OpenRaiser/Intern-Atlas huggingface dataset:
Illumination Distribution from Brightness in Shadows: Adaptive Estimation of Illumination Distribution with Unknown Reflectance Properties in Shadow Regions
Authors: Imari Sato, Yoichi Sato 0001, Katsushi Ikeuchi
Venue: ICCV
Year: 1999

An approach to extract watersheds and watercourses, as well as their corresponding valleys and hills, from images with subpixel precision is proposed. The critical points of the terrain are essential as the starting points for the construction of these separatrices. They are extracted efficiently with subpixel precision using an approach based on derivatives of Gaussian filters. The separatrices are extracted by integrating their defining differential equation. Finally, the hills and valleys are constructed by an efficient graph search algorithm. Examples show the quality of the results that can be achieved with the proposed approach.
Independent Motion Detection in 3D Scenes
Authors: Harpreet S. Sawhney, Yanlin Guo, Jane C. Asmuth, Rakesh Kumar 0001
Venue: ICCV
Year: 1999

Presents an algorithmic approach to the problem of detecting independently moving objects in 3D scenes that are viewed under camera motion. There are two fundamental constraints that can be exploited for the problem: (i) a two- (or multi-)view camera motion constraint (for instance, the epipolar/trilinear constraint), and (ii) a shape constancy constraint. Previous approaches to the problem either only used partial constraints or relied on dense correspondences or flow. We employ both of these fundamental constraints in an algorithm that does not demand a-priori availability of correspondences or flow. Our approach uses the plane-plus-parallax decomposition to enforce the two constraints. It is also demonstrated, for a class of scenes called sparse 3D scenes, in which genuine parallax and independent motions may be confounded, how the plane-plus-parallax decomposition allows progressive introduction and verification of the fundamental constraints. The results of applying the algorithm to some difficult sparse 3D scenes look promising.
Probabilistic Object Recognition and Localization
Authors: Bernt Schiele, Alex Pentland
Venue: ICCV
Year: 1999

Objects can be represented by regions of local structure as well as dependencies between these regions. The appearance of local structure can be characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters. This paper presents a technique in which the appearance of objects is represented by the joint statistics of local neighborhood operators. A probabilistic technique based on joint statistics is developed for the identification of multiple objects at arbitrary positions and orientations. Furthermore, by incorporating structural dependencies, a procedure for probabilistic localization of objects is obtained. The current recognition system runs at approximately 10 Hz on a Silicon 02. Experimental results are provided and an application using a head mounted camera is described.
Error Detection and DEM Fusion using Self-Consistency
Authors: Howard J. Schultz, Dong-Min Woo, Frank Stolle, Edward M. Riseman
Venue: ICCV
Year: 1999

The ability to efficiently and robustly recover accurate 3D terrain models from sets of stereoscopic images is important to many civilian and military applications. Our long-term goal is to develop an automatic, multi-image 3D reconstruction algorithm that can be applied to these domains. To develop an effective and practical terrain modeling system, methods must be found for detecting unreliable elevations in digital elevation maps (DEMs), and for fusing several DEMs from multiple sources into an accurate and reliable result. This paper focuses on two key factors for generating robust 3D terrain models, (1) the ability to detect unreliable elevations estimates, and (2) to fuse the reliable elevations into a single optimal terrain model. The techniques discussed in this paper are based on the concept of using self-consistency to identify potentially unreliable points. We apply the self-consistency methodology to both the two-image and multi-image scenarios. We demonstrate that the recently developed concept of self-consistency can be effectively employed to determine the reliability of values in a DEM. Estimates with a reliability below an error threshold can be excluded from further processing. We test the effectiveness of the methodology, as well as the relationship between error rate and scene geometry by processing both real and photo-realistic simulations.
Utilizing Scatter for Pixel Subspace Selection
Authors: Haim Schweitzer
Venue: ICCV
Year: 1999

Measures of scatter are used in statistical pattern recognition to identify and select important features, computed as linear combinations of the given features. Examples include principal components and linear discriminants. The classic computational procedures require eigenvector decomposition of large matrices, and in the case of images they are only practical for identifying a low dimensional feature subspace. We investigate the case in which the selected features are required to be a subset of the given features. It is shown that the same scatter measures used in the general case can also be used in this discrete selection case, but the computational procedure no longer involves matrix eigenvector decomposition. Instead, the selection of pixels that optimize scatter measures can be accomplished by a very simple and efficient discrete optimization technique that runs in linear time regardless of the subspace size. Applications to clustering and content based indexing are discussed.
About the Self-Calibration of a Rotating and Zooming Camera: Theory and Practice
Authors: Yongduek Seo, Ki-Sang Hong
Venue: ICCV
Year: 1999

This paper deals with the uniqueness of the self-calibration of a rotating and zooming camera theoretically. We assume that the principal point and the aspect ratio are fixed but the focal length changes as the camera moves. In this case, at least one inter-image homography is required in order to compute the internal calibration parameters as well as the rotation. We analyze the effects of the deviation of the principal point on the estimation of the focal length and the rotation. The more the camera changes its zoom, the larger the effects are, and the larger the rotation angle is, the smaller the effects are. Thus, we may take the image center as the principal point in practical applications. Experiments using real images are given.
Omnivergent Stereo
Authors: Heung-Yeung Shum, Adam Kalai, Steven M. Seitz
Venue: ICCV
Year: 1999

The notion of a virtual sensor for optimal 3D reconstruction is introduced. Instead of planar perspective images that collect many rays at a fixed viewpoint, omnivergent cameras collect a small number of rays at many different viewpoints. The resulting 2D manifold of rays are arranged into two multiple-perspective images for stereo reconstruction. We call such images omnivergent images, and the process of reconstructing the scene from such images, omnivergent stereo. This procedure is shown to produce 3D scene models with minimal reconstruction error due to the fact that for any point in the 3D scene, two rays with maximum vergence angle can be found in the omnivergent images. Furthermore, omnivergent images are shown to have horizontal epipolar lines, enabling the application of traditional stereo matching algorithms, without modification. Three types of omnivergent virtual sensors are presented: spherical omnivergent cameras, center-strip cameras and dual-strip cameras.
Stereo Reconstruction from Multiperspective Panoramas
Authors: Heung-Yeung Shum, Richard Szeliski
Venue: ICCV
Year: 1999

The paper presents a new approach to computing depth maps from a large collection of images where the camera motion has been constrained to planar concentric circles. We resample the resulting collection of regular perspective images into a set of multiperspective panoramas, and then compute depth maps directly from these resampled images. Only a small number of multiperspective panoramas is needed to obtain a dense and accurate 3D reconstruction, since our panoramas sample uniformly in three dimensions: rotation angle, inverse radial distance, and vertical elevation. Using multiperspective panoramas avoids the limited overlap between the original input images that causes problems in conventional multi-baseline stereo. Our approach differs from stereo matching of panoramic images taken from different locations, where the epipolar constraints are sine curves. For our multiperspective panoramas, the epipolar geometry to first order consists of horizontal lines. Therefore, any traditional stereo algorithm can be applied to multiperspective panoramas without modification. Experimental results show that our approach generates good depth maps that can be used for image based rendering tasks such as view interpolation and extrapolation.
The Hamilton-Jacobi Skeleton
Authors: Kaleem Siddiqi, Sylvain Bouix, Allen R. Tannenbaum, Steven W. Zucker
Venue: ICCV
Year: 1999

The eikonal equation and variants of it are of significant interest for problems in computer vision and image processing. It is the basis for continuous versions of mathematical morphology, stereo, shape-from-shading and for recent dynamic theories of shape. Its numerical simulation can be delicate, owing to the formation of singularities in the evolving front, and is typically based or, level set methods. However there are more classical approaches rooted in Hamiltonian physics, which have received little consideration in computer vision. In this paper we first introduce a new algorithm for simulating the eikonal equation, which offers a number of computational and conceptual advantages over the earlier methods when it comes to shock tracking. Next, we introduce a very efficient algorithm for shock detection, where the key idea is to measure the net outward flux of a vector field per unit volume, and to detect locations where a conservation of energy principle is violated. We illustrate the approach with several numerical examples including skeletons of complex 2D and 3D shapes.
Learning and Evaluating Visual Features for Pose Estimation
Authors: Robert Sim, Gregory Dudek
Venue: ICCV
Year: 1999

We present a method for learning a set of visual landmarks which are useful for pose estimation. The landmark learning mechanism is designed to be applicable to a wide range of environments, and generalized for different approaches to computing a pose estimate. Initially, each landmark is detected as a focal extremum of a measure of distinctiveness and represented by a principal components encoding which is exploited for matching. Attributes of the observed landmarks can be parameterized using a generic parameterization method and then evaluated in terms of their utility for pose estimation. We present experimental evidence that demonstrates the utility of the method.
Subpixel-Precise Extraction of Watersheds
Authors: Carsten Steger
Venue: ICCV
Year: 1999

An approach to extract watersheds and watercourses, as well as their corresponding valleys and hills, from images with subpixel precision is proposed. The critical points of the terrain are essential as the starting points for the construction of these separatrices. They are extracted efficiently with subpixel precision using an approach based on derivatives of Gaussian filters. The separatrices are extracted by integrating their defining differential equation. Finally, the hills and valleys are constructed by an efficient graph search algorithm. Examples show the quality of the results that can be achieved with the proposed approach.
Robust Estimation of Curvature Information from Noisy 3D Data for Shape Description
Authors: Chi-Keung Tang, Gérard G. Medioni
Venue: ICCV
Year: 1999

We describe an effective and novel approach to infer sign and direction of principal curvatures at each input site from noisy 3D data. Unlike most previous approaches, no local surface fitting, partial derivative computation of any kind, nor oriented normal vector recovery is performed in our method. These approaches are noise-sensitive since accurate, local, partial derivative information is often required, which is usually unavailable from real data because of the unavoidable outlier noise inherent in many measurement phases. Also, we can handle points with zero Gaussian curvature uniformly (i.e., without the need to localize and handle them first as a separate process). Our approach is based on Tensor Voting, a unified, salient structure inference process. Both the sign and the direction of principal curvatures are inferred directly from the input. Each input is first transformed into a synthetic tensor A novel and robust approach based on tensor voting is proposed for curvature information estimation. With faithfully inferred curvature information, each input ellipsoid is aligned with curvature-based dense tensor kernels to produce a dense tensor field. Surfaces and crease curves are extracted from this dense field, by using an extremal feature extraction process. The computation is non-iterative, does not require initialization, and robust to considerable amounts of outlier noise as its effect is reduced by collecting a large number of tensor votes. qualitative and quantitative results on synthetic as well as real and complex data are presented.
Epipolar Geometry Estimation by Tensor Voting in 8D
Authors: Chi-Keung Tang, Gérard G. Medioni, Mi-Suen Lee
Venue: ICCV
Year: 1999

We present a novel, efficient, initialization free approach to the problem of epipolar geometry estimation, by formulating it as one of hyperplane inference from a sparse and noisy point set in an 8D space. Given a set of noisy point correspondences in two images as obtained from two views of a static scene without correspondences, even in the presence of moving objects, our method pulls out inlier matches while rejecting outliers. Unlike most methods which optimize certain objective function, our approach does not involve initialization or any search in the parameter space, and therefore is free of the problem of local optima or poor convergence. Since no search is involved, it is unnecessary to impose simplifying assumption (such as affine camera or local planar homography) to the scene being analyzed for reducing the search complexity. Subject to the general epipolar constraint only, we detect wrong matches by establishing salient "extremalities" via a naval approach, 8D Tensor Voting: the input set of matches is first transformed into a sparse and discrete 8D point set. Dense tensor kernels are then applied to vote for the most salient hyperplane (normal and intercept) that captures all inliers inherent in the input. With this filtered set of matches, the normalized Eight-point Algorithm suffices for the accurate estimation of the fundamental matrix. By using efficient data structure and locality, our method is both time and space efficient despite the higher dimensionality. We demonstrate the general usefulness of our method using example image pairs (i) for aerial image analysis, (ii) with widely different views, and (iii) from non-static 3D scenes (e.g. basketball game in an indoor stadium). Each example contains a considerable amount of wrong matches.
Direction Diffusion
Authors: Bei Tang, Guillermo Sapiro, Vicent Caselles
Venue: ICCV
Year: 1999

In a number of disciplines, directional data provides a fundamental source of information. A novel framework for isotropic and anisotropic diffusion of directions is presented in this paper. The framework can be applied both to regularize directional data and to obtain multiscale representations of it. The basic idea is to apply and extend results from the theory of harmonic maps in liquid crystals. This theory deals with the regularization of vectorial data, while satisfying the unit norm constraint of directional data. We show the corresponding variational and partial differential equations formulations for isotropic diffusion, obtained from an L/sub 2/ norm, and edge preserving diffusion, obtained from an L/sub 1/ norm. In contrast with previous approaches, the framework is valid for directions in any dimensions, supports non-smooth data, and gives both isotropic and anisotropic formulations. We present a number of theoretical results, open questions, and examples for gradient vectors, optical flow, and color images.
Symmetry Maps of Free-Form Curve Segments Via Wave Propagation
Authors: Hüseyin Tek, Benjamin B. Kimia
Venue: ICCV
Year: 1999

This paper presents an approach for computing the symmetries (skeletons) of an edge map consisting of a collection of curve segments. This approach is a combination of analytic computations in the style of computational geometry and discrete propagations on a grid in the style of the numerical solutions of PDE's. Specifically, waves from each of the initial curve segments are initialized and propagated as a discrete wavefront along discrete directions. In addition, to avoid error built up due to the discrete nature of propagation, shockwaves are detected and explicitly propagated along a secondary dynamic grid. The propagation of shockwaves, integrated with the propagation of the wavefront along discrete directions, leads to an exact simulation of propagation by the Eikonal equation. The resulting symmetries are simply the collection of shockwaves formed in this process which can be manipulated locally, exactly, and efficiently under local changes in an edge map (gap completion, removal of spurious elements, etc.). The ability to express grouping operations in the language of symmetry maps makes it an appropriate intermediate representation between low-level edge maps and high level object hypotheses.
An Integrated Bayesian Approach to Layer Extraction from Image Sequences
Authors: Philip H. S. Torr, Richard Szeliski, P. Anandan 0001
Venue: ICCV
Year: 1999

This paper describes a Bayesian approach for modeling 3D scenes as a collection of approximately planar layers that are arbitrarily positioned and oriented in the scene. In contrast to much of the previous work on layer based motion modeling, which compute layered descriptions of 2D image motion, our work leads to a 3D description of the scene. We focus on the key problem of automatically segmenting the scene into layers as a precursor to recovery of stereo disparity data. The prior assumptions about the scene are formulated within a Bayesian decision making framework, and are then used to automatically determine the number of layers and the assignment of individual pixels to layers. Although using a collection of 3D layers has been previously proposed as an efficient and effective representation for multimedia applications, results to date have relied on hand segmentation. In contrast, the work described aims at fully automatic segmentation.
Three-Dimensional Scene Flow
Authors: Sundar Vedula, Simon Baker, Peter Rander, Robert T. Collins, Takeo Kanade
Venue: ICCV
Year: 1999

Scene flow is the three-dimensional motion field of points in the world, just as optical flow is the two-dimensional motion field of points in an image. Any optical flow is simply the projection of the scene flow onto the image plane of a camera. We present a framework for the computation of dense, non-rigid scene flow from optical flow. Our approach leads to straightforward linear algorithms and a classification of the task into three major scenarios: complete instantaneous knowledge of the scene structure; knowledge only of correspondence information; and no knowledge of the scene structure. We also show that multiple estimates of the normal flow cannot be used to estimate dense scene flow directly without some form of smoothing or regularization.
Parallel Hidden Markov Models for American Sign Language Recognition
Authors: Christian Vogler, Dimitris N. Metaxas
Venue: ICCV
Year: 1999

The major challenge that faces American Sign Language (ASL) recognition now is to develop methods that will scale well with increasing vocabulary size. Unlike in spoken languages, phonemes can occur simultaneously in ASL. The number of possible combinations of phonemes after enforcing linguistic constraints is approximately 5.5/spl times/10/sup 8/. Gesture recognition, which is less constrained than ASL recognition, suffers from the same problem. Thus, it is not feasible to train conventional hidden Markov models (HMMs) for large-scab ASL applications. Factorial HMMs and coupled HMMs are two extensions to HMMs that explicitly attempt to model several processes occuring in parallel. Unfortunately, they still require consideration of the combinations at training time. In this paper we present a novel approach to ASL recognition that aspires to being a solution to the scalability problems. It is based on parallel HMMs (PaHMMs), which model the parallel processes independently. Thus, they can also be trained independently, and do not require consideration of the different combinations at training time. We develop the recognition algorithm for PaHMMs and show that it runs in time polynomial in the number of states, and in time linear in the number of parallel processes. We run several experiments with a 22 sign vocabulary and demonstrate that PaHMMs can improve the robustness of HMM-based recognition even on a small scale. Thus, PaHMMs are a very promising general recognition scheme with applications in both gesture and ASL recognition.
3D Surface Topography from Intensity Images
Authors: Philip L. Worthington, Edwin R. Hancock
Venue: ICCV
Year: 1999

The paper demonstrates how a new shape from shading scheme can be used to extract topographic information from 2D intensity imagery. The shape-from-shading scheme has two novel ingredients. Firstly, it uses a geometric update procedure which allows the image irradiance equation to be satisfied as a hard constraint. This not only improves the data closeness of the recovered needle-map, but also removes the necessity for extensive parameter tuning. Secondly, we use curvature information to impose topographic constraints on the recovered needle-map. The topographic information is captured using the shape index of J.J. Koenderink and A.J. Van Doorn (1995) and consistency is imposed using a robust error function. We show that the new shape-from-shading scheme leads to a meaningful topographic labelling of 3D surface structures.
Equivalence of Julesz and Gibbs Texture Ensembles
Authors: Ying Nian Wu, Song Chun Zhu, Xiuwen Liu
Venue: ICCV
Year: 1999

Research on texture has been pursued along two different lines. The first line of research, pioneered by Julesz (1962), seeks the essential ingredients in terms of features and statistics in human texture perception. This leads us to a mathematical definition of texture as a Julesz ensemble. A Julesz ensemble is the maximum set of images that share the same value of some basic feature statistics as the image lattice /spl Lambda//spl rarr/Z/sup 2/, or equivalently it is a uniform distribution on this set. The second line of research studies statistical models, in particular, Markov random field (MRF) and FRAME models (Zhu et al., 1997), to characterize texture patterns locally. In this article, we bridge the two lines by the fundamental principle of equivalence of ensembles in statistical mechanics (Gibbs, 1902). We prove that 1) the conditional probability of an arbitrary image patch given its environment, under the Julesz ensemble or the uniform model, is inevitably a FRAME (MRF) model, and 2) the limit of the FRAME (MRF) model, which we called the Gibbs ensemble, is equivalent to a Julesz ensemble as /spl Lambda//spl rarr/Z/sup 2/. Thus the advantages of the two methodologies can be fully utilized.