Relevant bibliographies by topics / Improved histogram of oriented gradients (HOG)

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

Academic literature on the topic 'Improved histogram of oriented gradients (HOG)'

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Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Improved histogram of oriented gradients (HOG)"

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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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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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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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Zhang, Li, Weiyue Xu, Cong Shen, and Yingping Huang. "Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features." Sensors 24, no. 5 (2024): 1590. http://dx.doi.org/10.3390/s24051590.

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The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.
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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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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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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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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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Jing Zhao, Jing Zhao. "Sports Motion Feature Extraction and Recognition Based on a Modified Histogram of Oriented Gradients with Speeded Up Robust Features." 電腦學刊 33, no. 1 (2022): 063–70. http://dx.doi.org/10.53106/199115992022023301007.

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<p>Traditional motion recognition methods can extract global features, but ignore the local features. And the obscured motion cannot be recognized. Therefore, this paper proposes a modified Histogram of oriented gradients (HOG) combining speeded up robust features (SURF) for sports motion feature extraction and recognition. This new method can fully extract the local and global features of the sports motion recognition. The new algorithm first adopts background subtraction to obtain the motion region. Direction controllable filter can effectively describe the motion edge features. The HOG feature is improved by introducing direction controllable filter to enhance the local edge information. At the same time, the K-means clustering is performed on SURF to obtain the word bag model. Finally, the fused motion features are input to support vector machine (SVM) to classify and recognize the motion features. We make comparison with the state-of-the-art methods on KTH, UCF Sports and SBU Kinect Interaction data sets. The results show that the recognition accuracy of the proposed algorithm is greatly improved.</p> <p> </p>
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Bakheet, Samy, and Ayoub Al-Hamadi. "A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification." Brain Sciences 11, no. 2 (2021): 240. http://dx.doi.org/10.3390/brainsci11020240.

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Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.
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Dissertations / Theses on the topic "Improved histogram of 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 "Improved histogram of 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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Guzmán-Ramírez, Enrique, Ayax García, Esteban Guerrero-Ramírez, Antonio Orantes Molina, Oscar Ramírez, and Ignacio Arroyo. "Multi-object Recognition Using a Feature Descriptor and Neural Classifier." In Vision Sensors - Recent Advances [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.106754.

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In the field of object recognition, feature descriptors have proven to be able to provide accurate representations of objects facilitating the recognition task. In this sense, Histograms of Oriented Gradients (HOG), a descriptor that uses this approach, together with Support Vector Machines (SVM) have proven to be successful human detection methods. In this paper, we propose a scheme consisting of improved HOG and a classifier with a neural approach to producing a robust system for object recognition. The main contributions of this work are: First, we propose an improved gradient calculation that allows for better discrimination for the classifier system, which consists of performing a threshold over both the magnitude and direction of the gradients. This improvement reduces the rate of false positives. Second, although HOG is particularly suited for human detection, we demonstrate that it can be used to represent different objects accurately, and even perform well in multi-class applications. Third, we show that a classifier that uses a neuronal approach is an excellent complement to a HOG-based feature extractor. Finally, experimental results on the well-known Caltech 101 dataset illustrate the benefits of the proposed scheme.
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Conference papers on the topic "Improved histogram of 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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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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Reichman, Daniël, Leslie M. Collins, and Jordan M. Malof. "Learning improved pooling regions for the Histogram of Oriented Gradient (HOG) feature for buried threat detection in ground penetrating radar." In SPIE Defense + Security, edited by Steven S. Bishop and Jason C. Isaacs. SPIE, 2017. http://dx.doi.org/10.1117/12.2263108.

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Gonçalves, Camilo Lélis A., Ronaldo F. Zampolo, Fabrício José B. Barros, et al. "Improving the performance of a SVM+HOG classifier for detection and tracking of wagon components by using geometric constraints." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8336.

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The inspection of train and railway components that can cause derailment plays a key role in rail maintenance. To improve productivity and safety, service providers look for automatic and reliable inspection solutions. Although automatic inspection based on computer vision is a standard concept, such an application challenges development community due to the environmental and logistic factors to be considered. Previous publications presented automatic classifiers to evaluate integrity and placement of wagon components. Although the high classification accuracy reported, ineffective object detection affected the general performance. Our object detector/tracker consists of a descriptor based on the histogram of oriented gradients, a support vector machine classifier, and a set of geometric constraints, which takes in account the ideal trajectory path of the wagon’s components of interest and the distances between them. We detail training and validation procedures, together with the metrics used to assess the performance of the system. Presented results compare two other techniques with our approach, which exhibits a fair trade-off between reliability and computational complexity for the application of wagon component detection.
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P V, Deepa, and Abirami N. "Automatic Segmentation of Cloud Image from Satellite Image." In International Conference on Modern Trends in Engineering and Management (ICMTEM-25). International Journal of Advanced Trends in Engineering and Management, 2025. https://doi.org/10.59544/gfxg5093/icmtem25p54.

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The precise and reliable interpretation of the objects on land is hampered by the presence of clouds in satellite photography. Therefore, before allowing the satellite images to be used for any additional analysis, automatic cloud detection is an essential pre processing step. The different densities and thicknesses of clouds make this a difficult operation. A Deep Learning (DL) based system for automatically segmenting clouds from satellite data is proposed in this research to improve the accuracy of climate models. The system uses a Long Short Term Memory (LSTM) network that is connected with a systematic workflow that includes feature extraction of Histogram of Oriented Gradients (HOG), model training, and data preprocessing of Contrast Limited Adaptive Histogram Equalization (CLAHE). Raw satellite datasets are first preprocessed and pertinent features are extracted. An LSTM based model uses these features and trained to identify temporal patterns in order to improve cloud formation prediction. Future cloud topologies are predicted by the trained model using historical data, and users can access these predictions. The model is trained and verified using publicly accessible satellite image datasets and is implemented using PyTorch and its related libraries. A comparison with conventional rule based segmentation techniques demonstrates that the proposed method captures a variety of cloud structures.
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P, Mukilan, and Karputha Pandi P. "Dual-Path Mobile Net-Based Framework for Esophageal Cancer Detection using Enhanced Endoscopic Imaging." In International Conference on Modern Trends in Engineering and Management (ICMTEM-25). International Journal of Advanced Trends in Engineering and Management, 2025. https://doi.org/10.59544/zsdp4777/icmtem25p23.

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Esophageal Cancer (EC) is essential for increasing patient survival in early identification; yet, endoscopists find it difficult to identify the cancer cells. This paper proposed a Dual Path MobileNet for esophageal cancer screening is proposed to reduce the workload of physician and increase detection accuracy. Firstly, un sharp mask filter is applied to Esophageal Endoscopy Images to brighten the edge and to increase its sharpness for better quality of image. Next, the processed image is given to segmentation process using Self Organizing Map (SOM) clustering algorithm. Here the SOM convert the high resolution image into a lower resolution image. After that image is extracted using Histogram of Oriented Gradient (HOG) it capture the edge and shape of the gradient direction for further analysis. Finally, a Dual path MobileNet framework is processed to enhance the classification of esophageal cancer diagnosis. Using python software the proposed framework have an improved accuracy of 95% is accomplished when compared to other techniques.
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Cheng, Chi-Cheng, and Yi-Fan Wu. "A Pedestrian Counting Scheme for Video Images." In 3rd International Conference on Artificial Intelligence and Machine Learning (CAIML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121211.

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Pedestrian counting aims to compute the numbers of pedestrians entering and leaving an area of interest based on object detection and tracking techniques. This paper proposes a simple and effective approach of pedestrian counting that can effectively solve the problem of pedestrian occlusion.Firstly, the moving objects are detected by the median filtering and foreground extraction with the improved mixed Gaussian model. And then the HOG (Histogram of oriented gradient) features detection and the SVM (Support vector machine) classification are applied to identify the pedestrians. A pedestrian dataset containing 1500 positive samples, 12000 negative samples, and 420 hard examples, which gave the false discriminant results with the initial classifier, also considered as negative samples to enhance classification capability is employed. In addition, the Kalman filtering with BLOB analysis for dynamic target tracking is chosen to predict pedestrian trajectory.This method greatly reduces the target misjudgment caused by overlapping and completes the two-way counting. Experiments on pedestrian tracking and counting in video images demonstrate promising performance with satisfactory recognition rate and processing time.
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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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