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Journal articles on the topic 'Histogrammes de Gradient orienté'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 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

MATSUMOTO, Yohei. "Ship Image Recognition using HOG." Journal of Japan Institute of Navigation 129 (2013): 105–12. http://dx.doi.org/10.9749/jin.129.105.

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3

Déniz, O., G. Bueno, J. Salido, and F. De la Torre. "Face recognition using Histograms of Oriented Gradients." Pattern Recognition Letters 32, no. 12 (September 2011): 1598–603. http://dx.doi.org/10.1016/j.patrec.2011.01.004.

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4

Brookshire, Jonathan. "Person Following Using Histograms of Oriented Gradients." International Journal of Social Robotics 2, no. 2 (March 6, 2010): 137–46. http://dx.doi.org/10.1007/s12369-010-0046-y.

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5

Wu, Jiaxing, Zixuan Yang, and Ting Wang. "Histograms of Oriented Gradients for cats-dogs detection." Journal of Physics: Conference Series 1314 (October 2019): 012176. http://dx.doi.org/10.1088/1742-6596/1314/1/012176.

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6

Khalid, Madiha, Muhammad Murtaza Yousaf, Kashif Murtaza, and Syed Mansoor Sarwar. "Image de-fencing using histograms of oriented gradients." Signal, Image and Video Processing 12, no. 6 (March 12, 2018): 1173–80. http://dx.doi.org/10.1007/s11760-018-1266-0.

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7

Watanabe, Tomoki, Satoshi Ito, and Kentaro Yokoi. "Co-occurrence Histograms of Oriented Gradients for Human Detection." IPSJ Transactions on Computer Vision and Applications 2 (2010): 39–47. http://dx.doi.org/10.2197/ipsjtcva.2.39.

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8

Bratanič, Blaž, Franjo Pernuš, Boštjan Likar, and Dejan Tomaževič. "Real-Time Rotation Estimation Using Histograms of Oriented Gradients." PLoS ONE 9, no. 3 (March 24, 2014): e92137. http://dx.doi.org/10.1371/journal.pone.0092137.

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9

Li, Bin, and Guang Huo. "Face recognition using locality sensitive histograms of oriented gradients." Optik 127, no. 6 (March 2016): 3489–94. http://dx.doi.org/10.1016/j.ijleo.2015.12.032.

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10

Jebril, Noor A., Hussein R. Al-Zoubi, and Qasem Abu Al-Haija. "Recognition of Handwritten Arabic Characters using Histograms of Oriented Gradient (HOG)." Pattern Recognition and Image Analysis 28, no. 2 (April 2018): 321–45. http://dx.doi.org/10.1134/s1054661818020141.

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11

Tian, Qing, Wen Hua Zhao, Long Zhang, and Yun Wei. "Vehicle and Pedestrian Detection and Tracking." Applied Mechanics and Materials 401-403 (September 2013): 1432–35. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1432.

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Vehicle and pedestrian detection plays a critical role in the intelligent transportation system. The paper proposes an algorithm which can solve the problem effectively by Histograms of Oriented Gradients (HOG) features extraction and Support Vector Machine (SVM). This detection system is based on Histograms of Oriented Gradients features combined with Support Vector Machine for the recognition stage which is insensitive to lightings and noises. We use Kalman filter to track the objects. As shown in experiments, the method has high detection rate and can also satisfy the real-time intelligent transportation system.

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Hu, Xuejuan, Shuangchen Ruan, Chunyu Guo, and ChengxiangShenzhen Liu. "Improved histograms of oriented gradients for Chinese RMB currency recognition." Journal of Shenzhen University Science and Engineering 31, no. 5 (2014): 487. http://dx.doi.org/10.3724/sp.j.1249.2014.05487.

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13

Ge, Ping Shu, Guo Kai Xu, Xiu Chun Zhao, Peng Song, and Lie Guo. "Pedestrian Detection Based on Histograms of Oriented Gradients in ROI." Advanced Materials Research 542-543 (June 2012): 937–40. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.937.

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To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

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14

MITSUNARI, Koichi, Yoshinori TAKEUCHI, Masaharu IMAI, and Jaehoon YU. "Decomposed Vector Histograms of Oriented Gradients for Efficient Hardware Implementation." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E101.A, no. 11 (November 1, 2018): 1766–75. http://dx.doi.org/10.1587/transfun.e101.a.1766.

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15

JIA, Hui-Xing, and Yu-Jin ZHANG. "Multiple Kernels Based Object Tracking Using Histograms of Oriented Gradients." Acta Automatica Sinica 35, no. 10 (November 13, 2009): 1283–89. http://dx.doi.org/10.3724/sp.j.1004.2009.01283.

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16

Haibo, Pang, Chengming Liu, Zhe Zhao, and Shuyan Zhang. "Gesture Recognition Based on Hexagonal Structure Histograms of Oriented Gradients." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 8 (August 31, 2015): 239–50. http://dx.doi.org/10.14257/ijsip.2015.8.8.26.

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17

Lee, Seung Eun, Kyungwon Min, and Taeweon Suh. "Accelerating Histograms of Oriented Gradients descriptor extraction for pedestrian recognition." Computers & Electrical Engineering 39, no. 4 (May 2013): 1043–48. http://dx.doi.org/10.1016/j.compeleceng.2013.04.001.

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18

Meethongjan, Kittikhun, Thongchai Surinwarangkoon, and Vinh Truong Hoang. "Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 6 (December 1, 2020): 3019. http://dx.doi.org/10.12928/telkomnika.v18i6.16133.

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19

Qu, Zhi Yi, Ya Xin Jin, and Jie Feng. "Fast Human Detection Using Dynamic Contour and Histograms of Oriented Gradients." Applied Mechanics and Materials 347-350 (August 2013): 3600–3603. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3600.

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Human detection is a challenging problem, owing to variations in pose, body shape, appearance, clothing, illumination, and background clutter, in addition, the cameras or backgrounds make it even harder. But even so, it has many potential applications including net-meeting, security, human-computer interaction, gaming, and even health-care. Various new approaches have been proposed to solve this problem. We have studied and implemented a method by using dynamic contour [ and Histograms of Oriented Gradients [ to detecting human body fast and accurately in static images.

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20

Sun, Weihan, and Koichi Kise. "Cartoon Character Recognition Using Concentric Multi-Region Histograms of Oriented Gradients." IEEJ Transactions on Electronics, Information and Systems 132, no. 11 (2012): 1847–54. http://dx.doi.org/10.1541/ieejeiss.132.1847.

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21

Jhansi, Y. "Sketch-based Image Retrieval using Rotation-invariant Histograms of Oriented Gradients." International Journal of Computer Trends and Technology 49, no. 2 (July 25, 2017): 121–24. http://dx.doi.org/10.14445/22312803/ijctt-v49p118.

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22

WANG, CHI-CHEN RAXLE, JIN-YI WU, and JENN-JIER JAMES LIEN. "PEDESTRIAN DETECTION SYSTEM USING CASCADED BOOSTING WITH INVARIANCE OF ORIENTED GRADIENTS." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 04 (June 2009): 801–23. http://dx.doi.org/10.1142/s0218001409007363.

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This study presents a novel learning-based pedestrian detection system capable of automatically detecting individuals of different sizes and orientations against a wide variety of backgrounds, including crowds, even when the individual is partially occluded. To render the detection performance robust toward the effects of geometric and rotational variations in the original image, the feature extraction process is performed using both rectangular- and circular-type blocks of various sizes and aspect ratios. The extracted blocks are rotated in accordance with their dominant orientation(s) such that all the blocks extracted from the input images are rotationally invariant. The pixels within the cells in each block are then voted into rectangular- and circular-type 9-bin histograms of oriented gradients (HOGs) in accordance with their gradient magnitudes and corresponding multivariate Gaussian-weighted windows. Finally, four cell-based histograms are concatenated using a tri-linear interpolation technique to form one 36-dimensional normalized HOG feature vector for each block. The experimental results show that the use of the Gaussian-weighted window approach and tri-linear interpolation technique in constructing the HOG feature vectors improves the detection performance from 91% to 94.5%. In the proposed scheme, the detection process is performed using a cascaded detector structure in which the weak classifiers and corresponding weights of each stage are established using the AdaBoost self-learning algorithm. The experimental results reveal that the cascaded structure not only provides a better detection performance than many of the schemes presented in the literature, but also achieves a significant reduction in the computational time required to classify each input image.

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23

Mlakar, Uroš, and Božidar Potočnik. "Automated facial expression recognition based on histograms of oriented gradient feature vector differences." Signal, Image and Video Processing 9, S1 (August 13, 2015): 245–53. http://dx.doi.org/10.1007/s11760-015-0810-4.

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24

Thontadari, C., and C. J. Prabhakar. "Scale Space Co-Occurrence HOG Features for Word Spotting in Handwritten Document Images." International Journal of Computer Vision and Image Processing 6, no. 2 (July 2016): 71–86. http://dx.doi.org/10.4018/ijcvip.2016070105.

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In this paper, the authors proposed a Scale Space Co-occurrence Histograms of Oriented Gradients method (SS Co-HOG) for retrieving words from digitized handwritten documents. The poor performance of HOG based word spotting in handwritten documents is due to that HOG ignores spatial information of neighboring pixels whereas Co-HOG captures the spatial information of neighboring pixels through counting the occurrence of the gradient orientations of two or more neighboring pixels. The authors employed three scale parameter representation of an image and at each scale, they divide the word image into blocks and Co-HOG features are extracted from each block and finally concatenate them into form a feature descriptor. The proposed method is evaluated using precision and recall metrics through experimentation conducted on popular datasets such as IAM and GW and confirmed that their method outperforms for both the datasets.

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25

Jun Hu, Chun Guan, and Xin Zhou. "Method of Pedestrian Detection using Rough Set and Histograms of Oriented Gradients." International Journal of Advancements in Computing Technology 5, no. 3 (February 15, 2013): 430–38. http://dx.doi.org/10.4156/ijact.vol5.issue3.50.

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26

Sarpate, Pravin G., and Ramesh R. Manza. "Extraction of Face Texture Features Based on Histograms of Oriented Gradients (HOG)." International Journal of Computer Sciences and Engineering 6, no. 3 (March 30, 2018): 168–72. http://dx.doi.org/10.26438/ijcse/v6i3.168172.

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27

Miramontes-Jaramillo, D., V. I. Kober, V. H. Díaz-Ramírez, and V. N. Karnaukhov. "A novel image matching algorithm based on sliding histograms of oriented gradients." Journal of Communications Technology and Electronics 59, no. 12 (December 2014): 1446–50. http://dx.doi.org/10.1134/s1064226914120146.

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28

Torrione, Peter A., Kenneth D. Morton, Rayn Sakaguchi, and Leslie M. Collins. "Histograms of Oriented Gradients for Landmine Detection in Ground-Penetrating Radar Data." IEEE Transactions on Geoscience and Remote Sensing 52, no. 3 (March 2014): 1539–50. http://dx.doi.org/10.1109/tgrs.2013.2252016.

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29

Erazo-Aux, Jorge, H. Loaiza-Correa, and A. D. Restrepo-Giron. "Histograms of oriented gradients for automatic detection of defective regions in thermograms." Applied Optics 58, no. 13 (May 1, 2019): 3620. http://dx.doi.org/10.1364/ao.58.003620.

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30

Cerd Ng, Ri, Kian Ming Lim, Chin Poo Lee, and Siti Fatimah Abdul Razak. "Surveillance system with motion and face detection using histograms of oriented gradients." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 2 (May 1, 2019): 869. http://dx.doi.org/10.11591/ijeecs.v14.i2.pp869-876.

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<span>With the rapidly increasing crime rate in recent years, community safety issues aroused a wide concern among public community. Various security technologies had been invented and carried out, for example password door lock, alarm system, and closed-circuit televisions (CCTVs). Although the installation of CCTVs is common in most premises, they require extensive man power to manually monitor the videos. Moreover, the reliability of human operator greatly deteriorates when they are in fatigue condition. In view of this, our project aims to develop an automated computer vision based surveillance system. Unlike ordinary CCTV system that requires human operator to manually observe and detect intruder, a computer vision based surveillance system automatically monitor the security of premises and trigger actions once an intrusion is detected. Basically, it is a simple surveillance camera system that will be setup at the entrance of the house. The reliability is being enhanced by applying the motion detection and face recognition algorithm, using histogram of oriented gradients that could detect the existence of people at the main entrance and try to validate the user. Apart from recognizing the user, the propose system also support mobile interaction whereby user can monitor the camera, activate alarm, and even received notification when a stranger was being detected at the entrance of the house. By including such functionalities, proposed system had highly surpassed the existing surveillance system by not only support monitoring, but also try to recognize the people and inform the user at the exact moment when stranger detected, so that user could take immediate action about it, for example activating the alarm or report to police. The project was executed with expected outcome and objectives had been accomplished.</span>

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31

Yan, Gang, Ming Yu, Yang Yu, and Longfei Fan. "Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification." Optik 127, no. 19 (October 2016): 7941–51. http://dx.doi.org/10.1016/j.ijleo.2016.05.092.

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32

Vokhmintcev, A. V., I. V. Sochenkov, V. V. Kuznetsov, and D. V. Tikhonkikh. "Face recognition based on a matching algorithm with recursive calculation of oriented gradient histograms." Doklady Mathematics 93, no. 1 (January 2016): 37–41. http://dx.doi.org/10.1134/s1064562416010178.

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33

Cattell, Liam, Günther Platsch, Richie Pfeiffer, Jérôme Declerck, Julia A. Schnabel, and Chloe Hutton. "Classification of amyloid status using machine learning with histograms of oriented 3D gradients." NeuroImage: Clinical 12 (February 2016): 990–1003. http://dx.doi.org/10.1016/j.nicl.2016.05.004.

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34

Li, Bin, Fuqiang Sun, and Yonghan Zhang. "Building Recognition Using Gist Feature Based on Locality Sensitive Histograms of Oriented Gradients." Pattern Recognition and Image Analysis 29, no. 2 (April 2019): 258–67. http://dx.doi.org/10.1134/s1054661819020044.

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35

Arróspide, Jon, Luis Salgado, and Massimo Camplani. "Image-based on-road vehicle detection using cost-effective Histograms of Oriented Gradients." Journal of Visual Communication and Image Representation 24, no. 7 (October 2013): 1182–90. http://dx.doi.org/10.1016/j.jvcir.2013.08.001.

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36

Watanabe, Tomoki, Satoshi Ito, and Kentaro Yokoi. "Image Feature Descriptor using Co-occurrence Histograms of Oriented Gradients for Human Detection." Journal of the Institute of Image Information and Television Engineers 71, no. 1 (2017): J28—J34. http://dx.doi.org/10.3169/itej.71.j28.

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37

Hurney, Patrick, Edward Jones, Peter Waldron, Martin Glavin, and Fearghal Morgan. "Night-time pedestrian classification with histograms of oriented gradients-local binary patterns vectors." IET Intelligent Transport Systems 9, no. 1 (February 1, 2015): 75–85. http://dx.doi.org/10.1049/iet-its.2013.0163.

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38

Akimoto, Shohei, Tomokazu Takahashi, Masato Suzuki, Yasuhiko Arai, and Seiji Aoyagi. "Human Detection by Fourier Descriptors and Fuzzy Color Histograms with Fuzzyc-Means Method." Journal of Robotics and Mechatronics 28, no. 4 (August 19, 2016): 491–99. http://dx.doi.org/10.20965/jrm.2016.p0491.

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[abstFig src='/00280004/07.jpg' width='300' text='Result of specific person detection in Tsukuba Challenge' ] It is difficult to use histograms of oriented gradients (HOG) or other gradient-based features to detect persons in outdoor environments given that the background or scale undergoes considerable changes. This study involved the segmentation of depth images. Additionally, P-type Fourier descriptors were extracted as shape features from two-dimensional coordinates of a contour in the segmentation domains. With respect to the P-type Fourier descriptors, a person detector was created with the fuzzyc-means method (for general person detection). Furthermore, a fuzzy color histogram was extracted in terms of color features from the RGB values of the domain surface. With respect to the fuzzy color histogram, a detector of a person wearing specific clothes was created with the fuzzyc-means method (specific person detection). The study includes the following characteristics: 1) The general person detection requires less number of images used for learning and is robust against a change in the scale when compared to that in cases in which HOG or other methods are used. 2) The specific person detection gives results close to those obtained by human color vision when compared to the color indices such as RGB or CIEDE. This method was applied for a person search application at the Tsukuba Challenge, and the obtained results confirmed the effectiveness of the proposed method.

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Li, Jian Fu, and Wei Guo Gong. "Application of Thermal Infrared Imagery in Human Action Recognition." Advanced Materials Research 121-122 (June 2010): 368–72. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.368.

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Human action recognition has been widely researched and applied in intelligent visual surveillance fields nowadays. Most work on action recognition has been visible-spectrum oriented over the past decade, while the persistence of visual surveillance system increases the demand for night-time action recognition. This paper deals with the problem of night action recognition using thermal infrared imagery. A novel algorithm based on the human action silhouettes energy histograms is proposed. The algorithm first makes use of the statistical background model and background subtraction method to extract the human action silhouettes, while calculating the silhouette energy images for the action sequences. Then, the histograms of oriented gradients are computed from the silhouette energy images. Finally, the human action is represented by the energy histograms features, and recognized by using the Euclidean distance and nearest neighbor classifier. An infrared human action database was built to provide a foundation for night action recognition. Experimental results using the infrared thermal action data show the effective of this method.

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40

Song, Fengyi, Xiaoyang Tan, Xue Liu, and Songcan Chen. "Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients." Pattern Recognition 47, no. 9 (September 2014): 2825–38. http://dx.doi.org/10.1016/j.patcog.2014.03.024.

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41

Chen, Bo-Wei, Seungmin Rho, Muhammad Imran, Mohsen Guizani, and Wei-Kang Fan. "Cognitive Sensors Based on Ridge Phase-Smoothing Localization and Multiregional Histograms of Oriented Gradients." IEEE Transactions on Emerging Topics in Computing 7, no. 1 (January 1, 2019): 123–34. http://dx.doi.org/10.1109/tetc.2016.2585040.

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42

Miri, Mohammad Saleh, Michael D. Abràmoff, Young H. Kwon, and Mona K. Garvin. "Multimodal registration of SD-OCT volumes and fundus photographs using histograms of oriented gradients." Biomedical Optics Express 7, no. 12 (November 23, 2016): 5252. http://dx.doi.org/10.1364/boe.7.005252.

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Meşecan, İbrahim, İhsan Ömür Bucak, and Betim Çiço. "Comparison of histograms of oriented gradients (HOG) and n-Row average subtraction (nRAS) using GprMax." Microprocessors and Microsystems 63 (November 2018): 140–46. http://dx.doi.org/10.1016/j.micpro.2018.08.011.

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44

Beham, M. Parisa, S. M. Mansoor Roomi, J. Alageshan, and V. Kapileshwaran. "Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments." International Journal of Computer Vision and Image Processing 5, no. 1 (January 2015): 66–81. http://dx.doi.org/10.4018/ijcvip.2015010104.

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Face recognition and authentication are two significant and dynamic research issues in computer vision applications. There are many factors that should be accounted for face recognition; among them pose variation is a major challenge which severely influence in the performance of face recognition. In order to improve the performance, several research methods have been developed to perform the face recognition process with pose invariant conditions in constrained and unconstrained environments. In this paper, the authors analyzed the performance of a popular texture descriptors viz., Local Binary Pattern, Local Derivative Pattern and Histograms of Oriented Gradients for pose invariant problem. State of the art preprocessing techniques such as Discrete Cosine Transform, Difference of Gaussian, Multi Scale Retinex and Gradient face have also been applied before feature extraction. In the recognition phase K- nearest neighbor classifier is used to accomplish the classification task. To evaluate the efficiency of pose invariant face recognition algorithm three publicly available databases viz. UMIST, ORL and LFW datasets have been used. The above said databases have very wide pose variations and it is proved that the state of the art method is efficient only in constrained situations.

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45

Sun, Hao, Yu Li, Ge Liu, Hui Long, and Hong Qi Wang. "A New Ship Detection Method for Massive Data High-Resolution Remote Sensing Images." Advanced Materials Research 532-533 (June 2012): 1105–9. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1105.

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This paper proposes a new method for automatic ship targets detection in remote sensing images. The method uses adaptive segmentation algorithm for getting possible ship targets first, and then calculates Histograms of Oriented Gradient (HOG) feature to extract the structural information of ships, followed by supervised learning algorithm to identify the possible ship targets. Multi-scale sliding-window is used to handle targets with different scales. The experimental results prove that this new method has a good precision and robustness for most of the ship targets and give attention to the efficiency.

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46

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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47

Arafah, M., A. Achmad, Indrabayu, and I. S. Areni. "Face recognition system using Viola Jones, histograms of oriented gradients and multi-class support vector machine." Journal of Physics: Conference Series 1341 (October 2019): 042005. http://dx.doi.org/10.1088/1742-6596/1341/4/042005.

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48

Yano, Shinya, Yanlei Gu, and Shunsuke Kamijo. "Estimation of Pedestrian Pose and Orientation Using on-Board Camera with Histograms of Oriented Gradients Features." International Journal of Intelligent Transportation Systems Research 14, no. 2 (September 24, 2014): 75–84. http://dx.doi.org/10.1007/s13177-014-0103-2.

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49

Bukała, Andrzej, Michał Koziarski, Bogusław Cyganek, Osman Koç, and Alperen Kara. "A Study on Pattern Recognition with the Histograms of Oriented Gradients in Distorted and Noisy Images." JUCS - Journal of Universal Computer Science 26, no. 4 (April 28, 2020): 454–78. http://dx.doi.org/10.3897/jucs.2020.024.

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Histograms of oriented gradients (HOG) are still one of the most frequently used low-level features for pattern recognition in images. Despite their great popularity and simple implementation performance of the HOG features almost always has been measured on relatively high quality data which are far from real conditions. To fill this gap we experimentally evaluate their performance in the more realistic conditions, based on images affected by different types of noise, such as Gaussian, quantization, and salt-and-pepper, as well on images distorted by occlusions. Different noise scenarios were tested such anti-distortions during training as well as application of a proper denoising method in the recognition stage. As underpinned with experimental results, the negative impact of distortions and noise on object recognition with HOG features can be significantly reduced by employment of a proper denoising strategy.

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50

Sun, Zhi Hai, Teng Song, Wen Hui Zhou, and Hua Zhang. "Double Feature Combination: Region Contrast for Visual Salient Object Detection." Applied Mechanics and Materials 239-240 (December 2012): 811–15. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.811.

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Abstract:

Visual saliency detection has become an important step between computer vision and digital image processing. Recent methods almost form a computational model based on color, which are difficult to overcome the shortcoming with cluttered and textured background. This paper proposes a novel salient object detection algorithm integrating with region color contrast and histograms of oriented gradients (HoG). Extensively experiments show that our algorithm outperforms other state-of-art saliency methods, yielding higher precision and better recall rate, even lower mean absolution error.

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