OpenRaiser/Intern-Atlas huggingface dataset:
Reconstructing the Geometry of Flowing Water
Authors: Ivo Ihrke, Bastian Goldlücke, Marcus A. Magnor
Venue: ICCV
Year: 2005

We present a recording scheme, image formation model and reconstruction method that enables image-based modeling of flowing bodies of water from multivideo input data. The recorded water is dyed with a fluorescent chemical to measure the thickness of a column of water which leads to an image formation model based on integrated emissivities along a viewing ray. This model allows for a photo-consistency based error measure for a weighted minimal surface, which is recovered using a PDE obtained from the Euler-Lagrangian formulation of the problem. The resulting equation is solved using the level set method.
Multi-Modal Tensor Face for Simultaneous Super-Resolution and Recognition
Authors: Kui Jia, Shaogang Gong
Venue: ICCV
Year: 2005

Face images of non-frontal views under poor illumination resolution reduce dramatically face recognition accuracy. This is evident most compellingly by the very low recognition rate of all existing face recognition systems when applied to live CCTV camera input. In this paper, we present a Bayesian framework to perform multimodal (such as variations in viewpoint and illumination) face image super-resolution for recognition in tensor space. Given a single modal low-resolution face image, we benefit from the multiple factor interactions of training sensor and super-resolve its high-resolution reconstructions across different modalities for face recognition. Instead of performing pixel-domain super-resolution and recognition independently as two separate sequential processes, we integrate the tasks of super-resolution and recognition by directly computing a maximum likelihood identity parameter vector in high-resolution tensor space for recognition. We show results from multi-modal super-resolution and face recognition experiments across different imaging modalities, using low-resolution images as testing inputs and demonstrate improved recognition rates over standard tensorface and eigenface representations.
Eliminating Structure and Intensity Misalignment in Image Stitching
Authors: Jiaya Jia, Chi-Keung Tang
Venue: ICCV
Year: 2005

The aim of this paper is to achieve seamless image stitching for eliminating obvious visual artifact caused by severe intensity discrepancy, image distortion and structure misalignment, given that the input images are globally registered. Our approach is based on structure deformation and propagation while maintaining the overall appearance affinity of the result to the input images. This new approach is proven to be effective in solving the above problems, and has found applications in mosaic deghosting, image blending and intensity correction. Our new method consists of the following main processes. First, salient features or structures are robustly detected and aligned along the optimal partitioning boundary between the input images. From these features, we derive sparse deformation vectors to to uniformly encode the underlying structure and intensity misalignment. These sparse deformation cues will then be propagated robustly and smoothly into the interior of the target image by solving the associated Laplace equations in the image gradient domain. We present convincing results to show that our method can handle significant structure and intensity misalignment in image stitching.
Layered Active Appearance Models
Authors: Eagle Jones, Stefano Soatto
Venue: ICCV
Year: 2005

Active appearance models (AAMs) provide a framework for modeling the joint shape and texture of an image. An AAM is a compact representation of both factors in a conditionally linear model. However, the standard AAM framework does not handle images which have missing features, or allow modification of certain structures in the image while leaving neighboring ones undeformed. We introduce the layered active appearance model (LAAM), which allows for missing features, occlusion, substantial spatial rearrangement of features, and which provides a more general representation that extends the applicability of the active appearance model
Creating Efficient Codebooks for Visual Recognition
Authors: Frédéric Jurie, Bill Triggs
Venue: ICCV
Year: 2005

Visual codebook based quantization of robust appearance descriptors extracted from local image patches is an effective means of capturing image statistics for texture analysis and scene classification. Codebooks are usually constructed by using a method such as k-means to cluster the descriptor vectors of patches sampled either densely ('textons') or sparsely ('bags of features' based on key-points or salience measures) from a set of training images. This works well for texture analysis in homogeneous images, but the images that arise in natural object recognition tasks have far less uniform statistics. We show that for dense sampling, k-means over-adapts to this, clustering centres almost exclusively around the densest few regions in descriptor space and thus failing to code other informative regions. This gives suboptimal codes that are no better than using randomly selected centres. We describe a scalable acceptance-radius based clusterer that generates better codebooks and study its performance on several image classification tasks. We also show that dense representations outperform equivalent keypoint based ones on these tasks and that SVM or mutual information based feature selection starting from a dense codebook further improves the performance.
Multiple View Geometry and the L8-norm
Authors: Fredrik Kahl
Venue: ICCV
Year: 2005

This paper presents a new framework for solving geometric structure and motion problems based on L/sub /spl infin//-norm. Instead of using the common sum-of-squares cost-function, that is, the L/sub /spl infin//-norm, the model-fitting errors are measured using the L/sub /spl infin//-norm. Unlike traditional methods based on L/sub 2/ our framework allows for efficient computation of global estimates. We show that a variety of structure and motion problems, for example, triangulation, camera resectioning and homography estimation can be recast as a quasiconvex optimization problem within this framework. These problems can be efficiently solved using second order cone programming (SOCP) which is a standard technique in convex optimization. The proposed solutions have been validated on real data in different settings with small and large dimensions and with excellent performance.
Globally Optimal Estimates for Geometric Reconstruction Problems
Authors: Fredrik Kahl, Didier Henrion
Venue: ICCV
Year: 2005

We introduce a framework for computing statistically optimal estimates of geometric reconstruction problems. While traditional algorithms often suffer from either local minima or nonoptimality - or a combination of both - we pursue the goal of achieving global solutions of the statistically optimal cost-function. Our approach is based on a hierarchy of convex relaxations to solve nonconvex optimization problems with polynomials. These convex relaxations generate a monotone sequence of lower bounds and we show how one can detect whether the global optimum is attained at a given relaxation. The technique is applied to a number of classical vision problems: triangulation, camera pose, homography estimation and last, but not least, epipolar geometry estimation. Experimental validation on both synthetic and real data is provided. In practice, only a few relaxations are needed for attaining the global optimum
Detection and Tracking of Moving Objects from a Moving Platform in Presence of Strong Parallax
Authors: Jinman Kang, Isaac Cohen, Gérard G. Medioni, Chang Yuan
Venue: ICCV
Year: 2005

We present a novel approach to detect and track independently moving regions in a 3D scene observed by a moving camera in the presence of strong parallax. Detected moving pixels are classified into independently moving regions or parallax regions by analyzing two geometric constraints: the commonly used epipolar constraint, and the structure consistency constraint. The second constraint is implemented within a "plane+parallax" framework and represented by a bilinear relationship which relates the image points to their relative depths. This newly derived relationship is related to trilinear tensor, but can be enforced into more than three frames. It does not assume a constant reference plane in the scene and therefore eliminates the need for manual selection of reference plane. Then, a robust parallax filtering scheme is proposed to accumulate the geometric constraint errors within a sliding window and estimate a likelihood map for pixel classification. The likelihood map is integrated into our tracking framework based on the spatio-temporal joint probability data association filter (JPDAF). This tracking approach infers the trajectory and bounding box of the moving objects by searching the optimal path with maximum joint probability within a fixed size of buffer. We demonstrate the performance of the proposed approach on real video sequences where parallax effects are significant.
Dynamic Measurement Clustering to Aid Real Time Tracking
Authors: Christopher Kemp, Tom Drummond
Venue: ICCV
Year: 2005

We present a technique/or clustering measurements such that high-dimensional parameter estimation problems can be simplified. The key idea is to find rows of the measurement Jacobian whose rank is significantly less than its width. Such a set of rows gives a cluster of measurements which is affected only by a subset of the parameter space. This cluster can be used independently from other measurements to isolate parameter decisions. Unlike static partitioning techniques, the method presented dynamically generates clusters at each step of the estimation. This achieves substantial computational reductions, even for problems which cannot be partitioned in the traditional sense. The technique is applied to the task of tracking camera motions in real-time and video sequences are used to compare the resulting system to previous methods.
A Practical Single Image Based Approach for Estimating Illumination Distribution from Shadows
Authors: Taeone Kim, Ki-Sang Hong
Venue: ICCV
Year: 2005

This paper presents a practical method that estimates illumination distribution from shadows where the shadows are assumed to be cast on a textured, Lambertian surface. Previous methods usually require that the reflectance property of the surface be constant or uniform, or need an additional image to cancel out the effects of varying albedo of the textured surface. We deal with an estimation problem for which surface albedo information is not available. In this case, the estimation problem corresponds to an underdetermined one. We show that combination of regularization by correlation and some user-specified information can be a practical method for solving the problem. In addition, as an optimization tool for solving the problem, we develop a constrained nonnegative quadratic programming (NNQP) technique into which not only regularization but also user-specified information are easily incorporated. We test and validate our method on both synthetic and real images and present some experimental results.
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
Authors: Pushmeet Kohli, Philip H. S. Torr
Venue: ICCV
Year: 2005

In this paper, we present a fast new fully dynamic algorithm for the st-mincut/max-flow problem. We show how this algorithm can be used to efficiently compute MAP estimates for dynamically changing MRF models of labeling problems in computer vision, such as image segmentation. Specifically, given the solution of the max-flow problem on a graph, we show how to efficiently compute the maximum flow in a modified version of the graph. Our experiments showed that the time taken by our algorithm is roughly proportional to the number of edges whose weights were different in the two graphs. We test the performance of our algorithm on one particular problem: the object-background segmentation problem for video and compare it with the best known st-mincut algorithm. The results show that the dynamic graph cut algorithm is much faster than its static counterpart and enables real time image segmentation. It should be noted that our method is generic and can be used to yield similar improvements in many other cases that involve dynamic change in the graph
An Expectation Maximization Approach to the Synergy between Image Segmentation and Object Categorization
Authors: Iasonas Kokkinos, Petros Maragos
Venue: ICCV
Year: 2005

In this work, we deal with the problem of modelling and exploiting the interaction between the processes of image segmentation and object categorization. We propose a novel framework to address this problem that is based on the combination of the expectation maximization (EM) algorithm and generative models for object categories. Using a concise formulation of the interaction between these two processes, segmentation is interpreted as the E step, assigning observations to models, whereas object detection/analysis is modelled as the M-step, fitting models to observations. We present in detail the segmentation and detection processes comprising the E and M steps and demonstrate results on the joint detection and segmentation of the object categories of faces and cars.
What Metrics Can Be Approximated by Geo-Cuts, Or Global Optimization of Length/Area and Flux
Authors: Vladimir Kolmogorov, Yuri Boykov
Venue: ICCV
Year: 2005

In the work of the authors (2003), we showed that graph cuts can find hypersurfaces of globally minimal length (or area) under any Riemannian metric. Here we show that graph cuts on directed regular grids can approximate a significantly more general class of continuous non-symmetric metrics. Using submodularity condition (Boros and Hammer, 2002 and Kolmogorov and Zabih, 2004), we obtain a tight characterization of graph-representable metrics. Such "submodular" metrics have an elegant geometric interpretation via hypersurface functionals combining length/area and flux. Practically speaking, we attend 'geo-cuts' algorithm to a wider class of geometrically motivated hypersurface functionals and show how to globally optimize any combination of length/area and flux of a given vector field. The concept of flux was recently introduced into computer vision by Vasilevskiy and Siddiqi (2002) but it was mainly studied within variational framework so far. We are first to show that flux can be integrated into graph cuts as well. Combining geometric concepts of flux and length/area within the global optimization framework of graph cuts allows principled discrete segmentation models and advances the slate of the art for the graph cuts methods in vision. In particular we address the "shrinking" problem of graph cuts, improve segmentation of long thin objects, and introduce useful shape constraints.
Multi-View AAM Fitting and Camera Calibration
Authors: Seth Koterba, Simon Baker, Iain A. Matthews, Changbo Hu, Jing Xiao 0006, Jeffrey F. Cohn, Takeo Kanade
Venue: ICCV
Year: 2005

In this paper, we study the relationship between multi-view active appearance model (AAM) fitting and camera calibration. In the first part of the paper we propose an algorithm to calibrate the relative orientation of a set of N > 1 cameras by fitting an AAM to sets of N images. In essence, we use the human face as a (non-rigid) calibration grid. Our algorithm calibrates a set of 2 /spl times/ 3 weak-perspective camera projection matrices, protections of the world coordinate system origin into the images, depths of the world coordinate system origin, and focal lengths. We demonstrate that the performance of this algorithm is comparable to a standard algorithm using a calibration grid. In the second part of the paper, we show how calibrating the cameras improves tile performance of multi-view AAM fitting.
Learning Layered Motion Segmentation of Video
Authors: M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
Venue: ICCV
Year: 2005

We present an unsupervised approach for learning a generative layered representation of a scene from a video for motion segmentation. The learnt model is a composition of layers, which consist of one or more segments. Included in the model are the effects of image projection, lighting, and motion blur. The two main contributions of our method are: (i) a novel algorithm for obtaining the initial estimate of the model using efficient loopy belief propagation; and (ii) using /spl alpha//spl beta/-swap and /spl alpha/-expansion algorithms, which guarantee a strong local minima, for refining the initial estimate. Results are presented on several classes of objects with different types of camera motion. We compare our method with the state of the art and demonstrate significant improvements.
A Theory of Refractive and Specular 3D Shape by Light-Path Triangulation
Authors: Kiriakos N. Kutulakos, Eron Steger
Venue: ICCV
Year: 2005

We investigate the feasibility of reconstructing an arbitrarily-shaped specular scene (refractive or mirror-like) from one or more viewpoints. By reducing shape recovery to the problem of reconstructing individual 3D light paths that cross the image plane, we obtain three key results. First, we show how to compute the depth map of a specular scene from a single viewpoint, when the scene redirects incoming light just once. Second, for scenes where incoming light undergoes two refractions or reflections, we show that three viewpoints are sufficient to enable reconstruction in the general case. Third, we show that it is impossible to reconstruct individual light paths when light is redirected more than twice. Our analysis assumes that, for every point on the image plane, we know at least one 3D point on its light path. This leads to reconstruction algorithms that rely on an "environment matting" procedure to establish pixel-to-point correspondences along a light path. Preliminary results for a variety of scenes (mirror, glass, etc) are also presented.
A Maximum Entropy Framework for Part-Based Texture and Object Recognition
Authors: Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Venue: ICCV
Year: 2005

This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of scale- or affine- invariant keypoints. Object classes are represented using a dictionary of composite semi-local parts, or groups of neighboring keypoints with stable and distinctive appearance and geometric layout. A discriminative maximum entropy framework is used to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set. Experiments on two texture and two object databases demonstrate the effectiveness of this framework for visual classification.
Adaptive Enhancement of Cardiac Magnetic Resonance (CMR) Images
Authors: Erich T. H. Leung, John K. Tsotsos
Venue: ICCV
Year: 2005

This paper presents a wavelet-based framework for enhancing the coherent structures attributable to the target organ in cardiac magnetic resonance (MR) images. Previous approaches focus on the Rician nature of noise in magnitude MR images. Image noise is but only one of the confounding factors that obscure the anatomical structures of the target organ. This paper models the image noise in a magnitude MR image in terms of two noise classes which occur over different ranges of signal intensity. An adaptive enhancement scheme is developed to achieve simultaneous attenuation of the effects of these factors and improvement in image contrast
Kernel-Based Multifactor Analysis for Image Synthesis and Recognition
Authors: Yang Li, Yangzhou Du, Xueyin Lin
Venue: ICCV
Year: 2005

In many vision problems, the appearances of the observed images, e.g. the human facial images, are often influenced by multiple underlying factors. In this paper, a kernel-based factorization framework is proposed to analyze a multifactor dataset. Specifically, we perform N-mode singular value decomposition (N-mode SVD) in a higher dimensional feature space instead of the input space by using kernel approaches. Given an input sample, its specific underlying factors which may be all absent in the training set can be extracted and translated from one sample to another by using kernel-based 'translation'. Therefore our framework is suitable for tasks of new image synthesis and underlying factor recognition. We demonstrate the capabilities of our framework on ensembles of facial images subjected to different person identities, viewpoints and illuminations with high-quality synthetic faces and high face recognition accuracy.
Multi-Scale Gesture Recognition from Time-Varying Contours
Authors: Hong Li, Michael A. Greenspan
Venue: ICCV
Year: 2005

A novel method is introduced to recognize and estimate the scale of time-varying human gestures. It exploits the changes in contours along spatiotemporal directions. Each contour is first parameterized as a 2D function of radius vs. cumulative contour length, and a 3D surface is composed from a sequence of such functions. In a two-phase recognition process, dynamic time warping is employed to rule out significantly different gesture models, and then mutual information (MI) is applied for matching the remaining models. The system has been tested on 8 gestures performed by 5 subjects with varied time scales. The two-phase process is compared against exhaustively testing three similarity measures based upon MI, correlation, and nonparametric kernel density estimation. Experimental results demonstrate that the exhaustive application of MI is the most robust with a recognition rate of 90.6%, however, the two-phase approach is much more computationally efficient with a comparable recognition rate of 90.0%.
Shape Parameter Optimization for AdaBoosted Active Shape Model
Authors: Yuanzhong Li, Wataru Ito
Venue: ICCV
Year: 2005

Active shape model (ASM) has been shown to be a powerful tool to aid the interpretation of images, especially in face alignment. ASM local appearance model parameter estimation is based on the assumption that residuals between model fit and data have a Gaussian distribution. Moreover, to generate an allowable face shape, ASM truncates coefficients of shape principal components into the bounds determined by eigenvalues. In this paper, an algorithm of modeling local appearances, called AdaBoosted ASM, and a shape parameter optimization method are proposed. In the algorithm of modeling the local appearances, we describe our novel modeling method by using AdaBoosted histogram classifiers, in which the assumption of the Gaussian distribution is not necessary. In the shape parameter optimization, we describe that there is an inadequacy on controlling shape parameters in ASM, and our novel method on how to solve it. Experimental results demonstrate that the AdaBoosted histogram classifiers improve robustness of landmark displacement greatly, and the shape parameter optimization solves the inadequacy problem of ASM on shape constraint effectively.
Appearance Modeling Under Geometric Context
Authors: Jian Li 0022, Shaohua Kevin Zhou, Rama Chellappa
Venue: ICCV
Year: 2005

We propose a unified framework based on a general definition of geometric transform (GeT) for modeling appearance. GeT represents the appearance by applying designed functionals over certain geometric sets. We show that image warping, Radon transform, trace transform, etc. are special cases of our definition. Moreover, three different types of GeTs are designed to handle deformation, articulation and occlusion and applied to fingerprinting the appearance inside a contour. They include the contour-driven GeT, the feature curve based GeT and selecting functionals to model the appearance inside the convex hull of the contour. A multi-resolution representation that combines both shape and appearance information is also proposed. We apply our approach to image synthesis and object recognition. The proposed approach produces promising results when applied to fingerprinting the appearance of human and body parts despite the challenges due to articulated motion and deformations.