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2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Saha, Ashirbani Wu, Q. M. Jonathan Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781424442966

This paper proposes the use of the combination of digital curvelet transform and local binary patterns for recognizing facial expressions from still images. The curvelet transform is applied to the image of a face at a specific scale and orientation. local binary patterns are extracted from the selected curvelet sub-bands to form the descriptive feature set of the expressions. The average of the features of a particular class of expression is considered as the representative feature set of that class. The expression recognition is performed using a nearest neighbor classifier with Chi-square as the dissimilarity metric. Experiments show that our method yields recognition rates of 93% and 90% in JAFFE and Cohn-Kanade databases respectively.

关键词： Facial Expression Recognition Curvelets local binary patterns

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Automatic Detection of the Carotid Artery Location from Volumetric Ultrasound Images Using Anatomical Position-Dependent LBP Features

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2015年第7期E98D卷 1353-1364页

作者： Kawai, Fumi Kondo, Satoshi Hayata, Keisuke Ohmiya, Jun Ishikawa, Kiyoko Yamamoto, Masahiro Panason Healthcare Co Ltd Yokohama Kanagawa 2248539 Japan Yokohama Stroke & Brain Ctr Yokohama Kanagawa 2350012 Japan

We propose a fully automatic method for detecting the carotid artery from volumetric ultrasound images as a preprocessing stage for building three-dimensional images of the structure of the carotid artery. The proposed detector utilizes support vector machine classifiers to discriminate between carotid artery images and non-carotid artery images using two kinds of LBP-based features. The detector switches between these features depending on the anatomical position along the carotid artery. We evaluate our proposed method using actual clinical cases. Accuracies of detection are 100%, 87.5% and 68.8% for the common carotid artery, internal carotid artery, and external carotid artery sections, respectively.

关键词： ultrasound carotid artery detection support vector machine local binary patterns

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Scale- and rotation-invariant texture description with improved local binary pattern features

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SIGNAL PROCESSING 2015年 111卷 274-293页

作者： Davarzani, Reza Mozaffari, Saeed Yaghmaie, Khashayar Semnan Univ Fac Elect & Comp Engn Semnan Iran

local binary Pattern (LBP) is an effective image descriptor based on joint distribution of signed gray level differences. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are main properties of LBP. However, LBP is sensitive to scaling, rotation, viewpoint variations, and non-rigid deformations. In order to overcome these disadvantages of LBP, this paper proposes an improved LBP features. In our method, a circular neighboring radius and a dominant orientation are assigned to each pixel. To achieve scale invariance, we used the radius of blob-like structures to determine the circular neighboring set of each central pixel. Definition of LBP operator with respect to dominant orientation of each pixel can guarantee the rotation invariance of LBP features. Unlike original LBP operator which discards the magnitude information of the difference between the center and the neighbor gray values in a local neighborhood, a weighted LBP features is proposed in this paper. Several experiments are conducted to compare the proposed method with seven LBP-based descriptors for texture retrieval and classification using four databases: Brodatz, Outex, UIUC and UMD. Experimental results show that the proposed Weighted, Rotation- and Scale- Invariant local binary Pattern (WRSI_LBP) outperforms other LBP-based methods. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Blob-like structures local binary patterns Orientation assignment Scale-space theory Texture analysis

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Facial image clustering in stereoscopic videos using double spectral analysis

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2015年 33卷 86-105页

作者： Orfanidis, Georgios Tefas, Anastasios Nikolaidis, Nikos Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

In this work, we are focusing on facial image clustering techniques applied on stereoscopic videos. We introduce a novel spectral clustering algorithm which combines two well-known algorithms: normalized cuts and spectral clustering. Furthermore, we introduce two approach for evaluating the similarities between facial images, one based on Mutual Information and other based on local binary patterns, combined with facial fiducial points and an image registration procedure. Ways of exploring the extra information available in stereoscopic videos are also introduced. The proposed approaches are successfully tested on three stereoscopic feature films and compared against the state-of-the-art. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Stereoscopic video Stereo facial image clustering Spectral clustering local binary patterns local features Facial fiducial points

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Image orientation detection using LBP-based features and logistic regression

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第9期74卷 3013-3034页

作者： Ciocca, Gianluigi Cusano, Claudio Schettini, Raimondo Univ Milano Bicocca Dept Informat Syst & Commun DISCo I-20126 Milan Italy Univ Pavia Dept Elect Comp & Biomed Engn I-27100 Pavia Italy

Many imaging applications require that images are correctly orientated with respect to their content. In this work we present an algorithm for the automatic detection of the image orientation that relies on the image content as described by local binary patterns (LBP). The detection is efficiently performed by exploiting logistic regression. The proposed algorithm has been extensively evaluated on more than 100,000 images taken from the Scene UNderstanding (SUN) database. The results show that our algorithm outperformed similar approaches in the state of the art, and its accuracy is comparable with that of human observers in detecting the correct orientation of a wide range of image contents.

关键词： Image orientation detection Low-level features local binary patterns Logistic regression Image classification

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Detection of masses in mammograms with adaption to breast density using genetic algorithm, phylogenetic trees, LBP and SVM

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第22期42卷 8911-8928页

作者： de Sampaio, Wener Borges Silva, Aristofanes Correa de Paiva, Anselmo Cardoso Gattass, Marcelo Fed Univ Maranhao UFMA Appl Comp Grp NCA BR-65085580 Sao Luis MA Brazil Pontif Catholic Univ Rio de Janeiro PUC Rio BR-22453900 Rio De Janeiro RJ Brazil

Breast cancer is the second commonest type of cancer in the world, and the commonest among women, corresponding to 22% of the new cases every year. This work presents a new computational methodology, which helps the specialists in the detection of breast masses based on the breast density. The proposed methodology is divided into stages with the objective of overcoming several difficulties associated with the detection of masses. In many of these stages, we brought contributions to the areas. The first stage is intended to detect the type of density of the breast, which can be either dense or non-dense. We proposed an adaptive algorithm capable of analyzing and image and telling if it is dense or non-dense. The first stage consists in the segmentation of the regions that look like masses. We propose a novel use of the micro-genetic algorithm to create a texture proximity mask and select the regions suspect of containing lesions. The next stage is the reduction of false positives, which were generated in the previous stage. To this end, we proposed two new approaches. The first reduction of false positives used DBSCAN and a proximity ranking of the textures extracted from the ROIs. In the second reduction of false positives, the resulting regions have their textures analyzed by the combination of Phylogenetic Trees, local binary patterns and Support Vector Machines (SVM). A micro-genetic algorithm was used to choose the suspect regions that generate the best training models and maximize the classification of masses and non-masses used in the SVM. The best result produced a sensitivity of 92.99%, a rate of 0.15 false positives per image and an area under the FROC curve of 0.96 in the analysis of the non-dense breasts;and a sensitivity of 83.70%, a rate of 0.19 false positives per image and an area under the FROC curve of 0.85, in the analysis of the dense breasts. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Breast cancer Computer-aided detection Micro-genetic algorithm Phylogenetic trees local binary patterns Support vector machine

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Facial action unit recognition using multi-class classification

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NEUROCOMPUTING 2015年第PartB期150卷 440-448页

作者： Smith, R. S. Windeatt, T. Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

Within the context of facial expression classification using the facial action coding system (FACS), we address the problem of detecting facial action units (AUs). Feature extraction is performed by generating a large number of multi-resolution local binary pattern (MLBP) features and then selecting from these using fast correlation-based filtering (FCBF). The need for a classifier per AU is avoided by training a single error-correcting output code (ECOC) multi-class classifier to generate occurrence scores for each of several AU groups. A novel weighted decoding scheme is proposed with the weights computed using first order Walsh coefficients. Platt scaling is used to calibrate the ECOC scores to probabilities and appropriate sums are taken to obtain separate probability estimates for each AU individually. The bias and variance properties of the classifier are measured and we show that both these sources of error can be reduced by enhancing ECOC through bootstrapping and weighted decoding. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Facial expression recognition local binary patterns Feature selection Error-correcting output codes

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Automated Classification of Zooplankton for a Towed Imaging System

Automated Classification of Zooplankton for a Towed Imaging ...

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Oceans (conference)

作者： Xinsheng Yu Yajuan Wei Minliang Zhu Zhangguo, Zhou Key Lab. Of Submarine Geosciences and Prospecting Techniques MOE College of Marine Geosciences Ocean University of China Qingdao P. R. China Hisense Aingdao R&D Center Qingdao Pr. R. China Institute of Oceanology Chinese Academy of Sciences Qingdao Pr. R. China

ISBN: (纸本)9781467397254

Most studies on zooplankton image processing are focused on global texture feature. In this paper, an efficient method for classifying zooplankton images by combination of local binary pattern (LBP) is demonstrated. The classification performance of plankton image with global and local features have been evaluated and the overall performance of combination of these features for zooplankton identification shows that by combining conventional global features with local binary pattern texture feature have the similar classification performance for 6 taxonomic groups.

关键词： Zooplankton imge classification local binary patterns underwater image processing Automated

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Face recognition based on 2D and 3D data fusion

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INTERNATIONAL JOURNAL OF BIOMETRICS 2015年第1期7卷 69-81页

作者： Krotewicz, Pawel Sankowski, Wojciech Nowak, Piotr Stefan Lodz Univ Technol Dept Microelectron & Comp Sci Lodz Poland

The aim of the work presented in this paper is to present current state of the art of face recognition methods and describe proposal algorithms for face biometric identification that analyse 2D face images and 3D face geometry scans. Data for analysis gathered via 3D scanner are processed through different phases. These are: segmentation phase, feature extraction phase and comparison phase. Segmentation relies on localising characteristic landmark points of the face and projecting the face point cloud onto a plane constructed on the basis of these characteristic points. Feature extraction phase calculates separate feature vectors for 2D and 3D input data. Comparison phase applies fusion of 2D and 3D methods and calculates similarity value between two samples. All samples are compared against one another and results presented as DET curves are generated. By analysis of DET curves, conclusions are formulated.

关键词： biometrics face recognition biometric identification methods fusion contactless identification 3D face geometry local binary patterns multimodal biometrics detection error tradeoff DET biometric system

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Two novel local binary pattern descriptors for texture analysis

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APPLIED SOFT COMPUTING 2015年 34卷 728-735页

作者： Kaya, Yilmaz Ertugrul, Omer Faruk Tekin, Ramazan Siirt Univ Dept Comp Engn Siirt Turkey Batman Univ Dept Elect & Elect Engn Batman Turkey Batman Univ Dept Comp Engn Batman Turkey

The recent developments in the image quality, storage and data transmission capabilities increase the importance of texture analysis, which plays an important role in computer vision and image processing. local binary pattern (LBP) is an effective statistical texture descriptor, which has successful applications in texture classification. In this paper, two novel descriptors were proposed to search different patterns in images built on LBP. One of them is based on the relations between the sequential neighbors with a specified distance and the other one is based on determining the neighbors in the same orientation through central pixel parameter. These descriptors are tested with the Brodatz-1, Brodatz-2, Butterfly and Kylberg datasets to show the applicability of the proposed nLBP(d) and dLBP(alpha) descriptors. The proposed methods are also compared with classical LBP. The average accuracies obtained by ANN with 10 fold cross validation, which are 99.26% (LBPu2 and nLBP(d)), 94.44% (dLBP(alpha)), 95.71% (nLBP(d)(u2)) and %99.64 (nLBP(d)), for Brodatz-1, Brodatz-2, Butterfly and Kylberg datasets, respectively, show that the proposed methods outperform significant accuracies. (C) 2015 Elsevier B.V. All rights reserved.

关键词： local binary patterns nLBP(d) dLBP(alpha) Texture classification Feature extraction Image classification

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