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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

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Using Contours to Detect and Localize Junctions in Natural Images

Using Contours to Detect and Localize Junctions in Natural I...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Michael Maire Pablo Arbelaez Charless Fowlkes Jitendra Malik University of California Berkeley CA USA University of California Irvine CA USA

Contours and junctions are important cues for perceptual organization and shape recognition. Detecting junctions locally has proved problematic because the image intensity surface is confusing in the neighborhood of a junction. Edge detectors also do not perform well near junctions. Current leading approaches to junction detection, such as the Harris operator, are based on 2D variation in the intensity signal. However, a drawback of this strategy is that it confuses textured regions with junctions. We believe that the right approach to junction detection should take advantage of the contours that are incident at a junction;contours themselves can be detected by processes that use more global approaches. In this paper, we develop a new high-performance contour detector using a combination of local and global cues. this contour detector provides the best performance to date (F=0.70) on the Berkeley Segmentation Dataset (BSDS) benchmark. From the resulting contours, we detect and localize candidate junctions, taking into account both contour salience and geometric configuration. We show that improvements in our contour model lead to better junctions. Our contour and junction detectors both provide state of the art performance.

关键词： Detectors Image edge detection Semiconductor counters computer vision Brightness Filters Shape Image recognition Humans Object detection

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Recognizing Human Actions Using Multiple Features

Recognizing Human Actions Using Multiple Features

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Jingen Liu Saad Ali Mubarak Shah Computer Vision Laboratory University of Central Florida USA

In this paper, we propose a framework that fuses multiple features for improved action recognition in videos. the fusion of multiple features is important for recognizing actions as often a single feature based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc.). Hence, we use two types of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (or cuboids), and ii) a quantized vocabulary of spin-images, which aims to capture the shape deformation of the actor by considering actions as 3D objects (x, y, t). To optimally combine these features, we treat different features as nodes in a graph, where weighted edges between the nodes represent the strength of the relationship between entities. the graph is then embedded into a k-dimensional space subject to the criteria that similar nodes have Euclidian coordinates which are closer to each other. this is achieved by converting this constraint into a minimization problem whose solution is the eigenvectors of the graph Laplacian matrix. this procedure is known as Fiedler Embedding. the performance of the proposed framework is tested on publicly available data sets. the results demonstrate that fusion of multiple features helps in achieving improved performance, and allows retrieval of meaningful features and videos from the embedding space.

关键词： Humans Videos Vocabulary Fuses Image recognition Lighting Shape Laplace equations Matrix converters Testing

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Exploiting Side Information in Locality Preserving Projection

Exploiting Side Information in Locality Preserving Projectio...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Senjian An Wanquan Liu Svetha Venkatesh Department of Computing Curtin University of Technology Perth WA Australia

Even if the class label information is unknown, side information represents some equivalence constraints between pairs of patterns, indicating whether pairs originate from the same class. Exploiting side information, we develop algorithms to preserve both the intra-class and inter-class local structures. this new type of locality preserving projection (LPP), called LPP with side information (LPPSI), preserves the data's local structure in the sense that the close, similar training patterns will be kept close, whilst the close but dissimilar ones are separated. Our algorithms balance these conflicting requirements, and we further improve this technique using kernel methods. Experiments conducted on popular face databases demonstrate that the proposed algorithm significantly outperforms LPP. Further, we show that the performance of our algorithm with partial side information (that is, using only small amount of pair-wise similarity/dissimilarity information during training) is comparable with that when using full side information. We conclude that exploiting side information by preserving both similar and dissimilar local structures of the data significantly improves performance.

关键词： Face recognition Linear discriminant analysis Image retrieval Kernel Indexing Scattering Access control Information retrieval Australia Databases

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Robust Dual Motion Deblurring

Robust Dual Motion Deblurring

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Jia Chen Lu Yuan Chi-Keung Tang Long Quan Vision and Graphics Group Hong Kong University of Science and Technology Hong Kong China

this paper presents a robust algorithm to deblur two consecutively captured blurred photos from camera shaking. Previous dual motion deblurring algorithms succeeded in small and simple motion blur and are very sensitive to noise. We develop a robust feedback algorithm to perform iteratively kernel estimation and image deblurring. In kernel estimation, the stability and capability of the algorithm is greatly improved by incorporating a robust cost function and a set of kernel priors. the robust cost function serves to reject outliers and noise, while kernel priors, including sparseness and continuity, remove ambiguity and maintain kernel shape. In deblurring, we propose a novel and robust approach which takes two blurred images as input to infer the clear image. the deblurred image is then used as feedback to refine kernel estimation. Our method can successfully estimate large and complex motion blurs which cannot be handled by previous dual or single image motion deblurring algorithms. the results are shown to be significantly better than those of previous approaches.

关键词： Noise robustness Kernel Iterative algorithms Feedback Robust stability Cost function Cameras Image restoration Noise shaping Shape

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Bayesian Color Constancy Revisited

Bayesian Color Constancy Revisited

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Peter Vincent Gehler Carsten Rother Andrew Blake Tom Minka Toby Sharp Max-Planck Institute Tubingen Germany Microsoft Research Cambridge Cambridge UK

Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. this has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset.

关键词： Bayesian methods Layout Reflectivity Lighting Testing Cameras Image databases Object recognition Image resolution Yield estimation

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Large Margin Pursuit for a Conic Section Classifier

Large Margin Pursuit for a Conic Section Classifier

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Santhosh Kodipaka Arunava Banerjee Baba C. Vemuri Department of Computer & Information Science && Engineering University of Florida Gainesville FL USA

Learning a discriminant becomes substantially more difficult when the datasets are high-dimensional and the available samples are few. this is often the case in computer vision and medical diagnosis applications. A novel Conic Section classifier (CSC) was recently introduced in the literature to handle such datasets, wherein each class was represented by a conic section parameterized by its focus, directrix and eccentricity. the discriminant boundary was the locus of all points that are equi-eccentric relative to each class-representative conic section. Simpler boundaries were preferred for the sake of generalizability. In this paper, we improve the performance of the two-class classifier via a large margin pursuit. When formulated as a non-linear optimization problem, the margin computation is demonstrated to be hard, especially due to the high dimensionality of the data. Instead, we present a geometric algorithm to compute the distance of a point to the nonlinear discriminant boundary generated by the CSC in the input space. We then introduce a large margin pursuit in the learning phase so as to enhance the generalization capacity of the classifier. We validate the algorithm on real datasets and show favorable classification rates in comparison to many existing state-of-the-art binary classifiers as well as the CSC without margin pursuit.

关键词： Virtual colonoscopy Cancer computer vision Application software Kernel Supervised learning Information science Biomedical engineering Data engineering Medical diagnosis

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What Are the High-Level Concepts with Small Semantic Gaps?

What Are the High-Level Concepts with Small Semantic Gaps?

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Yijuan Lu Lei Zhang Qi Tian Wei-Ying Ma One UTSA Circle University of Texas San Antonio San Antonio TX USA Microsoft Research Asia Beijing China

Concept-based multimedia search has become more and more popular in Multimedia Information Retrieval (MIR). However, which semantic concepts should be used for data collection and model construction is still an open question. Currently, there is very little research found on automatically choosing multimedia concepts with small semantic gaps. In this paper, we propose a novel framework to develop a lexicon of high-level concepts with small semantic gaps (LCSS) from a large-scale web image dataset. By defining a confidence map and content-context similarity matrix, images with small semantic gaps are selected and clustered. the final concept lexicon is mined from the surrounding descriptions (titles, categories and comments) of these images. this lexicon offers a set of high-level concepts with small semantic gaps, which is very helpful for people to focus for data collection, annotation and modeling. It also shows a promising application potential for image annotation refinement and rejection. the experimental results demonstrate the validity of the developed concepts lexicon.

关键词： Information retrieval Europe Asia Large-scale systems Focusing Multimedia systems Power system modeling Object recognition Training data Vocabulary

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Visibility in Bad Weather from a Single Image

Visibility in Bad Weather from a Single Image

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Robby T. Tan Imperial College London Communications and Signal Processing Group NICTA-Australian National University Australia

Bad weather, such as fog and haze, can significantly degrade the visibility of a scene. Optically, this is due to the substantial presence of particles in the atmosphere that absorb and scatter light. In computer vision, the absorption and scattering processes are commonly modeled by a linear combination of the direct attenuation and the airlight. Based on this model, a few methods have been proposed, and most of them require multiple input images of a scene, which have either different degrees of polarization or different atmospheric conditions. this requirement is the main drawback of these methods, since in many situations, it is difficult to be fulfilled. To resolve the problem, we introduce an automated method that only requires a single input image. this method is based on two basic observations: first, images with enhanced visibility (or clear-day images) have more contrast than images plagued by bad weather;second, airlight whose variation mainly depends on the distance of objects to the viewer, tends to be smooth. Relying on these two observations, we develop a cost function in the framework of Markov random fields, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation. the method does not require the geometrical information of the input image, and is applicable for both color and gray images.

关键词： Optical scattering Layout Optical attenuators Light scattering Particle scattering Atmospheric modeling Degradation Atmosphere computer vision Absorption

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Approximate earth mover's distance in linear time

Approximate earth mover's distance in linear time

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Sameer Shirdhonkar David W. Jacobs Center for Automation Research University of Maryland College Park USA

the earth mover's distance (EMD) [16] is an important perceptually meaningful metric for comparing histograms, but it suffers from high (O(N{sup}3 log N)) computational complexity. We present a novel linear time algorithm for approximating the EMD for low dimensional histograms using the sum of absolute values of the weighted wavelet coefficients of the difference histogram. EMD computation is a special case of the Kantorovich-Rubinstein transshipment problem, and we exploit the Holder continuity constraint in its dual form to convert it into a simple optimization problem with an explicit solution in the wavelet domain. We prove that the resulting wavelet EMD metric is equivalent to EMD, i.e. the ratio of the two is bounded. We also provide estimates for the bounds. the weighted wavelet transform can be computed in time linear in the number of histogram bins, while the comparison is about as fast as for normal Euclidean distance or χ{sup}2 statistic. We experimentally show that wavelet EMD is a good approximation to EMD, has similar performance, but requires much less computation.

关键词： Earth Histograms Computational complexity Wavelet coefficients Wavelet domain Statistics Extraterrestrial measurements Image retrieval pattern matching Jacobian matrices

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Learning Subcategory Relevances for Category recognition

Learning Subcategory Relevances for Category Recognition

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Sinisa Todorovic Narendra Ahuja Beckman Institute University of Illinois Urbana-Champaign USA

A real-world object category can be viewed as a characteristic configuration of its parts, that are themselves simpler, smaller (sub)categories. recognition of a category can therefore be made easier by detecting its constituent subcategories and combing these detection results. Given a set of training images, each labeled by an object category contained in it, we present an approach to learning: (1) Taxonomy defined by recursive sharing of subcategories by multiple image categories;(2) Subcategory relevance as the degree of evidence a subcategory offers for the presence of its parent;(3) Likelihood that the image contains a subcategory;and (4) Prior that a subcategory occurs. the images are represented as points in a feature space spanned by confidences in the occurrences of the subcategories. the subcategory relevances are estimated as weights, necessary to rescale the corresponding axes of the feature space so that the images with the same label are closer to each other than to those with different labels. When a new image is encountered, the learned taxonomy, relevances, likelihoods, and priors are used by a linear classifier to categorize the image. On the challenging Caltech-256 dataset, the proposed approach significantly outperforms the best categorizations reported. this result is significant in that it not only demonstrates the advantages of exploiting subcategory taxonomy for recognition, but also suggests that a feature space spanned by part properties, instead of direct object properties, allows for linear separation of image classes.

关键词： Taxonomy Image recognition Maximum likelihood detection Character recognition Diversity reception Lighting Cameras Object detection Object recognition Wheels

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