Relevant bibliographies by topics / Dlib Histogram Oriented Gradients (HOG)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Dlib Histogram Oriented Gradients (HOG)'

Author: Grafiati
Published: 5 June 2025
Last updated: 20 July 2025

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Journal articles on the topic "Dlib Histogram Oriented Gradients (HOG)"

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>&nbsp;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.&nbsp;<strong>Methods:</strong>&nbsp;The proposed method is evaluated using Facial Expression Recognition &ndash; 2013 (FER &ndash; 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.&nbsp;<strong>Findings:</strong>&nbsp;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%.&nbsp;<strong>Novelty:</strong>&nbsp;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, X&shy;Gradient 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>&nbsp;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.&nbsp;<strong>Methods:</strong>&nbsp;The proposed method is evaluated using Facial Expression Recognition &ndash; 2013 (FER &ndash; 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.&nbsp;<strong>Findings:</strong>&nbsp;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%.&nbsp;<strong>Novelty:</strong>&nbsp;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, X&shy;Gradient 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:
Abstract <strong>Objectives:</strong>&nbsp;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.&nbsp;<strong>Methods:</strong>&nbsp;The proposed method is evaluated using Facial Expression Recognition &ndash; 2013 (FER &ndash; 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.&nbsp;<strong>Findings:</strong>&nbsp;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%.&nbsp;<strong>Novelty:</strong>&nbsp;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, X&shy;Gradient 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:
Abstract <strong>Objectives:</strong>&nbsp;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.&nbsp;<strong>Methods:</strong>&nbsp;The proposed method is evaluated using Facial Expression Recognition &ndash; 2013 (FER &ndash; 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.&nbsp;<strong>Findings:</strong>&nbsp;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%.&nbsp;<strong>Novelty:</strong>&nbsp;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, X&shy;Gradient 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:
Abstract <strong>Objectives:</strong>&nbsp;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.&nbsp;<strong>Methods:</strong>&nbsp;The proposed method is evaluated using Facial Expression Recognition &ndash; 2013 (FER &ndash; 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.&nbsp;<strong>Findings:</strong>&nbsp;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%.&nbsp;<strong>Novelty:</strong>&nbsp;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, X&shy;Gradient 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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Dissertations / Theses on the topic "Dlib Histogram Oriented Gradients (HOG)"

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Lienemann, Matthew A. "Automated Multi-Modal Search and Rescue using Boosted Histogram of Oriented Gradients." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1507.

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Unmanned Aerial Vehicles (UAVs) provides a platform for many automated tasks and with an ever increasing advances in computing, these tasks can be more complex. The use of UAVs is expanded in this thesis with the goal of Search and Rescue (SAR), where a UAV can assist fast responders to search for a lost person and relay possible search areas back to SAR teams. To identify a person from an aerial perspective, low-level Histogram of Oriented Gradients (HOG) feature descriptors are used over a segmented region, provided from thermal data, to increase classification speed. This thesis also introduces a dataset to support a Bird’s-Eye-View (BEV) perspective and tests the viability of low level HOG feature descriptors on this dataset. The low-level feature descriptors are known as Boosted Histogram of Oriented Gradients (BHOG) features, which discretizes gradients over varying sized cells and blocks that are trained with a Cascaded Gentle AdaBoost Classifier using our compiled BEV dataset. The classification is supported by multiple sensing modes with color and thermal videos to increase classification speed. The thermal video is segmented to indicate any Region of Interest (ROI) that are mapped to the color video where classification occurs. The ROI decreases classification time needed for the aerial platform by eliminating a per-frame sliding window. Testing reveals that with the use of only color data iv and a classifier trained for a profile of a person, there is an average recall of 78%, while the thermal detection results with an average recall of 76%. However, there is a speed up of 2 with a video of 240x320 resolution. The BEV testing reveals that higher resolutions are favored with a recall rate of 71% using BHOG features, and 92% using Haar-Features. In the lower resolution BEV testing, the recall rates are 42% and 55%, for BHOG and Haar-Features, respectively.
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Дрозд, В. П. "Застосування гістограми орієнтованих градієнтів (HOG) для виявлення пішохода на зображенні". Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/39124.

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Проблема виявлення пішохода полягає в тому, що люди дуже різноманітні за статурою та можуть приймати різні пози, у зображення можуть бути різні спотворення. Існує ряд методів для виявлення пішохода: методи основані на Haar wavelet признаках, нейронні мережі, гістограми направлених градієнтів та інші. В даній роботі пропонується розгляд варіанту застосування HOG.
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Chrápek, David. "Učení a detekce objektů různých tříd v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236481.

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This paper is focused on object learning and recognizing in the image and in the image stream. More specifically on learning and recognizing humans or theirs parts in case they are partly occluded, with possible usage on robotic platforms. This task is based on features called Histogram of Oriented Gradients (HOG) which can work quite well with different poses the human can be in. The human is split into several parts and those parts are detected individually. Then a system of voting is introduced in which detected parts votes for the final positions of found people. For training the detector a linear SVM is used. Then the Kalman filter is used for stabilization of the detector in case of detecting from image stream.
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Olejár, Adam. "Měření výšky postavy v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220426.

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The aim of this paper is a summary of the theory necessary for a modification, detection of person and the height calculation of the detected person in the image. These information were then used for implementation of the algoritm. The first half reveals teoretical problems and solutions. Shows the basic methods of image preprocessing and discusses the basic concepts of plane and projective geometry and transformations. Then describes the distortion, that brings into the picture imperfections of optical systems of cameras and the possibilities of removing them. Explains HOG algorithm and the actual method of calculating height of person detected in the image. The second half describes algoritm structure and statistical evaluation.
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Dočekal, Martin. "Porovnání klasifikačních metod." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403211.

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This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
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Book chapters on the topic "Dlib Histogram Oriented Gradients (HOG)"

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Nandi, Avishek, Paramartha Dutta, and Md Nasir. "Automatic Facial Expression Recognition Using Histogram Oriented Gradients (HoG) of Shape Information Matrix." In Intelligent Computing and Communication. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1084-7_33.

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Alekhya, Thanikonda, and S. Ranjan Mishra. "Object Recognition with Discriminately Trained Part-Based Model on HOG (Histogram of Oriented Gradients)." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2526-3_67.

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Tambi, Priya, Sarika Jain, and Durgesh Kumar Mishra. "Person-Dependent Face Recognition Using Histogram of Oriented Gradients (HOG) and Convolution Neural Network (CNN)." In International Conference on Advanced Computing Networking and Informatics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2673-8_5.

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Nithya, S., M. Revathi, A. Sathiya Sree, T. Sivapriya, and P. Vaishnavi. "Ensuring the Presence of a Person During Virtual Classes Using Histogram of Oriented Gradients (HOG) Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2177-3_29.

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Seng, Yeap Wei, Mohd Nadhir Ab Wahab, Wu Chia Chuan, Kevin Yeap Khai Wen, and Loo Tung Lun. "Enhanced the Face Recognition Accuracy by Using Histogram of Oriented Gradients (HOG) in Pre-processing Approach." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8129-5_6.

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Olejniczak, Michał, and Marek Kraft. "Taming the HoG: The Influence of Classifier Choice on Histogram of Oriented Gradients Person Detector Performance." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59063-9_49.

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Zhao, Yong, and Yong-feng Ju. "A Family of Efficient Appearance Models Based on Histogram of Oriented Gradients (HOG), Color Histogram and Their Fusion for Human Pose Estimation." In Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03766-6_94.

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Azam, Kazi Sultana Farhana, Farhin Farhad Riya, and Shah Tuhin Ahmed. "Leaf Detection Using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Classifying with SVM Utilizing Claim Dataset." In Intelligent Data Communication Technologies and Internet of Things. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9509-7_27.

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Asy’ari, Muhammad Zacky, Sebastian Filbert, and Zener Lie Sukra. "Histogram of Oriented Gradients (HOG) and Haar Cascade with Convolutional Neural Network (CNN) Performance Comparison in the Application of Edge Home Security System." In Lecture Notes in Electrical Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29078-7_2.

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Tanji Kaori, Itoh Hayato, and Imiya Atsushi. "Application of Directional Statistics to Classification of Three-Channel Colour Images." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-929-4-60.

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We extend the histogram of oriented gradient method to three-channel images which are captured by commercial RGB cameras. Firstly, we reformulate the oriented gradients (HoG) method from the viewpoints of gradient-based image pattern recognition and the directional statistics. Secondly, we develop operations for unification of three directional histograms constructed in channels of colour images.
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Conference papers on the topic "Dlib Histogram Oriented Gradients (HOG)"

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Gunawan, Wawan Setiawan, Bambang Sugiarto, Riffa Haviani Laluma, Teguh Nurhadi Suharsono, and Rini Nuraini Sukmana. "Car Detection Using Histogram of Oriented Gradients (HOG) Features on Autonomous Vehicle." In 2024 18th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2024. https://doi.org/10.1109/tssa63730.2024.10864315.

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Choudhry, Rashmi, Sivadharshini B, Madhav Dua, V. Nirmala, Saif O. Husain, and R. S. Arunkumar. "Breast Cancer Classification in Mammograms Using Support Vector Machines (SVMs) and Histogram of Oriented Gradients (HOG)." In 2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS). IEEE, 2024. http://dx.doi.org/10.1109/iicccs61609.2024.10763821.

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Arulananth, T. S., M. Sujitha, M. Nalini, B. Srividya, and K. Raviteja. "Fake shadow detection using local histogram of oriented gradients (HOG) features." In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2017. http://dx.doi.org/10.1109/iceca.2017.8212765.

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Lee, K. L., and M. M. Mokji. "Automatic target detection in GPR images using Histogram of Oriented Gradients (HOG)." In 2014 2nd International Conference on Electronic Design (ICED). IEEE, 2014. http://dx.doi.org/10.1109/iced.2014.7015795.

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Patel, Hitanshu A., and Ritesh D. Rajput. "Smart Surveillance System Using Histogram of Oriented Gradients (HOG) Algorithm and Haar Cascade Algorithm." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697464.

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Hannad, Yaâcoub, Imran Siddiqi, Youssef El Merabet, and Mohamed El Youssfi El Kettani. "Arabic Writer Identification System Using the Histogram of Oriented Gradients (HOG) of Handwritten Fragments." In the Mediterranean Conference. ACM Press, 2016. http://dx.doi.org/10.1145/3038884.3038900.

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Hosotani, Daisuke, Ikushi Yoda, and Katsuhiko Sakaue. "Wheelchair recognition by using stereo vision and histogram of oriented gradients (HOG) in real environments." In 2009 Workshop on Applications of Computer Vision (WACV). IEEE, 2009. http://dx.doi.org/10.1109/wacv.2009.5403043.

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Rosyidi, Lukman, Adrianto Prasetyo, and Muh Syaiful Romadhon. "Object Tracking with Raspberry Pi using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM)." In 2020 8th International Conference on Information and Communication Technology (ICoICT). IEEE, 2020. http://dx.doi.org/10.1109/icoict49345.2020.9166330.

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Correa, Thays, Fabíola De Oliveira, Matheus Baffa, and Lucas Lattari. "Unsupervised Segmentation of Breast Infrared Images in Lateral View Using Histogram of Oriented Gradients." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13477.

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Abstract:
Breast cancer is the second most common type of cancer in the world. It is estimated that 29.7% of new cases diagnosed in Brazil occur in any structures of the breasts. However, the disease has a good prognosis if detected early. Thus, the development of new technologies to help doctors to provide an accurate diagnosis is indispensable. The goal of this work is to develop a new method to automate parts of computer-aided diagnosis systems, performing the unsupervised segmentation of the Region of Interest (ROI) of infrared breast images acquired in lateral view. The segmentation proposed in this paper consists of three stages. The first stage pre-processes the infrared images of the lateral region of breasts. Later, features are extracted from a descriptor based on Histogram of Oriented Gradients (HOG). Concluding, a Machine Learning algorithm is used to perform the segmentation of the sample. The current method obtained an average of 89.9% accuracy and 94.3% specificity in our experiments, which is promising compared to other works.
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E., Mahitha, and Nagaraju V. "Analysis of Human Emotion via Speech Recognition Using Viola Jones Compared with Histogram of Oriented Gradients (HOG) Algorithm with Improved Accuracy." In First International Conference on Artificial Intelligence for Internet of things (AI4IOT): Accelerating Innovation in Industry and Consumer Electronics. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0012569300003739.

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