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Advanced Gaussian MRF rotation-invariant texture features for classification of remote sensing imagery

Advanced Gaussian MRF rotation-invariant texture features fo...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： Huawu Deng D.A. Clausi Systems Design Engineering University of Waterloo Canada

The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. The paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modeling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.

关键词： Remote sensing Least squares approximation image texture Discrete Fourier transforms Markov random fields Anisotropic magnetoresistance image retrieval Parameter estimation Synthetic aperture radar Sea ice

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Stereo matching with reflections and translucency

Stereo matching with reflections and translucency

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conference on computer Vision and pattern recognition (CVPR)

作者： Yanghai Tsin Sing Bing Kang R. Szeliski The Robotics Institute Carnegie Mellon University Pittsburgh PA USA Interactive Visual Media Group Microsoft Research Limited Redmond WA USA

In this paper, we address the stereo matching problem in the presence of reflections and translucency, where image formation can be modeled as the additive superposition of layers at different depth. The presence of such effects violates the Lambertian assumption underlying traditional stereo vision algorithms, making it impossible to recover component depths using direct color matching based methods. We develop several techniques to estimate both depths and colors of the component layers. Depth hypotheses are enumerated in pairs, one from each layer, in a nested plane sweep. For each pair of depth hypotheses, we compute a component-color-independent matching error per pixel, using a spatial-temporal differencing technique. We then use graph cut optimization to solve for the depths of both layers. This is followed by an iterative color update algorithm whose convergence is proven in our paper. We show convincing results of depth and color estimates for both synthetic and real image sequences.

关键词： Reflection Glass Layout Iterative algorithms Stereo vision computer society computer vision pattern recognition Attenuation Cameras

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Statistics of shape via principal geodesic analysis on Lie groups

Statistics of shape via principal geodesic analysis on Lie g...

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conference on computer Vision and pattern recognition (CVPR)

作者： P.T. Fletcher Conglin Lu S. Joshi Medical Image and Display Analysis Group North Carolina State University USA

Principal component analysis has proven to be useful for understanding geometric variability in populations of parameterized objects. The statistical framework is well understood when the parameters of the objects are elements of a Euclidean vector space. This is certainly the case when the objects are described via landmarks or as a dense collection of boundary points. We have been developing representations of geometry based on the medial axis description or m-rep. Although this description has proven to be effective, the medial parameters are not naturally elements of a Euclidean space. In this paper we show that medial descriptions are in fact elements of a Lie group. We develop methodology based on Lie groups for the statistical analysis of medially-defined anatomical objects.

关键词： Statistical analysis Shape Principal component analysis Lattices Atomic measurements image analysis computer vision Biomedical imaging Medical diagnostic imaging Displays

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Face alignment using statistical models and wavelet features

Face alignment using statistical models and wavelet features

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conference on computer Vision and pattern recognition (CVPR)

作者： Feng Jiao Stan Li Heung-Yeung Shum D. Schuurmans Department of Computer Science University of Waterloo Waterloo Canada Microsoft Research Asia Beijing China

Active shape model (ASM) is a powerful statistical tool for face alignment by shape. However, it can suffer from changes in illumination and facial expression changes, and local minima in optimization. In this paper, we present a method, W-ASM, in which Gabor wavelet features are used for modeling local image structure. The magnitude and phase of Gabor features contain rich information about the local structural features of face images to be aligned, and provide accurate guidance for search. To a large extent, this repairs defects in gray scale based search. An E-M algorithm is used to model the Gabor feature distribution, and a coarse-to-fine grained search is used to position local features in the image. Experimental results demonstrate the ability of W-ASM to accurately align and locate facial features.

关键词： Active shape model Active appearance model Facial animation Facial features Asia Lighting computer vision computer science Face recognition Active contours

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Structure from motion for scenes without features

Structure from motion for scenes without features

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conference on computer Vision and pattern recognition (CVPR)

作者： A.J. Yezzi S. Soatto Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Computer Science Department University of California Los Angeles CA USA

We describe an algorithm for reconstructing the 3D (three-dimensional) shape of the scene and the relative pose of a number of cameras from a collection of images under the assumption that the scene does not contain photometrically distinct "features". We work under the explicit assumption that the scene is made of a number of smooth surfaces that radiate constant energy isotropically in all directions, and setup a region-based cost functional that we minimize using local gradient flow techniques.

关键词： Layout Cameras Shape Photometry image reconstruction Surface reconstruction Cost function computer vision computer science Geometry

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Profile-based Pottery Reconstruction

Profile-based Pottery Reconstruction

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： M. Kampel R. Sablatnig Pattern Recognition and Image Processing Group Institute of Computer Aided Automation University of Technology Vienna Vienna Austria

A major obstacle to the broader use of 3D object reconstruction and modeling is the extent of manual intervention needed. Such interventions are currently extensive and exist throughout every phase of a 3D reconstruction project: collection of images, image management, establishment of sensor position and image orientation, extracting the geometric information describing an object, and merging geometric, texture and semantic data. We present a fully automated approach to pottery reconstruction based on the fragment profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. We demonstrate the method and give results on synthetic and real data.

关键词： image reconstruction Three dimensional displays Surface reconstruction pattern recognition conferences Shape computer vision

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image recognition using a quadratic convergent learning algorithm of synergetic neural network

Image recognition using a quadratic convergent learning algo...

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ieee International conference on Robotics, Intelligent Systems and Signal processing

作者： R.Q. Sheng Hong Qiao Bing Chen Department of Computer Science Shanghai Jiaotong University Shanghai China Department of Computation University of Manchester Institute of Science and Technology UK Institute of Artificial Intelligence and Robotics Xi''an Jiaotong University Xi'an China

ISBN: (纸本)078037925X

This paper presents a learning algorithm of synergetic neural network that can be realized by a 2-layered network. A main feature of the new algorithm is that it is of high accuracy and quadratic convergence rate. Further it avoids the error being accumulated. The experiment results show that the algorithm enables the synergetic neural network to recognize images more effectively compared with previous algorithms.

关键词： image recognition image converters Neural networks Signal processing.algorithms Prototypes Clustering algorithms Intelligent robots computer networks Convergence pattern recognition

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Shape and materials by example: a photometric stereo approach

Shape and materials by example: a photometric stereo approac...

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conference on computer Vision and pattern recognition (CVPR)

作者： A. Hertzmann S.M. Seitz University of Toronto Toronto Canada University of Washington Seattle WA USA

This paper presents a technique for computing the geometry of objects with general reflectance properties from images. For surfaces with varying material properties, a full segmentation into different material types is also computed. It is assumed that the camera viewpoint is fixed, but the illumination varies over the input sequence. It is also assumed that one or more example objects with similar materials and known geometry are imaged under the same illumination conditions. Unlike most previous work in shape reconstruction, this technique can handle objects with arbitrary and spatially-varying BRDFs. Furthermore, the approach works for arbitrary distant and unknown lighting environments. Finally, almost no calibration is needed, making the approach exceptionally simple to apply.

关键词： Shape Photometry Lighting Computational geometry Reflectivity Material properties image segmentation Cameras image reconstruction Calibration

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Adaptation for multiple cue integration

Adaptation for multiple cue integration

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conference on computer Vision and pattern recognition (CVPR)

作者： Zhaohui Sun Electric Imaging Products Research & Development Eastman Kodak (Japan) Limited Rochester NY USA

ISBN: (纸本)0769519008

Many classification tasks can be carried out by casting a domain-specific problem to general graph representation (with objects to be organized as graph nodes and pairwise similarities as graph edges) followed by a graph partition. In this paper, an adaptation scheme is proposed to integrate multiple graphs from various cues to a single graph, such that the distance between the ideal transition probability matrix to the one derived from cue integration is minimized. Four different distance measures, i.e., the Frobenius norm, the Kullback-Leibler directed divergence, the Jeffrey divergence and the cross entropy, are investigated to minimize the discrepancy. It is shown that the minimization leads to a closed-form nonlinear optimization that can be solved by the Levenberg-Marguardt method. Domain and task-specific knowledge is explored to facilitate the generic pattern classification task. Experimental results are demonstrated for image content description by multiple cue integration.

关键词： Casting Entropy Minimization methods Optimization methods image segmentation computer vision Sun pattern classification Pixel Joining processes

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Face recognition under variable lighting using harmonic image exemplars

Face recognition under variable lighting using harmonic imag...

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conference on computer Vision and pattern recognition (CVPR)

作者： Lie Zhang D. Samaras Department of Computer Science SUNY Stony Brook NY USA

We propose a new approach for face recognition under arbitrary illumination conditions, which requires only one training image per subject (if there is no pose variation) and no 3D shape information. Our method is based on the result of Basri and Jacobs (2001), which demonstrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace. In this paper, we show that we can recover basis images spanning this space from just one image taken under arbitrary illumination conditions. First, using a bootstrap set consisting of 3D face models, we compute a statistical model for each basis image. During training, given a novel face image under arbitrary illumination, we recover a set of images for this face. We prove that these images are the set of basis images with maximum probability. During testing, we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our method achieves comparable levels of accuracy with methods that have much more onerous training data requirements.

关键词： Face recognition Lighting Shape image recognition Testing computer science Probability Training data image reconstruction image texture analysis

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