OpenRaiser/Intern-Atlas huggingface dataset:
Variational Stereo Vision with Sharp Discontinuities and Occlusion Handling
Authors: Rami Ben-Ari, Nir A. Sochen
Venue: ICCV
Year: 2007

This paper addresses the problem of correspondence establishment in binocular stereo vision. We suggest a novel variational approach that considers both the discontinuities and occlusions. It deals with color images as well as gray levels. The proposed method divides the image domain into the visible and occluded regions where each region is handled differently. The depth discontinuities in the visible domain are preserved by use of the total variation term in conjunction with the Mumford-Shah framework. In addition to the dense disparity and the occlusion maps, our method also provides a discontinuity function revealing the location of the boundaries in the disparity map. We evaluate our method on data sets from Middlebury site showing superior performance in comparison to the state of the art variational technique.
Penrose Pixels Super-Resolution in the Detector Layout Domain
Authors: Moshe Ben-Ezra, Zhouchen Lin, Bennett Wilburn
Venue: ICCV
Year: 2007

We present a novel approach to reconstruction based super- resolution that explicitly models the detector's pixel layout. Pixels in our model can vary in shape and size, and there may be gaps between adjacent pixels. Furthermore, their layout can be periodic as well as aperiodic, such as penrose tiling or a biological retina. We also present a new variant of the well known error back-projection super-resolution algorithm that makes use of the exact detector model in its back projection operator for better accuracy. Our method can be applied equally well to either periodic or aperiodic pixel tiling. Through analysis and extensive testing using synthetic and real images, we show that our approach outperforms existing reconstruction based algorithms for regular pixel arrays. We obtain significantly better results using aperiodic pixel layouts. As an interesting example, we apply our method to a retina-like pixel structure modeled by a centroidal Voronoi tessellation. We demonstrate that, in principle, this structure is better for super-resolution than the regular pixel array used in today's sensors.
Finding a Closed Boundary by Growing Minimal Paths from a Single Point on 2D or 3D Images
Authors: Fethallah Benmansour, Stephane Bonneau, Laurent D. Cohen
Venue: ICCV
Year: 2007

In this paper, we present a new method for segmenting closed contours and surfaces. Our work builds on a variant of the Fast Marching algorithm. First, an initial point on the desired contour is chosen by the user. Next, new keypoints are detected automatically using a front propagation approach. We assume that the desired object has a closed boundary. This a-priori knowledge on the topology is used to devise a relevant criterion for stopping the keypoint detection and front propagation. The final domain visited by the front will yield a band surrounding the object of interest. Linking pairs of neighboring keypoints with minimal paths allows us to extract a closed contour from a 2D image. Detection of a variety of objects on real images is demonstrated. Using a similar same idea, we can extract networks of minimal paths from a 3D image called Geodesic Meshing. The proposed method is applied to 3D data with promising results.
Pairwise Similarities across Images for Multiple View Rigid/Non-Rigid Segmentation and Registration
Authors: Luca Bertelli, Marco Zuliani, B. S. Manjunath
Venue: ICCV
Year: 2007

A variational approach for the background/foreground segmentation of multiple views of the same scene is presented. The main novelty is the introduction of cost functions based on pairwise similarity between pixels across different images. These cost functions are minimized within a level set framework. In addition, a warping model (rigid or non-rigid) between the emerging foregrounds in the different views is imposed, thus avoiding the introduction of a specific shape term in the cost function to handle occlusions. The thin plate spline (TPS) warping is for the first time employed within the level set framework to model non- rigid deformations. The minimization of these cost functions leads to simultaneous segmentation and registration of the different views. Examples of segmentations of a variety of objects are shown and possible applications are proposed.
Instability of Projective Reconstruction of Dynamic Scenes near Critical Configurations
Authors: Marina Bertolini, Cristina Turrini, GianMario Besana
Venue: ICCV
Year: 2007

In the context of multiple view geometry in any dimension, we compute the minimum number of views necessary for projective reconstruction of both the set of cameras and of scenes. Within a unified approach to critical configurations and their loci, the paper focuses on the case of dynamic scenes of multiple bodies traveling along parallel straight-line trajectories with constant velocities, in the framework of higher dimensional projections introduced by Shashua and Wolf. Critical loci in this case are explicitly determined. A stratification of the resulting locus in terms of fixed common velocities is presented and leveraged to show, via a number of simulated experiments, instability of the reconstruction near critical configurations.
Robust Estimation of Albedo for Illumination-invariant Matching and Shape Recovery
Authors: Soma Biswas, Gaurav Aggarwal, Rama Chellappa
Venue: ICCV
Year: 2007

In this paper, we propose a non-stationary stochastic filtering framework for the task of albedo estimation from a single image. There are several approaches in literature for albedo estimation, but few include the errors in estimates of surface normals and light source directions to improve the albedo estimate. The proposed approach effectively utilizes the error statistics of surface normals and illumination direction for robust estimation of albedo. The albedo estimate obtained is further used to generate albedo-free normalized images for recovering the shape of an object. Illustrations and experiments are provided to show the efficacy of the approach and its application to illumination-invariant matching and shape recovery.
Fitting a Morphable Model to 3D Scans of Faces
Authors: Volker Blanz, Kristina Scherbaum, Hans-Peter Seidel
Venue: ICCV
Year: 2007

This paper presents a top-down approach to 3D data analysis by fitting a morphable model to scans of faces. In a unified framework, the algorithm optimizes shape, texture, pose and illumination simultaneously. The algorithm can be used as a core component in face recognition from scans. In an analysis-by-synthesis approach, raw scans are transformed into a PCA-based representation that is robust with respect to changes in pose and illumination. Illumination conditions are estimated in an explicit simulation that involves specular and diffuse components. The algorithm inverts the effect of shading in order to obtain the diffuse reflectance in each point of the facial surface. Our results include illumination correction, surface completion and face recognition on the FRGC database of scans.
Image Classification using Random Forests and Ferns
Authors: Anna Bosch, Andrew Zisserman, Xavier Muñoz
Venue: ICCV
Year: 2007

We explore the problem of classifying images by the object categories they contain in the case of a large number of object categories. To this end we combine three ingredients: (i) shape and appearance representations that support spatial pyramid matching over a region of interest. This generalizes the representation of Lazebnik et al., (2006) from an image to a region of interest (ROI), and from appearance (visual words) alone to appearance and local shape (edge distributions); (ii) automatic selection of the regions of interest in training. This provides a method of inhibiting background clutter and adding invariance to the object instance 's position; and (iii) the use of random forests (and random ferns) as a multi-way classifier. The advantage of such classifiers (over multi-way SVM for example) is the ease of training and testing. Results are reported for classification of the Caltech-101 and Caltech-256 data sets. We compare the performance of the random forest/ferns classifier with a benchmark multi-way SVM classifier. It is shown that selecting the ROI adds about 5% to the performance and, together with the other improvements, the result is about a 10% improvement over the state of the art for Caltech-256.
Detecting Cortical Surface Regions in Structural MR Data
Authors: Biswajit Bose, John W. Fisher III, Bruce Fischl, Oliver Hinds, Eric Grimson
Venue: ICCV
Year: 2007

We present a novel level-set method for evolving open surfaces embedded in three-dimensional volumes. We adapt the method for statistical detection and segmentation of cytoarchitectonic regions of the cortical ribbon (e.g., Brodmann areas). In addition, we incorporate an explicit interface appearance model which is oriented normal to the open surface, allowing one to model characteristics beyond voxel intensities and high gradients. We show that such models are well suited to detecting embedded cortical structures. Appearance models of the interface are used in two ways: firstly, to evolve an open surface in the normal direction for the purpose of detecting the location of the surface, and secondly, to evolve the boundary of the surface in a direction tangential to the surface in order to delineate the extent of a specific Brodmann area within the cortical ribbon. The utility of the method is demonstrated on a challenging ex-vivo structural MR dataset for detection of Brodmann area 17.
A Discrete Differential Operator for Direction-based Surface Morphometry
Authors: Maxime Boucher, Sue Whitesides, Oliver Lyttleton, Alan C. Evans
Venue: ICCV
Year: 2007

This paper presents a novel directional morphometry method for surfaces using first order derivatives. Non-directional surface morphometry has been previously used to detect regions of cortical atrophy using brain MRI data. However, evaluating directional changes on surfaces requires computing gradients to obtain a full metric tensor. Non-directionality reduces the sensitivity of deformationbased morphometry to area-preserving deformations. By proposing a method to compute directional derivatives, this paper enables analysis of directional deformations on surfaces. Moreover, the proposed method exhibits improved numerical accuracy when evaluating mean curvature, compared to the so-called cotangent formula. The directional deformation of folding patterns was measured in two groups of surfaces and the proposed methodology allowed to detect morphological differences that were not detected using previous non-directional morphometry. The methodology uses a closed-form analytic formalism rather than numerical approximation and is readily generalizable to any application involving surface deformation.
Rock, Paper, and Scissors: extrinsic vs. intrinsic similarity of non-rigid shapes
Authors: Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel
Venue: ICCV
Year: 2007

This paper explores similarity criteria between non-rigid shapes. Broadly speaking, such criteria are divided into intrinsic and extrinsic, the first referring to the metric structure of the objects and the latter to the geometry of the shapes in the Euclidean space. Both criteria have their advantages and disadvantages; extrinsic similarity is sensitive to non-rigid deformations of the shapes, while intrinsic similarity is sensitive to topological noise. Here, we present an approach unifying both criteria in a single distance. Numerical results demonstrate the robustness of our approach in cases where using only extrinsic or intrinsic criteria fail.
A Robust Graph-Based Method for The General Correspondence Problem Demonstrated on Image Stitching
Authors: Martin Bujnak, Radim Sára
Venue: ICCV
Year: 2007

We pose robust matching with parametric and non-parametric constraints as the problem of finding a stable independent set (SIS) in an oriented graph whose vertices are all possible correspondences, whose edges capture the structure of the constraints and whose edge orientation represents pairwise comparison 'is better' based on correspondence quality, including the uncertainty of this comparison. We show SIS possess properties of both robustness and weak optimality. The main contribution of this paper is algorithmic speedup that results from exploiting the dependence between the standard uniqueness constraint and the parametric constraint. The general theory is demonstrated on the example of image stitching using homography model. The algorithm needs at most 𝜅N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> calls of a procedure testing if two ellipse correspondences are consistent with a general homography. The previous known SIS algorithm needed O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) tests. Experiments show the method gives good results and is fast in practice with 𝜅≈0.3.
Improving Numerical Accuracy of Gröbner Basis Polynomial Equation Solvers
Authors: Martin Byröd, Klas Josephson, Kalle Åström
Venue: ICCV
Year: 2007

This paper presents techniques for improving the numerical stability of Grobner basis solvers for polynomial equations. Recently Grobner basis methods have been used successfully to solve polynomial equations arising in global optimization e.g. three view triangulation and in many important minimal cases of structure from motion. Such methods work extremely well for problems of reasonably low degree, involving a few variables. Currently, the limiting factor in using these methods for larger and more demanding problems is numerical difficulties. In the paper we (i) show how to change basis in the quotient space R[x]/I and propose a strategy for selecting a basis which improves the conditioning of a crucial elimination step, (ii) use this technique to devise a Grobner basis with improved precision and (iii) show how solving for the eigenvalues instead of eigenvectors can be used to improve precision further while retaining the same speed. We study these methods on some of the latest reported uses of Grobner basis methods and demonstrate dramatically improved numerical precision using these new techniques making it possible to solve a larger class of problems than previously.
Learning Graph Matching
Authors: Tibério S. Caetano, Li Cheng 0001, Quoc V. Le, Alexander J. Smola
Venue: ICCV
Year: 2007

As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. There are many ways in which the problem has been formulated, but most can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility functions and a quadratic term encodes edge compatibility functions. The main research focus in this theme is about designing efficient algorithms for solving approximately the quadratic assignment problem, since it is NP-hard. In this paper, we turn our attention to the complementary problem: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the "labels" are matchings between pairs of graphs. We present experimental results with real image data which give evidence that learning can improve the performance of standard graph matching algorithms. In particular, it turns out that linear assignment with such a learning scheme may improve over state-of-the-art quadratic assignment relaxations. This finding suggests that for a range of problems where quadratic assignment was thought to be essential for securing good results, linear assignment, which is far more efficient, could be just sufficient if learning is performed.
Position and radius of spheres from single off-axis catadioptric images
Authors: Vincenzo Caglioti, Simone Gasparini
Venue: ICCV
Year: 2007

In this paper we address the problem of sphere localization from a single image taken with a noncentral catadioptric camera. We propose a method for determining both the radius and the position of an unknown sphere from a single, catadioptric image. The method can find its application in the field of robotic vision, especially in mobile robots playing soccer in RoboCup contests, in order to improve robot capabilities related to playing with a flying ball. Recently, a method for sphere reconstruction from single image taken with a noncentral, axial-symmetric, catadioptric camera has been proposed. In an axial symmetric catadioptric cameras, the pinhole of the camera is placed on the mirror axis. Though axial symmetric cameras help to simplify the geometrical treatment of the problem, they are difficult to set up since they require a precise alignment, usually hard to check. In this paper we deal with the general case of off-axis catadioptric cameras, with the camera pinhole placed in a general position w.r.t. the mirror. We devise a simple geometrical method by which we determine both the position of a sphere and its radius from its apparent image contour. Since our approach is based on coplanar viewing rays, it has a wider applicability w.r.t. the previous method, as it relaxes the constraint on camera position, i.e. it does not require a precise alignment, and the constraint on mirror axial symmetry, i.e. it can be applied to a wider class of mirrors. Some preliminary experiments both on simulated and real image are also presented.
Methods for space line localization from single catadioptric images: new proposals and comparisons
Authors: Vincenzo Caglioti, Simone Gasparini, Pierluigi Taddei
Venue: ICCV
Year: 2007

Line localization from a single image of a central camera is an ill-posed problem unless other constraints or apriori knowledge are exploited. Recently, it has been proved that noncentral catadioptric cameras allow space lines to be localized from a single image. In this paper we propose two novel localization algorithms. The first method exploits a pair of coplanar viewing rays to localize the space line. The second method follows a constrained non-linear minimization procedure using a suitable parametrization to represent space lines. We compare the accuracy of the proposed method w.r.t. the classical line localization algorithm and two robust variants of it. We carried out both synthetic and real experiments and evaluated the performance in localizing a set of space lines. We also propose a quality index for the viewing surfaces associated to space lines in order to better evaluate the quality of the localization. The experimental results showed the effectiveness and the accuracy of both proposed methods.
Spectral Regression for Efficient Regularized Subspace Learning
Authors: Deng Cai 0001, Xiaofei He 0001, Jiawei Han 0001
Venue: ICCV
Year: 2007

Subspace learning based face recognition methods have attracted considerable interests in recent years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE) and marginal Fisher analysis (MFA). However, a disadvantage of all these approaches is that their computations involve eigen- decomposition of dense matrices which is expensive in both time and memory. In this paper, we propose a novel dimensionality reduction framework, called spectral regression (SR), for efficient regularized subspace learning. SR casts the problem of learning the projective functions into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. Computational analysis shows that SR has only linear-time complexity which is a huge speed up comparing to the cubic-time complexity of the ordinary approaches. Experimental results on face recognition demonstrate the effectiveness and efficiency of our method.
Semi-supervised Discriminant Analysis
Authors: Deng Cai 0001, Xiaofei He 0001, Jiawei Han 0001
Venue: ICCV
Year: 2007

Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. The projection vectors are commonly obtained by maximizing the between class covariance and simultaneously minimizing the within class covariance. In practice, when there is no sufficient training samples, the covariance matrix of each class may not be accurately estimated. In this paper, we propose a novel method, called Semi- supervised Discriminant Analysis (SDA), which makes use of both labeled and unlabeled samples. The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data. Specifically, we aim to learn a discriminant function which is as smooth as possible on the data manifold. Experimental results on single training image face recognition and relevance feedback image retrieval demonstrate the effectiveness of our algorithm.
Rectified Surface Mosaics
Authors: Robert E. Carroll, Steven M. Seitz
Venue: ICCV
Year: 2007

We approach mosaicing as a camera tracking problem within a known parameterized surface. From a video of a camera moving within a surface, we compute a mosaic representing the texture of that surface, flattened onto a planar image. Our approach works by defining a warp between images as a function of surface geometry and camera pose. Globally optimizing this warp to maximize alignment across all frames determines the camera trajectory, and the corresponding flattened mosaic image. In contrast to previous mosaicing methods which assume planar or distant scenes, or controlled camera motion, our approach enables mosaicing in cases where the camera moves unpredictably through proximal surfaces, such as in medical endoscopy applications.
Bilayer Stereo Matching
Authors: Dengfeng Chai, Qunsheng Peng 0001
Venue: ICCV
Year: 2007

This paper presents two novel approaches for stereo matching. First, a bitwise algorithm for stereo matching is proposed. It represents the disparity of each pixel as a binary number, treats each bit separately and determines them step by step, each step determines one bit of the disparities and involves only a single graph cut computation. It reduces the computation complexity to (9(log2 n). Second, bilayer stereo matching for scenes consisting of foreground and background is proposed. Disparity field of foreground and background layers are recovered independently, and the final disparity field is composed using the disparity field of two layers. Discontinuity between foreground and background can be preserved successfully by this schema. Experimental results show that the combination of bitwise algorithm and bilayer stereo matching can support realtime applications such as desktop-based 3D visual communication.