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1
Abdul Haris, Muhamad Amin Husni, and Sin Liang Lim. "Neural Network Facial Authentication for Public Electric Vehicle Charging Station." Journal of Engineering Technology and Applied Physics 3, no. 1 (2021): 17–21. http://dx.doi.org/10.33093/jetap.2021.3.1.4.
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This study is to investigate and compare the facial recognition accuracy performance of Dlib ResNet against a K-Nearest Neighbour (KNN) classifier. Particularly when used against a dataset from an Asian ethnicity as Dlib ResNet was reported to have an accuracy deficiency when it comes to Asian faces. The comparisons are both implemented on the facial vectors extracted using the Histogram of Oriented Gradients (HOG) method and use the same dataset for a fair comparison. Authentication of a user by facial recognition in an electric vehicle (EV) charging station demonstrates a practical use case for such an authentication system.
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Lee, Jia-Rou, Kok-Why Ng, and Yih-Jian Yoong. "Face and Facial Expressions Recognition System for Blind People Using ResNet50 Architecture and CNN." Journal of Informatics and Web Engineering 2, no. 2 (2023): 284–98. http://dx.doi.org/10.33093/jiwe.2023.2.2.20.
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Many blind individuals have difficulties in recognizing people’s facial expression which may impact their social interaction. With the recognition, the blind individuals can accurately interpret and respond to the emotions. There is a lack in the existing application with the combination of face and facial expressions recognition. The blind individuals have to rely on multiple applications to accomplish the same task, making it difficult and time-consuming for them to use. The paper aims to recognize faces and facial expressions for blind individuals and provides feedback in real-time. Three face detection algorithms of Haar Cascade Classifier, Dlib, and RetinaFace are compared. Dlib is chosen to process with Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). It loads the pre-trained model, computes the HOG features, slide the window scanning at different scales, classify the windows using the SVM classifier, generate bounding boxes, and applying non-maximum suppression. ResNet50 architecture is employed to recognize face and Convolutional Neural Networks (CNN) is applied to recognize facial expression. The training accuracy is 70% and validation accuracy is 60%.
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B, Meena Preethi, Sunitha C, Parameshvar M, Dharshini B, and Gokul S. "Emotion Detection using HOG for Crime Detection." Indian Journal of Science and Technology 16, no. 41 (2023): 3617–26. https://doi.org/10.17485/IJST/v16i41.1580.
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Abstract <strong>Objectives:</strong> The main objective of this study is to develop an advanced emotion detection system that can contribute to crime detection and prevention efforts. By using the power of machine learning, the system aims to enhance the effectiveness in identifying potential criminal activities by analyzing emotional cues of people. <strong>Methods:</strong> The proposed method is evaluated using Facial Expression Recognition – 2013 (FER – 2013) dataset from a Kaggle data science community. It consists of 3589 training data files with 48x48 pixel grayscale images of human faces of seven different emotions classified which are centered and occupying the same amount of space. Instead of using the same algorithm for face detection and emotion classification, separate algorithms were used. Histogram Oriented Gradients (HOG) will detect the face by parameters like eyes, nose, and face edges. The image detected will first divide the image into small cells and plots histogram for each and then brings all histogram together to form feature vector. The Random Forest Algorithm will get the image detected and the random parameters of the face were taken for the voting process by averaging the constructed decision tree. The emotion is obtained by the outcome of the voting process. The addition of boosting algorithm guarantees in increasing the computational speed and accuracy of the model. <strong>Findings:</strong> The use of HOG for face detection gives the best result by capturing the face apart from all noises and background disturbances like poor light etc. The accuracy of HOG face detection module is 99.57 %. The detected face was given as input to the combination of Random Forest Algorithm and X-Gradient Boosting Algorithm for classifying the emotion. The addition of a boosting algorithm gives the maximum accuracy and minimum loss of data during emotion classification. The accuracy of the model was achieved up to 95% and loss of data below 5%. <strong>Novelty:</strong> This is the first open paper highlighting the face detection and emotion classification process with different algorithms where the process was divided as face detection, feature extraction and emotion classification. <strong>Keywords:</strong> Facial emotion, Face detection, Feature Extraction, Dlib Histogram Oriented Gradients (HOG), Random Forest, X­Gradient Boosting, Emotion classification, Facial Expression Recognition (FER)
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B, Meena Preethi, Sunitha C, Parameshvar M, Dharshini B, and Gokul S. "Emotion Detection using HOG for Crime Detection." Indian Journal of Science and Technology 16, no. 41 (2023): 3617–26. https://doi.org/10.17485/IJST/v16i41.1580.
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Abstract <strong>Objectives:</strong> The main objective of this study is to develop an advanced emotion detection system that can contribute to crime detection and prevention efforts. By using the power of machine learning, the system aims to enhance the effectiveness in identifying potential criminal activities by analyzing emotional cues of people. <strong>Methods:</strong> The proposed method is evaluated using Facial Expression Recognition – 2013 (FER – 2013) dataset from a Kaggle data science community. It consists of 3589 training data files with 48x48 pixel grayscale images of human faces of seven different emotions classified which are centered and occupying the same amount of space. Instead of using the same algorithm for face detection and emotion classification, separate algorithms were used. Histogram Oriented Gradients (HOG) will detect the face by parameters like eyes, nose, and face edges. The image detected will first divide the image into small cells and plots histogram for each and then brings all histogram together to form feature vector. The Random Forest Algorithm will get the image detected and the random parameters of the face were taken for the voting process by averaging the constructed decision tree. The emotion is obtained by the outcome of the voting process. The addition of boosting algorithm guarantees in increasing the computational speed and accuracy of the model. <strong>Findings:</strong> The use of HOG for face detection gives the best result by capturing the face apart from all noises and background disturbances like poor light etc. The accuracy of HOG face detection module is 99.57 %. The detected face was given as input to the combination of Random Forest Algorithm and X-Gradient Boosting Algorithm for classifying the emotion. The addition of a boosting algorithm gives the maximum accuracy and minimum loss of data during emotion classification. The accuracy of the model was achieved up to 95% and loss of data below 5%. <strong>Novelty:</strong> This is the first open paper highlighting the face detection and emotion classification process with different algorithms where the process was divided as face detection, feature extraction and emotion classification. <strong>Keywords:</strong> Facial emotion, Face detection, Feature Extraction, Dlib Histogram Oriented Gradients (HOG), Random Forest, X­Gradient Boosting, Emotion classification, Facial Expression Recognition (FER)
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B, Meena Preethi, Sunitha C, Parameshvar M, Dharshini B, and Gokul S. "Emotion Detection using HOG for Crime Detection." Indian Journal of Science and Technology 16, no. 41 (2023): 3617–26. https://doi.org/10.17485/IJST/v16i41.1580.
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Abstract <strong>Objectives:</strong> The main objective of this study is to develop an advanced emotion detection system that can contribute to crime detection and prevention efforts. By using the power of machine learning, the system aims to enhance the effectiveness in identifying potential criminal activities by analyzing emotional cues of people. <strong>Methods:</strong> The proposed method is evaluated using Facial Expression Recognition – 2013 (FER – 2013) dataset from a Kaggle data science community. It consists of 3589 training data files with 48x48 pixel grayscale images of human faces of seven different emotions classified which are centered and occupying the same amount of space. Instead of using the same algorithm for face detection and emotion classification, separate algorithms were used. Histogram Oriented Gradients (HOG) will detect the face by parameters like eyes, nose, and face edges. The image detected will first divide the image into small cells and plots histogram for each and then brings all histogram together to form feature vector. The Random Forest Algorithm will get the image detected and the random parameters of the face were taken for the voting process by averaging the constructed decision tree. The emotion is obtained by the outcome of the voting process. The addition of boosting algorithm guarantees in increasing the computational speed and accuracy of the model. <strong>Findings:</strong> The use of HOG for face detection gives the best result by capturing the face apart from all noises and background disturbances like poor light etc. The accuracy of HOG face detection module is 99.57 %. The detected face was given as input to the combination of Random Forest Algorithm and X-Gradient Boosting Algorithm for classifying the emotion. The addition of a boosting algorithm gives the maximum accuracy and minimum loss of data during emotion classification. The accuracy of the model was achieved up to 95% and loss of data below 5%. <strong>Novelty:</strong> This is the first open paper highlighting the face detection and emotion classification process with different algorithms where the process was divided as face detection, feature extraction and emotion classification. <strong>Keywords:</strong> Facial emotion, Face detection, Feature Extraction, Dlib Histogram Oriented Gradients (HOG), Random Forest, X­Gradient Boosting, Emotion classification, Facial Expression Recognition (FER)
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B, Meena Preethi, Sunitha C, Parameshvar M, Dharshini B, and Gokul S. "Emotion Detection using HOG for Crime Detection." Indian Journal of Science and Technology 16, no. 41 (2023): 3617–26. https://doi.org/10.17485/IJST/v16i41.1580.
Full textAbstract:
Abstract <strong>Objectives:</strong> The main objective of this study is to develop an advanced emotion detection system that can contribute to crime detection and prevention efforts. By using the power of machine learning, the system aims to enhance the effectiveness in identifying potential criminal activities by analyzing emotional cues of people. <strong>Methods:</strong> The proposed method is evaluated using Facial Expression Recognition – 2013 (FER – 2013) dataset from a Kaggle data science community. It consists of 3589 training data files with 48x48 pixel grayscale images of human faces of seven different emotions classified which are centered and occupying the same amount of space. Instead of using the same algorithm for face detection and emotion classification, separate algorithms were used. Histogram Oriented Gradients (HOG) will detect the face by parameters like eyes, nose, and face edges. The image detected will first divide the image into small cells and plots histogram for each and then brings all histogram together to form feature vector. The Random Forest Algorithm will get the image detected and the random parameters of the face were taken for the voting process by averaging the constructed decision tree. The emotion is obtained by the outcome of the voting process. The addition of boosting algorithm guarantees in increasing the computational speed and accuracy of the model. <strong>Findings:</strong> The use of HOG for face detection gives the best result by capturing the face apart from all noises and background disturbances like poor light etc. The accuracy of HOG face detection module is 99.57 %. The detected face was given as input to the combination of Random Forest Algorithm and X-Gradient Boosting Algorithm for classifying the emotion. The addition of a boosting algorithm gives the maximum accuracy and minimum loss of data during emotion classification. The accuracy of the model was achieved up to 95% and loss of data below 5%. <strong>Novelty:</strong> This is the first open paper highlighting the face detection and emotion classification process with different algorithms where the process was divided as face detection, feature extraction and emotion classification. <strong>Keywords:</strong> Facial emotion, Face detection, Feature Extraction, Dlib Histogram Oriented Gradients (HOG), Random Forest, X­Gradient Boosting, Emotion classification, Facial Expression Recognition (FER)
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B, Meena Preethi, Sunitha C, Parameshvar M, Dharshini B, and Gokul S. "Emotion Detection using HOG for Crime Detection." Indian Journal of Science and Technology 16, no. 41 (2023): 3617–26. https://doi.org/10.17485/IJST/v16i41.1580.
Full textAbstract:
Abstract <strong>Objectives:</strong> The main objective of this study is to develop an advanced emotion detection system that can contribute to crime detection and prevention efforts. By using the power of machine learning, the system aims to enhance the effectiveness in identifying potential criminal activities by analyzing emotional cues of people. <strong>Methods:</strong> The proposed method is evaluated using Facial Expression Recognition – 2013 (FER – 2013) dataset from a Kaggle data science community. It consists of 3589 training data files with 48x48 pixel grayscale images of human faces of seven different emotions classified which are centered and occupying the same amount of space. Instead of using the same algorithm for face detection and emotion classification, separate algorithms were used. Histogram Oriented Gradients (HOG) will detect the face by parameters like eyes, nose, and face edges. The image detected will first divide the image into small cells and plots histogram for each and then brings all histogram together to form feature vector. The Random Forest Algorithm will get the image detected and the random parameters of the face were taken for the voting process by averaging the constructed decision tree. The emotion is obtained by the outcome of the voting process. The addition of boosting algorithm guarantees in increasing the computational speed and accuracy of the model. <strong>Findings:</strong> The use of HOG for face detection gives the best result by capturing the face apart from all noises and background disturbances like poor light etc. The accuracy of HOG face detection module is 99.57 %. The detected face was given as input to the combination of Random Forest Algorithm and X-Gradient Boosting Algorithm for classifying the emotion. The addition of a boosting algorithm gives the maximum accuracy and minimum loss of data during emotion classification. The accuracy of the model was achieved up to 95% and loss of data below 5%. <strong>Novelty:</strong> This is the first open paper highlighting the face detection and emotion classification process with different algorithms where the process was divided as face detection, feature extraction and emotion classification. <strong>Keywords:</strong> Facial emotion, Face detection, Feature Extraction, Dlib Histogram Oriented Gradients (HOG), Random Forest, X­Gradient Boosting, Emotion classification, Facial Expression Recognition (FER)
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Syarif Husin, Iskandar Lutfi, and Masayu Anisah. "PENGUJIAN PENGENALAN WAJAH REAL-TIME DENGAN DLIB PADA RASPBERRY PI 5." Jurnal Riset Multidisiplin Edukasi 2, no. 6 (2025): 254–69. https://doi.org/10.71282/jurmie.v2i6.442.
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Penelitian ini membahas pengujian sistem pengenalan wajah berbasis Raspberry Pi 5 menggunakan pustaka dlib dengan metode Histogram of Oriented Gradients (HOG). Tujuan utama pengujian ini adalah mengevaluasi performa sistem dalam berbagai kondisi pencahayaan dan jarak. Pengujian dilakukan terhadap dua sampel wajah dalam empat skenario: dekat-terang, jauh-terang, dekat-gelap, dan jauh-gelap. Hasil menunjukkan bahwa sistem bekerja optimal pada kondisi terang dan jarak dekat, namun performanya menurun pada pencahayaan rendah dan jarak jauh. Tingkat akurasi yang diperoleh adalah 25% untuk sampel pertama dan 50% untuk sampel kedua. Kelebihan sistem ini terletak pada efisiensi komputasi karena seluruh proses dilakukan secara lokal di Raspberry Pi 5 tanpa koneksi ke server eksternal. Kekurangannya adalah sensitivitas terhadap variasi pencahayaan dan sudut pandang wajah. Penelitian ini menunjukkan bahwa pengenalan wajah real-time pada perangkat edge dapat dilakukan secara cost-effective, dan direkomendasikan pengembangan lebih lanjut melalui peningkatan dataset serta integrasi dengan sensor cahaya untuk adaptasi otomatis terhadap kondisi lingkungan.
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Durak, Üsame, Ayşegül Ceren Koç, Hüseyin Daş, Oğuzhan Karahan, M. Fatih Kılıç, and Mehmet Fatih Akay. "Comparative Analysis of Face Recognition Algorithms for Facial Recognition in Diverse Environments." Cukurova University Journal of Natural and Applied Sciences 3, no. 2 (2024): 45–52. https://doi.org/10.70395/cunas.1504238.
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Facial recognition technology has evolved significantly over the last five decades and plays a central role in various applications such as biometrics, information security, access control, law enforcement and surveillance. In this study, the performance of two face recognition algorithms, Dlib and FaceNet, is evaluated using datasets obtained from video recordings in different environments. The Dlib algorithm uses the Histogram of Oriented Gradients (HOG) method for face detection, while FaceNet uses the Multi-Task Cascaded Convolutional Neural Network (MTCNN). The experimental results show that both algorithms achieve high accuracy in controlled environments, with Dlib showing greater robustness in complex scenarios. This study makes an important contribution to this topic by presenting a comparative analysis of the face recognition performance of the OpenFace, ArcFace, Exadel, and Dlib methods under different environmental conditions and scenarios. The results show that while the tested methods achieve high accuracy in controlled environments, their performance differs in more com-plex environments.In the results, OpenFace and ArcFace showed lower success rates than the other two algorithms. In particu-lar, Dlib proved superior in dynamic and challenging scenarios, achieving an overall accuracy of 96.1% compared to 94.6% for Exadel. Exadel, on the other hand, performed slightly better in certain controlled environments, highlighting its potential strength in certain applications. These results emphasize the importance of selecting the appropriate algorithm based on the specific environmental conditions and requirements of the application. This research not only improves our understanding of the performance characteristics of leading facial recognition technologies, but also provides practical insights into their use in real-world applications.
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Ilham Firman Ashari, Mohamad Idri, and M. Anas Nasrulah. "Analysis of Combination of Parking System with Face Recognition and QR Code using Histogram of Oriented Gradient Method." IT Journal Research and Development 7, no. 1 (2022): 94–110. http://dx.doi.org/10.25299/itjrd.2022.9958.
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Security is very important everywhere, including in the campus environment. To provide security and comfort for those who park their vehicles, a parking application is needed that can provide vehicle security while undergoing academic activities on campus. QR code (Quick Response Code) is a technology for converting written data into a two-dimensional code, which is printed on a more compact medium capable of storing various types of data. The most common individual part used to identify a person is the face because it has the unique characteristics of everyone. Histogram of Oriented Gradient (HOG) is a feature extraction used for face identification based on histogram of gradient orientation and gradient magnitude. This application is implemented using the Dlib library for facial recognition. The implementation of this method is expected to improve parking security and provide a record of parked vehicles. The results of testing the implementation of facial recognition methods into android applications show very satisfactory results. With the results of testing the QR code scanning accuracy of 100% and an accuracy of 90% for a 7% damage rate and an accuracy of 85% for a 15% damage rate, and the results of facial recognition testing of 90% on face photos wearing helmets and an accuracy of 92% on photo of face without helmet.
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Vukovic, Igor, Petar Cisar, Kristijan Kuk, Milos Bandjur, and Brankica Popovic. "Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification." Elektronika ir Elektrotechnika 27, no. 5 (2021): 49–58. http://dx.doi.org/10.5755/j02.eie.29081.
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In a criminal investigation, along with processing forensic evidence, different investigative techniques are used to identify the perpetrator of the crime. It includes collecting and analyzing unconstrained face images, mostly with low resolution and various qualities, making identification difficult. Since police organizations have limited resources, in this paper, we propose a novel method that utilizes off-the-shelf solutions (Dlib library Histogram of Oriented Gradients-HOG face detectors and the ResNet faces feature vector extractor) to provide practical assistance in unconstrained face identification. Our experiment aimed to establish which one (if any) of the basic image enhancement techniques should be applied to increase the effectiveness. Results obtained from three publicly available databases and one created for this research (simulating police investigators’ database) showed that resizing the image (especially with a resolution lower than 150 pixels) should always precede enhancement to improve face detection accuracy. The best results in determining whether they are the same or different persons in images were obtained by applying sharpening with a high-pass filter, whereas normalization gives the highest classification scores when a single weight value is applied to data from all four databases.
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Muhammad, Abbas Khan, Muhammad Zubair Khan Dr., Ehsan Bazai Sohaib, et al. "Motion based smart assistant for visually impaired people." Indian Journal of Science and Technology 13, no. 16 (2020): 1612–18. https://doi.org/10.17485/IJST/v13i16.18.
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Abstract <strong>Objectives/Aim:</strong> The present paper deals with building a smart assistant with the aim of assisting the visually impaired people in mobility with confidence by realizing the nearby obstacles and also implement image processing techniques to recognize people. <strong>Methods:</strong> We use Raspberry Pi 4 which has increased computing performance and is interfaced to the picamera, GPS and GSM modules. Arduino pro mini with buzzer, ultrasonic sensors and vibration motors are used for obstacle detection. Certain libraries important for image processing are used such as OpenCV, Dlib, face detection (Haar cascades, HOG + Linear SVM, or CNNs), espeak for converting text to speech. Programming was implemented through Python and Arduino compiler. <strong>Findings:</strong> Analysis was carried out using the proposed system for the blind and visually impaired people who could move around comfortably with confidence and were also able to detect objects and recognize people. Hence the proposed system removes the use for the cumbersome white cane in exchange for small and handy modules which can be used in the form of wearables mounted on shoulders, hands and legs to detect the obstacles from multiple directions and provide for a comfortable wear. It was determined that the Raspberry pi 4 was incapable of running CNN detection for which ideally computer GPU was required, so HOG detection method was used instead. <strong>Application/Improvements:</strong> This project can be implemented mainly in the commercial field of helping visually impaired people with poor eyesight or being completely blind. Industrial applications can be devised and enhanced like robots and machineries along with Security, Identifying and Tracking. <strong>Keywords:</strong> Face Recognition; Obstacle Detection; Open Source Computer Vision (OpenCV); Deep Learning; Histogram of Oriented Gradients (HOG); Support Vector Machine (SVM); Convolutional Neural Network (CNN)
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Kurian, Belvin, and Felix M. Philip. "Face Attendance System." YMER Digital 21, no. 05 (2022): 878–82. http://dx.doi.org/10.37896/ymer21.05/99.
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Attendances are taken in every school and college. The convention attendance system consists of registers marked by teachers which may lead to human error and a lot of maintenance. The system proposed in this study is to deviate from such a traditional system and introduce a new approach to taking attendance using image processing. The system uses a Histogram of Oriented Gradients (HOG) and python libraries such as OpenCV, Dlib, and NumPy. As a human, the brain automatically recognizes a face instantly, but the computer is not capable of this high-level generalization. The system automatically starts taking snaps and then applies face detection and recognition technique to the given image the recognized students are marked as present and their attendance is updated with the corresponding time[2]. The working of the system is that it first looks at a picture and finds all the faces in it. second, it focuses on each face and can understand that even if a face is directed in a weird direction or under bad lighting. Third, the system comes up with 68 specific points called landmarks, that exist on every face example the top of the chin, the inner edge of each eyebrow, etc., and then picks out unique features of the face that can be used to tell it apart from other people. Finally, compare the unique features of that face to those already determined faces. Then the person can clock in into the system after the person clocks out, the system automatically transfers the data into an excel sheet.
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Umar, Siyudi Shafi’I, Zaharaddeen S. Iro, Abubakar Y. Zandam, and Saifulllahi Sadi Shitu. "Accelerated Histogram of Oriented Gradients for Human Detection." Dutse Journal of Pure and Applied Sciences 9, no. 1a (2023): 44–56. http://dx.doi.org/10.4314/dujopas.v9i1a.5.
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Histogram of Oriented Gradients (HOG) is an object detection algorithm used to detect people from an image. It involves features extraction called ‘HOG descriptor’ which are used to identify a person in the image. Several operations are involved in the feature extraction process. Hence performing numerous computations in order to obtain HOG descriptors takes some considerable amount of time. This slow computation speed limits HOG’s application in real-time systems. This paper investigates HOG with a view to improve its speed, modify the feature computation process to develop a faster version of HOG and finally evaluate against existing HOG. The technique of asymptotic notation in particular Big-O notation was applied to each stage of HOG and the complexity for the binning stage was modified. This results in a HOG version with a reduced complexity from n4 to n2 thereby having an improved speed as compared to the original HOG.
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Anggraeny, Fetty Tri, Basuki Rahmat, and Singgih Putra Pratama. "Deteksi Ikan Dengan Menggunakan Algoritma Histogram of Oriented Gradients." Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer 15, no. 2 (2020): 114. http://dx.doi.org/10.30872/jim.v15i2.4648.
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Indonesia merupakan negara yang kaya akan sumber daya alam baik hayati maupun non-hayati. Salah satu sumber daya alam hayati yang sangat banyak jumlahnya di Indonesia adalah laut, Untuk mempermudah mengidentifikasikan ikan, dapat memanfaatkan sebuah teknologi yang dapat membantu manusia untuk dapat mengenali ikan dengan menggunakan visi komputer dan pendekatan pemrosesan gambar untuk deteksi ikan dan bukan ikan menggunakan algoritma Histogram of Oriented Gradients (HOG) dan AdaBoost-SVM. Hasil penelitian menunjukkan bahwa metode HOG dan AdaBoost-SVM dapat menghasilkan tingkat akurasi rata-rata sebesar 84.8%.
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Guo, Lie, Guang Xi Zhang, Ping Shu Ge, and Lin Hui Li. "Pedestrian Tracking with HOG and Color Histogram Features." Applied Mechanics and Materials 241-244 (December 2012): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.498.
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To improve the effectiveness of pedestrian tracking, the histograms of oriented gradients (HOG) and color histogram characteristics are adopted to track pedestrian based on particle filter. Firstly, the pedestrian is detected using the HOG features to determine the initial target position. Then the target is tracked based on particle filter utilizing color histogram, during which the HOG is used to modify particle heavy weights and particle sampling. Experimental results verify the accurateness and efficiency of the proposed method.
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Thilagavathy, J., and L. Surya. "Implementation of HOG based feature extraction method." i-manager's Journal on Digital Signal Processing 11, no. 2 (2023): 9. http://dx.doi.org/10.26634/jdp.11.2.20314.
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Human detection on emerging intelligent transportation systems is a challenging task in hardware implementation. The Histogram of Oriented Gradients (HOG)-based human detection is the most successful algorithm due to its superior performance. Unfortunately, more intensive computations and poor performance at a multi-scale and low-contrast make human detection more difficult and unreliable. To address the aforementioned problems, an efficient histogram of edge-oriented gradients-based human detection is proposed to preserve the edge gradients at low-contrast and support multi-scale detection. The proposed algorithm uses approximation methods and adopts a pipelined structure that utilizes low-cost and high-speed, respectively. Experiments conducted on various challenging human datasets show that the proposed method provides efficient detection. This algorithm has been synthesized on Xilinx Spartan 3 FPGA software and board, achieving better hardware utilization compared to other state-of-the-art approaches.
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Muradkhanli, Leyla, and Eshgin Mammadov. "Real-time face detection on a Raspberry PI." Problems of Information Society 13, no. 2 (2022): 38–45. http://dx.doi.org/10.25045/jpis.v13.i2.05.
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The article describes the implementation of different face detection algorithms to capture human faces from real-time video frames using a Raspberry PI microprocessor. This article examines this issue, proposes the implementation of two distinct real-time face detection algorithms, and presents a comprehensive architectural design. Used methods include Haar Cascades which is known as Viola-Jones algorithm, and Histogram of Oriented Gradients + Linear Support Vector Machines algorithm. The algorithms are implemented with the help of the OpenCV and Dlib libraries, and the Python programming language was used to build the face detection system. The OpenCV and Dlib libraries include a large number of built-in packages that assist with face detection and conduct face operations separately, resulting in reduced processing time and increased efficiency overall. The results confirm that both methods can detect faces in real time with acceptable accuracy and computation time but there are several differences. The Histogram of Oriented Gradients + Linear Support Vector Machines algorithm.method is much more preferable in terms of accuracy, but the image pyramid construction will be computationally demanding.
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Nuha, H. Hamada, and F. Kharbat Faten. "p-norms of histogram of oriented gradients for X-ray images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 423–4430. https://doi.org/10.11591/ijece.v11i5.pp4423-4430.
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Lebesgue spaces (L p over R n ) play a significant role in mathematical analysis. They are widely used in machine learning and artificial intelligence to maximize performance or minimize error. The well-known histogram of oriented gradients (HOG) algorithm applies the 2-norm (Euclidean distance) to detect features in images. In this paper, we apply different p-norm values to identify the impact that changing these norms has on the original algorithm. The aim of this modification is to achieve better performance in classifying X-ray medical images related to of COVID-19 patients. The efficiency of the p-HOG algorithm is compared with the original HOG descriptor using a support vector machine implemented in Python. The results of the comparisons are promising, and the p-HOG algorithm shows greater efficiency in most cases.
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El-Sayed, Rania Salah, and Mohamed Nour El-Sayed. "Classification of vehicles’ types using histogram oriented gradients: comparative study and modification." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (2020): 700. http://dx.doi.org/10.11591/ijai.v9.i4.pp700-712.
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This paper proposes an efficient model for recognizing and classifying a vehicle type. The model localizes each object in the image then identifies the vehicle type. The features of an image are extracted using the histogram oriented gradients (HOG) and ant colony optimization (ACO). A vehicle type is determined using different classifiers namely: the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and Softmax classifiers. The model is implemented and operated on two datasets of vehicles' images as test-beds. From the comparative study, the SVM outperforms the other adopted classifiers and is also better using HOG than that using ACO. A modification is done on HOG by adding the Laplacian filter to select the most significant image features. The accuracy of the SVM classifier using modified HOG outperforms that one using the traditional HOG. The proposed model is analyzed and discussed regardless the local geometric and photometric transformations like illumination variations.
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Hafidhoh, Nisa ul, and Septian Enggar Sukmana. "Deteksi Pemain Basket Terklasifikasi Berbasis Histogram of Oriented Gradients." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 3, no. 1 (2018): 6–11. http://dx.doi.org/10.25139/inform.v3i1.635.
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Pada olahraga basket jaman modern ini, kebutuhan analisis pergerakan pemain pada calon tim lawan olahraga basket perlu didukung oleh teknologi informasi yang mampu mengupayakan sistem yang otomatis. Analisis pergerakan pemain yang otomatis perlu didukung oleh sistem deteksi pemain yang handal dan akurat sehingga pemetaan pergerakan dapat dilakukan secara optimal. Tujuan dari penelitian ini adalah untuk mengembangkan metode Histogram of Oriented Gradients (HOG) menjadi sebuah metode deteksi yang handal untuk kasus deteksi pemain basket pada media. Tantangan pada penelitian ini adalah deteksi pemain tidak hanya pada saat berjalan dan berlari namun juga pada saat melompat. Untuk memperkuat fokus dan konsistensi terhadap objek yang terdeteksi, pemanfaatan metode klasifikasi Support Vector Machine (SVM) digunakan melalui kolaborasi terhadap HOG descriptor serta warna kostum pemain sehingga pembeda tim dari masing-masing pemain juga dapat dikenali. Tingkat akurasi dari evaluasi yang dihasilkan adalah 92% untuk true positive rate dan 40% untuk false positive rate.
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SIDDIK, MUHAMMAD ARSYAD, LEDYA NOVAMIZANTI, and I. NYOMAN APRAZ RAMATRYANA. "Deteksi Level Kolesterol melalui Citra Mata Berbasis HOG dan ANN." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 7, no. 2 (2019): 284. http://dx.doi.org/10.26760/elkomika.v7i2.284.
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ABSTRAKKolesterol merupakan lemak yang berada di dalam darah yang dibutuhkan untuk pembentukan hormon dan sel baru. Kadar kolesterol normal harus kurang dari 200 mg/dL, namun jika di atas 240 mg/dL akan berisiko tinggi terkena penyakit stroke dan jantung koroner. Penelitian ini menghasilkan suatu sistem yang dapat mendeteksi kadar kolesterol seseorang melalui citra mata menggunakan metode iridologi dan image processing. Citra mata diperoleh dari pasien laboratorium klinik sebanyak 120 citra mata. Proses sistem diawali dengan mengolah citra mata dengan metode cropping, resize, dan segmentasi. Metode ekstaksi ciri menggunakan Histogram of Oriented Gradients (HOG), dan klasifikasi menggunakan Artificial Neural Network (ANN). Sistem dapat mendeteksi kadar kolesterol dengan tiga level klasifikasi, yaitu normal, berisiko kolesterol tinggi, dan kolesterol tinggi dengan tingkat akurasi sebesar 93% dan waktu komputasi 0,0862 detik.Kata kunci: citra mata, kadar kolesterol, Histogram of Oriented Gradients, Artificial Neural Network ABSTRACTCholesterol is fat in the blood that is needed for the formation of hormones and new cells. Normal cholesterol levels should be less than 200 mg / dL, but if above 240 mg / dL will be at high risk of stroke and coronary heart disease. This study produced a system that can detect a person's cholesterol levels through eye images using iridology and image processing methods. Eye images obtained from clinical laboratory patients were 120 eye images. The system process begins with processing eye images using the method of cropping, resizing, and segmentation. Feature extraction method uses Histogram of Oriented Gradients (HOG), and classification using Artificial Neural Network (ANN). The system can detect cholesterol levels with three levels of classification, namely normal, at high risk of cholesterol, and high cholesterol with an accuracy rate of 93% and computing time of 0.0862 seconds.Keywords: eye image, cholesterol level, Histogram of Oriented Gradients, Artificial Neural Network
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Zhang, Li Hong, and Lin Li. "Improved Pedestrian Detection Based on Extended Histogram of Oriented Gradients." Applied Mechanics and Materials 347-350 (August 2013): 3815–20. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3815.
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In order to further improve pedestrian detection accuracy and avoid the disadvantage of original histogram of oriented gradients (HOG), differential template, overlap ratio and normalization method and so on are improved when HOG features are extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Multi-scale detection technique and non maxima suppression method are employed for precisely locating the pedestrians in the image. Experiments show that the human detection system improves detection accuracy and still maintains a relatively satisfactory speed.
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Li, Bin, Kaili Cheng, and Zhezhou Yu. "Histogram of Oriented Gradient Based Gist Feature for Building Recognition." Computational Intelligence and Neuroscience 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6749325.
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We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.
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Chen, Ji, Kaiping Zhan, Qingzhou Li, et al. "Spectral clustering based on histogram of oriented gradient (HOG) of coal using laser-induced breakdown spectroscopy." Journal of Analytical Atomic Spectrometry 36, no. 6 (2021): 1297–305. http://dx.doi.org/10.1039/d1ja00104c.
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Histogram of oriented gradients (HOG) was introduced in the unsupervised spectral clustering in LIBS. After clustering, the spectra of different matrices were clearly distinguished, and the accuracy of quantitative analysis of coal was improved.
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Hamada, Nuha H., and Faten F. Kharbat. "p-norms of histogram of oriented gradients for X-ray images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4423. http://dx.doi.org/10.11591/ijece.v11i5.pp4423-4430.
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<span>Lebesgue spaces (</span><em><span>L<sup>p</sup></span></em><span> over </span><em><span>R<sup>n</sup></span></em><span>) play a significant role in mathematical analysis. They are widely used in machine learning and artificial intelligence to maximize performance or minimize error. The well-known histogram of oriented gradients (HOG) algorithm applies the 2-norm (Euclidean distance) to detect features in images. In this paper, we apply different </span><em><span>p</span></em><span>-norm values to identify the impact that changing these norms has on the original algorithm. The aim of this modification is to achieve better performance in classifying X-ray medical images related to of COVID-19 patients. The efficiency of the </span><em><span>p</span></em><span>-HOG algorithm is compared with the original HOG descriptor using a support vector machine implemented in Python. The results of the comparisons are promising, and the </span><em><span>p</span></em><span>-HOG algorithm shows greater efficiency in most cases.</span>
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Wójcikowski, Marek. "Histogram of Oriented Gradients with Cell Average Brightness for Human Detection." Metrology and Measurement Systems 23, no. 1 (2016): 27–36. http://dx.doi.org/10.1515/mms-2016-0012.
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Abstract A modification of the descriptor in a human detector using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is presented. The proposed modification requires inserting the values of average cell brightness resulting in the increase of the descriptor length from 3780 to 3908 values, but it is easy to compute and instantly gives ≈ 25% improvement of the miss rate at 10‒4 False Positives Per Window (FPPW). The modification has been tested on two versions of HOG-based descriptors: the classic Dalal-Triggs and the modified one, where, instead of spatial Gaussian masks for blocks, an additional central cell has been used. The proposed modification is suitable for hardware implementations of HOG-based detectors, enabling an increase of the detection accuracy or resignation from the use of some hardware-unfriendly operations, such as a spatial Gaussian mask. The results of testing its influence on the brightness changes of test images are also presented. The descriptor may be used in sensor networks equipped with hardware acceleration of image processing to detect humans in the images.
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Zhang, Li Hong. "Human Detection Based on SVM and Improved Histogram of Oriented Gradients." Applied Mechanics and Materials 380-384 (August 2013): 3862–65. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3862.
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Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experiments show that while maintaining a relatively satisfactory speed the human detection system improves detection accuracy.
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Khalaf Fadel, Mohammed, and Mohammed Chachan Younis. "Facial Expressions Recognition Using Machine Learning Classifiers Based HOG Features." International Research Journal of Innovations in Engineering and Technology 09, no. 05 (2025): 457–65. https://doi.org/10.47001/irjiet/2025.905052.
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Facial Expression Recognition (FER) is a critical area of research in computer vision and human-computer interaction. This paper presents a comprehensive study on the use of Histogram of Oriented Gradients (HOG) and machine learning algorithms for FER.
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G, Neela Krishna Babu, and Joseph Peter V. "SKIN CANCER DETECTION USING SUPPORT VECTOR MACHINE WITH HISTOGRAM OF ORIENTED GRADIENTS FEATURES." ICTACT Journal on Soft Computing 11, no. 2 (2021): 2301–5. https://doi.org/10.21917/ijsc.2021.0329.
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This research work proposes an efficient skin cancer detection technique based on Support Vector Machine (SVM) with Histogram of Oriented Gradients (HOG) features. In this, skin cancer images from ISIC 2018 (International Skin Imaging Collaboration 2018) dataset are converted into gray scale and pre-processed using the median filter. The image resampling technique is then applied to rebalance the class distribution. The HOG features are extracted from these preprocessed images. After, the Radial Basis Function (RBF) kernel based SVM classification method is used to classify these extracted HOG features for detecting cancer class labels. These predicted class labels are compared with original labels for performing the evaluation. This proposed method is tested using and achieves 76% accuracy, 85% specificity, 84% precision, 76% recall and 75% F1-score.
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Sharma, Aditya. "Smart Face Attendance System using Facial Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem36137.
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The SmartFace Attendance system using facial recognition represents a modern solution to automate attendance tracking processes in educational institutions and organizations. Leveraging advanced facial recognition technology, it offers accurate and efficient attendance management while addressing privacy concerns and ensuring user acceptance. The system utilizes state-of-the-art algorithms such as Convolutional Neural Networks (CNN), Histogram of Oriented Gradients (HOG), and Support Vector Machines (SVM) to detect and recognize faces in real-time captured images. By providing touchless operation and seamless integration with existing infrastructure, SmartFace Attendance system offers convenience and scalability for users across diverse environments. With robust security measures, adherence to ethical considerations, and compliance with legal regulations, it is poised to revolutionize attendance tracking practices, enhancing operational efficiency and improving user experiences in educational and organizational settings. Keywords—Online Attendance System, Energy Efficiency, Facial Recognition, Support Vector Machine(SVM), Decision Tree, Histogram of Oriented Gradients(HOG), Convolutional Neural Network(CNN), Database Management, Model Train- ing.
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Gunadarma, Adhika, and Ken Ratri Retno Wardani. "Penerapan Histogram of Oriented Gradients, Principal Component Analysis dan AdaBoost untuk Sistem Pengenalan Wajah." Jurnal Telematika 13, no. 2 (2019): 93–98. http://dx.doi.org/10.61769/telematika.v13i2.225.
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This The human face image has a lot of information that can be used in the field of computer vision to create a human face recognition system. The method used in this study is the Histogram of Oriented Gradients (HOG) method used for feature extraction. The Principal Component Analysis (PCA) method is applied from the features of the HOG method to reduce the dimensionality of feature data from high to low without losing much of the information. Finally, the Adaptive Boosting method (AdaBoost) is used to process the resulting feature classification. Before performing facial recognition process, the initial treatment is done to detect and cut the face of the next part of the image pieces will be the same size so that the face taken has a uniform size. Based on the test results of cell, block and bins values, the best total eigenvalue and total iteration for this process were 8,16,4, -, 15 for the classifier using the HOG plus AdaBoost method with the resulting accuracy to recognize the face of 86% and 8.16,16,20,10 for classifier using HOG method, PCA with AdaBoost with accuracy level for face recognition of 96%.Citra wajah manusia memiliki banyak informasi yang dapat digunakan pada bidang komputer vision untuk membuat sistem pengenalan wajah manusia. Metode yang digunakan pada penelitian kali ini adalah metode Histogram of Oriented Gradients (HOG) yang digunakan untuk ekstraksi fitur. Metode Principal Component Analysis (PCA) diterapkan dari hasil fitur metode HOG untuk mereduksi dimensionalitas data fitur dari tinggi ke rendah tanpa menghilangkan banyak informasi. Terakhir, metode Adaptive Boosting (AdaBoost) dipakai untuk proses klasifikasi fitur yang dihasilkan. Sebelum melakukan proses pengenalan wajah, dilakukan pengolahan awal untuk mendeteksi dan memotong bagian wajah yang selanjutnya bagian potongan citra akan di samakan ukurannya agar wajah yang terambil mempunyai ukuran seragam. Berdasarkan hasil pengujian nilai sel, block dan bins, jumlah eigen dan jumlah iterasi terbaik untuk keseluruhan pada proses ini adalah 8,16,4,-,15 untuk classifier menggunakan metode HOG dan AdaBoost dengan tingkat akurasi yang dihasilkan untuk mengenali wajah sebesar 86% dan 8,16,16,20,10 untuk classifier menggunakan metode HOG, PCA dengan AdaBoost dengan tingkat akurasi untuk pengenalan wajah sebesar 96%.
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Ou, Jianping, and Jun Zhang. "Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine." Mathematical Problems in Engineering 2021 (October 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/6650367.
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In order to solve the problems such as big errors, lack of universality, and too much time consuming occurred in the recognition of overlapped fruits, an improved fuzzy least square support vector machine (FLS-SVM) is established based on the fruit ROI-HOG feature. First, the RGB image is transformed into saturation and value (HSV) image, and then the regions of interest (ROI) are detected from HSV color information. Finally, the histogram of oriented gradients (HOG) feature of ROI will be used as the input of FLS-SVM pattern recognizer to realize the recognition of picking fruit. In addition, the verified FLS-SVM is used to investigate the recognition performance of harvesting robot using regions of interest histogram of oriented gradients feature. The results reveal that the vector sizes are effectively reduced and a higher detection speed is achieved without compromising accuracy relative to conventional approaches. Similarly, the detection accuracy for the learning samples, the isolated fruit, the overlapped fruit, and the background can achieve 99.50%, 96.0%, 89.9%, and 97.0%, respectively, which shows the good performance of the proposed improved ROI-HOG feature recognition method.
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Thakur, Surendra, Emmanuel Adetiba, Oludayo O. Olugbara, and Richard Millham. "Experimentation Using Short-Term Spectral Features for Secure Mobile Internet Voting Authentication." Mathematical Problems in Engineering 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/564904.
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We propose a secure mobile Internet voting architecture based on the Sensus reference architecture and report the experiments carried out using short-term spectral features for realizing the voice biometric based authentication module of the architecture being proposed. The short-term spectral features investigated are Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Frequency Discrete Wavelet Coefficients (MFDWC), Linear Predictive Cepstral Coefficients (LPCC), and Spectral Histogram of Oriented Gradients (SHOGs). The MFCC, MFDWC, and LPCC usually have higher dimensions that oftentimes lead to high computational complexity of the pattern matching algorithms in automatic speaker recognition systems. In this study, higher dimensions of each of the short-term features were reduced to an 81-element feature vector per Speaker using Histogram of Oriented Gradients (HOG) algorithm while neural network ensemble was utilized as the pattern matching algorithm. Out of the four short-term spectral features investigated, the LPCC-HOG gave the best statistical results withRstatistic of 0.9127 and mean square error of 0.0407. These compact LPCC-HOG features are highly promising for implementing the authentication module of the secure mobile Internet voting architecture we are proposing in this paper.
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Soler, J. D., H. Beuther, M. Rugel, et al. "Histogram of oriented gradients: a technique for the study of molecular cloud formation." Astronomy & Astrophysics 622 (February 2019): A166. http://dx.doi.org/10.1051/0004-6361/201834300.
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We introduce the histogram of oriented gradients (HOG), a tool developed for machine vision that we propose as a new metric for the systematic characterization of spectral line observations of atomic and molecular gas and the study of molecular cloud formation models. In essence, the HOG technique takes as input extended spectral-line observations from two tracers and provides an estimate of their spatial correlation across velocity channels. We characterized HOG using synthetic observations of HI and 13CO (J = 1 → 0) emission from numerical simulations of magnetohydrodynamic (MHD) turbulence leading to the formation of molecular gas after the collision of two atomic clouds. We found a significant spatial correlation between the two tracers in velocity channels where vHI ≈ v13CO, almost independent of the orientation of the collision with respect to the line of sight. Subsequently, we used HOG to investigate the spatial correlation of the HI, from The HI/OH/recombination line survey of the inner Milky Way (THOR), and the 13CO (J = 1 → 0) emission from the Galactic Ring Survey (GRS), toward the portion of the Galactic plane 33°.75 ≤l ≤ 35°.25 and |b| ≤ 1°.25. We found a significant spatial correlation between the two tracers in extended portions of the studied region. Although some of the regions with high spatial correlation are associated with HI self-absorption (HISA) features, suggesting that it is produced by the cold atomic gas, the correlation is not exclusive to this kind of region. The HOG results derived for the observational data indicate significant differences between individual regions: some show spatial correlation in channels around vHI ≈ v13CO while others present spatial correlations in velocity channels separated by a few kilometers per second. We associate these velocity offsets to the effect of feedback and to the presence of physical conditions that are not included in the atomic-cloud-collision simulations, such as more general magnetic field configurations, shear, and global gas infall.
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Moldovanu, Simona, Lenuta Pană Toporaș, Anjan Biswas, and Luminita Moraru. "Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images." Entropy 22, no. 11 (2020): 1299. http://dx.doi.org/10.3390/e22111299.
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A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.
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Salfikar, Inzar, Indra Adji Sulistijono, and Achmad Basuki. "Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance." EMITTER International Journal of Engineering Technology 5, no. 2 (2018): 234–54. http://dx.doi.org/10.24003/emitter.v5i2.182.
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Finding victims at a disaster site is the primary goal of Search-and-Rescue (SAR) operations. Many technologies created from research for searching disaster victims through aerial imaging. but, most of them are difficult to detect victims at tsunami disaster sites with victims and backgrounds which are look similar. This research collects post-tsunami aerial imaging data from the internet to builds dataset and model for detecting tsunami disaster victims. Datasets are built based on distance differences from features every sample using Histogram-of-Oriented-Gradient (HOG) method. We use the longest distance to collect samples from photo to generate victim and non-victim samples. We claim steps to collect samples by measuring HOG feature distance from all samples. the longest distance between samples will take as a candidate to build the dataset, then classify victim (positives) and non-victim (negatives) samples manually. The dataset of tsunami disaster victims was re-analyzed using cross-validation Leave-One-Out (LOO) with Support-Vector-Machine (SVM) method. The experimental results show the performance of two test photos with 61.70% precision, 77.60% accuracy, 74.36% recall and f-measure 67.44% to distinguish victim (positives) and non-victim (negatives).
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De Ocampo, Anton Louise Pernez, Argel Bandala, and Elmer Dadios. "Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring." International Journal of Advances in Intelligent Informatics 6, no. 3 (2020): 223. http://dx.doi.org/10.26555/ijain.v6i3.514.
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In UAV-based human detection, the extraction and selection of the feature vector are one of the critical tasks to ensure the optimal performance of the detection system. Although UAV cameras capture high-resolution images, human figures' relative size renders persons at very low resolution and contrast. Feature descriptors that can adequately discriminate between local symmetrical patterns in a low-contrast image may improve a human figures' detection in vegetative environments. Such a descriptor is proposed and presented in this paper. Initially, the acquired images are fed to a digital processor in a ground station where the human detection algorithm is performed. Part of the human detection algorithm is the GeHOG feature extraction, where a bank of Gabor filters is used to generate textured images from the original. The local energy for each cell of the Gabor images is calculated to identify the dominant orientations. The bins of conventional HOG are enhanced based on the dominant orientation index and the accumulated local energy in Gabor images. To measure the performance of the proposed features, Gabor-enhanced HOG (GeHOG) and other two recent improvements to HOG, Histogram of Edge Oriented Gradients (HEOG) and Improved HOG (ImHOG), are used for human detection on INRIA dataset and a custom dataset of farmers working in fields captured via unmanned aerial vehicle. The proposed feature descriptor significantly improved human detection and performed better than recent improvements in conventional HOG. Using GeHOG improved the precision of human detection to 98.23% in the INRIA dataset. The proposed feature can significantly improve human detection applied in surveillance systems, especially in vegetative environments.
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Shidlovskiy, S. V., A. S. Bondarchuk, S. Poslavsky, and M. V. Shikhman. "Reducing dimensions of the histogram of oriented gradients (HOG) feature vector." Journal of Physics: Conference Series 1611 (August 2020): 012072. http://dx.doi.org/10.1088/1742-6596/1611/1/012072.
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T., Aditya Sai Srinivas, Kaif Khan Md., Karthik Reddy K., and Sai Chand G. "Artistry in Detail: Mastering HOG Feature Extraction." Journal of Advances in Computational Intelligence Theory 6, no. 1 (2023): 25–30. https://doi.org/10.5281/zenodo.10421202.
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<em>This abstract introduces the fascinating world of "Extracting HOG Features," where image analysis transcends traditional boundaries. HOG, or Histogram of Oriented Gradients, emerges as a powerful tool in unveiling intricate patterns within images. The paper delves into the artful craft of feature extraction, elucidating how HOG captures the nuanced textures and structures that define visual content. Through a lens of visual poetry, this work explores the transformative potential of HOGonomics, where the synthesis of pixels becomes a symphony. Join the journey into the realm of HOGwarts, where wizardry in feature extraction paints a vivid tapestry of insights in image analysis.</em>
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Sri., N. V. Phani Sai Kumar. "Crowd Monitoring using HOG." International Journal of Innovative Science and Research Technology 8, no. 1 (2023): 1031–38. https://doi.org/10.5281/zenodo.7599212.
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Many security and event management agencies throughout the world are beginning to understand the significance of crowd surveillance as public safety concerns increase. These organisations can avert any unforeseen mishaps or problems by estimating crowd dynamics. The goal of this research is to develop a system that can more effectively monitor crowds utilising Support Vector Machine (SVM) classifiers and Histogram of Oriented Gradients (HOG) features. According to our needs, we can interface two or more cameras to count the number of individuals in the input video of the cameras and to identify their locations in 3D space. This provides a sense of the density.
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Singh, Geetika, and Indu Chhabra. "Effective and Fast Face Recognition System Using Complementary OC-LBP and HOG Feature Descriptors With SVM Classifier." Journal of Information Technology Research 11, no. 1 (2018): 91–110. http://dx.doi.org/10.4018/jitr.2018010106.
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Selection and implementation of a face descriptor that is both discriminative and computationally efficient is crucial. Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) have been proven effective for face recognition. LBPs are fast to compute and are easy to extract the texture features. OC-LBP descriptors have been proposed to reduce the dimensionality of LBP while increasing the discrimination power. HOG features capture the edge features that are invariant to rotation and light. Owing to the fact that both texture and edge information is important for face representation, this article proposes a framework to combine OC-LBP and HOG. First, OC-LBP and HOG features are extracted, normalized and fused together. Next, classification is achieved using a histogram-based chi-square, square-chord and extended-canberra metrics and SVM with a normalized chi-square kernel. Experiments on three benchmark databases: ORL, Yale and FERET show that the proposed method is fast to compute and outperforms other similar state-of-the-art methods.
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Chang, Quan Yan, Thian Song Ong, and Siew Chin Chong. "Fusion of Active Appearance Model and Histogram of Oriented Gradient for Age Estimation." International Journal of Engineering & Technology 7, no. 4.29 (2018): 80–83. http://dx.doi.org/10.14419/ijet.v7i4.29.21849.
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In recent years, automated age estimation through face images has attracted the interest among the research due to its variety applications in law enforcement, human computer interaction etc. This paper presents the fusion of Active Appearances Model (AAM) with Histogram of Oriented Gradients (HOG) to form the face descriptors for automatic age estimation. AAM and HOG are known to be reliable feature extraction techniques for shape and texture images. The weaknesses of both are minimized and the strengths of both are utilized in the proposed method for better age estimation model. The proposed method is evaluated using two benchmarked age estimation datasets and promising results is generated.
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Pereira, Eduardo Lima, Warley Rocha Mendes, Flávio Garcia Pereira, and Daniel Cruz Cavalieri. "Classificação de folhas utilizando a fusão de classificadores Um estudo comparativo utilizando Binarização, HOG, SVM e PLS." Brazilian Journal of Development 9, no. 05 (2023): 17687–702. http://dx.doi.org/10.34117/bjdv9n5-213.
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Este trabalho apresenta uma visão comparativa entre os resultados obtidos pelos classificadores SVM (Support Vector Machine), PLS (Partial Least Square) e sua fusão, utilizando binarização e descritores HOG (Histogram of Oriented Gradients) aplicados a um conjunto de dados de 1907 fotos de folhas em 32 espécies de plantas diferentes. Os resultados obtidos pelos classificadores de fusão obtêm acima de 95% de assertividade.
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Mutia, Cut, Fitri Arnia, and Rusdha Muharar. "Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG." Bulletin of Electrical Engineering and Informatics 6, no. 3 (2017): 271–80. http://dx.doi.org/10.11591/eei.v6i3.657.
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The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories.
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Cut, Mutia, Arnia Fitri, and Muharar Rusdha. "Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG." Bulletin of Electrical Engineering and Informatics 6, no. 3 (2017): 271–80. https://doi.org/10.11591/eei.v6i3.657.
Full textAbstract:
The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories.
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Yohannes, Yohannes, Muhammad Ezar Al Rivan, Siska Devella, and Meiriyama Meiriyama. "Klasifikasi Motif Songket Palembang menggunakan Support Vector Machine berdasarkan Histogram of Oriented Gradients." Jurnal Teknologi Terpadu 9, no. 2 (2023): 143–49. http://dx.doi.org/10.54914/jtt.v9i2.1032.
Full textAbstract:
Songket Palembang merupakan salah satu warisan budaya takbenda dengan domain kemahiran dan kerajinan tradisional. Songket Palembang memiliki beberapa jenis motif antara lain Bunga Cina, Cantik Manis, dan Pulir. Upaya pelestarian dilakukan dengan memberikan pemahaman tentang motif songket palembang. Pada penelitian ini dilakukan pengklasifikasian motif songket Palembang berdasarkan fitur bentuk dengan menggunakan metode Histogram of Oriented Gradient (HOG). Berdasarkan hasil pengujian terhadap 45 citra data uji, bahwa metode HOG mampu menjadi fitur dalam klasifikasi citra motif Songket Palembang, yaitu motif Bunga Cina, Cantik Manis, dan Pulir. Metode Support Vector Machine (SVM) digunakan sebagai metode klasifikasi yang dapat mengenali motif Songket Palembang dengan kernel RBF, Linier dan Polinomial. Hasil pengujian menunjukkan bahwa kernel RBF menjadi kernel terbaik yang menghasilkan rata-rata nilai accuracy sebesar 88.1%, precision sebesar 84.1%, recall sebesar 82.2% dan f1-score sebesar 82.6% serta dari tiga motif songket Palembang yang diuji didapatkan hasil bahwa motif Songket Palembang yang paling mudah diklasifikasikan dengan baik adalah motif Cantik Manis untuk semua jenis kernel SVM.
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Luo, Jian, and Chang Lin. "Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System." Sensors 18, no. 4 (2018): 1174. http://dx.doi.org/10.3390/s18041174.
Full textAbstract:
In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.
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TR, Athira, and Abraham Varghese. "CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (2017): 8. http://dx.doi.org/10.11591/ijai.v6.i1.pp8-17.
Full textAbstract:
Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.
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Athira, TR, and Varghese Abraham. "CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (2017): 8–17. https://doi.org/10.5281/zenodo.4108200.
Full textAbstract:
Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.