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检索条件"任意字段=MIPPR 2007: Pattern Recognition and Computer Vision"

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A Fuzzy-interval based approach for explicit graph embedding

A Fuzzy-interval based approach for explicit graph embedding

引用

20th International Conference on pattern recognition, ICPR 2010

作者： Luqman, Muhammad Muzzamil Lladós, Josep Ramel, Jean-Yves Brouard, Thierry Laboratoire d’Informatique Université François Rabelais de Tours France Computer Vision Center Universitat Autónoma de Barcelona Spain

ISBN: (纸本)9783642177101

We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the number of edges. These are followed by w features for node degrees, m features for k node attributes and n features for l edge attributes — which represent the distribution of node degrees, node attribute values and edge attribute values, and are obtained by defining (in an unsupervised fashion), fuzzy-intervals over the list of node degrees, node attributes and edge attributes. Experimental results are provided for sample data of ICPR2010 contest GEPR. © 2010, Springer-Verlag Berlin Heidelberg.

关键词： Artificial intelligence

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Contextual pooling in image classification

Contextual pooling in image classification

引用

11th Asian Conference on computer vision, ACCV 2012

作者： Wu, Zifeng Huang, Yongzhen Wang, Liang Tan, Tieniu National Lab. of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642373305

The original bag-of-words (BoW) model in terms of image classification treats each local feature independently, and thus ignores the spatial relationships between a feature and its neighboring features, namely, the feature's context. However, our intuition and empirical studies tell the importance of such spatial information. Although the global spatial information can be captured with the spatial pyramid matching scheme, the subject of capturing local spatial relationships between features is still open. In this paper, we propose a new method to embed such local spatial (context) information into the BoW model. A vector reflecting context information is firstly extracted along with each feature, context patterns are then code-specifically trained, and thus the context information is elegantly embedded into the BoW model by contextual pooling according to different context patterns. Extensive experiments on the PASCAL VOC 2007 dataset show that our method greatly enhances the BoW model, and achieves the state-of-the-art performance. © 2013 Springer-Verlag.

关键词： Image classification

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Free-shape subwindow search for object localization

Free-shape subwindow search for object localization

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Zhiqi Zhang Yu Cao Dhaval Salvi Kenton Oliver Jarrell Waggoner Song Wang Department of Computer Science and Engineering University of South Carolina Columbia SC USA

Object localization in an image is usually handled by searching for an optimal subwindow that tightly covers the object of interest. However, the subwindows considered in previous work are limited to rectangles or other specified, simple shapes. With such specified shapes, no subwindow can cover the object of interest tightly. As a result, the desired subwindow around the object of interest may not be optimal in terms of the localization objective function, and cannot be detected by a subwindow search algorithm. In this paper, we propose a new graph-theoretic approach for object localization by searching for an optimal subwindow without pre-specifying its shape. Instead, we require the resulting subwindow to be well aligned with edge pixels that are detected from the image. This requirement is quantified and integrated into the localization objective function based on the widely-used bag of visual words technique. We show that the ratio-contour graph algorithm can be adapted to find the optimal free-shape subwindow in terms of the new localization objective function. In the experiment, we test the proposed approach on the PASCAL VOC 2006 and VOC 2007 databases for localizing several categories of animals. We find that its performance is better than the previous efficient subwindow search algorithm.

关键词： Object detection Shape Image edge detection Image recognition computer vision computer science Pixel Testing Visual databases Animals

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Two thresholds are better than one

Two thresholds are better than one

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Conference on computer vision and pattern recognition (CVPR)

作者： Tao Zhang Terrance E. Boult R.C. Johnson Vision And Security Technology Laboratory University of Colorado Colorado Springs CO USA

The concept of the Bayesian optimal single threshold is a well established and widely used classification technique. In this paper, we prove that when spatial cohesion is assumed for targets, a better classification result than the "optimal" single threshold classification can be achieved. Under the assumption of spatial cohesion and certain prior knowledge about the target and background, the method can be further simplified as dual threshold classification. In core-dual threshold classification, spatial cohesion within the target core allows "continuation" linking values to fall between the two thresholds to the target core; classical Bayesian classification is employed beyond the dual thresholds. The core-dual threshold algorithm can be built into a Markov random field model (MRF). From this MRF model, the dual thresholds can be obtained and optimal classification can be achieved. In some practical applications, a simple method called symmetric subtraction may be employed to determine effective dual thresholds in real time. Given dual thresholds, the quasi-connected component algorithm is shown to be a deterministic implementation of the MRF core-dual threshold model combining the dual thresholds, extended neighborhoods and efficient connected component computation.

关键词： Bayesian methods Solids Probability distribution Security Springs Joining processes Markov random fields Image segmentation Video surveillance Biomedical imaging

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Segmenting Motions of Different Types by Unsupervised Manifold Clustering

Segmenting Motions of Different Types by Unsupervised Manifo...

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Conference on computer vision and pattern recognition (CVPR)

作者： Alvina Goh Rene Vidal Center for Imaging Science Johns Hopkins University Baltimore MD USA

We propose a novel algorithm for segmenting multiple motions of different types from point correspondences in multiple affine or perspective views. Since point trajectories associated with different motions live in different manifolds, traditional approaches deal with only one manifold type: linear subspaces for affine views, and homographic, bilinear and trilinear varieties for two and three perspective views. As real motion sequences contain motions of different types, we cast motion segmentation as a problem of clustering manifolds of different types. Rather than explicitly modeling each manifold as a linear, bilinear or multilinear variety, we use nonlinear dimensionality reduction to learn a low-dimensional representation of the union of all manifolds. We show that for a union of separated manifolds, the LLE algorithm computes a matrix whose null space contains vectors giving the segmentation of the data. An analysis of the variance of these vectors allows us to distinguish them from other vectors in the null space. This leads to a new algorithm for clustering both linear and nonlinear manifolds. Although this algorithm is theoretically designed for separated manifolds, our experiments demonstrate its performance on real data where this assumption does not hold. We test our algorithm on the Hopkins 155 motion segmentation database and achieve an average classification error of 4.8%, which compares favorably against state-of-the art multiframe motion segmentation methods.

关键词： Clustering algorithms Motion segmentation computer vision Vectors Null space Analysis of variance Data analysis Functional analysis Algorithm design and analysis Testing

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On Constant Focal Length Self-Calibration From Multiple Views

On Constant Focal Length Self-Calibration From Multiple View...

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Conference on computer vision and pattern recognition (CVPR)

作者： Benoit Bocquillon Adrien Bartoli Pierre Gurdjos Alain Crouzil UPS-IRIT France CNRS-LASMEA France

We investigate the problem of finding the metric structure of a general 3D scene viewed by a moving camera with square pixels and constant unknown focal length. While the problem has a concise and well-understood formulation in the stratified framework thanks to the absolute dual quadric, two open issues remain. The first issue concerns the generic Critical Motion Sequences, i.e. camera motions for which self-calibration is ambiguous. Most of the previous work focuses on the varying focal length case. We provide a thorough study of the constant focal length case. The second issue is to solve the nonlinear set of equations in four unknowns arising from the dual quadric formulation. Most of the previous work either does local nonlinear optimization, thereby requiring an initial solution, or linearizes the problem, which introduces artificial degeneracies, most of which likely to arise in practice. We use interval analysis to solve this problem. The resulting algorithm is guaranteed to find the solution and is not subject to artificial degeneracies. Directly using interval analysis usually results in computationally expensive algorithms. We propose a carefully chosen set of inclusion functions, making it possible to find the solution within few seconds. Comparisons of the proposed algorithm with existing ones are reported for simulated and real data.

关键词： Cameras Collision mitigation Image reconstruction Layout Nonlinear equations Algorithm design and analysis Computational modeling computer vision Surface reconstruction Uninterruptible power systems

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Scene Segmentation and Categorization Using NCuts

Scene Segmentation and Categorization Using NCuts

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Conference on computer vision and pattern recognition (CVPR)

作者： Yanjun Zhao Tao Wang Peng Wang Wei Hu Yangzhou Du Yimin Zhang Guangyou Xu Department CS. & Technology Tsinghua University Beijing China Intel China Research Center Limited Beijing China

For video summarization and retrieval, one of the important modules is to group temporal-spatial coherent shots into high-level semantic video clips namely scene segmentation. In this paper, we propose a novel scene segmentation and categorization approach using normalized graph cuts(NCuts). Starting from a set of shots, we first calculate shot similarity from shot key frames. Then by modeling scene segmentation as a graph partition problem where each node is a shot and the weight of edge represents the similarity between two shots, we employ NCuts to find the optimal scene segmentation and automatically decide the optimum scene number by Q function. To discover more useful information from scenes, we analyze the temporal layout patterns of shots, and automatically categorize scenes into two different types, i.e. parallel event scenes and serial event scenes. Extensive experiments are tested on movie, and TV series. The promising results demonstrate that the proposed NCuts based scene segmentation and categorization methods are effective in practice.

关键词： Layout Information analysis pattern analysis Motion pictures Partitioning algorithms Clustering algorithms Hidden Markov models Event detection Testing TV

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Simultaneous Optimization of Structure and Motion in Dynamic Scenes Using Unsynchronized Stereo Cameras

Simultaneous Optimization of Structure and Motion in Dynamic...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Akihito Seki Masatoshi Okutomi Graduate School of Science and Engineering Tokyo Institute of Technology Tokyo Japan

In this paper, we propose a simultaneous estimation method of structure and motion in dynamic scenes. Usual methods for obtaining structure and motion using stereo cameras require two kinds of operations: stereo correspondence and tracking. Therefore, we must separately determine the correspondence between stereo images and sequential images. This necessity complicates the algorithm and increases the possibility of mismatches because of the object's motion and visibility change in the images. Our proposed method makes two contributions. The first contribution is the method of corresponding all stereo images and sequential images at once. Therefore, we can obtain the structure and motion simultaneously and more accurately. On the other hand, most stereo correspondence algorithms are limited to use under a synchronized status. In a stereo rig using unsynchronized cameras, as are most commercially available cameras, the structure cannot be obtained by stereo correspondence and triangulation because of the unknown time offset between cameras. Therefore, our second contribution is a method of estimating structure, motion, and time offset simultaneously using unsynchronized stereo cameras. This latter task is accomplished by taking advantage of the first contribution scheme. Additionally, our method requires no preprocessing such as motion segmentation for separating identical-motion objects and advance calibration of the time offset. Finally, we present the experimental results using both synthetic and real images.

关键词： Layout Cameras Motion estimation Tracking Frequency synchronization Motion segmentation computer vision Timing Image motion analysis Image generation

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Learning to Group Objects

Learning to Group Objects

引用

IEEE Conference on computer vision and pattern recognition

作者： Victoria Yanulevskaya Jasper Uijlings Nicu Sebe University of Trento

ISBN: (纸本)9781479951192

This paper presents a novel method to generate a hypothesis set of class-independent object regions. It has been shown that such object regions can be used to focus computer vision techniques on the parts of an image that matter most leading to significant improvements in both object localisation and semantic segmentation in recent years. Of course, the higher quality of class-independent object regions, the better subsequent computer vision algorithms can perform. In this paper we focus on generating higher quality object hypotheses. We start from an oversegmentation for which we propose to extract a wide variety of region-features. We group regions together in a hierarchical fashion, for which we train a Random Forest which predicts at each stage of the hierarchy the best possible merge. Hence unlike other approaches, we use relatively powerful features and classifiers at an early stage of the generation of likely object regions. Finally, we identify and combine stable regions in order to capture objects which consist of dissimilar parts. We show on the PASCAL 2007 and 2012 datasets that our method yields higher quality regions than competing approaches while it is at the same time more computationally efficient.

关键词： Image color analysis Image segmentation Histograms Feature extraction Merging Radio frequency computer vision

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Combination of OCR Engines for Page Segmentation Based on Performance Evaluation

Combination of OCR Engines for Page Segmentation Based on Pe...

引用

International Conference on Document Analysis and recognition

作者： M. Ferrer E. Valveny Computer Vision Center-Department Ciències Computació Universitat Autònoma Barcelona Bellaterra Spain

ISBN: (纸本)9780769528229;0769528228

In this paper we present a method to improve the performance of individual page segmentation engines based on the combination of the output of several engines. The rules of combination are designed after analyzing the results of each individual method. This analysis is performed using a performance evaluation framework that aims at characterizing each method according to its strengths and weaknesses rather than computing a single performance measure telling which is the "best" segmentation method.

关键词： Optical character recognition software Engines Image segmentation computer vision Performance analysis Performance evaluation Particle measurements Text analysis pattern recognition

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