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Robust Object Segmentation Using Graph Cut with Object and Background Seed Estimation

Robust Object Segmentation Using Graph Cut with Object and B...

引用

International conference on pattern recognition

作者： Jung-Ho Ahn KilCheon Kim Hyeran Byun Department Of Computer Science Yonsei University Seoul South Korea

ISBN: (纸本)0769525210

In this paper we propose a new robust way of extracting accurate human silhouettes indoors with an active stereo camera. We first infer the parts of object and background areas of high confidence by fusing color, stereo matching information and image segmentation methods. Then the inferred areas (seeds) are incorporated in a graph cut. The experimental results were presented with image sequences taken with pan-tilt stereo camera. Our proposed algorithms were evaluated with respect to the ground truth data. We proved that our algorithms can outperform other methods that are based on either color/contrast or stereo/contrast principles alone

关键词： Robustness Object segmentation Cameras Image segmentation Robot vision systems computer science Data mining Image sequences Human robot interaction Feature extraction

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A Unified Camera Calibration Using Geometry and Blur of Feature Points

A Unified Camera Calibration Using Geometry and Blur of Feat...

引用

International conference on pattern recognition

作者： M. Baba M. Mukunoki N. Asada Department of Intelligent Systems Hiroshima City University Hiroshima Japan

Although a lot of camera calibration methods have been proposed so far, most of them deal with the pinhole camera model, which neglects the defocus effects introduced by real lens systems. In this paper, we present a unified camera calibration method which determines both internal and external camera parameters from several blurred edges detected in a single image. First, we show the calibration procedure using our thin lens based camera model. Then, we describe how to determine the position and the width of blurred features in an image. Experiments have been done with real images and those results have shown the validity of our method

关键词： Calibration Geometry Lenses Focusing Smart cameras Image edge detection Iris Intelligent systems computer vision computer graphics

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Recognizing Interaction Activities using Dynamic Bayesian Network

Recognizing Interaction Activities using Dynamic Bayesian Ne...

引用

International conference on pattern recognition

作者： Youtian Du Feng Chen Wenli Xu Yongbin Li Department of Automation Tsinghua University Beijing China

Activity recognition is significant in intelligent surveillance. In this paper, we present a novel approach to the recognition of interacting activities based on dynamic Bayesian network (DBN). In this approach the features representing the object motion are divided into two classes: global features and local features, which are at two different spatial scales. Global features describe object motion at a large spatial scale and relations between objects or between the object and environment, and local ones represent the motion details of objects of interest. We propose a new DBN model structure with state duration to model human interacting activities. This DBN model structure combines the global features with local ones harmoniously. The effectiveness of this novel approach is demonstrated by experiment

关键词： Bayesian methods Hidden Markov models Feature extraction Humans computer vision Automation Surveillance Data mining Exponential distribution pattern recognition

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Supervised Image Classification by SOM Activity Map Comparison

Supervised Image Classification by SOM Activity Map Comparis...

引用

International conference on pattern recognition

作者： G. Lefebvre C. Laurent J. Ros C. Garcia TECH/IRIS/CIM France Telecom Research and Development Center Cesson-Sevigne France

This article presents a method aiming at quantifying the visual similarity between two images. This kind of problem is recurrent in many applications such as object recognition, image classification, etc. In this paper, we propose to use self-organizing feature maps (SOM) to measure image similarity. To reach this goal, we feed local signatures associated to salient patches into the neural network. At the end of the learning step, each neural unit is tuned to a particular local signature prototype. During the recognition step, each image presented to the network generates a neural map that can be represented by an activity histogram. Image similarity is then computed by a quadratic distance between histograms. This scheme offers very promising results for image classification with a percentage of 84.47% of correct classification rates

关键词： Image classification Histograms Prototypes Feature extraction computer vision Data mining pattern recognition Humans Telecommunications Research and development

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Shape recognition using Curve Segment Hausdorff Distance

Shape Recognition using Curve Segment Hausdorff Distance

引用

International conference on pattern recognition

作者： Xiaozhou Yu M.K.H. Leung SCE Nanyang Technological University Singapore

ISBN: (纸本)0769525210

A novel shape recognition approach (CsHD) combining the template and syntactic matching schemes is proposed in this paper to generalize the Hausdorff distance (HD) from matching two sets of points to matching two sets of lines/curves. The new approach retains the desirable features of HD. These include non-explicit feature correspondence (natural allowance for portions of one shape to be compared with another) and simplicity in computation. In addition, a fast searching technique is employed to identify a list of likely matches. The proposed system has been applied to logo, palmprint and stationery recognitions with superior results. It has greatly out-performed the HD and is robust to noise, occlusion, skewing and minor broken line effect

关键词： Shape High definition video Magnetohydrodynamics Noise shaping Working environment noise Face recognition Noise robustness computer vision Image processing Video sequences

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Euclidean Structure from Confocal Conics: Theory and Application to Camera Calibration

Euclidean Structure from Confocal Conics: Theory and Applica...

引用

conference on computer vision and pattern recognition (CVPR)

作者： P. Gurdjos Jun-Sik Kim In-So Kweon UPS IRIT-TCI Toulouse France Department of EECS KAIST Daejeon South Korea

ISBN: (纸本)0769525970

Plane-based calibration is now a very popular procedure because of its flexibility. One key step consists in detecting a set of coplanar features, from which the Euclidean structure of the corresponding 3D plane has to be computed. We suggest to use confocal conics as calibration targets, as they offer undeniable advantages over other ones (e.g., points or lines) in terms of detection and estimation, especially in the presence of partial occlusion. We introduce important projective and Euclidean properties of the linear family of conics (i.e., the confocal conic range), spanned by two confocal conics. In particular, we rely on the fact that the circular point-envelope - a rank-2 conic that encodes the 2D Euclidean structure - is a degenerate member of any confocal conic range. This allows us to give closed-form solutions in three cases: one conic with known foci, two confocal conics with known product of ratios of semi axes, and two unknown confocal conics. The performances of the proposed algorithms (consisting of a few lines of Matlab-like code) show up high accuracies for both intrinsic and extrinsic camera parameters. In addition to experiments with synthetic data, a video sequence is processed, showing off the interest of using confocal conics as calibration targets, for augmented reality purposes.

关键词： Cameras Calibration Closed-form solution pattern matching computer vision Uninterruptible power systems computer languages Video sequences Augmented reality Solid modeling

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Efficient, Simultaneous Detection of Multiple Object Classes

Efficient, Simultaneous Detection of Multiple Object Classes

引用

International conference on pattern recognition

作者： P. Zehnder E. Koller-Meier L. Van Gool Computer Vision Laboratory ETH Zurich Zurich Switzerland

At present, the object categorisation literature is still dominated by the use of individual class detectors. Detecting multiple classes then implies the subsequent application of multiple such detectors, but such an approach is not scalable towards high numbers of classes. This paper presents an alternative strategy, where multiple classes are detected in a combined way. This includes a decision tree approach, where ternary rather than binary nodes are used, and where nodes share features. This yields an efficient scheme, which scales much better. The paper proposes a strategy where the object samples are first distinguished from the background. Then, in a second stage, the actual object class membership of each sample is determined. The focus of the paper lies entirely on the first stage, i.e. the distinction from background. The tree approach for this step is compared against two alternative strategies, one of them being the popular cascade approach. While classification accuracy tends to be better or comparable, the speed of the proposed method is systematically better. This advantage gets more outspoken as the number of object classes increases

关键词： Object detection Detectors Decision trees Face detection Costs computer vision Laboratories Application software Focusing Computational efficiency

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Fuzzy-Granular Gene Selection from Microarray Expression Data

Fuzzy-Granular Gene Selection from Microarray Expression Dat...

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IEEE International conference on Data Mining workshops (ICDM workshops)

作者： Yuanchen He Yuchun Tang Yan-qing Zhang Rajshekhar Sunderraman Department of Computer Science Georgia State University Atlanta GA USA CipherTrust Inc. Alpharetta GA USA

Selecting informative and discriminative genes from huge microarray gene expression data is an important and challenging bioinformatics research topic. This paper proposes a fuzzy-granular method for the gene selection task. Firstly, genes are grouped into different function granules with the fuzzy C-means algorithm (FCM). And then informative genes in each cluster are selected with the signal to noise metric (S2N). With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. The simulation results on two publicly available microarray expression datasets show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies

关键词： Clustering algorithms Gene expression Biological system modeling Data mining Prostate cancer computer science Bioinformatics Classification algorithms pattern recognition Predictive models

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The Facial Features Extraction for Face recognition Based on Geometrical Approach

The Facial Features Extraction for Face Recognition Based on...

引用

Canadian conference on Electrical and computer Engineering (CCECE)

作者： Basavaraj Amarapur Nagaraj Patil P.D.A. College of Engineering Gulbarga India S.L.N. College of Engineering Raichur India

Over the last two decades, several different techniques have been proposed for face recognition, which is one of the challenging areas of research in the field of image processing, pattern recognition and vision applications. Automatic human face identification system, e.g. security checks and crime investigation, etc. involves face recognition. The basic process consists of extraction of potential, facial features such as eyes, nose, mouth, eyebrows, etc. In the present paper, a geometrical face model proposed by Shi-Hong Jeng et al. for frontal face images is improved by the inclusion of ears and chin also as potential facial features, since it enhances the discrimination ability of the proposed face model during face recognition .The developed approach is divided into four main steps. The first step is pre processing, the goal of this step is to get rid of high intensity noises and to transform the input image into binary one. The second step includes a labeling process, which label the facial feature candidates by block by block. Then find the center, area and the orientation of each feature candidate. Third step is a geometrical model, used to measure relative distances and to locate the actual position of the entire facial features. Finally, the matching process. The modified face model has been experimented with test images and an enhanced success rate of 94% is achieved

关键词： Facial features Face recognition Face detection Solid modeling Image processing pattern recognition Humans Security Eyes Nose

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Optimal Estimation of Perspective Camera Pose

Optimal Estimation of Perspective Camera Pose

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International conference on pattern recognition

作者： C. Olsson F. Kahl M. Oskarsson Centre for Mathematical Sciences Lund University Sweden

In this paper we propose a practical and efficient method for finding the globally optimal solution to the problem of camera pose estimation for calibrated cameras. While traditional methods may get trapped in local minima, due to the non-convexity of the problem, we have developed an approach that guarantees global optimality. The scheme is based on ideas from global optimization theory, in particular, convex under-estimators in combination with branch and bound. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data

关键词： Cameras Iterative algorithms computer vision Application software Numerical stability Councils Nonlinear equations Gaussian noise Noise measurement Maximum likelihood estimation

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