Relevant bibliographies by topics / Histogram of Oriented Gradients

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

Academic literature on the topic 'Histogram of Oriented Gradients'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 May 2022

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Journal articles on the topic "Histogram of Oriented Gradients"

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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 (December 1, 2011): 293–98. http://dx.doi.org/10.5391/ijfis.2011.11.4.293.

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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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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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K. Alilou, Vahid, and Farzin Yaghmaee. "Non-texture image inpainting using histogram of oriented gradients." Journal of Visual Communication and Image Representation 48 (October 2017): 43–53. http://dx.doi.org/10.1016/j.jvcir.2017.06.003.

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Gupta, Sheifali, Gurleen Kaur, Deepali Gupta, and Udit Jindal. "Brazilian Coins Recognition Using Histogram of Oriented Gradients Features." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4170–78. http://dx.doi.org/10.1166/jctn.2019.8498.

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This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.
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Zhao, Yong, Yongjun Zhang, Ruzhong Cheng, Daimeng Wei, and Guoliang Li. "An Enhanced Histogram of Oriented Gradients for Pedestrian Detection." IEEE Intelligent Transportation Systems Magazine 7, no. 3 (2015): 29–38. http://dx.doi.org/10.1109/mits.2015.2427366.

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Anggraeny, Fetty Tri, Basuki Rahmat, and Singgih Putra Pratama. "Deteksi Ikan Dengan Menggunakan Algoritma Histogram of Oriented Gradients." Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer 15, no. 2 (September 10, 2020): 114. http://dx.doi.org/10.30872/jim.v15i2.4648.

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Indonesia merupakan negara yang kaya akan sumber daya alam baik hayati maupun non-hayati. Salah satu sumber daya alam hayati yang sangat banyak jumlahnya di Indonesia adalah laut, Untuk mempermudah mengidentifikasikan ikan, dapat memanfaatkan sebuah teknologi yang dapat membantu manusia untuk dapat mengenali ikan dengan menggunakan visi komputer dan pendekatan pemrosesan gambar untuk deteksi ikan dan bukan ikan menggunakan algoritma Histogram of Oriented Gradients (HOG) dan AdaBoost-SVM. Hasil penelitian menunjukkan bahwa metode HOG dan AdaBoost-SVM dapat menghasilkan tingkat akurasi rata-rata sebesar 84.8%.
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Hafidhoh, Nisa ul, and Septian Enggar Sukmana. "Deteksi Pemain Basket Terklasifikasi Berbasis Histogram of Oriented Gradients." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 3, no. 1 (January 31, 2018): 6–11. http://dx.doi.org/10.25139/inform.v3i1.635.

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Pada olahraga basket jaman modern ini, kebutuhan analisis pergerakan pemain pada calon tim lawan olahraga basket perlu didukung oleh teknologi informasi yang mampu mengupayakan sistem yang otomatis. Analisis pergerakan pemain yang otomatis perlu didukung oleh sistem deteksi pemain yang handal dan akurat sehingga pemetaan pergerakan dapat dilakukan secara optimal. Tujuan dari penelitian ini adalah untuk mengembangkan metode Histogram of Oriented Gradients (HOG) menjadi sebuah metode deteksi yang handal untuk kasus deteksi pemain basket pada media. Tantangan pada penelitian ini adalah deteksi pemain tidak hanya pada saat berjalan dan berlari namun juga pada saat melompat. Untuk memperkuat fokus dan konsistensi terhadap objek yang terdeteksi, pemanfaatan metode klasifikasi Support Vector Machine (SVM) digunakan melalui kolaborasi terhadap HOG descriptor serta warna kostum pemain sehingga pembeda tim dari masing-masing pemain juga dapat dikenali. Tingkat akurasi dari evaluasi yang dihasilkan adalah 92% untuk true positive rate dan 40% untuk false positive rate.
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Zhang, Xiangyu, Fengwei An, Ikki Nakashima, Aiwen Luo, Lei Chen, Idaku Ishii, and Hans Jürgen Mattausch. "A hardware-oriented histogram of oriented gradients algorithm and its VLSI implementation." Japanese Journal of Applied Physics 56, no. 4S (January 30, 2017): 04CF01. http://dx.doi.org/10.7567/jjap.56.04cf01.

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Dissertations / Theses on the topic "Histogram of Oriented Gradients"

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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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Norris, Michael K. "INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1629.

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This work presents improvements to Monte Carlo Localization (MCL) for a mobile robot using computer vision. Solutions to the localization problem aim to provide fine resolution on location approximation, and also be resistant to changes in the environment. One such environment change is the kidnapped/teleported robot problem, where a robot is suddenly transported to a new location and must re-localize. The standard method of "Augmented MCL" uses particle filtering combined with addition of random particles under certain conditions to solve the kidnapped robot problem. This solution is robust, but not always fast. This work combines Histogram of Oriented Gradients (HOG) computer vision with particle filtering to speed up the localization process. The major slowdown in Augmented MCL is the conditional addition of random particles, which depends on the ratio of a short term and long term average of particle weights. This ratio does not change quickly when a robot is kidnapped, leading the robot to believe it is in the wrong location for a period of time. This work replaces this average-based conditional with a comparison of the HOG image directly in front of the robot with a cached version. This resulted in a speedup ranging from from 25.3% to 80.7% (depending on parameters used) in localization time over the baseline Augmented MCL.
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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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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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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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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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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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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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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.
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Book chapters on the topic "Histogram of Oriented Gradients"

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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, 125–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30447-2_11.

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Arora, Ridhi, and Parvinder Singh. "Histogram of Oriented Gradients for Image Mosaicing." In Innovations in Computational Intelligence, 211–25. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4555-4_14.

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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, 649–82. Cham: 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, 61–69. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6887-6_6.

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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, 118–25. Singapore: 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, 86–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71804-6_7.

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Mirabdollah, M. Hossein, Mahmoud A. Mohamed, and Bärbel Mertsching. "Distributed Averages of Gradients (DAG): A Fast Alternative for Histogram of Oriented Gradients." In RoboCup 2016: Robot World Cup XX, 97–108. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68792-6_8.

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Yang, Wei-Jong, Yu-Xiang Su, Pau-Choo Chung, and Jar-Ferr Yang. "Weighted Histogram of Oriented Uniform Gradients for Moving Object Detection." In Lecture Notes in Networks and Systems, 250–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12388-8_18.

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Tsolakidis, Dimitris G., Dimitrios I. Kosmopoulos, and George Papadourakis. "Plant Leaf Recognition Using Zernike Moments and Histogram of Oriented Gradients." In Artificial Intelligence: Methods and Applications, 406–17. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07064-3_33.

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Rashwan, Hatem A., Mahmoud A. Mohamed, Miguel Angel García, Bärbel Mertsching, and Domenec Puig. "Illumination Robust Optical Flow Model Based on Histogram of Oriented Gradients." In Lecture Notes in Computer Science, 354–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40602-7_38.

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Conference papers on the topic "Histogram of Oriented Gradients"

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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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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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Yadav, Ankush Arvindkumar, and A. S. Bhalchandra. "Vehicle Logo Detection Using Histogram of Oriented Gradients." In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. http://dx.doi.org/10.1109/iccons.2018.8662837.

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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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Maurya, Aparna, and Sandhya Tarar. "Spoofed Video Detection Using Histogram of Oriented Gradients." In VisionNet'16: Third International Symposium on Computer Vision and the Internet. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2983402.2983408.

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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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Yibo Cui, Lifeng Sun, and Shiqiang Yang. "Pedestrian detection using improved histogram of oriented gradients." In 5th International Conference on Visual Information Engineering (VIE 2008). IEE, 2008. http://dx.doi.org/10.1049/cp:20080344.

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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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