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Moving Forward in Structure From Motion

Moving Forward in Structure From Motion

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Andrea Vedaldi Gregorio Guidi Stefano Soatto Computer Science Department University of California Los Angeles Los Angeles CA USA

It is well-known that forward motion induces a large number of local minima in the instantaneous least-squares reprojection error. This is caused in part by singularities in the error landscape around the forward direction, and presents a challenge in using existing algorithms for structure-from-motion in autonomous navigation applications. In this paper we prove that imposing a bound on the reconstructed depth of the scene makes the least-squares re-projection error continuous. This has implications for autonomous navigation, as it suggests simple modifications for existing algorithms to minimize the effects of local minima in forward translation.

关键词： Layout Navigation Cameras Geometry computer errors Image reconstruction Algorithm design and analysis Mathematics computer science computer vision

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Discriminant Interest Points are Stable

Discriminant Interest Points are Stable

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Dashan Gao Nuno Vasconcelos Department of Electrical and Computer Engineering University of California San Diego USA

A study of the performance of recently introduced discriminant methods for interest point detection [6,14] is presented. It has been previously shown that the resulting interest points are more informative for object recognition than those produced by the detectors currently used in computer vision. Little is, however, known about the properties of discriminant points with respect to the metrics, such as repeatability, that have been traditionally used to evaluate interest point detection. A thorough experimental evaluation of the stability of discriminant points is presented, and this stability compared to those of four popular methods. In particular, we consider image correspondence under geometric and photometric transformations, and extend the experimental protocol proposed by Mikolajczyk et al. [13] for the evaluation of stability with respect to such transformations. The extended protocol is suitable for the evaluation of both bottom-up and top-down (learned) detectors. It is shown that the stability of discriminant interest points is comparable, and frequently superior, to those of interest points produced by various currently popular techniques.

关键词： Detectors Protocols computer vision Image recognition Object detection Photometry Stability criteria Robust stability Training data Object recognition

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Beyond Local Appearance: Category recognition from Pairwise Interactions of Simple Features

Beyond Local Appearance: Category Recognition from Pairwise ...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Marius Leordeanu Martial Hebert Rahul Sukthankar Carnegie Mellon University USA Intel Research Pittsburgh USA

We present a discriminative shape-based algorithm for object category localization and recognition. Our method learns object models in a weakly-supervised fashion, without requiring the specification of object locations nor pixel masks in the training data. We represent object models as cliques of fully-interconnected parts, exploiting only the pairwise geometric relationships between them. The use of pairwise relationships enables our algorithm to successfully overcome several problems that are common to previously-published methods. Even though our algorithm can easily incorporate local appearance information from richer features, we purposefully do not use them in order to demonstrate that simple geometric relationships can match (or exceed) the performance of state-of-the-art object recognition algorithms.

关键词： Shape Solid modeling Object recognition Humans Deformable models Image recognition Training data Animals Cognitive science computer vision

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Feature Extraction by Maximizing the Average Neighborhood Margin

Feature Extraction by Maximizing the Average Neighborhood Ma...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Fei Wang Changshui Zhang State Key Laboratory of Intelligent Technologies and Systems Department of Automation Tsinghua University Beijing China

A novel algorithm called Average Neighborhood Margin Maximization (ANMM) is proposed for supervised linear feature extraction. For each data point, ANMM aims at pulling the neighboring points with the same class label towards it as near as possible, while simultaneously pushing the neighboring points with different labels away from it as far as possible. We will show that features extracted from ANMM can separate the data from different classes well, and it avoids the small sample size problem existed in traditional Linear Discriminant Analysis (LDA). The kernelized (nonlinear) counterpart of ANMM is also established in this paper. Moreover, as in many computer vision applications the data are more naturally represented by higher order tensors (e.g. images and videos), we develop a tensorized (multilinear) form of ANMM, which can directly extract features from tensors. The experimental results of applying ANMM to face recognition are presented to show the effectiveness of our method.

关键词： Feature extraction Linear discriminant analysis Principal component analysis Tensile stress Data mining computer vision Scattering pattern recognition Covariance matrix Kernel

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Regularized Mixed Dimensionality and Density Learning in computer vision

Regularized Mixed Dimensionality and Density Learning in Com...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Gloria Haro Gregory Randall Guillermo Sapiro Department Teoria del Senyal i Comunicacions Universitat Poliltècnica de Catalunya Spain Instituto de Ingeniería Elèctrica Universidad de la República Uruguay Department of Electrical Engineering University of Minnesota USA

A framework for the regularized estimation of nonuniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well. The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computer vision applications.

关键词： computer vision Lighting Principal component analysis Manifolds Application software Embedded computing Videos Humans Clouds Fractals

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Part-based Face recognition Using Near Infrared Images

Part-based Face Recognition Using Near Infrared Images

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Ke Pan Shengcai Liao Zhijian Zhang Stan Z. Li Peiren Zhang University of Science and Technology Hefei China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

Recently, the authors developed NIR based face recognition for highly accurate face recognition under illumination variations. In this paper, we present a part-based method for improving its robustness with respect to pose variations. An NIR face is decomposed into parts. A part classifier is built for each part, using the most discriminative LBP histogram features selected by AdaBoost learning. The outputs of part classifiers are fused to give the final score. Experiments show that the present method outperforms the whole face-based method by 4.53%.

关键词： Face recognition Infrared imaging Face detection Lighting Robustness Histograms Support vector machines Support vector machine classification Image recognition Biomedical optical imaging

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High-Speed Visual Tracking of the Nearest Point of an Object Using 1,000-fps Adaptive pattern Projection

High-Speed Visual Tracking of the Nearest Point of an Object...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Tomoyuki Tnoue Shingo Kagami Joji Takei Koichi Hashimoto Kenkichi Yamamoto Idaku Ishii Tohoku University Aoba-ku Sendai Japan Hiroshima University Higashi-Hiroshima Japan

A 1,000-jps camera-projector system in which projected patterns are adoptively controlled according to image processing results is described. Adaptive structured light projection enables fast and efficient 3-D information acquisition. The prototype system is applied to the tracking of the nearest point of an object, and experimental results show that the system successfully tracked an apex of a fast-moving large! object.

关键词： Pixel Cameras Machine vision Control systems Shape Prototypes Head Field programmable gate arrays Image processing Layout

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On the Direct Estimation of the Fundamental Matrix

On the Direct Estimation of the Fundamental Matrix

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Yaser Sheikh Asaad Hakeem Mubarak Shah Robotics Institute Carnegie Mellon University USA Computer Vision Laboratory University of Central Florida USA

The fundamental matrix is a central construct in the analysis of images captured from a pair of cameras and many feature-based methods have been proposed for its computation. In this paper, we propose a direct method for estimating the fundamental matrix where the motion between the frames is small (e.g. between successive frames of a video). To achieve this, a warping function is presented for the fundamental matrix by using the brightness constancy constraint in conjunction with geometric constraints. Using this warping function, an iterative hierarchical algorithm is described to recover accurate estimates of the fundamental matrix. We present results of experimentation to evaluate the performance of the proposed approach and demonstrate improved accuracy in the computation of the fundamental matrix.

关键词： Transmission line matrix methods Motion estimation computer vision Cameras Iterative algorithms Iterative methods Robot vision systems Laboratories Image analysis Brightness

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Riemannian Analysis of Probability Density Functions with Applications in vision

Riemannian Analysis of Probability Density Functions with Ap...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Anuj Srivastava Ian Jermyn Shantanu Joshi Department of Statistics Florida State University USA ARIANA Group INRIA Sophia-Antipolis France Department of Electrical Engineering Florida State University USA

Applications in computer vision involve statistically analyzing an important class of constrained, non-negative functions, including probability density functions (in texture analysis), dynamic time-warping functions (in activity analysis), and re-parametrization or non-rigid registration functions (in shape analysis of curves). For this one needs to impose a Riemannian structure on the spaces formed by these functions. We propose a "spherical" version of the Fisher-Rao metric that provides closed-form expressions for geodesies and distances, and allows fast computation of sample statistics. To demonstrate this approach, we present an application in planar shape classification.

关键词： Probability density function Application software computer vision Shape Geophysics computing Statistics Pixel Statistical analysis Closed-form solution Frequency

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New real-time needle segmentation technique using gray-scale Hough transformation

New real-time needle segmentation technique using gray-scale...

引用

Conference on Medical Imaging, Parallel Processing of Images, and Optimization Techniques

作者： Qiu, Wu Zhou, Hua Ding, Mingyue Zhang, Songgeng Huazhong Univ Sci & Technol State Key Lab Image Proc & Intelligent Control Inst Pattern Recognit & Artificial Intelligence 1037 Luoyu Rd Wuhan 430074 Peoples R China Beijing Tsinghua R&D Ind Inst Beijing 100085 Peoples R China

ISBN: (纸本)9780819469533

Real-time needle segmentation and tracking is very important in image-guided surgery, biopsy, and therapy. In this paper, we described an automated technique to provide real-time needle segmentation from a sequence of 2-D ultrasound images for the use of guidance of a needle to the target in soft tissues. The Hough transform is used to find straight lines or analytic curves in binary image. Hough transform is applied usually to binary images. Hence one needs to convert, initially, the gray level image to a binary one (through thresholding, edge detection, or thinning) in order to apply the HT. While in the process of binarization, some information about line segments in the image may be lost when an inappropriate threshold is used. Gray-Scale Hough Transform can detect the line without binarization. Unfortunately, its high computational cost often prevents it from being applied in real-time applications without the help of specially designed hardware. In this paper, we proposed a needle segmentation technique based on a real-time gray-scale Hough transform. It is composed of an improved Gray Hough Transformation and a coarse-fine search strategy. Furthermore, the RTGHT (Real-Time Gray-Scale Hough Transform) technique is evaluated by patient breast biopsy images. Experiments with patient breast biopsy ultrasound (US) image sequences showed that our approach can segment the biopsy needle in real time (i.e., less than 60 ms) with the angular rms error of about 1 degrees and the position rms error of about 0.5 nun. an affordable PC computer without the help of specially designed hardware.

关键词： real-time processing gray-level Hough transform needle segmentation biopsy tracking coarse-fine search

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