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International Conference on Management, Manufacturing and Materials Engineering (ICMMM 2011)

作者： Miyata, Shigeharu Ishikawa, Takahiro Nakamura, Hitomi Takehara, Shin Kinki Univ Sch Engn Dept Intelligent Mech Engn Takayaumenobe Higahihiroshima Japan

ISBN: (纸本)9783037853511

This study explains that a method utilizing the eigen spaces obtained by the KL transform for automatic recognition by camera of the speed on a speed limit sign has the following advantages: it is robust in response to changes in intensity patterns caused by the direction the sign is facing and by the amount of light striking the sign, and it is able to reduce the recognition processing time by reducing the number of feature vector dimensions during analysis. The method for recognition of traffic signs previously proposed by the authors of this study was a method for recognition based on extracting geometric shapes from the sign and recognizing them based on their aspect ratios. As such, this method was not able to identify the numbers on a speed limit sign, all of which have identical aspect ratios. It will be shown that the method in this study is able to recognize nearly all speed limits indicated on traffic signs within several 100s of ms after image acquisition. This method was applied to still images and its effectiveness was verified from the perspective of the following requirements for providing accurate information concerning the vehicle surroundings to the driver: high processing speed, high recognition accuracy, detection of all detectable objects without omission, and robustness in response to changes in the surrounding environment.

关键词： pattern recognition Image processing computer vision Traffic sign KL transform Feature extraction Eigen space method

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Personalization and user verification in wearable systems using biometric walking patterns

Personalization and user verification in wearable systems us...

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作者： Casale, Pierluigi Pujol, Oriol Radeva, Petia Computer Vision Center Barcelona Spain Departamento de Matematica Aplicada i Analisi Computer Vision Center Universitat de Barcelona Barcelona Spain

In this article, a novel technique for user's authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a 'wild' scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies. © Springer-Verlag London Limited 2011.

关键词： Biometrics

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A distance measure between labeled combinatorial maps

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computer vision AND IMAGE UNDERSTANDING 2012年第12期116卷 1168-1177页

作者： Wang, Tao Dai, Guojun Ni, Bingbing Xu, De Siewe, Francois Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Hangzhou Dianzi Univ Comp Sch Hangzhou 310018 Zhejiang Peoples R China Adv Digital Sci Ctr Singapore 138632 Singapore De Montfort Univ Fac Technol Software Technol Res Lab Leicester LE1 9BH Leics England

Combinatorial maps are widely used in image representation and processing, however map matching problems have not been extensively researched. This paper addresses the problem of inexact matching between labeled combinatorial maps. First, the concept of edit distance is extended to combinatorial maps, and then used to define mapping between combinatorial maps as a sequence of edit operations that transforms one map into another. Subsequently, an optimal approach based on A* algorithm and an approximate approach based on Greedy algorithm are proposed to compute the distance between combinatorial maps. Experimental results show that the proposed inexact map matching approach produces richer search results than the exact map matching technique by tolerating small difference between maps. The proposed approach performs better in practice than the previous approach based on maximum common submap which cannot be directly used for comparing labels on the maps. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Combinatorial map Edit distance pattern recognition Graph

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Text detection of two major Indian scripts in natural scene images

Text detection of two major Indian scripts in natural scene ...

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4th International Workshop on Camera-Based Document Analysis and recognition, CBDAR 2011

作者： Roy Chowdhury, Aruni Bhattacharya, Ujjwal Parui, Swapan K. Department of Information Technology Heritage Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783642293634

In this article, we present a robust scheme for detection of Devanagari or Bangla texts in scene images. These are the two most popular scripts in India. The proposed scheme is primarily based on two major characteristics of such texts - (i) variations in stroke thickness for text components of a script are low compared to their non-text counterparts and (ii) presence of a headline along with a few vertical downward strokes originating from this headline. We use the Euclidean distance transform to verify the general characteristics of texts in (i). Also, we apply the probabilistic Hough line transform to detect the characteristic headline of Devanagari and Bangla texts. Further, similarity and adjacency measures are applied to identify text regions, which do not satisfy the verification in (ii). The proposed approach has been simulated on a repository of 120 images taken from Indian roads and the results are encouraging. Also, we have discussed the applicability of the proposed scheme for detection of English texts. Towards this end, we have considered the training and test samples from the image database of ICDAR 2003 Robust Reading Competition. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Character recognition

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Psychologically inspired, rule-based outlier detection in noisy data

Psychologically inspired, rule-based outlier detection in no...

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13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Reiz, Beata Pongor, Sandor Hungarian Acad Sci Biol Res Ctr Temesvari Krt 67 H-6726 Szeged Hungary Int Ctr Genet Engn & Biotechnol Prot Struct & Bioinformat Grp I-34012 Trieste Italy Univ Szeged Inst Informat Aradi Vertanuk Tere 1 H-6720 Szeged Hungary Pazmany Peter Catholic Univ Fac Informat Technol Prater Utca 50-A H-1083 Budapest Hungary

ISBN: (纸本)9780769546308

Outlier detection is widely applied in several fields such as data mining, pattern recognition and bioinformatics. The algorithms used for outlier detection are based mainly on statistics and artificial intelligence. Our long-term goal is to study and apply the principles of human vision for solving outlier detection problems. Here we present an algorithm suitable for outlier detection based on the principles of Gestalt psychology. We demonstrate the algorithm's main properties on an example taken from human perception, the recognition of continuous curves formed of Gabor patches embedded into a noisy background. We show that the algorithm is tolerant with respect to added noise and is orientation independent. As a potential application we present the problem of filtering proteomics mass spectrometry data. The true peaks within a measured mass spectrum can be represented as a graph in which nodes are fragment peaks while edges represent equivalents of proximity, similarity and continuity defined in terms of chemical rules. The applicability of the principle to further problems is discussed.

关键词： circular pattern data filtering outlier detection

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Real-time viewpoint-invariant hand localization with cluttered backgrounds

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IMAGE AND vision COMPUTING 2012年第1期30卷 26-37页

作者： Sangineto, Enver Cupelli, Marco Ist Italian Tecnol Pattern Anal & Comp Vis PAVIS I-16163 Genoa Italy Gepin SpA I-00143 Rome Italy

Over the past few years there has been a growing interest in visual interfaces based on gestures. Using gestures as a mean to communicate with a computer can be helpful in applications such as gaming platforms, domotic environments, augmented reality or sign language interpretation to name a few. However, a serious bottleneck for such interfaces is the current lack of accurate hand localization systems, which are necessary for tracking (re-)initialization and hand pose understanding. In fact, human hand is an articulated object with a very large degree of appearance variability which is difficult to deal with. For instance, recent attempts to solve this problem using machine learning approaches have shown poor generalization capabilities over different viewpoints and finger spatial configurations. In this article we present a model based approach to articulated hand detection which splits this variability problem by separately searching for simple finger models in the input image. A generic finger silhouette is localized in the edge map of the input image by combining curve and graph matching techniques. Cluttered backgrounds and thick textured images, which usually make it hard to compare edge information with silhouette models (e.g., using chamfer distance or voting based methods) are dealt with in our approach by simultaneously using connected curves and topological information. Finally, detected fingers are clustered using geometric constraints. Our system is able to localize in real time a hand with variable finger configurations in images with complex backgrounds, different lighting conditions and different positions of the hand with respect to the camera. Experiments with real images and videos and a simple visual interface are presented to validate the proposed method. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Hand detection Articulated object recognition Model based techniques Geometric constraints Graph matching Curve matching

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Segment-Based Features for Time Series Classification

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computer JOURNAL 2012年第9期55卷 1088-1102页

作者： Zhang, Zhang Cheng, Jun Li, Jun Bian, Wei Tao, Dacheng Univ Technol Sydney Ctr Quantum Computat & Intelligent Syst Fac Engn & Informat Technol Sydney NSW 2007 Australia Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

In this paper, we propose an approach termed segment-based features (SBFs) to classify time series. The approach is inspired by the success of the component- or part-based methods of object recognition in computer vision, in which a visual object is described as a number of characteristic parts and the relations among the parts. Utilizing this idea in the problem of time series classification, a time series is represented as a set of segments and the corresponding temporal relations. First, a number of interest segments are extracted by interest point detection with automatic scale selection. Then, a number of feature prototypes are collected by random sampling from the segment set, where each feature prototype may include single segment or multiple ordered segments. Subsequently, each time series is transformed to a standard feature vector, i.e. SBF, where each entry in the SBF is calculated as the maximum response (maximum similarity) of the corresponding feature prototype to the segment set of the time series. Based on the original SBF, an incremental feature selection algorithm is conducted to form a compact and discriminative feature representation. Finally, a multi-class support vector machine is trained to classify the test time series. Extensive experiments on different time series datasets, including one synthetic control dataset, two sign language datasets and one gait dynamics dataset, have been performed to evaluate the proposed SBF method. Compared with other state-of-the-art methods, our approach achieves superior classification performance, which clearly validates the advantages of the proposed method.

关键词： time series classification segment-based features matching

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2011 IEEE computer Society Conference on computer vision and pattern recognition Workshops, CVPRW 2011

2011 IEEE Computer Society Conference on Computer Vision and...

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2011 IEEE computer Society Conference on computer vision and pattern recognition Workshops, CVPRW 2011

ISBN: (纸本)9781457705298

The proceedings contain 146 papers. The topics discussed include: textured 3D face recognition using biological vision-based facial representation and optimized weighted sum fusion;a method for object localization in a multiview multimodal camera system;real-time tracking of unconstrained full-body motion using niching swarm filtering combined with local optimization;activity related biometric authentication using spherical harmonics;joint gait-pose manifold for video-based human motion estimation;learning human behaviour patterns in work environments;an optimized silicon retina stereo matching algorithm using time-space correlation;face recognition system using extended curvature Gabor classifier bunch for low-resolution face image;analysis of patterns of motor behavior in gamers with down syndrome;dense shape correspondences using spectral high-order graph matching;and pedestrian sensing using time-of-flight range camera.

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A skin detection approach based on the Dempster-Shafer theory of evidence

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2012年第4期53卷 636-659页

作者： Shoyaib, Mohammad Abdullah-Al-Wadud, M. Chae, Oksam Kyung Hee Univ Dept Comp Engn Yongin 446701 Kyunggi Do South Korea Hankuk Univ Foreign Studies Dept Ind & Management Engn Yongin 449791 Gyonggi South Korea

Skin detection is an important step for a wide range of research related to computer vision and image processing and several methods have already been proposed to solve this problem. However, most of these methods suffer from accuracy and reliability problems when they are applied to a variety of images obtained under different conditions. Performance degrades further when fewer training data are available. Besides these issues, some methods require long training times and a significant amount of parameter tuning. Furthermore, most state-of-the-art methods incorporate one or more thresholds, and it is difficult to determine accurate threshold settings to obtain desirable performance. These problems arise mostly because the available training data for skin detection are imprecise and incomplete, which leads to uncertainty in classification. This requires a robust fusion framework to combine available information sources with some degree of certainty. This paper addresses these issues by proposing a fusion-based method termed Dempster-Shafer-based Skin Detection (DSSD). This method uses six prominent skin detection criteria as sources of information (Sol), quantifies their reliabilities (confidences), and then combines their confidences based on the Dempster-Shafer Theory (DST) of evidence. We use the DST as it offers a powerful and flexible framework for representing and handling uncertainties in available information and thus helps to overcome the limitations of the current state-of-the-art methods. We have verified this method on a large dataset containing a variety of images, and achieved a 90.17% correct detection rate (CDR). We also demonstrate how DSSD can be used when very little training data are available, achieving a CDR as high as 87.47% while the best result achieved by a Bayesian classifier is only 68.81% on the same dataset. Finally, a generalized DSSD (GDSSD) is proposed achieving 91.12% CDR. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Dempster-Shafer theory of evidence Image processing pattern recognition Skin detection

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Point pattern Matching Algorithm for Planar Point Sets under Euclidean Transform

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JOURNAL OF APPLIED MATHEMATICS 2012年第unknown期000卷 139014-1-139014-12页

作者： Wang, Xiaoyun Zhang, Xianquan Yangtze Normal Univ Coll Math & Comp Sci Chongqing 408100 Peoples R China Guangxi Normal Univ Dept Comp Sci Guilin 541004 Peoples R China

Point pattern matching is an important topic of computer vision and pattern recognition. In this paper, we propose a point pattern matching algorithm for two planar point sets under Euclidean transform. We view a point set as a complete graph, establish the relation between the point set and the complete graph, and solve the point pattern matching problem by finding congruent complete graphs. Experiments are conducted to show the effectiveness and robustness of the proposed algorithm.

关键词： pattern matching computer vision Set of points Transform Planar Euclidean pattern recognition

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