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A Multiple Features Image Tracking Algorithm

A Multiple Features Image Tracking Algorithm

2012 Fifth International Symposium on Computational Intelligence and Design [第五届计算智能与设计国际会议 ISCID 2012]

作者： Wenhua Guo Zuren Feng Shuai Wang Qin Nie Systems Engineering Institute Slate Key Laboratory' for Manufacturing Systems Engineering Xi 'an Jiaotong University Xi 'an 710049

In Mean Shift algorithm, the features of the tracked target and the image matching similarity criterion have great influence on the result of tracking. A new algorithm of target tracking is proposed. The algorithm combine local binary pattern and color information to form a new feature CL, which tracks target by using a method of centroid iteration based on maximum posterior probability. Thanks to the simplification of the LBP, the CL has higher differentiation ability and lower computational complexity. Experimental results show that the new algorithm have significantly improved the tracking performance, in comparison with original Mean Shift algorithm. In complex background, the algorithm can track the target robustly.

关键词： object tracking local binary patterns maximum posterior probability multiple features fusion Mean Shift

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Shift and gray scale invariant features for palmprint identification using complex directional wavelet and local binary pattern

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NEUROCOMPUTING 2011年第17期74卷 3351-3360页

作者： Mu, Meiru Ruan, Qiuqi Guo, Song Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China

In this paper, a novel feature extraction framework is presented for palmprint identification, which provides a shiftable and gray scale invariant description of image achieving high identification accuracy at a low computational cost. The image is firstly decomposed by the shiftable complex directional filter bank (CDFB) transform which provides a two-dimensional (2-D) decomposition of energy shiftable and scalable multiresolution, arbitrarily directional resolution, low redundant ratio, and efficient implementation. Further, the subband coefficients of CDFB decomposition are operated by the uniform local binary pattern (LBP) which is gray scale invariant and contains information about the distribution of the local micro-patterns. The resulting LBP mappings are divided into many subblocks, over which the statistical histograms are achieved independently. Finally, a Fisher linear discriminant (FLD) classifier is learned in the statistical histogram feature space for palmprint identification. Experiments are executed over the HongKong PolyU palmprint database of 7752 images. To verify the high performance of our proposed feature descriptor, several other multiresolution and multidirectional transforms are also investigated including Gabor filter, dual-tree complex wavelet and Contourlet transforms. The experimental results demonstrate that CDFB yields the most promising performance balancing the identification accuracy, storage requirement and computational complexity for our proposed feature extraction framework. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Palmprint identification Complex directional wavelet Pyramidal dual-tree directional filter bank local binary patterns

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Defect detection in patterned fabrics using modified local binary patterns

Defect detection in patterned fabrics using modified Local B...

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7th International Conference on Computational Intelligence and Multimedia Applications

作者： Tajeripour, F. Kabir, E. Sheikhi, A. Islamic Azad Univ Dept Elect Engn Fasa Branch Tehran Iran Tarbiat Modares Univ Dept Elect Engn Tehran Iran Shiraz Univ Dept Elect & Elect Engn Shiraz Iran

ISBN: (纸本)0769530508

local binary patterns, LBP, is one of the features which has been used for texture classification. In this paper, a method based oil using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row, (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the defection stage, a test image is divided into windows and using the threshold, defective windows call be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well.

关键词： defect detection texture local binary patterns fabric machine vision

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Periocular Biometrics in the Visible Spectrum

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2011年第1期6卷 96-106页

作者： Park, Unsang Jillela, Raghavender Reddy Ross, Arun Jain, Anil K. Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA W Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26505 USA Korea Univ Brain & Cognit Engn Dept Seoul 136713 South Korea

The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular biometric traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular biometrics, 6) use of the periocular biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers.

关键词： Biometrics face fusion gradient orientation histogram local binary patterns periocular recognition scale invariant feature transform

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CENSUS HISTOGRAMS: A SIMPLE FEATURE EXTRACTION AND MATCHING APPROACH FOR FACE RECOGNITION

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2011年第8期25卷 1337-1348页

作者： Chiachia, Giovani Marana, Aparecido Nilceu Ruf, Tobias Ernst, Andreas Sao Paulo State Univ Dept Comp BR-17033360 Sao Paulo Brazil Fraunhofer Inst Integrated Circuits IIS Elect Imaging Dept D-91058 Erlangen Germany

Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance.

关键词： Face recognition census transform local binary patterns histogram matching feature extraction

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Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings

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SIGNAL PROCESSING 2011年第10期91卷 2395-2403页

作者： Blotta, Eduardo Ballarin, Virginia Brun, Marcel Rabal, Hector UNMDP Fac Ingn Lab Proc & Medic Senales Mar Del Plata Buenos Aires Argentina CONICET CIC CIOp La Plata Buenos Aires Argentina

In the industrial process of painting, paint-drying is an important stage because of its high impact in the final result. Its study is of relevance to improve the properties of the resulting coating. Amalvy's experiments to measure the speed of drying on surfaces, based on techniques of speckle interferometry, have been used as a starting point in the evaluation of other methods, which allow to measure the process with greater accuracy. Haralick's descriptors have been studied in depth, then filters based on mathematical morphology techniques, a natural complexity measure and, finally, local binary patterns. Measures of speed of drying based on gravimetrical information were obtained and used as a gold standard. The comparison of different techniques was based on their ability to predict its values through a linear regression model. Morphological descriptors showed a low dependance with the sampling time, a desired property. Permutation entropy and local binary patterns evinced similar drying curves, showing a remarkable inflection point, coincident with the passage on the constant drying area to a later state, defined by a slower diffusion of the solvent through the dry coat of the surface. More precise descriptors of drying phenomena have been identified in this study. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Speckle interferometry Co-occurrence matrix Mathematical morphology Permutation entropy local binary patterns Drying-of-coatings Latex paint

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On the use of dynamic features in face biometrics: recent advances and challenges

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SIGNAL IMAGE AND VIDEO PROCESSING 2011年第4期5卷 495-506页

作者： Hadid, Abdenour Dugelay, Jean-Luc Pietikainen, Matti Univ Oulu Machine Vis Grp Oulu Finland EURECOM Sophia Antipolis France

The way a person is moving his/her head and facial parts (such as the movements of the mouth when a person is talking) defines so called facial dynamics and characterizes personal behaviors. An emerging direction in automatic face analysis consists of also using such dynamic cues, in addition to facial structure, in order to enhance the performance of static image-based methods. This is inspired by psychophysical and neural studies indicating that behavioral characteristics do also provide valuable information to face analysis in the human visual system. This survey article presents the motivations, reviews the recent developments and discusses several other important issues related to the use of facial dynamics in computer vision. As a case study of using facial dynamics, two LBP-based baseline methods are considered and experimental results in different face-related problems, including face recognition, gender recognition, age estimation and ethnicity classification are reported and discussed. Furthermore, remaining challenges are highlighted and some promising directions are pointed out.

关键词： Facial dynamics Biometrics Spatio-temporal analysis Behavioral features local binary patterns

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Combining different local binary pattern variants to boost performance

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 6209-6216页

作者： Nanni, Loris Brahnam, Sheryl Lumini, Alessandra Univ Bologna DEIS I-47023 Cesena Italy Missouri State Univ Springfield MO 65804 USA

This paper focuses on the combination of variants of local binary patterns (LBP), widely considered the state of the art among texture descriptors, using the same radius and the same number of neighborhoods. We report new experiments exploring several LBP-based descriptors and propose a set of variants for the representation of images. Our experiments are of two main types. In the first set, the Fourier transform is used to extract features starting from the histogram of uniform patterns. In these experiments we test different methods of extracting features from the histogram and each method is used to train a set of support vector machines (SVMs) which are then combined. In the second set of experiments, features are extracted from histograms using different definitions of uniform patterns. These are used to train SVMs, and the results are then combined. Our results show that descriptors extracted from LPB using the same radius and the same number of neighborhoods can be combined to improve classifier performance. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Image analysis Texture descriptors Support vector machine local binary patterns local binary pattern histogram Fourier features

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Face Image Retrieval Using Sparse Representation Classifier with Gabor-LBP Histogram

Face Image Retrieval Using Sparse Representation Classifier ...

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11th International Workshop on Information Security Applications (WISA 2010)

作者： Lee, Hansung Chung, Yunsu Kim, Jeongnyeo Park, Daihee Korea Univ Dept Comp & Informat Sci Seoul South Korea Elect & Telecommun Res Inst Taejon South Korea

ISBN: (纸本)9783642179549

Face image retrieval is an important issue in the practical applications such as mug shot searching and surveillance systems. However, it is still a challenging problem because face images are fairly similar due to the same geometrical configuration of facial features. In this paper, we present a face image retrieval method which is robust to the variations of face image condition and with high accuracy. Firstly, we choose the Gabor-LBP histogram for face image representation. Secondly, we use the sparse representation classification for the face image retrieval. Using the Gabor-LBP histogram and sparse representation classifier, we achieved effective and robust retrieval results with high accuracy. Finally, experiments are conducted on ETRI and XM2VTS database to verify a proposed method. It showed rank 1 retrieval accuracy rate of 98.9% on ETRI face set, and of 99.3% on XM2VTS face set, respectively.

关键词： face retrieval face recognition Gabor filter local binary patterns sparse representation classifier

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MODIFIED local binary PATTERN (MLBP) FOR ROBUST FACE RECOGNITION

MODIFIED LOCAL BINARY PATTERN (MLBP) FOR ROBUST FACE RECOGNI...

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International Conference on Neural Computation Theory and Applications

作者： Islam, Mohammad Moinul Asari, Vijayan K. Islam, Mohammed Nazrul Karim, Mohammad A. Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA Farmingdale SUNY Dept Secur Syst Farmingdale NY 11735 USA

ISBN: (纸本)9789898425843

This paper presents an improvement of local binary Pattern (LBP) for robust face representation under varying lighting conditions. Original LBP operator compares pixels in a local neighbourhood with the centre pixel and converts the resultant binary string to 8-bit integer value. So, it is less effective under difficult lighting conditions where variation between pixels is negligible. Our proposed MLBP uses two stage encoding procedure which is more robust in detecting this variation in a local patch. The performance of the proposed method is compared with the baseline LBP under different illumination conditions.

关键词： local binary patterns Object recognition Illumination invariance Modified local binary pattern

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