检索结果-内蒙古大学图书馆

Adaptive Weber-face for robust illumination face recognition

COMPUTING 2019年第6期101卷 605-619页

作者： Yang, Chao Wu, Shiqian Fang, Hongping Er, Meng Joo Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Hubei Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, an adaptive Weber-face method is presented to deal with varying lighting and extract illumination-insensitive representation for face recognition. A novel pixel-wise adaptive gamma transformation, which has been proved mathematically, is proposed to achieve a local uniform-illumination assumption in Weber-face. Next, the local binary patterns are extracted from the Weber-face images, and then further alleviate the effect of varying illumination. Experiments demonstrate that the proposed method achieves the high recognition rates of 99.55% and 96.63% on extended Yale B and CMU-PIE databases respectively, which outperforms the state-of-the-art methods.

关键词： Face recognition Illumination normalization Adaptive Weber-face Gamma correct local binary patterns

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Toward practical remote iris recognition: A boosting based framework

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NEUROCOMPUTING 2019年 330卷 238-252页

作者： Zhang, Man He, Zhaofeng Zhang, Hui Tan, Tieniu Sun, Zhenan Chinese Acad Sci Ctr Res Intelligent Percept & Comp CAS Ctr Excellence Brain Sci & Intelligence Techn Natl Lab Pattern RecognitInst Automat Beijing Peoples R China Beijing IrisKing Co Ltd Beijing Peoples R China

In this paper, we present a generalized boosting framework to tackle some challenging problems in practical remote iris recognition, namely, iris detection, iris mislocalization detection, iris spoof detection as well as iris recognition. This solution takes advantages of a set of carefully designed features and well-tuned boosting algorithms. Basically, there are two major contributions. The first one is an exploration into the intrinsic properties of remote iris recognition as well as the carefully designed robust features for specific problems. For example, the randomness of iris texture is explored, and ordinal measures are adopted as features for iris representation. The second major contribution is the methodology on how to tune Adaboost learning for specific problems. For instance, an effective similarity oriented boosting algorithm is proposed for iris recognition inspired by the similarity property of the training samples. Other specific contributions include: an efficient topological model of Haar-like features for robust iris detection, a texture and Adaboost based method for efficient iris spoof detection and iris mislocalization detection, a novel Gaussian model for adaptive decision making, etc. Extensive experiments on challenging iris image databases are conducted to evaluate the usefulness of the proposed methods, and the results show that state-of-the-art performance is achieved. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Biometrics Remote iris recognition Adaboost learning Iris detection Iris mislocalization detection Iris spoof detection Iris recognitions Ordinal features local binary patterns

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Efficient and distinctive binary descriptor for rotated circular image recognition

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MACHINE VISION AND APPLICATIONS 2019年第4期30卷 749-761页

作者： Zhang, Dongbo Chen, Honglei Yin, Feng Chen, Zhiqiang Tang, Hongzhong Xu, Haixia Xiangtan Univ Coll Informat Engn Xiangtan Peoples R China Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan Peoples R China

Based on pairs of spatial symmetric patches, a novel efficient and distinctive binary descriptor is proposed in this paper for rotated circular image recognition. To achieve rotation invariance during feature computation, a local coordinate system is first found with radial transform technology. On the basis of that, local binary patterns against rotation can be extracted. Meanwhile, the circular image is divided into a set of overlapped annular regions, and pairs of patch description are constructed with histogram within the ring. Finally, the rotation-invariant image description can be obtained by concatenating the ring features from inner to outer. The performance of proposed method is tested with three datasets, i.e., the euro coins, QQ expression and car logo. The test results show that its recognition accuracies reach 100, 100 and 97.07%, respectively, which are superior to the results of traditional methods based on LBP feature. And our method presents competitive performance contrasted to recently proposed LBP invariants, i.e., R_LBP, CS_LBP and PRI_COLBP, and conventional floating and binary descriptors, such as SIFT, SURF, BRISK and FREAK. Moreover, the algorithm is efficient, as single-point feature computation needed only 0.045ms.

关键词： Image descriptor local binary patterns Image recognition Rotation invariance

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1-D local binary patterns BASED VAD USED INHMM-BASED IMPROVED SPEECH RECOGNITION

1-D LOCAL BINARY PATTERNS BASED VAD USED INHMM-BASED IMPROVE...

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20th European Signal Processing Conference (EUSIPCO)

作者： Zhu, Qiming Chatlani, Navin Soraghan, John J. Univ Strathclyde Ctr Excellence Signal & Image Proc CeSIP Glasgow Lanark Scotland

ISBN: (纸本)9781467310680

In this paper, 1-D local binary patterns (LBP) are proposed to be used in speech signal segmentation and voice activation detection (VAD) and combined with hidden Markov model (HMM) for advanced speech recognition. Speech is firstly de-noised by Adaptive Empirical Model Decomposition (AEMD), and then processed using LBP based VAD. The short-time energy of the speech activity detected from the VAD is finally smoothed and used as the input of the HMM recognition process. The enhanced performance of the proposed system for speech recognition is compared with other VAD techniques at different SNRs ranging from 15 dB to a robust noisy condition at -5 dB.

关键词： Speech Enhancement using Adaptive Empirical Model Decomposition (AEMD) local binary patterns Voice Activity Detection Noise Reduction Hidden Markov Model

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Multi-view recognition system for human activity based on multiple features for video surveillance system

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第12期78卷 17165-17196页

作者： Singh, Roshan Kushwaha, Alok Kumar Singh Srivastava, Rajeev BHU IIT Dept Comp Sci & Engn Varanasi Uttar Pradesh India IK Gujral Punjab Tech Univ Dept Comp Sci & Engn Main Campus Kapurthala Punjab India

Recognition of the activities of human, image sequences is most active area of research in computer vision and most of the previous projects, the focus of the activity is to acknowledge the recognition, from a single view and ignored issues of multiple view invariance. In this paper, the proposed framework for the recognition of a view-invariant human activity, solves the above problem. The components of the proposed framework are three consecutive modules: (i) the detection and positioning of the person's background subtraction, (ii) the function extraction (iii) and the final activity is referenced by using a set of hidden Markov models (HMMs). During features extraction phase in the proposed method for activity representation a combination of contour-based distance signal feature, optical flow-based motion feature and uniform rotation local binary patterns has been used. Due to its rotation invariant nature the uniform LBP provides view-invariant recognition of multi-view human activities. A successful testing of the proposed approach was done on our own viewpoint dataset, KTH action recognition dataset, i3DPost multi-view dataset, and MSR view-point action dataset. From the experimental results and analysis over the chosen datasets, it is observed that the proposed framework is robust, flexible and efficient with respect to multiple views activity recognition, scale and phase variations.

关键词： Human activity recognition General framework Features extraction local binary patterns Optical flow features Hidden Markov models

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local binary patterns versus signal processing texture analysis: a study from a performance evaluation perspective

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SENSOR REVIEW 2012年第2期32卷 149-162页

作者： Ghita, Ovidiu Ilea, Dana Fernandez, Antonio Whelan, Paul Dublin City Univ Sch Elect Engn Dublin 9 Ireland Univ Vigo Sch Ind Engn Vigo 36310 Spain

Purpose - The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro-level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. Design/methodology/approach - The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases. Findings - The most important finding resulting from this study is that although the LBP/C and the multi-channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture. Practical implications - Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding. Originality/value - This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi-channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.

关键词： Signal processing Pattern recognition Texture classification local binary patterns Rotational invariance Gabor filters Isotropic filters

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A new patch-based LBP with adaptive weights for gender classification of human face

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2020年第5期43卷 451-457页

作者： Chen, Wen-Shiung Jeng, Ren-He Natl Chi Nan Univ Dept Elect Engn 1 Univ Rd Nantou 545 Taiwan

Recognition methods based on local binary patterns (LBP) depend on a pre-defined structure, such as pixel-to-pixel or pixel-to-patch comparison, for the feature extraction of picture descriptions without the addition of weights. This paper proceeds on the assumption that patch-based LBP should be based on a pyramid structure to enable the computation of gradients using weight parameters pre-defined by eigen theory. The proposed method, namely Adaptive-Weight Patch-based LBP (AWPLBP), seeks to determine the transformation that identifies sets on the hyper-plane capable of maximizing variance in projections. The transformation and quantization loss are used to train the AWPLBP descriptors to facilitate the efficient feature extraction of descriptions. Finally, a classifier with a support vector machine (SVM) is trained using the AWPLBP features for the gender classification of human faces. The experimental results show that the proposed AWPLBP descriptor may achieve better recognition performances than other LBP methods and is competitive with CNN-based methods in gender classification application.

关键词： Gender classification local binary patterns patch-based LBP eigen theory

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local appearance modeling for objects class recognition

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PATTERN ANALYSIS AND APPLICATIONS 2019年第2期22卷 439-455页

作者： Taffar, Mokhtar Miguet, Serge Univ Jijel Dept Comp Sci BP 98 Ouled Aissa 18000 Jijel Algeria Univ Lyon 2 Univ Lyon UMR CNRS 5205 LIRIS 5 Ave Pierre Mendes FranceBat C 123 F-69676 Lyon France

In this work, we propose a new formulation of the objects modeling combining geometry and appearance;it is useful for detection and recognition. The object local appearance location is referenced with respect to an invariant which is a geometric landmark. The appearance (shape and texture) is a combination of Harris-Laplace descriptor and local binary pattern (LBP), all being described by the invariant local appearance model (ILAM). We use an improved variant of LBP traits at regions located by Harris-Laplace detector to encode local appearance. We applied the model to describe and learn object appearances (e.g., faces) and to recognize them. Given the extracted visual traits from a test image, ILAM model is carried out to predict the most similar features to the facial appearance: first, by estimating the highest facial probability and then in terms of LBP histogram-based measure, by computing the texture similarity. Finally, by a geometric calculation the invariant allows to locate an appearance in the image. We evaluate the model by testing it on different face images databases. The experiments show that the model results in high accuracy of detection and provides an acceptable tolerance to the appearance variability.

关键词： Invariant descriptors local binary patterns Features matching Probabilistic learning Appearance modeling Object class recognition Facial detection

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BACKGROUND MODELING USING local binary patterns OF MOTION VECTOR

BACKGROUND MODELING USING LOCAL BINARY PATTERNS OF MOTION VE...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Wang, Tingting Liang, Jiuzhen Wang, Xiaolong Wang, Shizheng Jiangnan Univ Sch Internet Things Engn Wuxi Peoples R China Chinese Acad Sci R& Ctr Internet Things Beijing Peoples R China

ISBN: (纸本)9781467344050;9781467344067

Pixel-domain analysis methods are widely adopted in background modeling, some of which are not only concerned by academia but also coming into view of industry. However, as the increasing data volume of video, how to process and analysis videos in a fast and effective way has still been an intractable problem in practical applications. Under this circumstance, surveillance video analysis in the compressed domain is indeed of strategic importance from the angle of balancing visual perception and processing speed, especially in modeling background and segmenting moving objects. Therefore, a background modeling method in the compressed domain is proposed to quickly extract moving objects in this paper. Our main contributions are: 1) a method to calculate MVLBP features based on MV amplitude in the compressed domain is presented;2) a background modeling and moving objects extraction method is designed in the compressed domain based on local binary patterns of Motion Vector (MVLBP). Experimental results show that our approach gives a stable performance in a shorter time in H. 264 compressed domain.

关键词： Background modeling motion vector compressed domain local binary patterns

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Quantitative evaluation of multiple-point simulations using image segmentation and texture descriptors

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COMPUTATIONAL GEOSCIENCES 2019年第6期23卷 1349-1368页

作者： Abdollahifard, Mohammad Javad Mariethoz, Gregoire Ghavim, Maryam Tafersh Univ Elect Engn Dept Tafresh *** Iran Univ Lausanne Inst Earth Surface Dynam Lausanne Switzerland

Continuous growth of multiple-point simulation algorithms for modeling environmental variables necessitates a straightforward, reliable, robust, and distinctive method for evaluating the quality of output images. A good simulation method should produce realizations consistent with the training image (TI). Moreover, it should be capable of producing diverse realizations to effectively model the variability of real fields. In this paper, the pattern innovation capability is evaluated by estimating the coherence map using keypoint detection and matching, without assuming any access to the simulation process. local binary patterns, as distinctive and effective texture descriptors, are also employed to evaluate the consistency of realizations with the TI. Our proposed method provides absolute measures in the interval [0,1], allowing MPS algorithms to be evaluated on their own. Experiments show that the produced scores are consistent with human perception and robust for different realizations obtained using the same method, allowing for a reliable judgment using a few realizations. While a human observer is highly sensitive to discontinuities and insensitive to verbatim copies, the proposed method considers both factors simultaneously.

关键词： Geostatistics Variability Texture analysis local binary patterns Coherence map

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