OpenRaiser/Intern-Atlas huggingface dataset:
Applying the Information Bottleneck Principle to Unsupervised Clustering of Discrete and Continuous Image Representations
Authors: Shiri Gordon, Hayit Greenspan, Jacob Goldberger
Venue: ICCV
Year: 2003

We present a method for unsupervised clustering of image databases. The method is based on a recently introduced information-theoretic principle, the information bottleneck (IB) principle. Image archives are clustered such that the mutual information between the clusters and the image content is maximally preserved. The IB principle is applied to both discrete and continuous image representations, using discrete image histograms and probabilistic continuous image modeling based on mixture of Gaussian densities, respectively. Experimental results demonstrate the performance of the proposed method for image clustering on a large image database. Several clustering algorithms derived from the IB principle are explored and compared.
Recovery of Epipolar Geometry as a Manifold Fitting Problem
Authors: Liran Goshen, Ilan Shimshoni, P. Anandan 0001, Daniel Keren
Venue: ICCV
Year: 2003

The introduction of the joint image manifold allows to treat the problem of recovering camera motion and epipolar geometry as the problem of fitting a manifold to the data measured in a stereo pair. The manifold has a singularity and boundary, therefore care must be taken when fitting it. This paper reviews the notion of joint image manifold, and how previous motion recovery methods can be viewed in its context, and then offers a new fitting method, which improves upon previous results, especially when the extent of the data and/or the motion are small.
Surface Classification Using Conformal Structures
Authors: Xianfeng Gu, Shing-Tung Yau
Venue: ICCV
Year: 2003

3D surface classification is a fundamental problem in computer vision and computational geometry. Surfaces can be classified by different transformation groups. Traditional classification methods mainly use topological transformation groups and Euclidean transformation groups. We introduce a novel method to classify surfaces by conformal transformation groups. Conformal equivalent class is refiner than topological equivalent class and coarser than isometric equivalent class, making it suitable for practical classification purposes. For general surfaces, the gradient fields of conformal maps form a vector space, which has a natural structure invariant under conformal transformations. We present an algorithm to compute this conformal structure, which can be represented as matrices, and use it to classify surfaces. The result is intrinsic to the geometry, invariant to triangulation and insensitive to resolution. To the best of our knowledge, this is the first paper to classify surfaces with arbitrary topologies by global conformal invariants. The method introduced here can also be used for surface matching problems.
Natural Image Statistics for Natural Image Segmentation
Authors: Matthias Heiler, Christoph Schnörr
Venue: ICCV
Year: 2003

Building on recent progress in modeling filter response statistics of natural images we integrate a statistical model into a variational framework for image segmentation. Incorporated in a sound probabilistic distance measure the model drives level sets toward meaningful segmentations of complex textures and natural scenes. Since each region comprises two model parameters only the approach is computationally efficient and enables the application of variational segmentation to a considerably larger class of real-world images. We validate the statistical basis of our approach on thousands of natural images and demonstrate that our model outperforms recent variational segmentation methods based on second-order statistics.
Real Time Pattern Matching Using Projection Kernels
Authors: Yacov Hel-Or, Hagit Hel-Or
Venue: ICCV
Year: 2003

A novel approach to pattern matching is presented, which reduces time complexity by two orders of magnitude compared to traditional approaches. The suggested approach uses an efficient projection scheme which bounds the distance between a pattern and an image window using very few operations. The projection framework is combined with a rejection scheme which allows rapid rejection of image windows that are distant from the pattern. Experiments show that the approach is effective even under very noisy conditions. The approach described here can also be used in classification schemes where the projection values serve as input features that are informative and fast to extract.
Bayesian Clustering of Optical Flow Fields
Authors: Jesse Hoey, James J. Little
Venue: ICCV
Year: 2003

We present a method for unsupervised learning of classes of motions in video. We project optical flow fields to a complete, orthogonal, a-priori set of basis functions in a probabilistic fashion, which improves the estimation of the projections by incorporating uncertainties in the flows. We then cluster the projections using a mixture of feature-weighted Gaussians over optical flow fields. The resulting model extracts a concise probabilistic description of the major classes of optical flow present. The method is demonstrated on a video of a person's facial expressions.
Large-Scale Event Detection Using Semi-Hidden Markov Models
Authors: Somboon Hongeng, Ramakant Nevatia
Venue: ICCV
Year: 2003

We present a new approach to recognizing events in videos. We first detect and track moving objects in the scene. Based on the shape and motion properties of these objects, we infer probabilities of primitive events frame-by-frame by using Bayesian networks. Composite events, consisting of multiple primitive events, over extended periods of time are analyzed by using a hidden, semi-Markov finite state model. This results in more reliable event segmentation compared to the use of standard HMMs in noisy video sequences at the cost of some increase in computational complexity. We describe our approach to reducing this complexity. We demonstrate the effectiveness of our algorithm using both real-world and perturbed data.
Circular Motion Geometry by Minimal 2 Points in 4 Images
Authors: Guang Jiang, Long Quan, Hung-Tat Tsui
Venue: ICCV
Year: 2003

We describe a new and simple method of recovering the geometry of uncalibrated circular motion or single axis motion using a minimal data set of 2 points in 4 images. This problem has been solved using nonminimal data either by computing the fundamental matrix and trifocal tensor in 3 images, or by fitting conics to tracked points in 5 images. Our new method first computes a planar homography from a minimum of 2 points in 4 images. It is shown that two eigenvectors of this homography are the images of the circular points. Then, other fixed image entities and rotation angles can be straightforwardly computed. The crux of the method lies in relating this planar homography from two different points to a homology naturally induced by corresponding points on different conic loci from a circular motion. The experiments on real image sequences demonstrate the simplicity, accuracy and robustness of the new method.
High resolution terrain mapping using low altitude aerial stereo imagery
Authors: Il-Kyun Jung, Simon Lacroix
Venue: ICCV
Year: 2003

This paper presents an approach to build high resolution digital elevation maps from a sequence of unregistered low altitude stereovision image pairs. The approach first uses a visual motion estimation algorithm that determines the 3D motions of the cameras between consecutive acquisitions, on the basis of visually detected and matched environment features. An extended Kalman filter then estimates both the 6 position parameters and the 3D positions of the memorized features as images are acquired. Details are given on the filter implementation and on the estimation of the uncertainties on the feature observations and motion estimations. Experimental results show that the precision of the method enables to build spatially consistent very large maps.
Integrated Edge and Junction Detection with the Boundary Tensor
Authors: Ullrich Köthe
Venue: ICCV
Year: 2003

The boundaries of image regions necessarily consist of edges (in particular, step and roof edges), corners, and junctions. Currently, different algorithms are used to detect each boundary type separately, but the integration of the results into a single boundary representation is difficult. Therefore, a method for the simultaneous detection of all boundary types is needed. We propose to combine responses of suitable polar separable filters into what we will call the boundary tensor. The trace of this tensor is a measure of boundary strength, while the small eigenvalue and its difference to the large one represent corner/junction and edge strengths respectively. We prove that the edge strength measure behaves like a rotationally invariant quadrature filter. A number of examples demonstrate the properties of the new method and illustrate its application to image segmentation.
Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification
Authors: Sanjiv Kumar, Martial Hebert
Venue: ICCV
Year: 2003

In this work we present discriminative random fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the labels as well as the observed data. The discriminative random fields offer several advantages over the conventional Markov random field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used in the MRF framework. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where likelihood parameters are usually learned separately from the field parameters. We illustrate the advantages of the DRFs over the MRF framework in an application of man-made structure detection in natural images taken from the Corel database.
The Local Projective Shape of Smooth Surfaces and their Outlines
Authors: Svetlana Lazebnik, Jean Ponce
Venue: ICCV
Year: 2003

We examine projectively invariant local properties of smooth curves and surfaces. Oriented projective differential geometry is proposed as a theoretical framework for establishing such invariants and describing the local shape of surfaces and their outlines. This framework is applied to two problems: a projective proof of Koenderink's famous characterization of convexities, concavities, and inflections of apparent contours; and the determination of the relative orientation of rim tangents at frontier points.
Learning Pedestrian Models for Silhouette Refinement
Authors: L. Lee, Gerald Dalley, Kinh Tieu
Venue: ICCV
Year: 2003

We present a model-based method for accurate extraction of pedestrian silhouettes from video sequences. Our approach is based on two assumptions, 1) there is a common appearance to all pedestrians, and 2) each individual looks like him/herself over a short amount of time. These assumptions allow us to learn pedestrian models that encompass both a pedestrian population appearance and the individual appearance variations. Using our models, we are able to produce pedestrian silhouettes that have fewer noise pixels and missing parts. We apply our silhouette extraction approach to the NIST gait data set and show that under the gait recognition task, our model-based silhouettes result in much higher recognition rates than silhouettes directly extracted from background subtraction, or any nonmodel-based smoothing schemes.
A Model-Based Approach for Automated Feature Extraction in Fundus Images
Authors: Huiqi Li, Opas Chutatape
Venue: ICCV
Year: 2003

A new approach to automatically extract the main features in color fundus images is proposed. The optic disk is localized by principal component analysis (PCA) and its shape is detected by a modified active shape model (ASM). Exudates are extracted by the combined region growing and edge detection. A fundus coordinate system is further set up based on fovea localization to provide a better description of the features in fundus images. The success rates achieved are 99%, 94%, and 100% for disk localization, disk boundary detection, and fovea localization respectively. The sensitivity and specificity for exudate detection are 100% and 71%. The success of the proposed algorithms can be attributed to utilization of the model-based methods.
Meshfree Particle Method
Authors: Huafeng Liu 0003, Pengcheng Shi
Venue: ICCV
Year: 2003

Many of the computer vision algorithms have been posed in various forms of differential equations, derived from minimization of specific energy functionals, and the finite element representation and computation have become the de facto numerical strategies for solving these problems. However, for cases where domain mappings between numerical iterations or image frames involve large geometrical shape changes, such as deformable models for object segmentation and nonrigid motion tracking, these strategies may exhibit considerable loss of accuracy when the mesh elements become extremely skewed or compressed. We present a new computational paradigm, the meshfree particle method, where the object representation and the numerical calculation are purely based on the nodal points and do not require the meshing of the analysis domain. This meshfree strategy can naturally handle large deformation and domain discontinuity issues and achieve desired numerical accuracy through adaptive node and polynomial shape function refinement. We discuss in detail the element-free Galerkin method, including the shape function construction using the moving least square approximation and the Galerkin weak form formulation, and we demonstrate its applications to deformable model based segmentation and mechanically motivated left ventricular motion analysis.
Eye Design in the Plenoptic Space of Light Rays
Authors: Jan Neumann, Cornelia Fermüller, Yiannis Aloimonos
Venue: ICCV
Year: 2003

Natural eye designs are optimized with regard to the tasks the eye-carrying organism has to perform for survival. This optimization has been performed by the process of natural evolution over many millions of years. Every eye captures a subset of the space of light rays. The information contained in this subset and the accuracy to which the eye can extract the necessary information determines an upper limit on how well an organism can perform a given task. In this work we propose a new methodology for camera design. By interpreting eyes as sample patterns in light ray space we can phrase the problem of eye design in a signal processing framework. This allows us to develop mathematical criteria for optimal eye design, which in turn enables us to build the best eye for a given task without the trial and error phase of natural evolution. The principle is evaluated on the task of 3D ego-motion estimation.
Automatic Video Summarization by Graph Modeling
Authors: Chong-Wah Ngo, Yufei Ma 0006, HongJiang Zhang
Venue: ICCV
Year: 2003

We propose a unified approach for summarization based on the analysis of video structures and video highlights. Our approach emphasizes both the content balance and perceptual quality of a summary. Normalized cut algorithm is employed to globally and optimally partition a video into clusters. A motion attention model based on human perception is employed to compute the perceptual quality of shots and clusters. The clusters, together with the computed attention values, form a temporal graph similar to Markov chain that inherently describes the evolution and perceptual importance of video clusters. In our application, the flow of a temporal graph is utilized to group similar clusters into scenes, while the attention values are used as guidelines to select appropriate subshots in scenes for summarization.
Phenomenological Eigenfunctions for Image Irradiance
Authors: Peter Nillius, Jan-Olof Eklundh
Venue: ICCV
Year: 2003

We present a framework for calculating low-dimensional bases to represent image irradiance from surfaces with isotropic reflectance under arbitrary illumination. By representing the illumination and the bidirectional reflectance distribution function (BRDF) in frequency space, a model for the image irradiance is derived. This model is then reduced in dimensionality by analytically constructing the principal component basis for all images given the variations in both the illumination and the surface material. The principal component basis are constructed in such a way that all the symmetries (Helmholtz reciprocity and isotropy) of the BRDF are preserved in the basis functions. Using the framework we calculate a basis using a database of natural illumination and the CURET database containing BRDFs of real world surface materials.
Autocalibration of a Projector-Screen-Camera System: Theory and Algorithm for Screen-to-Camera Homography Estimation
Authors: Takayuki Okatani, Koichiro Deguchi
Venue: ICCV
Year: 2003

This paper deals with the autocalibration of a system that consists of a planar screen, multiple projectors, and a camera. In the system, either multiple projectors or a single moving projector projects patterns on a screen while a stationary camera placed in front of the screen takes images of the patterns. We treat the case in which the patterns that the projectors project toward space are assumed to be known (i.e., the projectors are calibrated), whereas poses of the projectors are unknown. Under these conditions, we consider the problem of estimating screen-to-camera homography from the images alone. This is intended for cases where there is no clue on the screen surface that enables direct estimation of the screen-to-camera homography. One application is a 6DOF input device; poses of a multibeam projector freely moving in space are computed from the images of beam spots on the screen. The primary contribution of the paper is theoretical results on the uniqueness of solutions and a noniterative algorithm for the problem. The effectiveness of the method is shown by experimental results on synthetic as well as on real images.
Variational Stereovision and 3D Scene Flow Estimation with Statistical Similarity Measures
Authors: Jean-Philippe Pons, Renaud Keriven, Olivier D. Faugeras, Gerardo Hermosillo
Venue: ICCV
Year: 2003

We present a common variational framework for dense depth recovery and dense three-dimensional motion field estimation from multiple video sequences, which is robust to camera spectral sensitivity differences and illumination changes. For this purpose, we first show that both problems reduce to a generic image matching problem after backprojecting the input images onto suitable surfaces. We then solve this matching problem in the case of statistical similarity criteria that can handle frequently occurring nonaffine image intensities dependencies. Our method leads to an efficient and elegant implementation based on fast recursive filters. We obtain good results on real images.
Unsupervised Image Translation
Authors: Rómer Rosales, Kannan Achan, Brendan J. Frey
Venue: ICCV
Year: 2003

An interesting and potentially useful vision/graphics task is to render an input image in an enhanced form or also in an unusual style; for example with increased sharpness or with some artistic qualities. In previous work [10, 5], researchers showed that by estimating the mapping from an input image to a registered (aligned) image of the same scene in a different style or resolution, the mapping could be used to render a new input image in that style or resolution. Frequently a registered pair is not available, but instead the user may have only a source image of an unrelated scene that contains the desired style. In this case, the task of inferring the output image is much more difficult since the algorithm must both infer correspondences between features in the input image and the source image, and infer the unknown mapping between the images. We describe a Bayesian technique for inferring the most likely output image. The prior on the output image P(X) is a patch-based Markov random field obtained from the source image. The likelihood of the input P(Y/spl bsol/X) is a Bayesian network that can represent different rendering styles. We describe a computationally efficient, probabilistic inference and learning algorithm for inferring the most likely output image and learning the rendering style. We also show that current techniques for image restoration or reconstruction proposed in the vision literature (e.g., image super-resolution or de-noising) and image-based nonphotorealistic rendering could be seen as special cases of our model. We demonstrate our technique using several tasks, including rendering a photograph in the artistic style of an unrelated scene, de-noising, and texture transfer.
A New Paradigm for Recognizing 3-D Object Shapes from Range Data
Authors: Salvador Ruiz-Correa, Linda G. Shapiro, Marina Meila
Venue: ICCV
Year: 2003

Most of the work on 3D object recognition from range data has used an alignment-verification approach in which a specific 3D object is matched to an exact instance of the same object in a scene. This approach has been successfully used in industrial machine vision, but it is not capable of dealing with the complexities of recognizing classes of similar objects. This paper undertakes this task by proposing and testing a component-based methodology encompassing three main ingredients: 1) a new way of learning and extracting shape-class components from surface shape information; 2) a new shape representation called a symbolic surface signature that summarizes the geometric relationships among components; and 3) an abstract representation of shape classes formed by a hierarchy of classifiers that learn object-class parts and their spatial relationships from examples.
Fast Intensity-based 2D-3D Image Registration of Clinical Data Using Light Fields
Authors: Daniel B. Russakoff, Torsten Rohlfing, Calvin R. Maurer Jr.
Venue: ICCV
Year: 2003

Registration of a preoperative CT (3D) image to one or more X-ray projection (2D) images, a special case of the pose estimation problem, has been attempted in a variety of ways with varying degrees of success. Recently, there has been a great deal of interest in intensity-based methods. One of the drawbacks to such methods is the need to create digitally reconstructed radiographs (DRRs) at each step of the optimization process. DRRs are typically generated by ray casting, an operation that requires O(n/sup 3/) time, where we assume that n is approximately the size (in voxels) of one side of the DRR as well as one side of the CT volume. We address this issue by extending light field rendering techniques from the computer graphics community to generate DRRs instead of conventional rendered images. Using light fields allows most of the computation to be performed in a preprocessing step; after this precomputation, very accurate DRRs can be generated in O(n/sup 2/) time. Another important issue for 2D-3D registration algorithms is validation. Previously reported 2D-3D registration algorithms were validated using synthetic data or phantoms but not clinical data. We present an intensity-based 2D-3D registration system that generates DRRs using light fields; we validate its performance using clinical data with a known gold standard transformation.