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Fast Algorithms for Large Scale Conditional 3D Prediction

Fast Algorithms for Large Scale Conditional 3D Prediction

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Liefeng Bo Cristian Sminchisescu Atul Kanaujia Dimitris Metaxas TTI-C University of Bonn Germany Rutgers University USA

the potential success of discriminative learning approaches to 3D reconstruction relies on the ability to efficiently train predictive algorithms using sufficiently many examples that are representative of the typical configurations encountered in the application domain. Recent research indicates that sparse conditional Bayesian Mixture of Experts (cMoE) models (e.g. BME [21]) are adequate modeling tools that not only provide contextual 3D predictions for problems like human pose reconstruction, but can also represent multiple interpretations that result from depth ambiguities or occlusion. However, training conditional predictors requires sophisticated double-loop algorithms that scale unfavorably with the input dimension and the training set size, thus limiting their usage to 10,000 examples of less, so far. In this paper we present large-scale algorithms, referred to as fBME, that combine forward feature selection and bound optimization in order to train probabilistic, BME models, with one order of magnitude more data (100,000 examples and up) and more than one order of magnitude faster. We present several large scale experiments, including monocular evaluation on the HumanEva dataset [19], demonstrating how the proposed methods overcome the scaling limitations of existing ones.

关键词： Large-scale systems Bayesian methods Humans Predictive models Iterative algorithms Image reconstruction Context modeling Boosting Optimization methods computer vision

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Shading Models for Illumination and Reflectance Invariant Shape Detectors

Shading Models for Illumination and Reflectance Invariant Sh...

引用

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

作者： Peter Nillius Josephine Sullivan Antonis Argyros Department of Physics Royal Institute of Technology Stockholm Sweden Computer Science & Communication Royal Institute of Technology Stockholm Sweden Vassilika Vouton Institute of Computer Science-FORTH Heraklion Greece

Many objects have smooth surfaces of a fairly uniform color, thereby exhibiting shading patterns that reveal information about its shape, an important clue to the nature of the object. this papers explores extracting this information from images, by creating shape detectors based on shading. Recent work has derived low-dimensional models of shading that can handle realistic unknown lighting conditions and surface reflectance properties. We extend this theory by also incorporating variations in the surface shape. In doing so it enables the creation of very general models for the 2D appearance of objects, not only coping with variations in illumination and BRDF but also in shape alterations such as small scale and pose changes. Using this framework we propose a scheme to build shading models that can be used for shape detection in a bottom up fashion without any a priori knowledge about the scene. From the developed theory we construct detectors for two basic shape primitives, spheres and cylinders. their performance is evaluated by extensive synthetic experiments as well as experiments on real images.

关键词： Lighting Reflectivity Shape Light sources Frequency Detectors Principal component analysis Data mining Power harmonic filters computer science

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Efficient subdivision-based image and volume warping

Efficient subdivision-based image and volume warping

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Gady Agam Ravinder Singh Department of Computer Science Illinois Institute of Technology Chicago IL USA

Warping is fundamental to multiple algorithms in computer vision and medical imaging such as image and volume registration. Warping is performed by determining a continuous deformation map and applying it to a given image or volume. In registration the deformation map is determined based on correspondence between two images. It is often the case that the deformation map can only be determined at discrete locations and so has to be interpolated. the discrete locations where the deformation map is determined form irregular sampling of the unknown continuous deformation map. thin-plate splines are commonly used to perform the interpolation and provide an optimal solution in the sense of bending energy minimization. Assuming N samples of the deformation map and n{sup}2 image pixels, thin plate splines require solving a N × N dense linear system with O(N{sup}3) complexity for determining spline coefficients and N computations per pixel with O(N n{sup}2) complexity for determining interpolated values. When N and n are large as in the case of volumetric medical image analysis this cost becomes prohibitive. the approach proposed in this paper is based on subdivision surfaces and is capable of achieving similar quality results with O(N log N) complexity for coefficient determination and O(n{sup}2) complexity for computing interpolated values. Experimental results demonstrate two orders of magnitude performance improvement on actual clinical data.

关键词： Interpolation Biomedical imaging Pixel Image registration Deformable models computer vision Image analysis computer science Image sampling Linear systems

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Enhanced Biologically Inspired Model

Enhanced Biologically Inspired Model

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

作者： Yongzhen Huang Kaiqi Huang Liangsheng Wang Dacheng Tao Tieniu Tan Xuelong Li National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Biometrics Research Centre Department of Computing Hong Kong Polytechnic University Hong Kong China School of Computer Science and Information Systems Birkbeck University of London London UK

It has been demonstrated by Serre et al. that the biologically inspired model (BIM) is effective for object recognition. It outperforms many state-of-the-art methods in challenging databases. However, BIM has the following three problems: a very heavy computational cost due to dense input, a disputable pooling operation in modeling relations of the visual cortex, and blind feature selection in a feedforward framework. To solve these problems, we develop an enhanced BIM (EBIM), which removes uninformative input by imposing sparsity constraints, utilizes a novel local weighted pooling operation with stronger physiological motivations, and applies a feedback procedure that selects effective features for combination. Empirical studies on the CalTech5 database and CalTech101 database show that EBIM is more effective and efficient than BIM. We also apply EBIM to the MIT-CBCL street scene database to show it achieves comparable performance in comparison with the current best performance. Moreover, the new system can process images with resolution 128 × 128 at a rate of 50 frames per second and enhances the speed 20 times at least in comparison with BIM in common applications.

关键词： Biological system modeling Spatial databases Image databases Brain modeling Object recognition Visual databases Computational efficiency Feedforward systems Feedback Layout

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Directional Independent Component Analysis with Tensor Representation

Directional Independent Component Analysis with Tensor Repre...

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

作者： Lei Zhang Quanxue Gao David Zhang Biometric Research Center Hong Kong Polytechnic University Hong Kong China School of Telecommunications Engineering XIDIAN University Xi'an China

Conventional independent component analysis (ICA) learns the statistical independencies of 2D variables from the training images that are unfolded to vectors. the unfolded vectors, however, make the ICA suffer from the small sample size (SSS) problem that leads to the dimensionality dilemma. this paper presents a novel directional multilinear ICA method to solve those problems by encoding the input image or high dimensional data array as a general tensor. In addition, the mode-k matrix of the tensor is re-sampled and re-arranged to form a mode-k directional image to better exploit the directional information in training. An algorithm called mode-k directional ICA is then presented for feature extraction. Compared with the conventional ICA and other subspace analysis algorithms, the proposed method can greatly alleviate the SSS problem, reduce the computational cost in the learning stage by representing the data in lower dimension, and simultaneously exploit the directional information in the high dimensional dataset. Experimental results on well-known face and palmprint databases show that the proposed method has higher recognition accuracy than many existing ICA, PCA and even supervised FLD schemes while using a low dimension of features.

关键词： Independent component analysis Tensile stress Image coding Feature extraction Information analysis Algorithm design and analysis Computational efficiency Spatial databases Face recognition Principal component analysis

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Boosted Deformable Model for Human Body Alignment

Boosted Deformable Model for Human Body Alignment

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Xiaoming Liu Ting Yu thomas Sebastian Peter Tu Visualization and Computer Vision Laboratory GE Global Research Center Niskayuna NY USA

this paper studies image alignment, the problem of learning a shape and appearance model from labeled data and efficiently fitting the model to a non-rigid object with large variations. Given a set of images with manually labeled landmarks, our model representation consists of a shape component represented by a Point Distribution Model and an appearance component represented by a collection of local features, trained discriminatively as a two-class classifier using boosting. Images with ground truth landmarks are the positive training samples while those with perturbed landmarks are considered as negatives. Enabled by piece-wise affine warping, corresponding local feature positions across all training samples form a hypothesis space for boosting. Image alignment is performed by maximizing the boosted classifier score, which is our distance measure, through iteratively mapping the feature positions to the image, and computing the gradient direction of the score with respect to the shape parameter. We apply this approach to human body alignment from surveillance-type images. We conduct experiments on the MIT pedestrian database where the body size is approximately 110 × 46 pixels, and demonstrate our real-time alignment capability.

关键词： Deformable models Humans Biological system modeling Boosting Shape measurement Active appearance model Magnesium compounds Active shape model Image resolution Performance evaluation

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Dense Specular Shape from Multiple Specular Flows

Dense Specular Shape from Multiple Specular Flows

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Yuriy Vasilyev Yair Adato Todd Zickler Ohad Ben-Shahar Harvard School of Engineering and Applied Sciences Cambridge USA Computer Science Department Ben-Gurion University of the Negev Beersheba Israel

the inference of specular (mirror-like) shape is a particularly difficult problem because an image of a specular object is nothing but a distortion of the surrounding environment. Consequently, when the environment is unknown, such an image would seem to convey little information about the shape itself. It has recently been suggested (Adato et al., ICCV 2007) that observations of relative motion between a specular object and its environment can dramatically simplify the inference problem and allow one to recover shape without explicit knowledge of the environment content. However, this approach requires solving a non-linear PDE (the 'shape from specular flow equation') and analytic solutions are only known to exist for very constrained motions. In this paper, we consider the recovery of shape from specular flow under general motions. We show that while the 'shape from specular flow' PDE for a single motion is non-linear, we can combine observations of multiple specular flows from distinct relative motions to yield a linear set of equations. We derive necessary conditions for this procedure, discuss several numerical issues with their solution, and validate our results quantitatively using image data.

关键词： Shape Nonlinear equations Motion analysis Nonlinear distortion Optical reflection Surface reconstruction Image reconstruction computer science Art Mirrors

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Unified Principal Component Analysis with Generalized Covariance Matrix for Face recognition

Unified Principal Component Analysis with Generalized Covari...

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

作者： Shiguang Shan Bo Cao Yu Su Laiyun Qing Xilin Chen Wen Gao Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Sigma Center Microsoft Advanced Technology Center Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China Graduate School of Chinese Academy of Sciences Beijing China

Recently, 2DPCA and its variants have attracted much attention in face recognition area. In this paper, some efforts are made to discover the underlying fundaments of these methods, and a novel framework called Unified Principal Component Analysis (UPCA) is proposed. First, we introduce a novel concept, named Generalized Covariance Matrix (GCM), which is naturally derived from the traditional Covariance Matrix (CM). Each element of GCM is a generalized covariance of two random vectors rather than two scalar variables in CM. Based on GCM, the UPCA framework is proposed, from which the traditional PCA and its 2D counterparts can be deduced as special cases. Furthermore, under the UPCA framework, we not only revisit the existing 2D PCA methods and their limitations, but also propose two new methods: the grid-sampling method (GridPCA) and the intra-group correlation reduction method. Extensive experimental results on the FERET face database support the theoretical analysis and validate the feasibility of the proposed methods.

关键词： Principal component analysis Covariance matrix Face recognition Laboratories Information processing computer science Smart pixels Image representation Image databases Usability

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Image Based Rendering for Motion Compensation in Angiographic Roadmapping

Image Based Rendering for Motion Compensation in Angiographi...

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

作者： Christian Unger Martin Groher Nassir Navab Computer Aided Medical Procedures (CAMP) Technische Universität München Germany

2D angiographic roadmapping is used frequently during image guided interventions to superimpose vessel structures onto currently acquired fluoroscopic images. While the fluoroscopic images, acquired with 12-15 frames per second, show patient bone anatomy as well as the current location of the inserted catheter, the roadmap delineates vessels to provide path information and to avoid accidental vessel wall punctures during catheter advancement. this technique successfully reduces the injection of contrast agent, which is hazardous for the patient;however, it suffers from inaccuracy due to inevitable patient movement, which yields a misalignment of the roadmap laid over the current fluoroscopic frame. We propose a method for rigid patient motion compensation via the trifocal tensor and Image Based Rendering (IBR). the method uses two contrasted and slightly shifted views and the current fluoroscopic frame. Different to the existing solutions, we perform the motion compensation inherently in 3D, increasing reliability and accuracy of the resulting vascular rendering. Moreover, with the IBR technique, we avoid an explicit reconstruction, thus achieving reasonable results even for very small patient movements, which are common in interventional scenarios.

关键词： Rendering (computer graphics) Motion compensation Catheters Tensile stress Navigation X-ray imaging Anatomy Image reconstruction Visualization Bones

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A Robust Descriptor based on Weber's Law

A Robust Descriptor based on Weber's Law

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

作者： Jie Chen Shiguang Shan Guoying Zhao Xilin Chen Wen Gao Matti Pietikainen School of Computer Science and Technology Harbin Institute of Technology Harbin China Machine Vision Group Department of Electrical and Information Engineering University of Oulu Finland Languages Informatics Systems and Software Engineering Department Faculty of Computer Science Chinese Academy and Sciences Beijing China

Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends on not only the change of a stimulus (such as sound, lighting, et al.) but also the original intensity of the stimulus. Specifically, WLD consists of two components: its differential excitation and orientation. A differential excitation is a function of the ratio between two terms: One is the relative intensity differences of its neighbors against a current pixel;the other is the intensity of the current pixel. An orientation is the gradient orientation of the current pixel. For a given image, we use the differential excitation and the orientation components to construct a concatenated WLD histogram feature. Experimental results on Brodatz textures show that WLD impressively outperforms the other classical descriptors (e.g., Gabor). Especially, experimental results on face detection show a promising performance. Although we train only one classifier based on WLD features, the classifier obtains a comparable performance to state-of-the-art methods on MIT+CMU frontal face test set, AR face dataset and CMU profile test set.

关键词： Robustness Face detection Humans Content addressable storage Testing Psychology Laboratories Machine intelligence Information processing computers

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