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Journal articles on the topic 'Histogram of Oriented Gradient'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

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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2

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

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3

Overbeek, Marlinda Vasty. "HISTOGRAM OF ORIENTED GRADIENT UNTUK DETEKSI EKSPRESI WAJAH MANUSIA." High Education of Organization Archive Quality: Jurnal Teknologi Informasi 10, no. 2 (December 31, 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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Adhinata, Faisal Dharma, Muhammad Ikhsan, and Wahyono Wahyono. "People counter on CCTV video using histogram of oriented gradient and Kalman filter methods." Jurnal Teknologi dan Sistem Komputer 8, no. 3 (May 26, 2020): 222–27. http://dx.doi.org/10.14710/jtsiskom.2020.13660.

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CCTV cameras have an important function in the field of public service, especially for convenience. The objects recorded through CCTV cameras are processed into information to support service satisfaction in the community. This study uses the function of CCTV for people counting from objects recorded by a camera. Currently, the process of detecting and tracking people takes a long time to detect all frames. In this study, the frame selection into keyframes uses the mutual information entropy method. The keyframes processing uses the Histogram of Oriented Gradient (HOG) and Kalman filter methods. The proposed method results F1 value of 0.85, recall of 76 %, and precision of 97 % with winStride parameter (12,12), scale 1.05, and the distance of the human object to CCTV 4 meters.

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5

Das, Dipankar. "Activity Recognition Using Histogram of Oriented Gradient Pattern History." International Journal of Computer Science, Engineering and Information Technology 4, no. 4 (August 31, 2014): 23–31. http://dx.doi.org/10.5121/ijcseit.2014.4403.

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6

Lei, Zhen. "Histogram of oriented gradient detector with color-invariant gradients in Gaussian color space." Optical Engineering 49, no. 10 (October 1, 2010): 109701. http://dx.doi.org/10.1117/1.3503944.

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7

Ouanan, Hamid, Mohammed Ouanan, and Brahim Aksasse. "Gabor-HOG Features based Face Recognition Scheme." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 2015): 331. http://dx.doi.org/10.11591/tijee.v15i2.1546.

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Extraction of invariant features is the core of Face RecognitionSystems (FRS). This work proposes a novel feature extractor-fusion scheme using two powerful feature descriptor known as Gabor Filters (GFs) and Histogram of Oriented Gradient (HOG), which the face image is filtered with the multiscale multiresolution Gabor filter bank to generate multiple Gabor magnitude images (GMIs), then the down-sampled GMIs and apply Histogram of Oriented Gradient to form the features. The experimental results on the FERET face database show the effectiveness of our methods.

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8

Jiao, Jichao, and Zhongliang Deng. "Deep combining of local phase quantization and histogram of oriented gradients for indoor positioning based on smartphone camera." International Journal of Distributed Sensor Networks 13, no. 1 (January 2017): 155014771668697. http://dx.doi.org/10.1177/1550147716686978.

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To achieve high accuracy in indoor positioning using a smartphone, there are two limitations: (1) limited computational and memory resources of the smartphone and (2) the human walking in large buildings. To address these issues, we propose a new feature descriptor by deeply combining histogram of oriented gradients and local phase quantization. This feature is a local phase quantization of a salient histogram of oriented gradient visualizing image, which is robust in indoor scenarios. Moreover, we introduce a base station–based indoor positioning system for assisting to reduce the image matching at runtime. The experimental results show that accurate and efficient indoor location positioning is achieved.

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9

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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10

Xu, Liangpeng, Yong Li, Chunxiao Fan, Hongbin Jin, and Xiang shi. "Incorporating Gradient Magnitude in Computation of Edge Oriented Histogram Descriptor." Electronic Imaging 2016, no. 2 (February 14, 2016): 1–7. http://dx.doi.org/10.2352/issn.2470-1173.2016.2.vipc-241.

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11

Hmood, Ali K., Ching Y. Suen, and Louisa Lam. "An Enhanced Histogram of Oriented Gradient Descriptor for Numismatic Applications." Pattern Recognition and Image Analysis 28, no. 4 (October 2018): 569–87. http://dx.doi.org/10.1134/s1054661818040028.

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12

Kong Yueping, 孔月萍, 刘霞 Liu Xia, 谢心谦 Xie Xinqian, and 李凤洁 Li Fengjie. "Face Liveness Detection Method Based on Histogram of Oriented Gradient." Laser & Optoelectronics Progress 55, no. 3 (2018): 031009. http://dx.doi.org/10.3788/lop55.031009.

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13

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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14

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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15

Dong, Jun, Xue Yuan, and Fanlun Xiong. "Global and Local Oriented Edge Magnitude Patterns for Texture Classification." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 03 (February 2017): 1750007. http://dx.doi.org/10.1142/s0218001417500070.

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In this paper, we propose a gray-scale texture descriptor, name the global and local oriented edge magnitude patterns (GLOEMP), for texture classification. GLOEMP is a framework, which is able to effectively combine local texture, global structure information and contrast of texture images. In GLOEMP, the principal orientation is determined by Histogram of Gradient (HOG) feature, then each direction is respectively shown in detail by a local binary patterns (LBP) occurrence histogram. Due to the fact that GLOEMP characterizes image information across different directions, it contains very abundant information. The global-level rotation compensation method is proposed, which shifts the principal orientation of the HOG to the first position, thus allowing GLOEMP to be robust to rotations. In addition, gradient magnitudes are used as weights to add to the histogram, making GLOEMP robust to lighting variances as well, and it also possesses a strong ability to express edge information. The experimental results obtained from the representative databases demonstrate that the proposed GLOEMP framework is capable of achieving significant improvement, in some cases reaching classification accuracy of 10% higher than over the traditional rotation invariant LBP method.

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16

Ibrahim, Shafaf. "Histogram of Oriented Gradient (HOG) for Off-Line Handwritten Signature Authentication." International Journal of Emerging Trends in Engineering Research 8, no. 1.1 (September 15, 2020): 102–07. http://dx.doi.org/10.30534/ijeter/2020/1681.12020.

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17

Jeong, Joon-Yong, Byung-Man Jung, and Kyu-Won Lee. "Multiple Pedestrians Tracking using Histogram of Oriented Gradient and Occlusion Detection." Journal of the Korean Institute of Information and Communication Engineering 16, no. 4 (April 30, 2012): 812–20. http://dx.doi.org/10.6109/jkiice.2012.16.4.812.

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18

Song, Dan, Lin-bo Tang, and Bao-jun Zhao. "The Object Recognition Algorithm Based on Affine Histogram of Oriented Gradient." Journal of Electronics & Information Technology 35, no. 6 (February 17, 2014): 1428–34. http://dx.doi.org/10.3724/sp.j.1146.2012.01241.

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19

Manchalwar, Mrunalini, and Krishna Warhade. "Detection of Cataract and Conjunctivitis Disease Using Histogram of Oriented Gradient." International Journal of Engineering and Technology 9, no. 3 (June 30, 2017): 2400–2406. http://dx.doi.org/10.21817/ijet/2017/v9i3/1709030214.

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20

Jamshed, Muhammed, Shahnaj Parvin, and Subrina Akter. "Significant HOG-Histogram of Oriented Gradient Feature Selection for Human Detection." International Journal of Computer Applications 132, no. 17 (December 17, 2015): 20–24. http://dx.doi.org/10.5120/ijca2015907704.

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21

Sun, Li, Yiqing Liu, and Guizhong Liu. "Multiple pedestrians tracking algorithm by incorporating histogram of oriented gradient detections." IET Image Processing 7, no. 7 (October 1, 2013): 653–59. http://dx.doi.org/10.1049/iet-ipr.2012.0500.

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22

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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23

Li, Zeyi, Haitao Zhang, and Yihang Huang. "A Rotation-Invariant Optical and SAR Image Registration Algorithm Based on Deep and Gaussian Features." Remote Sensing 13, no. 13 (July 4, 2021): 2628. http://dx.doi.org/10.3390/rs13132628.

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Traditional feature matching methods of optical and synthetic aperture radar (SAR) used gradient are sensitive to non-linear radiation distortions (NRD) and the rotation between two images. To address this problem, this study presents a novel approach to solving the rigid body rotation problem by a two-step process. The first step proposes a deep learning neural network named RotNET to predict the rotation relationship between two images. The second step uses a local feature descriptor based on the Gaussian pyramid named Gaussian pyramid features of oriented gradients (GPOG) to match two images. The RotNET uses a neural network to analyze the gradient histogram of the two images to derive the rotation relationship between optical and SAR images. Subsequently, GPOG is depicted a keypoint by using the histogram of Gaussian pyramid to make one-cell block structure which is simpler and more stable than HOG structure-based descriptors. Finally, this paper designs experiments to prove that the gradient histogram of the optical and SAR images can reflect the rotation relationship and the RotNET can correctly predict them. The similarity map test and the image registration results obtained on experiments show that GPOG descriptor is robust to SAR speckle noise and NRD.

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24

Ragb, Hussin K., and Vijayan K. Asari. "Histogram of Oriented Phase and Gradient (HOPG) Descriptor for Improved Pedestrian Detection." Electronic Imaging 2016, no. 3 (February 14, 2016): 1–6. http://dx.doi.org/10.2352/issn.2470-1173.2016.3.vstia-511.

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25

Mahto, M. K., K. Bhatia, and R. K. Sharma. "Robust Offline Gurmukhi Handwritten Character Recognition using Multilayer Histogram Oriented Gradient Features." International Journal of Computer Sciences and Engineering 6, no. 6 (June 30, 2018): 915–25. http://dx.doi.org/10.26438/ijcse/v6i6.915925.

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26

Park, Taejin, SeungKwan Beack, and Taejin Lee. "Noise Robust Automatic Speech Recognition Scheme with Histogram of Oriented Gradient Features." IEIE Transactions on Smart Processing and Computing 3, no. 5 (October 31, 2014): 259–66. http://dx.doi.org/10.5573/ieiespc.2014.3.5.259.

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27

Sugianela, Yuna, and Nanik Suciati. "EKSTRAKSI FITUR PADA PENGENALAN KARAKTER AKSARA JAWA BERBASIS HISTOGRAM OF ORIENTED GRADIENT." JUTI: Jurnal Ilmiah Teknologi Informasi 17, no. 1 (March 12, 2019): 64. http://dx.doi.org/10.12962/j24068535.v17i1.a819.

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28

Setumin, Samsul, and Shahrel Azmin Suandi. "Difference of Gaussian Oriented Gradient Histogram for Face Sketch to Photo Matching." IEEE Access 6 (2018): 39344–52. http://dx.doi.org/10.1109/access.2018.2855208.

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29

Mueid, Rifat Muhammad, Chandrama Ahmed, and Md Atiqur Rahman Ahad. "Pedestrian activity classification using patterns of motion and histogram of oriented gradient." Journal on Multimodal User Interfaces 10, no. 4 (May 27, 2015): 299–305. http://dx.doi.org/10.1007/s12193-015-0178-3.

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30

Xu, Fang, and Jing-hong Liu. "Ship detection and extraction using visual saliency and histogram of oriented gradient." Optoelectronics Letters 12, no. 6 (November 2016): 473–77. http://dx.doi.org/10.1007/s11801-016-6179-y.

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31

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 (November 14, 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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32

Widodo, Agus Wahyu, and Agus Harjoko. "Sistem Verifikasi Tanda Tangan Off-Line Berdasar Ciri Histogram Of Oriented Gradient (HOG) Dan Histogram Of Curvature (HoC)." Jurnal Teknologi Informasi dan Ilmu Komputer 2, no. 1 (August 19, 2015): 1. http://dx.doi.org/10.25126/jtiik.201521121.

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33

Chen, Ji, Kaiping Zhan, Qingzhou Li, Zhiyang Tang, Chenwei Zhu, Ke Liu, and Xiangyou Li. "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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34

Ghaffari, Sina, Parastoo Soleimani, Kin Fun Li, and David W. Capson. "A Novel Hardware–Software Co-Design and Implementation of the HOG Algorithm." Sensors 20, no. 19 (October 2, 2020): 5655. http://dx.doi.org/10.3390/s20195655.

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The histogram of oriented gradients is a commonly used feature extraction algorithm in many applications. Hardware acceleration can boost the speed of this algorithm due to its large number of computations. We propose a hardware–software co-design of the histogram of oriented gradients and the subsequent support vector machine classifier, which can be used to process data from digital image sensors. Our main focus is to minimize the resource usage of the algorithm while maintaining its accuracy and speed. This design and implementation make four contributions. First, we allocate the computationally expensive steps of the algorithm, including gradient calculation, magnitude computation, bin assignment, normalization and classification, to hardware, and the less complex windowing step to software. Second, we introduce a logarithm-based bin assignment. Third, we use parallel computation and a time-sharing protocol to create a histogram in order to achieve the processing of one pixel per clock cycle after the initialization (setup time) of the pipeline, and produce valid results at each clock cycle afterwards. Finally, we use a simplified block normalization logic to reduce hardware resource usage while maintaining accuracy. Our design attains a frame rate of 115 frames per second on a Xilinx® Kintex® Ultrascale™ FPGA while using less hardware resources, and only losing accuracy marginally, in comparison with other existing work.

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35

DEORE, S. P., and A. PRAVIN. "Ensembling: Model of histogram of oriented gradient based handwritten devanagari character recognition system." Traitement du signal 34, no. 1-2 (October 28, 2017): 7–20. http://dx.doi.org/10.3166/ts.34.7-20.

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36

Yang Guowei, 杨国威, 闫树明 Yan Shuming, and 王以忠 Wang Yizhong. "V-Shaped Seam Tracking Based on Particle Filter with Histogram of Oriented Gradient." Chinese Journal of Lasers 47, no. 7 (2020): 0702002. http://dx.doi.org/10.3788/cjl202047.0702002.

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37

Syaputra, R., D. Syamsuar, and E. S. Negara. "Multiple Smile Detection Using Histogram of Oriented Gradient and Support Vector Machine Methods." IOP Conference Series: Materials Science and Engineering 1071, no. 1 (February 1, 2021): 012027. http://dx.doi.org/10.1088/1757-899x/1071/1/012027.

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38

Salfikar, Inzar, Indra Adji Sulistijono, and Achmad Basuki. "Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance." EMITTER International Journal of Engineering Technology 5, no. 2 (January 13, 2018): 234–54. http://dx.doi.org/10.24003/emitter.v5i2.182.

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Finding victims at a disaster site is the primary goal of Search-and-Rescue (SAR) operations. Many technologies created from research for searching disaster victims through aerial imaging. but, most of them are difficult to detect victims at tsunami disaster sites with victims and backgrounds which are look similar. This research collects post-tsunami aerial imaging data from the internet to builds dataset and model for detecting tsunami disaster victims. Datasets are built based on distance differences from features every sample using Histogram-of-Oriented-Gradient (HOG) method. We use the longest distance to collect samples from photo to generate victim and non-victim samples. We claim steps to collect samples by measuring HOG feature distance from all samples. the longest distance between samples will take as a candidate to build the dataset, then classify victim (positives) and non-victim (negatives) samples manually. The dataset of tsunami disaster victims was re-analyzed using cross-validation Leave-One-Out (LOO) with Support-Vector-Machine (SVM) method. The experimental results show the performance of two test photos with 61.70% precision, 77.60% accuracy, 74.36% recall and f-measure 67.44% to distinguish victim (positives) and non-victim (negatives).

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Hong, Yameng, Chengcai Leng, Xinyue Zhang, Zhao Pei, Irene Cheng, and Anup Basu. "HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor." Remote Sensing 13, no. 12 (June 14, 2021): 2328. http://dx.doi.org/10.3390/rs13122328.

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Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

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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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41

Chitlangia, Aditya, and G. Malathi. "Handwriting Analysis based on Histogram of Oriented Gradient for Predicting Personality traits using SVM." Procedia Computer Science 165 (2019): 384–90. http://dx.doi.org/10.1016/j.procs.2020.01.034.

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42

Su, Ang, Xiaoliang Sun, Yueqiang Zhang, and Qifeng Yu. "Efficient rotation‐invariant histogram of oriented gradient descriptors for car detection in satellite images." IET Computer Vision 10, no. 7 (April 19, 2016): 634–40. http://dx.doi.org/10.1049/iet-cvi.2015.0333.

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43

Adetiba, Emmanuel, and Oludayo O. Olugbara. "Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features." Scientific World Journal 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/786013.

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This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

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Rahman Ahad, Md Atiqur, Md Nazmul Islam, and Israt Jahan. "Action recognition based on binary patterns of action-history and histogram of oriented gradient." Journal on Multimodal User Interfaces 10, no. 4 (September 7, 2016): 335–44. http://dx.doi.org/10.1007/s12193-016-0229-4.

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45

Manikonda, Santhosh K. G., and Dattatraya N. Gaonkar. "Islanding detection method based on image classification technique using histogram of oriented gradient features." IET Generation, Transmission & Distribution 14, no. 14 (July 17, 2020): 2790–99. http://dx.doi.org/10.1049/iet-gtd.2019.1824.

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46

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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Xiao, Yong Hao, and Hong Zhen. "Pedestrian Crowd Detection Based Unmanned Aerial Vehicle Infrared Imagery." Applied Mechanics and Materials 873 (November 2017): 347–52. http://dx.doi.org/10.4028/www.scientific.net/amm.873.347.

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Human detection is a keyproblem in computer vision. Recently, some research has been focusing on the detection ofpedestrianusing infrared images. The infrared images have outstanding merit. It depends only on object's temperature, but not on color or texture. In this paper, the pedestrian crowd detection approach is proposed. The approach is compose of ROI blocks extraction and crowd block recognition. ROI blocks can be extracted with circle gradient operator and weighted geometric filtering. Crowd blocks are recognized by support vector machine, which combines histogram of oriented gradient and circle gradient. The experimental results show thatthe approach works effectively in different scenes.

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