OpenRaiser/Intern-Atlas huggingface dataset:
Recovering Camera Motion Using L\infty Minimization
Authors: Kristy Sim, Richard I. Hartley
Venue: CVPR
Year: 2006

Recently, there has been interest in formulating various geometric problems in Computer Vision as L\infty optimization problems. The advantage of this approach is that under L\infty norm, such problems typically have a single minimum, and may be efficiently solved using Second-Order Cone Programming (SOCP). This paper shows that such techniques may be used effectively on the problem of determining the track of a camera given observations of features in the environment. The approach to this problem involves two steps: determination of the orientation of the camera by estimation of relative orientation between pairs of views, followed by determination of the translation of the camera. This paper focusses on the second step, that of determining the motion of the camera. It is shown that it may be solved effectively by using SOCP to reconcile translation estimates obtained for pairs or triples of views. In addition, it is observed that the individual translation estimates are not known with equal certainty in all directions. To account for this anisotropy in uncertainty, we introduce the use of covariances into the L\infty optimization framework.
A Generalized EM Approach for 3D Model Based Face Recognition under Occlusions
Authors: Michaël De Smet, Rik Fransens, Luc Van Gool
Venue: CVPR
Year: 2006

This paper describes an algorithm for pose and illumination invariant face recognition from a single image under occlusions. The method iteratively estimates the parameters of a 3D morphable face model to approximate the appearance of a face in an image. Simultaneously, a visibility map is computed which segments the image into visible and occluded regions. The visibility map is incorporated into a probabilistic image formation model as a set of spatially correlated random variables. This leads to a Generalized Expectation-Maximization algorithm in which the estimation of the morphable model related parameters is interleaved with visibility computations. The validity of the algorithm is verified by a face recognition experiment using images from the publicly available AR Face Database.
Face Recognition using 2.5D Shape Information
Authors: William A. P. Smith, Edwin R. Hancock
Venue: CVPR
Year: 2006

In this paper we investigate whether the 2.5D shape information delivered by a novel shape-from-shading algorithm can be used for illumination insensitive face recognition. We present a robust and efficient facial shape-fromshading algorithm which uses principal geodesic analysis to model the variation in surface orientation across a face. We show how this algorithm can be used to recover accurate facial shape and albedo from real world images. Our second contribution is to use the recovered 2.5D shape information in a variety of recognition methods. We present a novel recognition strategy in which similarity is measured in the space of the principal geodesic parameters. We also use the recovered shape information to generate illumination normalised prototype images on which recognition can be performed. Finally we show that, from a single input image, we are able to generate the basis images employed by a number of well known illumination-insensitive recognition algorithms. We also demonstrate that the principal geodesics provide an efficient parameterisation of the space of harmonic basis images.
Fast Variational Segmentation using Partial Extremal Initialization
Authors: Jan Erik Solem, Niels Chr. Overgaard, Markus Persson, Anders Heyden
Venue: CVPR
Year: 2006

In this paper we consider region-based variational segmentation of two- and three-dimensional images by the minimization of functionals whose fidelity term is the quotient of two integrals. Users often refrain from quotient functionals, even when they seem to be the most natural choice, probably because the corresponding gradient descent PDEs are nonlocal and hence require the computation of global properties. Here it is shown how this problem may be overcome by employing the structure of the Euler-Lagrange equation of the fidelity term to construct a good initialization for the gradient descent PDE, which will then converge rapidly to the desired (local) minimum. The initializer is found by making a one-dimensional search among the level sets of a function related to the fidelity term, picking the level set which minimizes the segmentation functional. This partial extremal initialization is tested on a medical segmentation problem with velocity- and intensity data from MR images. In this particular application, the partial extremal initialization speeds up the segmentation by two orders of magnitude compared to straight forward gradient descent.
Globally Optimal Grouping for Symmetric Boundaries
Authors: Joachim S. Stahl, Song Wang 0002
Venue: CVPR
Year: 2006

Many natural and man-made structures have a boundary that shows certain level of bilateral symmetry, a property that has been used to solve many computer-vision tasks. In this paper, we present a new grouping method for detecting closed boundaries with symmetry. We first construct a new type of grouping token in the form of a symmetric trapezoid, with which we can flexibly incorporate various boundary and region information into a unified grouping cost function. Particularly, this grouping cost function integrates Gestalt laws of proximity, closure, and continuity, besides the desirable boundary symmetry. We then develop a graph algorithm to find the boundary that minimizes this grouping cost function in a globally optimal fashion. Finally, we test this method by some experiments on a set of natural and medical images.
Nonlinear Mean Shift for Clustering over Analytic Manifolds
Authors: Raghav Subbarao, Peter Meer
Venue: CVPR
Year: 2006

The mean shift algorithm is widely applied for nonparametric clustering in Euclidean spaces. Recently, mean shift was generalized for clustering on matrix Lie groups. We further extend the algorithm to a more general class of nonlinear spaces, the set of analytic manifolds. As examples, two specific classes of frequently occurring parameter spaces, Grassmann manifolds and Lie groups, are considered. When the algorithm proposed here is restricted to matrix Lie groups the previously proposed method is obtained. The algorithm is applied to a variety of robust motion segmentation problems and multibody factorization. The motion segmentation method is robust to outliers, does not require any prior specification of the number of independent motions and simultaneously estimates all the motions present.
Image Comparison by Compound Disjoint Information
Authors: Zhaohui Sun, Anthony Hoogs
Venue: CVPR
Year: 2006

In this paper, we study disjoint information as a metric for image comparison and its applications in image matching, alignment, and video tracking. Disjoint information is the joint entropy of random variables excluding the mutual information. This measure of statistical dependence and information redundancy satisfies more rigorous metric conditions than mutual information. For image comparison, compound disjoint information is derived from the marginal densities of the image distributions. By using marginal densities other than color histograms, it can overcome the difficulties (such as a lack of spatial information) inherent in histogram-based mutual information methods and enrich the vocabulary of image description. Disjoint information is not sensitive to illumination and appearance changes, and it is particularly suited for multimodal applications.
Tracking With Sobolev Active Contours
Authors: Ganesh Sundaramoorthi, Jeremy D. Jackson, Anthony J. Yezzi, Andrea Mennucci
Venue: CVPR
Year: 2006

Recently proposed Sobolev active contours introduced a new paradigm for minimizing energies defined on curves by changing the traditional cost of perturbing a curve and thereby redefining their gradients. Sobolev active contours evolve more globally and are less attracted to certain intermediate local minima than traditional active contours. In this paper we analyze Sobolev active contours in the Fourier domain in order to understand their evolution across different scales. This analysis shows an important and useful behavior of Sobolev contours, namely, that they move successively from coarse to increasingly finer scale motions in a continuous manner. Along with other properties, the previous observation reveals that Sobolev active contours are ideally suited for tracking problems that use active contours. Our purpose in this work is to show how a variety of active contour based tracking methods can be significantly improved merely by evolving the active contours according to the Sobolev method.
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion
Authors: Xiaoyang Tan, Songcan Chen, Jun Li, Zhi-Hua Zhou
Venue: CVPR
Year: 2006

The performance of many computer vision and machine learning algorithms critically depends on the quality of the similarity measure defined over the feature space. Previous works usually utilize metric distances which are ofen epistemologically different from the perceptual distance of human beings. In this paper a novel non-metric partial similarity measure is introduced, which is born to automatically capture the prominent partial similarity between two images while ignoring the confusing unimportant dissimilarity. This measure is potentially useful in face recognition since it can help identify the inherent intra-personal similarity and thus reducing the influence caused by large variations such as expression and occlusions. Moreover; to make this method practical, this paper proposes an automatic and class-dependent similarity threshold setting mechanism based on the maximal margin criterion, and uses a Self- Organization Map-based embedding technique to alleviate the computational problem. Experimental results show the feasibility and effectiveness of the proposed method.
Separation of Highlight Reflections on Textured Surfaces
Authors: Ping Tan 0002, Long Quan, Stephen Lin 0001
Venue: CVPR
Year: 2006

We present a method for separating highlight reflections on textured surfaces. In contrast to previous techniques that use diffuse color information from outside the highlight area to constrain the solution, the proposed method further capitalizes on the spatial distributions of colors to resolve ambiguities in separation that often arise in real images. For highlight pixels in which a clear-cut separation cannot be determined from color space analysis, we evaluate possible separation solutions based on their consistency with diffuse texture characteristics outside the highlight. With consideration of color distributions in both the color space and the image space, appreciably enhanced separation performance can be attained in challenging cases.
Estimating Intrinsic Component Images using Non-Linear Regression
Authors: Marshall F. Tappen, Edward H. Adelson, William T. Freeman
Venue: CVPR
Year: 2006

Images can be represented as the composition of multiple intrinsic component images, such as shading, albedo, and noise images. In this paper, we present a method for estimating intrinsic component images from a single image, which we apply to the problems of estimating shading and albedo images and image denoising. Our method is based on learning estimators that predict filtered versions of the desired image. Unlike previous approaches, our method does not require unnatural discretizations of the problem. We also demonstrate how to learn a weighting function that properly weights the local estimates when constructing the estimated image. For shading estimation, we introduce a new training set of real-world images. The accuracy of our method is measured both qualitatively and quantitatively, showing better performance on the shading/albedo separation problem than previous approaches. The performance on denoising is competitive with the current state of the art.
Conditional Random People: Tracking Humans with CRFs and Grid Filters
Authors: Leonid Taycher, David Demirdjian, Trevor Darrell, Gregory Shakhnarovich
Venue: CVPR
Year: 2006

We describe a state-space tracking approach based on a Conditional Random Field (CRF) model, where the observation potentials are learned from data. We find functions that embed both state and observation into a space where similarity corresponds to L1 distance, and define an observation potential based on distance in this space. This potential is extremely fast to compute and in conjunction with a grid-filtering framework can be used to reduce a continuous state estimation problem to a discrete one. We show how a state temporal prior in the grid-filter can be computed in a manner similar to a sparse HMM, resulting in real-time system performance. The resulting system is used for human pose tracking in video sequences.
Towards Multi-View Object Class Detection
Authors: Alexander Thomas, Vittorio Ferrari, Bastian Leibe, Tinne Tuytelaars, Bernt Schiele, Luc Van Gool
Venue: CVPR
Year: 2006

We present a novel system for generic object class detection. In contrast to most existing systems which focus on a single viewpoint or aspect, our approach can detect object instances from arbitrary viewpoints. This is achieved by combining the Implicit Shape Model for object class detection proposed by Leibe and Schiele with the multi-view specific object recognition system of Ferrari et al. After learning single-view codebooks, these are interconnected by so-called activation links, obtained through multi-view region tracks across different training views of individual object instances. During recognition, these integrated codebooks work together to determine the location and pose of the object. Experimental results demonstrate the viability of the approach and compare it to a bank of independent single-view detectors
Extracting Subimages of an Unknown Category from a Set of Images
Authors: Sinisa Todorovic, Narendra Ahuja
Venue: CVPR
Year: 2006

Suppose a set of images contains frequent occurrences of objects from an unknown category. This paper is aimed at simultaneously solving the following related problems: (1) unsupervised identification of photometric, geometric, and topological (mutual containment) properties of multiscale regions defining objects in the category; (2) learning a region-based structural model of the category in terms of these properties from a set of training images; and (3) segmentation and recognition of objects from the category in new images. To this end, each image is represented by a tree that captures a multiscale image segmentation. The trees are matched to find the maximally matching subtrees across the set, the existence of which is itself viewed as evidence that a category is indeed present. The matched subtrees are fused into a canonical tree, which represents the learned model of the category. Recognition of objects in a new image and image segmentation delineating all object parts are achieved simultaneously by finding matches of the model with subtrees of the new image. Experimental comparison with state-of-the-art methods shows that the proposed approach has similar recognition and superior localization performance while it uses fewer training examples.
Graph Partitioning by Spectral Rounding: Applications in Image Segmentation and Clustering
Authors: David Tolliver, Gary L. Miller
Venue: CVPR
Year: 2006

We introduce a family of spectral partitioning methods. Edge separators of a graph are produced by iteratively reweighting the edges until the graph disconnects into the prescribed number of components. At each iteration a small number of eigenvectors with small eigenvalue are computed and used to determine the reweighting. In this way spectral rounding directly produces discrete solutions where as current spectral algorithms must map the continuous eigenvectors to discrete solutions by employing a heuristic geometric separator (e.g. k-means). We show that spectral rounding compares favorably to current spectral approximations on the Normalized Cut criterion (NCut). Results are given for natural image segmentation, medical image segmentation, and clustering. A practical version is shown to converge.
Inferring Facial Action Units with Causal Relations
Authors: Yan Tong, Wenhui Liao, Qiang Ji
Venue: CVPR
Year: 2006

A system that could automatically analyze the facial actions in real time have applications in a number of different fields. However, developing such a system is always a challenging task due to the richness, ambiguity, and dynamic nature of facial actions. Although a number of research groups attempt to recognize action units (AUs) by either improving facial feature extraction techniques, or the AU classification techniques, these methods often recognize AUs individually and statically, therefore ignoring the semantic relationships among AUs and the dynamics of AUs. Hence, these approaches cannot always recognize AUs reliably, robustly, and consistently. In this paper, we propose a novel approach for AUs classification, that systematically accounts for relationships among AUs and their temporal evolution. Specifically, we use a dynamic Bayesian network (DBN) to model the relationships among different AUs. The DBN provides a coherent and unified hierarchical probabilistic framework to represent probabilistic relationships among different AUs and account for the temporal changes in facial action development. Under our system, robust computer vision techniques are used to get AU measurements. And such AU measurements are then applied as evidence into the DBN for inferencing various AUs. The experiments show the integration of AU relationships and AU dynamics with AU image measurements yields significant improvements in AU recognition.
Grouping with Asymmetric Affinities: A Game-Theoretic Perspective
Authors: Andrea Torsello, Samuel Rota Bulò, Marcello Pelillo
Venue: CVPR
Year: 2006

Pairwise grouping and clustering approaches have traditionally worked under the assumption that the similarities or compatibilities between the elements to be grouped are symmetric. However, asymmetric compatibilities arise naturally in many areas of computer vision and pattern recognition. Hence, there is a need for a new generic approach to clustering and grouping that can deal with asymmetries in the compatibilities. In this paper, we present a generic framework for grouping and clustering derived from a game-theoretic formalization of the competition between the hypotheses of group membership, and apply it to perceptual grouping. In the proposed approach groups correspond to evolutionary stable strategies, a classic notion in evolutionary game theory. We also provide a combinatorial characterization of the stable strategies, and, hence, of the elements that belong to a group. Experiments show that our approach outperforms both state-of-the-art clustering-based perceptual grouping approacheswith symmetric compatibilities, and other approaches explicitly designed to make use of asymmetric compatibilities.
Integration of Top-down and Bottom-up Information for Image Labeling
Authors: Takahiro Toyoda, Keisuke Tagami, Osamu Hasegawa
Venue: CVPR
Year: 2006

This paper proposes a novel framework that integrates bottom-up information and top-down information for scene understanding. Bottom-up information is derived from local features of texture and color. Top-down information is generated from a holistic image context. The information is integrated effectively by extension of the Ising model, which is a simple model of ferromagnetism. Locally and globally consistent image recognition is achieved through an iterative process. The proposed method showed 91.8% accuracy in road-image labeling, which is superior to results obtained using only bottom-up information (81.9%) and the best accuracy obtained using the other method (90.7%).
Instant 3Descatter
Authors: Tali Treibitz, Yoav Y. Schechner
Venue: CVPR
Year: 2006

Imaging in scattering media such as fog and water is important but challenging. Images suffer from poor visibility due to backscattering and signal attenuation. Most prior methods for visibility improvement use active illumination scanners (structured and gated), which are slow and cumbersome. On the other hand, natural illumination is inapplicable to dark environments. The current paper counters these deficiencies. We study the formation of images under wide field (non-scanning) artificial illumination. We discovered some characteristics of backscattered light empirically. Based on these, the paper presents a visibility recovery approach which also yields a rough estimate of the 3D scene structure. The method is simple and requires compact hardware, using active wide field polarized illumination. Two images of the scene are instantly taken, with different states of a camera-mounted polarizer. A recovery algorithm then follows. We demonstrate the approach in underwater field experiments.
AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition
Authors: Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh, Hung Hai Bui
Venue: CVPR
Year: 2006

Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithmto a home video surveillance application and demonstrate its efficacy.
Wire Structure Pattern Extraction and Tracking From X-Ray Images of Composite Mechanisms
Authors: David Tschumperlé, Jalal Fadili
Venue: CVPR
Year: 2006

This paper introduces a complete pipeline of image processing methods in order to analyze and track the internal structures of a composite material. As a first step, input Xray images are denoised, enhanced, separated into different morphological components, and geometrically filtered in order to isolate the interesting fiber inside the composite material. This requires the design of specific algorithms preserving very thin image details while being able to remove undesired image regions that may be sometimes large. For this purpose, we use state-of the-art techniques based on recent achievements in diffusion PDE’s and modern harmonic analysis tools. Then, the shapes of the remaining fibered composite are individually analyzed by the use of a tensor-based tracking algorithm. We illustrate how this set of techniques allows the dynamic analysis of the composite structure when submitted to external mechanical loads.
Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment
Authors: Zhuowen Tu, Xiang Sean Zhou, Luca Bogoni, Adrian Barbu, Dorin Comaniciu
Venue: CVPR
Year: 2006

Automatic polyp detection is an increasingly important task in medical imaging with virtual colonoscopy [15] being widely used. In this paper, we present a 3D object detection algorithm and show its application on polyp detection from CT images. We make the following contributions: (1) The system adopts Probabilistic Boosting Tree (PBT) to probabilistically detect polyps. Integral volume and 3D Haar filters are introduced to achieve fast feature computation. (2) We give an explicit convergence rate analysis for the AdaBoost algorithm [2] and prove that the error at each step \in t+1. is tightly bounded by the previous error \in t. (3) For a 3D polyp template, a generative model is defined. Given the bound and convergence analysis, we analyze the role of "sample alignment" in the template design and devise a robust and efficient algorithm for polyp detection. The overall system has been tested on 150 volumes and the results obtained are very encouraging.