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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

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Radiometric alignment of image sequences

Radiometric alignment of image sequences

引用

conference on computer vision and pattern recognition (cvpr)

作者： Seon Joo Kim M. Pollefeys Department of Computer Science North Carolina State University Chapel Hill NC USA

Color values in an image are related to image irradiance by a nonlinear function called radiometric response function. Since this function depends on the aperture and the shutter speed, image intensity of a same object may vary during the acquisition of an image sequence due to auto exposure feature of the camera. While this is desirable to make optimal use of the limited dynamic range of most cameras, this causes problems for a number of applications in computer vision. In this paper we propose a method for estimating the radiometric response function and apply it to radiometrically align images so that the color values are consistent for all images of a sequence. Our approach computes the response function, exposure and white balance changes between images (up to some ambiguity) for a moving camera without any prior knowledge about exposures. We show the performance of our algorithm by estimating the response function from synthetic images and also from real world data, using it to radiometrically align the images.

关键词： Radiometry Image sequences Layout Dynamic range computer vision Brightness Shape measurement Application software Digital cameras Iterative algorithms

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Using Multiple Segmentations to Discover Objects and their Extent in Image Collections

Using Multiple Segmentations to Discover Objects and their E...

引用

conference on computer vision and pattern recognition (cvpr)

作者： B.C. Russell W.T. Freeman A.A. Efros J. Sivic A. Zisserman CS and AI Laboratory Massachusetts Institute of Technology Cambridge MA USA School of Computer Science Carnegie Mellon University Pittsburgh PA USA Department of Engineering Science University of Oxford Oxford UK

Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe.

关键词： Image segmentation Layout Image recognition Text analysis Artificial intelligence Laboratories computer science Object recognition Concrete Information retrieval

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A fast multigrid implicit algorithm for the evolution of geodesic active contours

A fast multigrid implicit algorithm for the evolution of geo...

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Papandreou P. Maragos School of Electrical and Computer Engin National and Technical University of Athens Athens Greece

Active contour models are among the most popular PDE-based tools in computer vision. In this paper we present a new algorithm for the fast evolution of geodesic active contours and compare it with other established numerical schemes. The new algorithm employs a full time-implicit and unconditionally stable numerical scheme and applies multigrid methods for the efficient solution of the occurring sparse linear system. When we utilize very big time-steps for the numerical evolution of the front, the proposed scheme has increased accuracy and better rotational invariance properties compared with the alternative AOS scheme. This allows for the rapid evolution and convergence of the contour to its final configuration after only very few iterations. Standard pyramidal and/or narrowband techniques can be easily integrated into our algorithm and further accelerate the curve evolution. Experimental results in object boundary detection demonstrate the power of the method.

关键词： Active contours computer vision Object detection Level set Geophysics computing Narrowband Solid modeling Stability Multigrid methods Linear systems

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Seamless Image Stitching of Scenes with Large Motions and Exposure Differences

Seamless Image Stitching of Scenes with Large Motions and Ex...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Eden M. Uyttendaele R. Szeliski Computer Science Division University of California Berkeley USA Interactive Visual Media Group Microsoft Research Limited USA

This paper presents a technique to automatically stitch multiple images at varying orientations and exposures to create a composite panorama that preserves the angular extent and dynamic range of the inputs. The main contribution of our method is that it allows for large exposure differences, large scene motion or other misregistrations between frames and requires no extra camera hardware. To do this, we introduce a two-step graph cut approach. The purpose of the first step is to fix the positions of moving objects in the scene. In the second step, we fill in the entire available dynamic range. We introduce data costs that encourage consistency and higher signal-to-noise ratios, and seam costs that encourage smooth transitions. Our method is simple to implement and effective. We demonstrate the effectiveness of our approach on several input sets with varying exposures and camera orientations.

关键词： Layout Cameras Dynamic range Pixel Hardware Costs Radiometry computer science Signal to noise ratio Calibration

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Spectral Methods for Automatic Multiscale Data Clustering

Spectral Methods for Automatic Multiscale Data Clustering

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Azran Z. Ghahramani Gatsby Computational Neuroscience Unit University College London London UK Learning Department Carnegie Mellon University Pittsburgh USA Department of Engineering University of Cambridge Cambridge UK

Spectral clustering is a simple yet powerful method for finding structure in data using spectral properties of an associated pairwise similarity matrix. This paper provides new insights into how the method works and uses these to derive new algorithms which given the data alone automatically learn different plausible data partitionings. The main theoretical contribution is a generalization of a key result in the field, the multicut lemma [7]. We use this generalization to derive two algorithms. The first uses the eigenvalues of a given affinity matrix to infer the number of clusters in data, and the second combines learning the affinity matrix with inferring the number of clusters. A hierarchical implementation of the algorithms is also derived. The algorithms are theoretically motivated and demonstrated on nontrivial data sets.

关键词： Clustering algorithms Partitioning algorithms computer vision Machine learning algorithms Machine learning Power engineering computing Educational institutions Data engineering Power engineering and energy Eigenvalues and eigenfunctions

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Fuzzy Systems and Knowledge Discovery: Second International conference, FSKD 2005. Proceedings

Fuzzy Systems and Knowledge Discovery: Second International ...

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Second International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005

The proceedings contain 169 papers from the Fuzzy Systems and Knowledge Discovery: Second International conference, FSKD 2005. Proceedings. The topics discussed include: dimensionality reduction for semi-supervised face recognition;cross-document transliterated personal name conference resolution;difference-similitude matrix in text classification;a study on feature selection for toxicity prediction;application of feature selection for unsupervised learning in prosecutors' office;supervised learning for classification;robust nonlinear dimension reduction: a self-organizing approach;pattern classification using rectified nearest feature line segment;and nearest neighbor classification using cam weighted distance.

关键词： Fuzzy sets

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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

Beyond Bags of Features: Spatial Pyramid Matching for Recogn...

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Lazebnik C. Schmid J. Ponce Beckman Institute University of Illinois Urbana-Champaign USA INRIA Rhône-Alpes Montbonnot Saint Martin France Ecole Normale Supéerieure Paris France

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.

关键词： Layout Robustness Histograms Image databases Spatial databases Object recognition Image representation Image recognition Shape Image segmentation

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Modeling and Classifying Breast Tissue Density in Mammograms

Modeling and Classifying Breast Tissue Density in Mammograms

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Bosch X. Munoz A. Oliver J. Marti Computer Vision and Robotics Group University of Girona Girona Spain

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal.

关键词： Breast tissue Breast cancer computer vision Mammography Medical diagnostic imaging Dictionaries Layout Robot vision systems Delta-sigma modulation Proposals

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Patch-based convolutional neural network for whole slide tissue image classification

Patch-based convolutional neural network for whole slide tis...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Hou, Le Samaras, Dimitris Kurc, Tahsin M. Gao, Yi Davis, James E. Saltz, Joel H. Dept. of Computer Science Stony Brook University United States Dept. of Biomedical Informatics Stony Brook University United States Dept. of Applied Mathematics and Statistics Stony Brook University United States Oak Ridge National Laboratory United States Dept. of Pathology Stony Brook Hospital United States Cancer Center Stony Brook Hospital United States

ISBN: (纸本)9781467388511

Convolutional Neural Networks (CNN) are state-of-theart models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN.

关键词： Image classification

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The spectrum enhancement algorithm for feature extraction and pattern recognition

The spectrum enhancement algorithm for feature extraction an...

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Intelligent Robots and computer vision XXIII: Algorithms, Techniques, and Active vision

作者： Crosta, Giovanni Franco Inverse Problems and Mathematical Morphology Unit Department of Environmental Sciences Università Degli Studi Milano - Bicocca 1 Piazza delia Scienza I-20126 Milan Italy

The "enhanced spectrum" of an image g[&middot] is a fonction h[&middot] of wavenumber u obtained as follows. A reflection operation Q[&middot] is applied to g[&middot];the power spectral dens... 详细信息

ISBN: (纸本)0819460303

The "enhanced spectrum" of an image g[·] is a fonction h[·] of wavenumber u obtained as follows. A reflection operation Q[·] is applied to g[·];the power spectral density | G[u]| 2 of Q[g[·]] is converted to the Log scale and averaged over a suitable arc;the function s[·] of u alone is thus obtained, from which a known function, the "model" m[u], is subtracted: this yields h[u]· Models m(p)[·] used herewith have a roll-off like -10Log10[up]. As a consequence spectrum enhancement is a non-linear image filter which is shown to include partial spatial differentiation of Q[g[·]] of suitable order. The function h[·] emphasizes deviations of s[·] from the prescribed behaviour m (P)W[·]. The enhanced spectrum is used herewith as the morphological descriptor of the image after polynomial interpolation. Multivariate statistical analysis of enhanced spectra by means of principal components analysis is applied with the objective, of maximizing discrimination between classes of images. Recent applications to materials science, cell biology and environmental monitoring are reviewed.

关键词： Image enhancement

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