OpenRaiser/Intern-Atlas huggingface dataset:
Integrated Surface, Curve and Junction Inference from Sparse 3-D Data Sets
Authors: Chi-Keung Tang, Gérard G. Medioni
Venue: ICCV
Year: 1998

We are interested in descriptions of 3-D data sets, as obtained from stereo or a 3-D digitizer. We therefore consider as input a sparse set of points, possibly associated with orientation information. In this paper, we address the problem of inferring integrated high-level descriptions such as surfaces, curves, and junctions from a sparse point set. While the method described previously provides excellent results for smooth structures, it only detects discontinuities, but does not localize them. For precise localization, we propose a non-iterative cooperative algorithm in which surfaces, curves, and junctions work together: Initial estimates are computed based on previous results, where each point in the given sparse and possibly noisy point set is convolved with a predefined vector mask to produce dense saliency maps. These maps serve as input to our novel maximal surface and curve marching algorithms for initial surface and curve extraction. Refinement of initial estimates is achieved by hybrid voting using excitatory and inhibitory fields for inferring reliable and natural extension so that surface/curve and curve/junction discontinuities are preserved. Results on several synthetic as well as real data sets are presented.
Segmentating Cortical Gray Matter for Functional MRI Visualization
Authors: P. C. Teo, Guillermo Sapiro, Brian A. Wandell
Venue: ICCV
Year: 1998

We describe a system that is being used to segment gray matter and create connected cortical representations from MRI. The method exploits knowledge of the anatomy of the cortex and incorporates structural constraints into the segmentation. First, the white matter and CSF regions in the MR volume are segmented using some novel techniques of posterior anisotropic diffusion. Then, the user selects the cortical white matter component of interest, and its structure is verified by checking for cavities and handles. After this, a connected representation of the gray matter is created by a constrained growing-out from the white matter boundary. Because the connectivity is computed, the segmentation can be used as input to several methods of visualizing the spatial pattern of cortical activity within gray matter. In our case, the connected representation of gray matter is used to create a representation of the flattened cortex. Then, fMRI measurements are overlaid on the flattened representation, yielding a representation of the volumetric data within a single image.
Maintaining Multiple Motion Model Hypotheses Through Many Views to Recover Matching and Structure
Authors: Philip H. S. Torr, Andrew W. Fitzgibbon, Andrew Zisserman
Venue: ICCV
Year: 1998

In order to recover structure from images it is desirable to use many views to obtain the best possible estimates. However, whilst recovering projective structure and motion from such extended sequences problems arise that are not apparent from a general view-point/structure approach. Foremost amongst these are (a) maintaining image correspondences consistently through many images, and (b) identifying images, within the sequence, for which structure cannot be reliably recovery. Within this paper the use of multiple motion model hypotheses is explored as an aid to solve both of these problems.
A Cascaded Hough Transform as an Aid in Aerial Image Interpretation
Authors: Tinne Tuytelaars, Luc Van Gool, Marc Proesmans, Theo Moons
Venue: ICCV
Year: 1998

Cartography and other applications of remote sensing have led to an increased interest in the (semi-)automatic interpretation of structures in aerial images of urban and suburban areas. Although these areas are particularly challenging because of their complexity, the degree of regularity in such man-made structures also helps to tackle the problems. The paper presents the iterated application of the Hough transform as a means to exploit such regularities. It shows how such 'Cascaded Hough Transform' (or CHT for short) yields straight lines, vanishing points, and vanishing lines. It also illustrates how the latter assist in improving the precision of the former. The examples are based on real aerial photographs.
Snake Pedals: Geometric Models with Physics-Based Control
Authors: Baba C. Vemuri, Yanlin Guo
Venue: ICCV
Year: 1998

In this paper, we introduce a novel geometric shape modeling scheme which allows for representation, of global and local shape characteristics of an object. Geometric models are traditionally well suited for representing global shapes but not the local details. However, in this paper we propose a powerful geometric shape modeling scheme which allows for the representation of global shapes with local detail and permits model shaping as well as topological changes via physics-based control. The proposed modeling scheme consists of representing shapes by pedal curves and surfaces-pedal curves/surfaces are the loci of the foot of perpendiculars to the tangents of a fixed curve/surface from a fixed point called the pedal point. By varying the location of the pedal point, one can synthesize a large class of shapes which exhibit both local and global deformations. We introduce physics-based control for shaping these geometric models by letting the pedal point vary and use a dynamic spline to represent the position of this varying pedal point. The model dubbed as a "snake pedal" allows for interactive manipulation via forces applied to the snake. We demonstrate the applicability of this modeling scheme via examples of shape synthesis and shape estimation from real image data.
Finding the Epipole from Uncalibrated Optical Flow
Authors: Alessandro Verri, Emanuele Trucco
Venue: ICCV
Year: 1998

This paper presents a novel method for determining the location of the instantaneous epipole in a sequence of images acquired by an uncalibrated camera and containing a single, rigid motion (e.g., the camera moves in a static environment). The method uses the full perspective camera model and requires the estimation of the optical flow at a minimum of six image locations. The key observation is that the optical flow equations can be written in terms of the epipole in a strikingly simple form if the translational and rotational flow components are not separated as done usually. The epipole location can then be obtained as the minimum of a least-square residual function associated to the computed optical flow. We report and discuss initial experiments on both synthetic and real data and illustrate possible developments of this method towards the use of uncalibrated optical flow for 3-D motion and structure reconstruction.
ASL Recognition Based on a Coupling Between HMMs and 3D Motion Analysis
Authors: Christian Vogler, Dimitris N. Metaxas
Venue: ICCV
Year: 1998

We present a framework for recognizing isolated and continuous American Sign Language (ASL) sentences from three-dimensional data. The data are obtained by using physics-based three-dimensional tracking methods and then presented as input to Hidden Markov Models (HMMs) for recognition. To improve recognition performance, we model context-dependent HMMs and present a novel method of coupling three-dimensional computer vision methods and HMMs by temporally segmenting the data stream with vision methods. We then use the geometric properties of the segments to constrain the HMM framework for recognition. We show in experiments with a 53 sign vocabulary that three-dimensional features outperform two-dimensional features in recognition performance. Furthermore, we demonstrate that context-dependent modeling and the coupling of vision methods and HMMs improve the accuracy of continuous ASL recognition.
3D Shape and Motion Analysis from Image Blur and Smear: A Unified Approach
Authors: Yuan-Fang Wang, Ping Liang
Venue: ICCV
Year: 1998

This paper addresses 3D shape recovery and motion estimation using a realistic camera model with an aperture and a shutter. The spatial blur and temporal smear effects induced by the camera's finite aperture and shutter speed are used for inferring both the shape and motion of the imaged objects.
Intensity and Feature Based Stereo Matching by Disparity Parametrization
Authors: Guo-Qing Wei, Gerd Hirzinger
Venue: ICCV
Year: 1998

In this paper, we propose a new solution to the stereo correspondence problem by including features an intensity based matching. The features we use are intensity gradients in both the x and y directions of the left and the deformed right images. Although a uniform smoothness constraint is still used, it is nevertheless applied only to non-feature regions. To avoid local minima in function minimization, we propose to parameterize the disparity function by hierarchical Gaussians. A simple stochastic gradient method is used to estimate the Gaussian weights. Experiments with various real stereo images show robust performances.
Consensus Surfaces for Modeling 3D Objects from Multiple Range Images
Authors: Mark D. Wheeler, Yoichi Sato 0001, Katsushi Ikeuchi
Venue: ICCV
Year: 1998

In this paper, we present a robust method for creating a triangulated surface mesh from multiple range images. Our method merges a set of range images into a volumetric implicit-surface representation which is converted to a surface mesh using a variant of the marching-cubes algorithm. Unlike previous techniques based on implicit-surface representations, our method estimates the signed distance to the object surface by finding a consensus of locally coherent observations of the surface. We call this method the consensus-surface algorithm. This algorithm effectively eliminates many of the troublesome effects of noise and extraneous surface observations without sacrificing the accuracy of the resulting surface. We utilize octrees to represent volumetric implicit surfaces-effectively reducing the computation and memory requirements of the volumetric representation without sacrificing accuracy of the resulting surface. We present results which demonstrate that our consensus-surface algorithm can construct accurate geometric models from rather noisy input range data.
Optical Flow Estimation Using Wavelet Motion Model
Authors: Yu-Te Wu, Takeo Kanade, Jeffrey F. Cohn, Ching-Chung Li
Venue: ICCV
Year: 1998

A motion estimation algorithm using wavelet approximation as an optical flow model has been developed to estimate accurate dense optical flow from an image sequence. This wavelet motion model is particularly useful in estimating optical flows with large displacement. Traditional pyramid methods which use the coarse-to-fine image pyramid by image burring in estimating optical flow often produce incorrect results when the coarse-level estimates contain large errors that cannot be corrected at the subsequent finer levels. This happens when regions of low texture become flat or certain patterns result in spatial aliasing due to image blurring. Our method, in contrast, uses large-to-small full-resolution regions without blurring images, and simultaneously optimizes the coarser and finer parts of optical flow so that the large and small motion can be estimated correctly. We compare results obtained by using our method with those obtained by using one of the leading optical flow methods, the Szeliski pyramid spline-based method. The experiments include cases of small displacement (less than 4 pixels under 128/spl times/128 image size or equivalent displacement under other image sizes), and those of large displacement (10 pixels). While both methods produce comparable results when the displacements are small, our method outperforms pyramid spline-based method when the displacements are large.
Learned Temporal Models of Image Motion
Authors: Yaser Yacoob, Larry S. Davis
Venue: ICCV
Year: 1998

An approach for learning and estimating temporal-flow models from image sequences is proposed. The temporal-flow models are represented as a set of orthogonal temporal-flow bases that are learned using principal component analysis of instantaneous flow measurements. Spatial constraints on the temporal-flow are also developed for modeling the motion of regions in rigid and coordinated motion. The performance of these models is demonstrated on several long image sequences of rigid and articulated bodies in motion.
Schwarz Representation for Matching and Similarity Analysis
Authors: Qing Yang 0002, Songde Ma
Venue: ICCV
Year: 1998

This paper presents a novel multiscale representation of one-dimensional signal based on complex analysis. We show that a signal and its derivative at different scales can be represented by one analytic function defined on the unit disc in the complex plane, which is called the Schwarz representation of a signal. This representation is applied to the matching problem. Using the theory of analytic functions, we are able to define the inverse of a signal. The matching function between two signals can be defined as the composition of one signal's Schwarz representation and another signal's inverse. The matching function determined by this method has a group structure and is close-formed.
Automatic Generation of Robot Program Code: Learning from Perceptual Data
Authors: Mohammed Yeasin, Subhasis Chaudhuri
Venue: ICCV
Year: 1998

We propose a novel approach to program a robot by demonstrating the task multiple number of times in front of a vision system. Here we integrate human dexterity with sensory data using computer vision techniques in a single platform. A simultaneous feature detection and tracking framework is used to track various features (finger tips and the wrist joint). A Kalman filter does the tracking by predicting the tentative feature location and a HOS-based data clustering algorithm extracts the feature. Color information of the features are used for establishing correspondences. A fast, efficient and robust algorithm for the vision system thus developed process a binocular video sequence to obtain the trajectories and the orientation information of the end effector. The concept of a trajectory bundle is introduced to avoid singularities and to obtain an optimal path.
Utilization of Stereo Disparity and Optical Flow Information for Human Interaction
Authors: Ikushi Yoda, Katsuhiko Sakaue
Venue: ICCV
Year: 1998

To attain smooth human interaction we propose a system which simultaneously utilizes the stereo disparity and optical flow information of real-time stereo gray and multi resolution images to recognize objects and gestures. For real-time calculation of the disparity and optical flow information of a stereo image the system first creates pyramid images by utilizing a Gaussian filter. The system then determines the disparity and optical flow of a low density image and extracts regions in front of a certain depth. The three foremost regions are recognized by higher order local autocorrelation features and a linear discriminant analysis. With this process the system recognizes the face and hand signs of users which are displayed foremost, and roughly recognizes movements within the region in real-time. With this framework, the system can discriminate, the face of a user, can monitor the basic movements of the user, can smoothly learn a presented object by users and can communicate with users from hand signs learned in advance.
Visual Motion Estimation and Prediction: A Probabilistic Network Model for Temporal Coherence
Authors: Alan L. Yuille, Pierre-Yves Burgi, Norberto M. Grzywacz
Venue: ICCV
Year: 1998

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate motion flows in the image sequences. Our theory is expressed in terms of the Bayesian generalization of standard Kalman filtering which allows us to solve temporal grouping in conjunction with prediction and estimation. As demonstrated for tracking isolated contours the Bayesian formulation is superior to approaches which use data association as a first stage followed by conventional Kalman filtering. Our computer simulations demonstrate that our theory qualitatively accounts for several psychophysical experiments on motion occlusion and motion outliers.
Signfinder: Using Color to Detect, Localize and Identify Informational Signs
Authors: Alan L. Yuille, Daniel Snow, Mark Nitzberg
Venue: ICCV
Year: 1998

We describe an approach to detecting, locating and normalizing road signs. The approach will apply provided: (i) the signs have stereotypical boundary shapes (i.e. rectangular, or hexagonal-of course, we allow for these shapes to be distorted by projection to unknown viewpoint), (ii) the writing on the sign has one uniform color and the rest of the sign has a second uniform color (we allow for the color of the illuminant to be unknown). We show that the approach works even under significant illuminant color changes, viewpoint direction, shadowing, and occlusion. This work is part of a project intended to help people who are blind, or whose sight is impaired.
Understanding the Relationship Between the Optimization Criteria in Two-View Motion Analysis
Authors: Zhengyou Zhang
Venue: ICCV
Year: 1998

The three best known criteria in two-view motion analysis are based respectively, on the distances between points and their corresponding epipolar lines, on the gradient-weighted epipolar errors, and on the distances between points and the reprojections of their reconstructed paints. The last one has a better statistical interpretation, but is, however much slower than the first two. In this paper we show that the last two criteria are equivalent when the epipoles are at infinity, and differ from each other only a little even when the epipoles are in the image. The first two criteria are equivalent only when the epipoles are at infinity and when the observed object has the same scale in the two images. This suggests that the second criterion is sufficient in practice because of its computational efficiency. The result is valid for both calibrated and uncalibrated images.
Modeling Geometric Structure and Illumination Variation of a Scene from Real Images
Authors: Zhengyou Zhang
Venue: ICCV
Year: 1998

We present in this paper a system which automatically builds, from real images, a scene model containing both 3D geometric information of the scene structure and its photometric information under various illumination conditions. The geometric structure is recovered from images taken from distinct viewpoints. Structure-from-motion and correlation-based stereo techniques are used to match pixels between images of different viewpoints and to reconstruct the scene in 3D space. The photometric property is extracted from images taken under different illumination conditions (orientation, position and intensity of the light sources). This is achieved by computing a low-dimensional linear space of the spatio-illumination volume, and is represented by a set of basis images. The model that has been built can be used to create realistic renderings from different viewpoints and illumination conditions. Applications include object recognition, virtual reality and product advertisement.
Euclidean Structure from Uncalibrated Images Using Fuzzy Domain Knowledge: Application to Facial Images Synthesis
Authors: Zhengyou Zhang, Katsunori Isono, Shigeru Akamatsu
Venue: ICCV
Year: 1998

Use of uncalibrated images has found many applications such as image synthesis. However, it is not easy to specify the desired position of the new image in projective or affine space. This paper proposes to recover Euclidean structure from uncalibrated images using domain knowledge such as distances and angles. The knowledge we have is usually about an object category, but not very precise for the particular object being considered. The variation (fuzziness) is modeled as a Gaussian variable. Six types of common knowledge are formulated. Once we have an Euclidean description, the task to specify the desired position in Euclidean space becomes trivial. The proposed technique is then applied to synthesis of new facial images. A number of difficulties existing in image synthesis are identified and solved. For example, we propose to use edge points to deal with occlusion.
Acquiring 3D Object Models from Specular Motion Using Circular Lights Illumination
Authors: Jiang Yu Zheng, Akio Murata
Venue: ICCV
Year: 1998

This work recovers 3D graphics models of objects with specular surfaces. An object is rotated and continuous images of it are taken. Circular lights that generate cones of rays are used to illuminate the rotating object. When the lights are properly set each point on the object can be highlighted during the rotation. The shape for each rotational plane is measured independently using its corresponding epipolar plane image. A 3D graphics model is subsequently reconstructed by combining shapes at different rotation planes. Computing a shape is simple and requires only the motion of the highlight on each rotation plane. Results not obtained before are given in the 3D shape recovery experiments on real objects.
Object Tracking Using Deformable Templates
Authors: Yu Zhong 0001, Anil K. Jain 0001, Marie-Pierre Dubuisson-Jolly
Venue: ICCV
Year: 1998

We propose a novel method for object tracking using prototype-based deformable template models. To track an object in an image sequence, we use a criterion which combines two terms: the deviation of the object shape from its shape in the previous frame, and the fidelity of the detected shape to the input image. Shape and gradient information are used to track the object. We have also used the consistency between corresponding object regions throughout the sequence to help in trading the object of interest. Inter-frame motion is also used to track the boundary of moving objects. We have applied the algorithm to a number of image sequences from different sources. The inherent structure in the deformable template, together with region, motion, and image gradient cues, make the algorithm relatively insensitive to the adverse effects of weak image features and moderate partial occlusion.
GRADE: Gibbs Reaction and Diffusion Equation
Authors: Song Chun Zhu, David Mumford
Venue: ICCV
Year: 1998

Recently there have been increasing interests in using nonlinear PDEs for applications in computer vision and image processing. In this paper, we propose a general statistical framework for designing a new class of PDEs. For a given application, a Markov random field model p(I) is learned according to the minimax entropy principle so that p(I) should characterize the ensemble of images in our application. P(I) is a Gibbs distribution whose energy terms can be divided into two categories. Subsequently the partial differential equations given by gradient descent on the Gibbs potential are essentially reaction-diffusion equations, where the energy terms in one category produce anisotropic diffusion while the inverted energy terms in the second category produce reaction associated with pattern formation. We call this new class of PDEs the Gibbs Reaction And Diffusion Equations-GRADE and we demonstrate experiments where GRADE are used for texture pattern formation, denoising, image enhancement, and clutter removal.