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Robust RVM regression using sparse outlier model

Robust RVM regression using sparse outlier model

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conference on computer vision and pattern recognition (CVPR)

作者： Kaushik Mitra Ashok Veeraraghavan Rama Chellappa Department of Electrical and Computer Engineering and Center for Automation Research UMIACS University of Maryland College Park MD USA Mitsubishi Electric Research Laboratories Inc. Cambridge MA USA

Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision problems such as age, head pose, 3D human pose and lighting estimation. However, the presence of outliers in the training dataset makes the estimates from these regression techniques unreliable. In this paper, we propose robust versions of the RVM regression that can handle outliers in the training dataset. We decompose the noise term in the RVM formulation into a (sparse) outlier noise term and a Gaussian noise term. We then estimate the outlier noise along with the model parameters. We present two approaches for solving this estimation problem: (1) a Bayesian approach, which essentially follows the RVM framework and (2) an optimization approach based on Basis Pursuit Denoising. In the Bayesian approach, the robust RVM problem essentially becomes a bigger RVM problem with the advantage that it can be solved efficiently by a fast algorithm. Empirical evaluations, and real experiments on image de-noising and age estimation demonstrate the better performance of the robust RVM algorithms over that of the RVM reg ression.

关键词： Gaussian noise Noise robustness Bayesian methods Kernel Gaussian processes computer vision Magnetic heads Humans Noise reduction Image denoising

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High-resolution modeling of moving and deforming objects using sparse geometric and dense photometric measurements

High-resolution modeling of moving and deforming objects usi...

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conference on computer vision and pattern recognition (CVPR)

作者： Yi Xu Daniel G. Aliaga Department of Computer Science Purdue University USA

Modeling moving and deforming objects requires capturing as much information as possible during a very short time. When using off-the-shelf hardware, this often hinders the resolution and accuracy of the acquired model. Our key observation is that in as little as four frames both sparse surface-positional measurements and dense surface-orientation measurements can be acquired using a combination of structured light and photometric stereo, resulting in high-resolution models of moving and deforming objects. Our system projects alternating geometric and photometric patterns onto the object using a set of three projectors and captures the object using a synchronized camera. Small motion among temporally close frames is compensated by estimating the optical flow of images captured under the uniform illumination of the photometric light. Then spatial-temporal photogeometric reconstructions are performed to obtain dense and accurate point samples with a sampling resolution equal to that of the camera. Temporal coherence is also enforced. We demonstrate our system by successfully modeling several moving and deforming real-world objects.

关键词： Solid modeling Deformable models Photometry Cameras Hardware Motion estimation Geometrical optics Optical variables control Image motion analysis Lighting

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Bidirectional relighting for 3D-aided 2D face recognition

Bidirectional relighting for 3D-aided 2D face recognition

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conference on computer vision and pattern recognition (CVPR)

作者： G. Toderici G. Passalis S. Zafeiriou G. Tzimiropoulos M. Petrou T. Theoharis I. A. Kakadiaris Computational Biomedicine Lab Department of CS ECE Biomedical Engineering University of Houston Houston TX USA Computer Graphics Lab University of Athens (NKUA) Greece Department of Electrical and Electronic Engineering Imperial College London UK CERTH ITI Greece

In this paper, we present a new method for bidirectional relighting for 3D-aided 2D face recognition under large pose and illumination changes. During subject enrollment, we build subject-specific 3D annotated models by using the subjects' raw 3D data and 2D texture. During authentication, the probe 2D images are projected onto a normalized image space using the subject-specific 3D model in the gallery. Then, a bidirectional relighting algorithm and two similarity metrics (a view-dependent complex wavelet structural similarity and a global similarity) are employed to compare the gallery and probe. We tested our algorithms on the UHDB11 and UHDB12 databases that contain 3D data with probe images under large lighting and pose variations. The experimental results show the robustness of our approach in recognizing faces in difficult situations.

关键词： Face recognition Authentication Probes Lighting Biomedical engineering Image databases Face detection Biomedical computing computer graphics Educational institutions

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Natural gradients for deformable registration

Natural gradients for deformable registration

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conference on computer vision and pattern recognition (CVPR)

作者： Darko Zikic Ali Kamen Nassir Navab Computer Aided Medical Procedures CAMP Technische Universität München Germany Siemens AG Corporate Research and Development Princeton NJ USA

We apply the concept of natural gradients to deformable registration. The motivation stems from the lack of physical interpretation for gradients of image-based difference measures. The main idea is to endow the space of deformations with a distance metric which reflects the variation of the difference measure between two deformations. This is in contrast to standard approaches which assume the Euclidean frame. The modification of the distance metric is realized by treating the deformations as a Riemannian manifold. In our case, the manifold is induced by the Riemannian metric tensor based on the approximation of the Fisher Information matrix, which takes into account the information about the chosen difference measure and the input images. Thus, the resulting natural gradient defined on this manifold inherently takes into account this information. The practical advantages of the proposed approach are the improvement of registration error and faster convergence for low-gradient regions. The proposed scheme is applicable to arbitrary difference measures and can be readily integrated into standard variational deformable registration methods with practically no computational overhead.

关键词： Tensile stress Convergence Distortion measurement Stochastic processes Erbium Biomedical imaging Thyristors Measurement standards Impedance Euclidean distance

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Figure-ground segmentation improves handled object recognition in egocentric video

Figure-ground segmentation improves handled object recogniti...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaofeng Ren Chunhui Gu Intel Laboratories Seattle Seattle WA USA University of California Berkeley Berkeley CA USA

Identifying handled objects, i.e. objects being manipulated by a user, is essential for recognizing the person's activities. An egocentric camera as worn on the body enjoys many advantages such as having a natural first-person view and not needing to instrument the environment. It is also a challenging setting, where background clutter is known to be a major source of problems and is difficult to handle with the camera constantly and arbitrarily moving. In this work we develop a bottom-up motion-based approach to robustly segment out foreground objects in egocentric video and show that it greatly improves object recognition accuracy. Our key insight is that egocentric video of object manipulation is a special domain and many domain-specific cues can readily help. We compute dense optical flow and fit it into multiple affine layers. We then use a max-margin classifier to combine motion with empirical knowledge of object location and background movement as well as temporal cues of support region and color appearance. We evaluate our segmentation algorithm on the large Intel Egocentric Object recognition dataset with 42 objects and 100K frames. We show that, when combined with temporal integration, figure-ground segmentation improves the accuracy of a SIFT-based recognition system from 33% to 60%, and that of a latent-HOG system from 64% to 86%.

关键词： Object recognition Cameras Robustness Optical computing Image motion analysis Instruments Mobile computing Wearable computers Motion segmentation computer vision

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Shape and refractive index recovery from single-view polarisation images

Shape and refractive index recovery from single-view polaris...

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conference on computer vision and pattern recognition (CVPR)

作者： Cong Phuoc Huynh Antonio Robles-Kelly Edwin Hancock School of Engineering Australian National University Canberra ACT Australia NICTA Canberra ACT Australia Department of Computer Science University of York Heslington Yorkshire UK

In this paper, we propose an approach to the problem of simultaneous shape and refractive index recovery from multispectral polarisation imagery captured from a single viewpoint. The focus of this paper is on dielectric surfaces which diffusely polarise light transmitted from the dielectric body into the air. The diffuse polarisation of the reflection process is modelled using a Transmitted Radiance Sinusoid curve and the Fresnel transmission theory. We provide a method of estimating the azimuth angle of surface normals from the spectral variation of the phase of polarisation. Moreover, to render the problem of simultaneous estimation of surface orientation and index of refraction well-posed, we enforce a generative model on the material dispersion equations for the index of refraction. This generative model, together with the Fresnel transmission ratio, permit the recovery of the index of refraction and the zenith angle simultaneously. We show results on shape recovery and rendering for real world and synthetic imagery.

关键词： Shape Refractive index Optical polarization Dielectrics Focusing Optical reflection Fresnel reflection Phase estimation Azimuth Equations

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Local features are not lonely – Laplacian sparse coding for image classification

Local features are not lonely – Laplacian sparse coding for...

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conference on computer vision and pattern recognition (CVPR)

作者： Shenghua Gao Ivor Wai-Hung Tsang Liang-Tien Chia Peilin Zhao School of Computer Engineering Nanyang Technological University Singapore

Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization process of BoW based image representation. However, in the feature quantization process of sparse coding, some similar local features may be quantized into different visual words of the codebook due to the sensitiveness of quantization. In this paper, to alleviate the impact of this problem, we propose a Laplacian sparse coding method, which will exploit the dependence among the local features. Specifically, we propose to use histogram intersection based kNN method to construct a Laplacian matrix, which can well characterize the similarity of local features. In addition, we incorporate this Laplacian matrix into the objective function of sparse coding to preserve the consistence in sparse representation of similar local features. Comprehensive experimental results show that our method achieves or outperforms existing state-of-the-art results, and exhibits excellent performance on Scene 15 data set.

关键词： Laplace equations Image coding Image classification Quantization Signal processing Sparse matrices Signal generators Image representation Histograms Layout

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Minimum length in the tangent bundle as a model for curve completion

Minimum length in the tangent bundle as a model for curve co...

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conference on computer vision and pattern recognition (CVPR)

作者： Guy Ben-Yosef Ohad Ben-Shahar Computer Science Department Ben-Gurion University of the Negev Beersheba Israel

The phenomenon of visual curve completion, where the visual system completes the missing part (e.g., due to occlusion) between two contour fragments, is a major problem in perceptual organization research. Previous computational approaches for the shape of the completed curve typically follow formal descriptions of desired, image-based perceptual properties (e.g, minimum total curvature, roundedness, etc.). Unfortunately, however, it is difficult to determine such desired properties psychophysically and indeed there is no consensus in the literature for what they should be. Instead, in this paper we suggest to exploit the fact that curve completion occurs in early vision in order to formalize the problem in a space that explicitly abstracts the primary visual cortex. We first argue that a suitable abstraction is the unit tangent bundle R 2 × S 1 and then we show that a basic principle of “minimum energy consumption” in this space, namely a minimum length completion, entails desired perceptual properties for the completion in the image plane. We present formal theoretical analysis and numerical solution methods, we show results on natural images and their advantage over existing popular approaches, and we discuss how our theory explains recent findings from the perceptual literature using basic principles only.

关键词： Shape Visual system computer science Psychology Abstracts Energy consumption Image analysis Image segmentation Computational modeling computer vision

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Efficient alignments of metabolic networks with bounded treewidth

Efficient alignments of metabolic networks with bounded tree...

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10th ieee International conference on Data Mining workshops, ICDMW 2010

作者： Cheng, Qiong Berman, Piotr Harrison, Rob Zelikovsky, Alex Department of Computer Science University of Miami Miami FL 33146 United States Department of Computer Science and Engineering Pennsylvania State University University Park PA 16802 United States Department of Computer Science Georgia State University Atlanta GA 30303-4110 United States

ISBN: (纸本)9780769542577

The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks becomes computationally challenging. Alignment of the reconstructed networks can help to catch model inconsistencies and infer missing elements. We have formulated the network alignment problem which asks for the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. This paper gives the first efficient algorithm for optimal aligning of metabolic pathways with bounded tree width. In particular, the optimal alignment from pathway P to pathway T can be found in time O(|VP||VT|αa+1), where VP and VT are the vertex sets of pathways and a is the tree width of P. This significantly improves alignment tools since the *** metabolic network has tree width 3 and more than 90% of pathways of several organisms are series-parallel. We have implemented the algorithm for alignment of metabolic pathways of tree width 2 with arbitrary metabolic networks. Our experiments show that allowing pattern vertex deletion significantly improves alignment. We also have applied the network alignment to identifying inconsistency, inferring missing enzymes, and finding potential candidates for filling the holes. © 2010 ieee.

关键词： Metabolism

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Efficient additive kernels via explicit feature maps

Efficient additive kernels via explicit feature maps

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conference on computer vision and pattern recognition (CVPR)

作者： Andrea Vedaldi Andrew Zisserman Department of Engineering Science University of Oxford UK

Maji and Berg have recently introduced an explicit feature map approximating the intersection kernel. This enables efficient learning methods for linear kernels to be applied to the non-linear intersection kernel, expanding the applicability of this model to much larger problems. In this paper we generalize this idea, and analyse a large family of additive kernels, called homogeneous, in a unified framework. The family includes the intersection, Hellinger's, and χ 2 kernels commonly employed in computer vision. Using the framework we are able to: (i) provide explicit feature maps for all homogeneous additive kernels along with closed form expression for all common kernels; (ii) derive corresponding approximate finite-dimensional feature maps based on the Fourier sampling theorem; and (iii) quantify the extent of the approximation. We demonstrate that the approximations have indistinguishable performance from the full kernel on a number of standard datasets, yet greatly reduce the train/test times of SVM implementations. We show that the χ 2 kernel, which has been found to yield the best performance in most applications, also has the most compact feature representation. Given these train/test advantages we are able to obtain a significant performance improvement over current state of the art results based on the intersection kernel.

关键词： Kernel Testing Support vector machines Learning systems computer vision Probability distribution Histograms Sampling methods Large-scale systems Machine learning

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