Relevant bibliographies by topics / Histogram oriented

Contents

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

Academic literature on the topic 'Histogram oriented'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Histogram oriented"

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Essa, Almabrok, and Vijayan K. Asari. "Histogram of Oriented Directional Features for Robust Face Recognition." International Journal of Monitoring and Surveillance Technologies Research 4, no. 3 (2016): 35–51. http://dx.doi.org/10.4018/ijmstr.2016070103.

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This paper presents an illumination invariant face recognition system that uses directional features and modular histogram. The proposed Histogram of Oriented Directional Features (HODF) produces multi-region histograms for each face image, then concatenates these histograms to form the final feature vector. This feature vector is used to recognize the face image by the help of k nearest neighbors classifier (KNN). The edge responses and the relationship among pixels are very important and play the main role for improving the face recognition accuracy. Therefore, this work presents the effectiveness of using different directional masks for detecting the edge responses on face recognition accuracy, such as Prewitt kernels, Kirsch masks, Sobel kernels, and Gaussian derivative masks. The performance evaluation of the proposed HODF algorithm is conducted on several publicly available databases and observed promising recognition rates.
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Gadetska, Svitlana, Valeriy Dubnitskiy, Alexander Khodyrev, and Yuri Kushneruk. "Excel-oriented calculator for calculating results of entropy analysis of data distributed by categories." Advanced Information Systems 7, no. 2 (2023): 28–40. http://dx.doi.org/10.20998/2522-9052.2023.2.05.

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The goal of the work. Development of EXCEL-oriented calculator for calculating the results of entropy analysis of data, which are distributed by categories. The subject of research is histograms of arbitrary distribution laws and conjugation tables 2×2. Research methods: Entropy and information analysis of histograms of arbitrary distribution laws and conjugation tables. The obtained results. It is proposed to use methods of entropy analysis for the analysis of data distributed by categories; information on the structure of the EXCEL-oriented calculator designed for this purpose is given. The calculator makes it possible to calculate entropy characteristics of histograms, namely: histogram entropy, histogram dispersion, histogram confidence intervals, diversity information index. The calculator performs a pairwise comparison of entropies of histograms using the Hutcheson method, determines Hellinger and Kullback-Leibler distances between histograms of arbitrary distribution laws and thus complements the chi-square criterion, determines the informational correlation coefficient. The correspondence between the Pearson correlation coefficient and the information correlation coefficient is established by the method of statistical modeling. For 2×2 conjugation tables, the calculator makes it possible to estimate the significance of the interaction between the row factor and the column factor. The calculator determines the values of conditional entropies for 2×2 conjugation tables. The proposed calculator fills the gaps in existing software products and can be used to process data distributed by categories using entropy analysis methods. It is shown that entropy methods of analysis are appropriate to use in cases where histograms determine arbitrary distribution laws.
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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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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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Cheon, Min-Kyu, Won-Ju Lee, Chang-Ho Hyun, and Mignon Park. "Rotation Invariant Histogram of Oriented Gradients." International Journal of Fuzzy Logic and Intelligent Systems 11, no. 4 (2011): 293–98. http://dx.doi.org/10.5391/ijfis.2011.11.4.293.

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Jia, Wei, Rong-Xiang Hu, Ying-Ke Lei, Yang Zhao, and Jie Gui. "Histogram of Oriented Lines for Palmprint Recognition." IEEE Transactions on Systems, Man, and Cybernetics: Systems 44, no. 3 (2014): 385–95. http://dx.doi.org/10.1109/tsmc.2013.2258010.

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Overbeek, Marlinda Vasty. "HISTOGRAM OF ORIENTED GRADIENT UNTUK DETEKSI EKSPRESI WAJAH MANUSIA." High Education of Organization Archive Quality: Jurnal Teknologi Informasi 10, no. 2 (2018): 81–86. http://dx.doi.org/10.52972/hoaq.vol10no2.p81-86.

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This research focuses on the detection of human facial expressions using the Histogram of Oriented Gradient algorithm. Whereas for the classification algorithm, Convolutional Neural Network is used. Image data used in the form of seven different expressions of humans with the extraction of 48x48 pixels. The use of Histogram of Oriented Gradient as a feature extracting algorithm, because Histogram of Oriented Gradient is good to be used in detecting moving objects. Whereas Convolutional Neural Network is used because it is an improvement of the Multi Layer Perceptron algorithm. Of the three epoches done, it produced the best accuracy of 77% re-introduction of human facial expressions. These results are quite convincing because it only uses three epochs.
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Liu, Yun-Fu, Jing-Ming Guo, and Jie-Cyun Yu. "Contrast Enhancement Using Stratified Parametric-Oriented Histogram Equalization." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 6 (2017): 1171–81. http://dx.doi.org/10.1109/tcsvt.2016.2527338.

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Shu, Chang, Xiaoqing Ding, and Chi Fang. "Histogram of the oriented gradient for face recognition." Tsinghua Science and Technology 16, no. 2 (2011): 216–24. http://dx.doi.org/10.1016/s1007-0214(11)70032-3.

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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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Dissertations / Theses on the topic "Histogram oriented"

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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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Kuřátko, Jiří. "Počítání lidí ve videu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255470.

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This master's thesis prepared the programme which is able to follow the trajectories of the movement of people and based on this to create various statistics. In practice it is an effective marketing tool which can be used for instance for customer flow analyses, optimal evaluation of opening hours, visitor traffic analyses and for a lot of other benefits. Histograms of oriented gradients, SVM classificator and optical flow monitoring were used to solve this problem. The method of multiple hypothesis tracking was selected for the association data. The system's quality was evaluated from the video footage of the street with the large concentration of pedestrians and from the school's camera system, where the movement in the corridor was monitored and the number of people counted.
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Dvořák, Michal. "Detekce a rozpoznání dopravního značení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221299.

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The goal of this thesis is the utilization of computer vision methods, in a way that will lead to detection and identification of traffic signs in an image. The final application is to analyze video feed from a video camcorder placed in a vehicle. With focus placed on effective utilization of computer resources in order to achieve real time identification of signs in a video stream.
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Vajhala, Rohith, Rohith Maddineni, and Preethi Raj Yeruva. "Weapon Detection In Surveillance Camera Images." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13565.

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Now a days, Closed Circuit Television (CCTV) cameras are installedeverywhere in public places to monitor illegal activities like armedrobberies. Mostly CCTV footages are used as post evidence after theoccurrence of crime. In many cases a person might be monitoringthe scene from CCTV but the attention can easily drift on prolongedobservation. Eciency of CCTV surveillance can be improved by in-corporation of image processing and object detection algorithms intomonitoring process.The object detection algorithms, previously implemented in CCTVvideo analysis detect pedestrians, animals and vehicles. These algo-rithms can be extended further to detect a person holding weaponslike rearms or sharp objects like knives in public or restricted places.In this work the detection of weapon from CCTV frame is acquiredby using Histogram of Oriented Gradients (HOG) as feature vector andarticial neural networks performing back-propagation algorithm forclassication.As a weapon in the hands of a human is considered to be greaterthreat as compared to a weapon alone, in this work the detection ofhuman in an image prior to a weapon detection has been found advan-tageous. Weapon detection has been performed using three methods.In the rst method, the weapon in the image is detected directly with-out human detection. Second and third methods use HOG and back-ground subtraction methods for detection of human prior to detectionof a weapon. A knife and a gun are considered as weapons of inter-est in this work. The performance of the proposed detection methodswas analysed on test image dataset containing knives, guns and im-ages without weapon. The accuracy rate 84:6% has been achievedby a single-class classier for knife detection. A gun and a knife havebeen detected by the three-class classier with an accuracy rate 83:0%.
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Němec, Jiří. "Detekce pohybujících se objektů ve video sekvenci." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-412865.

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This thesis deals with methods for the detection of people and tracking objects in video sequences. An application for detection and tracking of players in video recordings of sport activities, e.g. hockey or basketball matches, is proposed and implemented. The designed application uses the combination of histograms of oriented gradients and classification based on SVM (Support Vector Machines) for detecting players in the picture. Moreover, a particle filter is used for tracking detected players. The whole system was fully tested and the results are shown in the graphs and tables with verbal descriptions.
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Černín, Jan. "Vizuální detekce osob v komerčních aplikacích." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219704.

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The aim of the master thesis is to derive and implement image porcessing methods for people detection and tracking in images or videos. The overall solution was chosen as a combination of modern approaches and methods which were recently presented. The proposed algorithm is able to create trajectory of the person moving in indoor building spaces even under influence of full or partial occlusion for a short period of time. The scene of interest is surveyed by a static camera having direct view on targets. Selected methods are implemented in C# programming language based on OpenCV library. Graphical user interface was created to show the final output of algorithm.
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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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Memarzadeh, Milad. "Automated 2D Detection and Localization of Construction Resources in Support of Automated Performance Assessment of Construction Operations." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76908.

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This study presents two computer vision based algorithms for automated 2D detection of construction workers and equipment from site video streams. The state-of-the-art research proposes semi-automated detection methods for tracking of construction workers and equipment. Considering the number of active equipment and workers on jobsites and their frequency of appearance in a camera's field of view, application of semi-automated techniques can be time-consuming. To address this limitation, two new algorithms based on Histograms of Oriented Gradients and Colors (HOG+C), 1) HOG+C sliding detection window technique, and 2) HOG+C deformable part-based model are proposed and their performance are compared to the state-of-the-art algorithm in computer vision community. Furthermore, a new comprehensive benchmark dataset containing over 8,000 annotated video frames including equipment and workers from different construction projects is introduced. This dataset contains a large range of pose, scale, background, illumination, and occlusion variation. The preliminary results with average performance accuracies of 100%, 92.02%, and 89.69% for workers, excavators, and dump trucks respectively, indicate the applicability of the proposed methods for automated activity analysis of workers and equipment from single video cameras. Unlike other state-of-the-art algorithms in automated resource tracking, these methods particularly detects idle resources and does not need manual or semi-automated initialization of the resource locations in 2D video frames.<br>Master of Science
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Novák, Pavel. "Vyhledávání objektů v obraze na základě předlohy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220583.

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This Thesis is focused to Image Object Detection using Template. Main Benefit of this Work is a new Method for sympthoms extraction from Histogram of Oriented Gradients using set of Comparators. In this used Work Methods of Image comparing and Sympthoms extraction are described. Main Part is given to Histogram of Oriented Gradients Method. We came out from this Method. In this Work is used small training Data Set (100 pcs.) verified by X-Validation, followed by tests on real Sceneries. Achieved success Rate using X-Validation is 98%. for SVM Algorithm.
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Vídeňský, František. "Počítačová podpora rozpoznávání a klasifikace rodových erbů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363773.

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This master thesis describes the design and development of the system for detection and recognition of whole coat of arms as well as each heraldic parts. In the thesis are presented methods of computer vision for segmentation and detection of an object and selected methods that are the most suitable. Most of the heraldic parts are segmented using a convolution neural networks and the rest using active contours. The Histogram of the gradient method was selected for coats of arms detection in an image. For training and functionality verification is used my own data set. The resulting system can serve as an auxiliary tool used in auxiliary sciences of history.
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Book chapters on the topic "Histogram oriented"

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Lech, Piotr, and Krzysztof Okarma. "Binary Line Oriented Histogram." In Image Processing and Communications Challenges 9. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68720-9_1.

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Calvillo, Alberto Dzul, Roberto A. Vazquez, Jose Ambrosio, and Axel Waltier. "Face Recognition Using Histogram Oriented Gradients." In Intelligent Computing Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30447-2_11.

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Tan, Ching Soon, Phooi Yee Lau, and Tang Jung Low. "Macroalgae Recognition Based on Histogram Oriented Gradient." In 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1721-6_28.

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Arora, Ridhi, and Parvinder Singh. "Histogram of Oriented Gradients for Image Mosaicing." In Innovations in Computational Intelligence. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4555-4_14.

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Mousavi, Hossein, Moin Nabi, Hamed Kiani Galoogahi, Alessandro Perina, and Vittorio Murino. "Abnormality Detection with Improved Histogram of Oriented Tracklets." In Image Analysis and Processing — ICIAP 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23234-8_66.

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Kelly, Colm, Roger Woods, Moslem Amiri, Fahad Siddiqui, and Karen Rafferty. "Programmable Architectures for Histogram of Oriented Gradients Processing." In Handbook of Signal Processing Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91734-4_18.

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Purushothaman, P., S. Srihari, Alex Noel Joseph Raj, and M. Bhaskar. "Hardware Implementation of Pyramidal Histogram of Oriented Gradients." In Advances in Intelligent Systems and Computing. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6887-6_6.

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Chen, Shizeng, Bijun Li, Yuan Guo, and Jian Zhou. "Lane Detection Based on Histogram of Oriented Vanishing Points." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3651-9_1.

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Jangid, Mahesh, Sumit Srivastava, and Vivek Kumar Verma. "Real-Time Bottle Detection Using Histogram of Oriented Gradients." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8240-5_13.

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Wattanapanich, Chirawat, Hong Wei, and Wei Xu. "Analysis of Histogram of Oriented Gradients on Gait Recognition." In Pattern Recognition and Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71804-6_7.

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Conference papers on the topic "Histogram oriented"

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Srivastava, Shubham, Ajay Verma, and Shekhar Sharma. "Hindi Handwritten Character Recognition Using Histogram of Oriented Gradients Feature." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985080.

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Thomas, Rajesh, Suleiman Y. Yerima, and Khaled Shaalan. "Botnet Detection Using Network Traffic Visualization and Histogram of Oriented Gradients." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2024. https://doi.org/10.1109/cicn63059.2024.10847448.

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Palecek, Karel. "Lipreading using spatiotemporal histogram of oriented gradients." In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760575.

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Ortiz, Javier, Slawomir Bak, Michal Koperski, and Francois Bremond. "Minimizing hallucination in histogram of Oriented Gradients." In 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2015. http://dx.doi.org/10.1109/avss.2015.7301764.

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Tang, Chunhui, and Qijun Chen. "Zenithal people counting using histogram of oriented gradients." In 2012 5th International Congress on Image and Signal Processing (CISP). IEEE, 2012. http://dx.doi.org/10.1109/cisp.2012.6469949.

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Nickfarjam, A. M., A. Pourshabanan Najafabadi, and H. Ebrahimpour-komleh. "Efficient parameter tuning for histogram of oriented gradients." In 2014 22nd Iranian Conference on Electrical Engineering (ICEE). IEEE, 2014. http://dx.doi.org/10.1109/iraniancee.2014.6999687.

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Zhang, Ling, Wei Zhou, Jingwei Li, Juan Li, and Xin Lou. "Histogram of Oriented Gradients Feature Extraction Without Normalization." In 2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). IEEE, 2020. http://dx.doi.org/10.1109/apccas50809.2020.9301715.

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Salsabila, Haifa, Ema Rachmawati, and Febryanti Sthevanie. "Sundanese Aksara Recognition Using Histogram of Oriented Gradients." In 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2019. http://dx.doi.org/10.1109/isriti48646.2019.9034589.

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Ren, Haoyu, and Ze-Nian Li. "Object detection using edge histogram of oriented gradient." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025824.

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Kumari, Jyoti, and R. Rajesh. "Facial Expression Recognition Using Histogram of Oriented Gradients." In International Conference on Recent Advances in Mathematics, Statistics and Computer Science 2015. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789814704830_0055.

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Reports on the topic "Histogram oriented"

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Emma, Olsson. Kolinlagring med biokol : Att nyttja biokol och hydrokol som kolsänka i östra Mellansverige. Linköping University Electronic Press, 2025. https://doi.org/10.3384/9789180759496.

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Pest inventory of a field is a way of knowing when the thresholds for pest control is reached. It is of increasing interest to use machine learning to automate this process, however, many challenges arise with detection of small insects both in traps and on plants. This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machine were implemented. Trap detection with neural network models and a check size function were tested for narrowing the detections down to pests of a certain size. The results indicates that with further refinement and more training images this approach might hold potential for fungus gnat and rape beetles. Further, this thesis also investigates detection performance of Mask R-CNN and YOLOv5 on different insects in fields for the purpose of automating the data gathering process. The models showed promise for detection of rape beetles. YOLOv5 also showed promise as a multi-class detector of different insects, where sizes ranged from small rape beetles to larger bumblebees.
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