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From Active Contours to Active Surfaces

From Active Contours to Active Surfaces

引用

IEEE Conference on computer vision and pattern recognition

作者： Akshaya Mishra Paul W. Fieguth David A. Clausi Tornado Medical Systems University of Waterloo

ISBN: (纸本)9781457703942

Identifying the surfaces of three-dimensional static objects or of two-dimensional objects over time are key to a variety of applications throughout computer vision. Active surface techniques have been widely applied to such tasks, such that a deformable spline surface evolves by the influence of internal and external (typically opposing) energies until the model converges to the desired surface. Present deformable model surface extraction techniques are computationally expensive and are not able to reliably identify surfaces in the presence of noise, high curvature, or clutter. This paper proposes a novel active surface technique, decoupled active surfaces, with the specific objectives of robustness and computational efficiency. Motivated by recent results in two-dimensional object segmentation, the internal and external energies are treated separately, which leads to much faster convergence. A truncated maximum likelihood estimator is applied to generate a surface consistent with the measurements (external energy), and a Bayesian linear least squares estimator is asserted to enforce the prior (internal energy). To maintain tractability for typical three-dimensional problems, the density of vertices is dynamically resampled based on curvature, a novel quasi-random search is used as a substitute for the ML estimator, and sparse conjugate-gradient is used to execute the Bayesian estimator. The performance of the proposed method is presented using two natural and two synthetic image volumes.

关键词： effective surface Active contours Object segmentation maximum likelihood estimation images computer vision Surfaces

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Evaluation of face recognition system in heterogeneous environments (visible vs NIR)

Evaluation of face recognition system in heterogeneous envir...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Debaditya Goswami Chi Ho Chan David Windridge Josef Kittler Centre for Vision Speech and Signal Processing University of Surrey Guildford UK Centre of Vision Speech and Signal Processing University of Surrey Guildford UK

Performing facial recognition between Near Infrared (NIR) and visible-light (VIS) images has been established as a common method of countering illumination variation problems in face recognition. In this paper we present a new database to enable the evaluation of cross-spectral face recognition. A series of preprocessing algorithms, followed by Local Binary pattern Histogram (LBPH) representation and combinations with Linear Discriminant Analysis (LDA) are used for recognition. These experiments are conducted on both NIR→VIS and the less common VIS→NIR protocols, with permutations of uni-modal training sets. 12 individual baseline algorithms are presented. In addition, the best performing fusion approaches involving a subset of 12 algorithms are also described.

关键词： Face Databases Feature extraction Face recognition Training Histograms Protocols

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Object recognition and centroid detection based on machine vision

Object recognition and centroid detection based on machine v...

引用

International Conference on Mechanic Automation and Control Engineering (MACE)

作者： Bin Liu Hui Ju Yuqin Yao Chengdu University of Information and Technology Chengdu Sichuan China

Machine vision based on image processing, pattern recognition, computer technology and physiology, exploit computer simulation human brain to process, understanding, description visual the information extracted, is an important branch of artificial intelligence. This paper Proposes a object recognition and centroid detection method based on the machine vision, send the centroid of each extracted object to controller of the Mechanical arm in factory production line, which is used to sort and package the actual industrial production automatically .

关键词： Object recognition Presses Artificial intelligence Visualization

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TVParser: An Automatic TV Video Parsing Method

TVParser: An Automatic TV Video Parsing Method

引用

IEEE Conference on computer vision and pattern recognition

作者： Chao Liang Changsheng Xu Jian Cheng Hanqing Lu National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781457703942

In this paper, we propose an automatic approach to simultaneously name faces and discover scenes in TV shows. We follow the multi-modal idea of utilizing script to assist video content understanding, but without using timestamp (provided by script-subtitles alignment) as the connection. Instead, the temporal relation between faces in the video and names in the script is investigated in our approach, and an global optimal video-script alignment is inferred according to the character correspondence. The contribution of this paper is two-fold: (1) we propose a generative model, named TVParser, to depict the temporal character correspondence between video and script, from which face-name relationship can be automatically learned as a model parameter, and meanwhile, video scene structure can be effectively inferred as a hidden state sequence;(2) we find fast algorithms to accelerate both model parameter learning and state inference, resulting in an efficient and global optimal alignment. We conduct extensive comparative experiments on popular TV series and report comparable and even superior performance over existing methods.

关键词： Hidden Markov models TV Histograms Kernel Semantics Labeling Mathematical model

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NonLinear Refinement of Structure from Motion Reconstruction by Taking Advantage of a Partial Knowledge of the Environment

NonLinear Refinement of Structure from Motion Reconstruction...

引用

IEEE Conference on computer vision and pattern recognition

作者： Mohamed Tamaazousti Vincent Gay-Bellile Sylvie Naudet Collette Steve Bourgeois Michel Dhome CEA LIST Vision and Content Engineering Lab LASMEA (CNRS/UBP)

ISBN: (纸本)9781457703942

We address the challenging issue of camera localization in a partially known environment, i.e. for which a geometric 3D model that covers only a part of the observed scene is available. When this scene is static, both known and unknown parts of the environment provide constraints on the camera motion. This paper proposes a nonlinear refinement process of an initial SfM reconstruction that takes advantage of these two types of constraints. Compare to those that exploit only the model constraints i.e. the known part of the scene, including the unknown part of the environment in the optimization process yields a faster, more accurate and robust refinement. It also presents a much larger convergence basin. This paper will demonstrate these statements on varied synthetic and real sequences for both 3D object tracking and outdoor localization applications.

关键词： Environments Refining Optimization Procedure Reconstructive Surgical Procedures Cameras Constraint nonlinearity Statement

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Action recognition by Dense Trajectories

Action Recognition by Dense Trajectories

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IEEE Conference on computer vision and pattern recognition

作者： Heng Wang Alexander Klaser Cordelia Schmid Cheng-Lin Liu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences LEAR INRIA Grenoble LJK

ISBN: (纸本)9781457703942

Feature trajectories have shown to be efficient for representing videos. Typically, they are extracted using the KLT tracker or matching SIFT descriptors between frames. However, the quality as well as quantity of these trajectories is often not sufficient. Inspired by the recent success of dense sampling in image classification, we propose an approach to describe videos by dense trajectories. We sample dense points from each frame and track them based on displacement information from a dense optical flow field. Given a state-of-the-art optical flow algorithm, our trajectories are robust to fast irregular motions as well as shot boundaries. Additionally, dense trajectories cover the motion information in videos well. We, also, investigate how to design descriptors to encode the trajectory information. We introduce a novel descriptor based on motion boundary histograms, which is robust to camera motion. This descriptor consistently outperforms other state-of-the-art descriptors, in particular in uncontrolled realistic videos. We evaluate our video description in the context of action classification with a bag-of-features approach. Experimental results show a significant improvement over the state of the art on four datasets of varying difficulty, i.e. KTH, YouTube, Hollywood2 and UCF sports.

关键词： trajectory Videotapes Dense Descriptors Subject Headings Image classification Optic Flow

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Development of a human-tracking robot using QR code recognition

Development of a human-tracking robot using QR code recognit...

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Korea-Japan Joint Workshop on Frontiers of computer vision

作者： Takashi Anezaki Koki Eimon Suriyon Tansuriyavong Yasushi Yagi Okinawa National College of Technology Nago Okinawa Japan Institute of Scientific and Industrial Research Osaka University Ibaraki Osaka Japan

In this paper, we propose a human-tracking robot that can be used in commercial establishments such as airports and factories. The human-tracking process involves three main steps. The first is robust person identification using QR code recognition, and is the most important step in human tracking. The second is location detection using shape-based pattern matching in order to determine the position of the QR code when the person being tracked moves far from the robot. Finally, the third step is robot control to maintain an appropriate distance for human tracking. In experiments measuring QR code recognition rates, it was shown that QR code recognition was 99.9% and that location detection is robust. In a robot-control experiment, tracking was shown to be accurate, and during tracking, the robot maintained an appropriate distance from the human.

关键词： pattern matching Cameras Humans Robot vision systems Service robots

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2D Nonrigid Partial Shape Matching Using MCMC and Contour Subdivision

2D Nonrigid Partial Shape Matching Using MCMC and Contour Su...

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IEEE Conference on computer vision and pattern recognition

作者： Yu Cao Zhiqi Zhang Irina Czogiel Ian Dryden Song Wang Department of Computer Science & Engineering University of South Carolina Max Planck Institute for Molecular Genetics Department of Statistics University of South Carolina

ISBN: (纸本)9781457703942

Shape matching has many applications in computer vision, such as shape classification, object recognition, object detection, and localization. In 2D cases, shape instances are 2D closed contours and matching two shape contours can usually be formulated as finding a one-to-one dense point correspondence between them. However, in practice, many shape contours are extracted from real images and may contain partial occlusions. This leads to the challenging partial shape matching problem, where we need to identify and match a subset of segments of the two shape contours. In this paper, we propose a new MCMC (Markov chain Monte Carlo) based algorithm to handle partial shape matching with mildly non-rigid deformations. Specifically, we represent each shape contour by a set of ordered landmark points. The selection of a subset of these landmark points into the shape matching is evaluated and updated by a posterior distribution, which is composed of both a matching likelihood and a prior distribution. This prior distribution favors the inclusion of more and consecutive landmark points into the matching. To better describe the matching likelihood, we develop a contour-subdivision technique to highlight the contour segment with highest matching cost from the selected subsequences of the points. In our experiments, we construct 1,600 test shape instances by introducing partial occlusions to the 40 shapes chosen from different categories in MPEG-7 dataset. We evaluate the performance of the proposed algorithm by comparing with three well-known partial shape matching methods.

关键词： contour images computer vision Outlines Shape Subset

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Object recognition and centroid detection based on machine vision

Object recognition and centroid detection based on machine v...

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2011 2nd International Conference on Mechanic Automation and Control Engineering, MACE 2011

作者： Liu, Bin Ju, Hui Yao, Yuqin Chengdu University of Information Technology Chengdu sichuan China

ISBN: (纸本)9781424494392

关键词： Edge detection

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RAPID INFRARED MULTI-SPECTRAL SYSTEMS DESIGN USING A HYPERSPECTRAL BENCHMARKING FRAMEWORK

RAPID INFRARED MULTI-SPECTRAL SYSTEMS DESIGN USING A HYPERSP...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Dacal-Nieto, A. Formella, A. Carrion, P. Vazquez-Fernandez, E. Fernandez-Delgado, M. Univ Vigo Dept Comp Sci Vigo Spain GRADIANT Galician R&D Ctr Adv Telecommun Vigo Spain Univ Santiago CITIUS Santiago Spain

ISBN: (纸本)9781612843490

We present a benchmarking framework to design multi-spectral systems working in the NIR range for multiple purposes. This framework is composed of a hyperspectral imaging hardware and an ad-hoc software that performs pattern recognition experiments (image acquisition, segmentation, feature extraction, feature selection, classification and evaluation steps) comparing different algorithms in every step. For each experiment, we obtain a solution using a generic hyperspectral system, but we also obtain enough data to design a specific multi-spectral system in order to decrease the overall execution time. This improvement is based in the feature selection step, that provides the most relevant wavelengths for the problem. The framework has been tested for detecting internal and external features in potatoes, determining the origin of honey, and studying fecundity parameters in hen eggs.

关键词： hyperspectral multi-spectral computer vision image processing infrared

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