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Analytical study of two feature extraction methods in comparison with deep learning methods for classification of small metal objects

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 146-158页

作者： Somaieh Amraee Maryam Chinipardaz Mohammadali Charoosaei Department of Electrical and Computer Engineering Jundi-Shapur University of TechnologyDezful64615/334Iran

This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws,nuts,keys,and coins:the histogram of oriented gradients(HOG)and local binary pattern(LBP).The desired features for the labeled images are first extracted and saved in the form of a feature *** three different classification methods(non-parametric K-nearest neighbors algorithm,support vector machine,and naïve Bayesian method),the images are classified into four different ***,by examining the resulting confusion matrix,the performances of the HOG and LBP approaches are compared for these four *** effectiveness of these two methods is also compared with the“You Only Look Once”and faster region-based convolutional neural network approaches,which are based on deep *** collected image set in this paper includes 800 labeled training images and 180 test *** results show that the use of the HOG is more efficient than the use of the ***,a combination of the HOG and LBP provides better results than either alone.

关键词： histogram of oriented gradients Local binary pattern Support vector machine k-nearest neighbors Deep learning

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Object tracking based on particle filter with discriminative features

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控制理论与应用（英文版） 2013年第1期11卷 42-53页

作者： Yunji ZHAO Hailong PEI Key Laboratory of Autonomous Systems and Networked Control (Ministry of Education) South China University of Technology

This paper presents a particle filter-based visual tracking method with online feature selection mechanism. In color-based particle filter algorithm the weights of particles do not always represent the importance correctly, this may cause that the object tracking based on particle filter converge to a local region of the object. In our proposed visual tracking method, the Bhattacharyya distance and the local discrimination between the object and background are used to define the weights of the particles, which can solve the existing local convergence problem. Experiments demonstrates that the proposed method can work well not only in single object tracking processes but also in multiple similar objects tracking processes.

关键词： histogram of oriented gradients Local discrimination Particle filter Multiple object tracking

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Traffic light detection and recognition for autonomous vehicles

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The Journal of China Universities of Posts and Telecommunications 2015年第1期22卷 50-56页

作者： Guo Mu Zhang Xinyu Li Deyi Zhang Tianlei An Lifeng Department of Computer Science and Technology Tsinghua University

Traffic light detection and recognition is essential for autonomous driving in urban environments. A camera based algorithm for real-time robust traffic light detection and recognition was proposed, and especially designed for autonomous vehicles. Although the current reliable traffic light recognition algorithms operate well under way, most of them are mainly designed for detection at a fixed position and the effect on autonomous vehicles under real-world conditions is still limited. Some methods achieve high accuracy on autonomous vehicle, but they can＇t work normally without the aid of high-precision priori map. The authors presented a camera-based algorithm for the problem. The image processing flow can be divided into three steps, including pre-processing, detection and recognition. Firstly, red-green-blue （RGB） color space is converted to hue-saturation-value （HSV） as main content of pre-processing. In detection step, the transcendental color threshold method is used for initial filterings, meanwhile, the prior knowledge is performed to scan the scene in order to quickly establish candidate regions. For recognition, this article use histogram of oriented gradients （HOG） features and support vector machine （SVM） as well to recognize the state of traffic light. The proposed system on our autonomous vehicle was evaluated. With voting schemes, the proposed can provide a sufficient accuracy for autonomous vehicles in urban enviroment.

关键词： autonomous vehicle traffic light detection and recognition histogram of oriented gradients

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Facial expression recognition using threestage support vector machines

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Visual Computing for Industry,Biomedicine,and Art 2019年第1期2卷 236-244页

作者： Issam Dagher Elio Dahdah Morshed Al Shakik Computer Engineering Department University of BalamandTripoli P.O.BOX 100Lebanon

Herein,a three-stage support vector machine(SVM)for facial expression recognition is *** first stage comprises 21 SVMs,which are all the binary combinations of seven *** one expression is dominant,then the first stage will suffice;if two are dominant,then the second stage is used;and,if three are dominant,the third stage is *** multilevel stages help reduce the possibility of experiencing an error as much as *** image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification *** expressions are created as a result of muscle movements on the *** subtle movements are detected by the histogram-oriented gradient feature,because it is sensitive to the shapes of *** features attained are then used to train the three-stage *** different validation methods were used:the leave-one-out and K-fold *** results on three databases(Japanese Female Facial Expression,Extended Cohn-Kanade Dataset,and Radboud Faces Database)show that the proposed system is competitive and has better performance compared with other works.

关键词： Facial expression recognition Support vector machine histogram of oriented gradients Viola-Jones Validation

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Palm Print Identification Using Improved histogram of oriented Lines

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Circuits and Systems 2016年第8期7卷 1665-1676页

作者： M. Arunkumar S. Valarmathy Department of ECE Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India

Automatic palmprint identification has received much attention in security applications and law enforcement. The performance of a palmprint identification system is improved by means of feature extraction and classification. Feature extraction methods such as Subspace learning are highly sensitive to the rotation variances, translation and illumination in image identification. Thus, histogram of oriented Lines (HOL) has not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named Improved histogram of oriented Lines (IHOL), which is an alternative of HOL. Improved HOL is not very sensitive to changes of translation and illumination, and has the robustness against small transformations whereas the small translation and rotations make no change in histogram value adjustment of the proposed work. The experiment results show that based on IHOL, with Principal Component Analysis (PCA) subspace learning can achieve high recognition rates. The proposed method (IHOL-Cosine distance) improves 1.30% on PolyU I database, and similarly (IHOL-Euclidean distance) improves 2.36% on COEP database compared with existing HOL method.

关键词： histogram of oriented gradients histogram of oriented Lines Improved histogram of oriented Lines Principal Component Analysis

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Fusion of LDB and HOG for Face Recognition

Fusion of LDB and HOG for Face Recognition

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第37届中国控制会议

作者： Hua Wang DingSheng Zhang ZhongHua Miao Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and AutomationShanghai University

Face Recognition plays a very important role in numerous occasions based on visual security in recent days. The current methods of face recognition are to extract the different features of different faces to distinguish from others, so feature extraction has become a vital step in face recognition. For this reason, this paper presents a new fusion of local difference binary（LDB） and histogram of oriented gradients（HOG） for face recognition. We use LDB descriptor to extract the local pattern features of a face image. At the same time, the edge features of the original image are extracted by using HOG descriptor. The proposed new fusion of features improves the shortcomings of the low accuracy and avoids the problems that the dimension of the general fusion of features is too high. The experimental results on ORL and Yale face database verify the validity of the proposed fusion of features.

关键词： Face recognition Local difference binary histogram of oriented gradients Fusion of features

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