OpenRaiser/Intern-Atlas huggingface dataset:
A Task Driven 3d Object Recognition System using Bayesian Networks
Authors: Björn Krebs, Bernd Korn, M. Burkhardt
Venue: ICCV
Year: 1998

In this paper we propose a general framework to build a task oriented 3D object recognition system for CAD based vision (CBV). Features from 3D space curves representing the object's rims provide sufficient information to allow identification and pose estimation of industrial CAD models. However, features relying on differential surface properties tend to be very vulnerable with respect to noise. To model the statistical behavior of the data we introduce Bayesian nets which model the relationship between objects and observable features. Furthermore, task oriented selection of the optimal action to reduce the uncertainty of recognition results is incorporated into the Bayesian nets. This enables the integration of intelligent recognition strategies depending on the already acquired evidence into a robust, and efficient, 3D CAD based recognition system.
Morphological Corner Detection
Authors: Robert Laganière
Venue: ICCV
Year: 1998

This paper presents a new operator for corner detection. This operator uses a variant of the morphological closing operator, which we have called asymmetrical closing. It consists of the successive application of different morphological transformations using different structuring elements. Each of these structuring elements used to probe the image under study is tuned to affect corners of different orientation and brightness. We found that this kind of approach, based on brightness comparisons, leads to better quality results than others and is achieved at a lower computational cost.
Estimation with Bilinear Constraints in Computer Vision
Authors: Yoram Leedan, Peter Meer
Venue: ICCV
Year: 1998

A complete analysis of the statistical issues related to the estimation of a bilinear form, one of the fundamental problems in computer vision, is presented. It is shown why already at moderate noise levels most available techniques fail to provide a satisfactory solution. A new estimation procedure is proposed in which the nonlinear nature of the errors is taken into account and the implementation is based on the generalized singular value decomposition for superior numerical behavior. As an example, the ellipse fitting problem is discussed, and the performance of the new algorithm is compared with the current state-of-the-art.
PIMs and Invariant Parts for Shape Recognition
Authors: Zhibin Lei, Tolga Tasdizen, David B. Cooper
Venue: ICCV
Year: 1998

We present completely new very powerful solutions to two fundamental problems central to computer vision. Given data sets representing C objects to be stored in a database, and given a new data set for an object, determine the object in the database that is most like the object measured. We solve this problem through use of PIMs ("Polynomial Interpolated Measures"), which is a new representation integrating implicit polynomial curves and surfaces, explicit polynomials, and discrete data sets which may be sparse. The method provides high accuracy at low computational cost. 2. Given noisy 2D data along a curve (or 3D data along a surface), decompose the data into patches such that new data taken along affine transformations or Euclidean transformations of the curve (or surface) can be decomposed into corresponding patches. Then recognition of complex or partially occluded objects can be done in terms of invariantly determined patches. We briefly outline a low computational cost image-database indexing-system based on this representation for objects having complex shape-geometry.
Representation and Self-Similarity of Shapes
Authors: Tyng-Luh Liu, Davi Geiger, Robert Kohn
Venue: ICCV
Year: 1998

Representing shapes is a significant problem for vision systems that must recognize or classify objects. We derive a representation for a given shape by investigating its self-similarities, and constructing its shape axis (SA) and shape axis tree (SA-tree). We start with a shape, its boundary contour, and two different parameterizations for the contour. To measure its self-similarity we consider matching pairs of points (and their tangents) along the boundary contour, i.e., matching the two parameterizations. The matching, of self-similarity criteria may vary, e.g., co-circularity, parallelism, distance, region homogeneity. The loci of middle points of the pairing contour points are the shape axis and they can be grouped into a unique tree graph, the SA-tree. The shape axis for the co-circularity criteria is compared to the symmetry axis. An interpretation in terms of object parts is also presented.
2D Affine Transformations are Unlikely to Account for Human 3D Object Recognition
Authors: Zili Liu, Daniel J. Kersten
Venue: ICCV
Year: 1998

Converging evidence has shown that human object recognition depends on observers' familiarity with objects' appearance. The more similar the objects are, the stronger this dependence will be, and the more important two-dimensional (2D) image information will be. The degree to which 3D structural information is used, however, remains an area of strong debate. Previously, we showed that all models that allow rotations in the image plane of independent 2D templates could not account for human performance in discriminating novel object views. We now present results from models of generalized radial basis functions (GRBF), 2D nearest neighbor matching that allows 2D affine transformations, and a Bayesian statistical estimator that integrates over all possible 2D affine transformations. The performance of the human observers relative to each of the models is better for the novel views than for the template views, suggesting that humans generalize better to novel views from template views. The Bayesian estimator yields the optimal performance with 2D affine transformations and independent 2D templates. Therefore, no models of 2D affine operations with independent 2D templates account for the human performance.
Independent 3D Motion Detection Using Residual Parallax Normal Flow Fields
Authors: Manolis I. A. Lourakis, Antonis A. Argyros, Stelios C. Orphanoudakis
Venue: ICCV
Year: 1998

This paper considers a specific problem of visual perception of motion, tamely the problem of visual detection of independent 3D motion. Most of the existing techniques for solving this problem rely on restrictive assumptions about the environment, the observer's motion, or both. Moreover, they are based on the computation of a dense optical flow field, which amounts to solving the ill-posed correspondence problem. In this work independent motion detection is formulated as a problem of robust parameter estimation applied to the visual input acquired by a rigidly moving observer. The proposed method automatically selects a planar surface in the scene and the residual planar parallax normal flow field with respect to the motion of this surface is computed at two successive time! instants. The two resulting normal flow fields are then combined in a linear model. The parameters of this model are related to the parameters of self-motion (ego-motion) and their robust estimation leads to a segmentation of the scene based on 3D motion. The method avoids a complete solution to the correspondence problem by selectively matching subsets of image points and by employing normal flow fields. Experimental results demonstrate the effectiveness of the proposed method in detecting independent motion in scenes with large depth variations and unrestricted observer motion.
A Probabilistic Contour Discriminant for Object Localisation
Authors: John MacCormick, Andrew Blake 0001
Venue: ICCV
Year: 1998

A method of localising objects in images is proposed. Possible configurations are evaluated using the contour discriminant, a likelihood ratio which is derived from a probabilistic model of the feature detection process. We treat each step in this process probabilistically, including the occurrence of clutter features, and derive the observation densities for both correct "target" configurations and incorrect "clutter" configurations. The contour discriminant distinguishes target objects from the background even in heavy clutter, making only the most general assumptions about the form that clutter might take. The method generates samples stochastically to avoid the cost of processing an entire image, and promises to be particularly suited to the task of initialising contour trackers based on sampling methods.
Geotensity: Combining Motion and Lighting for 3D Surface Reconstruction
Authors: Atsuto Maki, Mutsumi Watanabe, Charles Wiles
Venue: ICCV
Year: 1998

This paper is about automatically reconstructing the full 3D surface of an object observed in motion by a single static camera. We introduce the geotensity constraint that governs the relationship between four images of a moving object under fairly general lighting conditions. We show that it is possible in theory to solve for 3D surface structure for both the case of a single point light source and a pair of point, light sources and propose that a solution exists for an arbitrary number point light sources. The surface may or may not be textured. We then give an example of automatic surface reconstruction of a face under a point light source. The geotensity constraint provides the theoretical foundation for the full automatic 3D reconstruction of Lambertian objects using a single fixed camera, and arbitrary unknown object motion under arbitrary lighting conditions.
A Real-Time Algorithm for Medical Shape Recovery
Authors: Ravi Malladi, James A. Sethian
Venue: ICCV
Year: 1998

In this paper, we present a shape recovery technique in 2D and 3D with specific applications in visualizing and measuring anatomical shapes from medical images. This algorithm models extremely corrugated structures like the brain, is topologically adaptable, is robust, and runs in O(N log N) time where N is the total number of points in the domain. Our two-stage technique is based on the level set shape recovery scheme and the fast marching method for computing solutions to static Hamilton-Jacobi equations.
Shape Recovery Using Dynamic Subdivision Surfaces
Authors: Chhandomay Mandal, Baba C. Vemuri, Hong Qin 0001
Venue: ICCV
Year: 1998

A new dynamic subdivision surface model is proposed for shape recovery from 3D data sets. The model inherits the attractive properties of the Catmull-Clark subdivision scheme and is set in a physics-based modeling paradigm. Unlike other existing methods, our model does not require a parameterized input mesh to recover shapes of arbitrary topology, allows direct manipulation of the limit surface via application of forces and provides a fast, robust, and hierarchical approach to recover complex shapes from 3D data with very few degrees of freedom (control vertices). We provide an analytic formulation and introduce the physical quantities required to develop the dynamic subdivision surface model which can be deformed by applying forces synthesized from the data. Our experiments demonstrate that this new dynamic model has a promising future in shape recovery from volume and range data sets.
Stereo Depth Estimation: A Confidence Interval Approach
Authors: Robert Mandelbaum, Gerda Kamberova, Max Mintz
Venue: ICCV
Year: 1998

We describe an estimation technique which, given a measurement of the depth of a target from a wide-field-of-view (WFOV) stereo camera pair, produces a minimax risk fixed-size confidence interval estimate for the target depth. This work constitutes the first application to the computer vision domain of optimal fixed-size confidence-interval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with a target on an optical bench. The method is compared to several other procedures including the Kalman Filter. The minimax approach is found to dominate all the other methods in performance. In particular for the minimax approach, a very close agreement is achieved between theoretical capture probability and empirical capture frequency. This allows performance to be accurately predicted, greatly facilitating the system design, and delineating the tasks that may be performed with a given system.
Ambiguity in Reconstruction from Images of Six Points
Authors: Stephen J. Maybank, Amnon Shashua
Venue: ICCV
Year: 1998

Let S be a set of six points in space, let /spl psi/ be any hyperboloid of one sheet containing S, and let I be a sequence of images of S taken by an uncalibrated camera moving over /spl psi/. Then reconstruction from I is subject to a three way ambiguity which is unbroken as long as the optical centre of the camera remains on /spl psi/. Let p be an image of S taken from a point on /spl psi/. The images 'near' p define a tangent space which splits into a direct sum W/sub p//spl oplus/N/sub p//spl oplus/F/sub p/, where W/sub p/ corresponds to images near p for which the ambiguity is maintained, N/sub p/ corresponds to images for which the ambiguity is broken and F/sub p/ corresponds to images which are physically impossible.
A Multigrid Approach for Hierarchical Motion Estimation
Authors: Étienne Mémin, Patrick Pérez
Venue: ICCV
Year: 1998

This paper focuses on the estimation of the apparent motion field between two consecutive frames in an image sequence. The approach developed here is a tradeoff between methods based on global parameterized flow models and local dense optic flow estimators. The method relies on an adaptive multigrid minimization approach. In addition to accelerated convergence toward good estimates, it allows to mix different parameterizations of the estimate relative to adaptive partitions of the image. The performances of the resulting algorithms are demonstrated in the difficult context of a non-convex energy. Experimental results on real world Meteosat sequences are presented.
Human Face Recognition: A Minimal Evidence Approach
Authors: Ali Reza Mirhosseini, Catherine Chen, Tuan D. Pham, Hong Yan 0001
Venue: ICCV
Year: 1998

A face recognition system is described which employs a fuzzy information fusion technique to increase the overall recognition rate. The face images are searched for locating head area and face boundary. The eyes, and mouth are detected using rigid and deformable templates. Assuming a 3D head model, the face rotations are estimated, which allows for compensating rotated facial features back to a front, upright view. Each facial feature forms a source of information for classification. Based on a correlation technique using eye-forehead, mouth and nose windows, three classifiers are established. The output of each classifier is taken as a partial evidence in classification. The importance of each source is measured using a fuzzy density measure and the final classification is achieved using a fuzzy evidence aggregation method. The performance of the system is evaluated using a combined match score.
A Unified Factorization Algorithm for Points, Line Segments and Planes with Uncertainty Models
Authors: Daniel D. Morris, Takeo Kanade
Venue: ICCV
Year: 1998

In this paper we present a unified factorization algorithm for recovering structure and motion from image sequences by using point features, line segments and planes. This new formulation is based on directional uncertainty model for features. Points and line segments are both described by the same probabilistic models and so can be recovered in the same way. Prior information on the coplanarity of features is shown to fit naturally into the new factorization formulation and provides additional constraints for the shape recovery. This formulation leads to a weighted least squares motion and shape recovery problem which is solved by an efficient quasi-linear algorithm. The statistical uncertainty model also enables us to recover uncertainty estimates for the reconstructed three dimensional feature locations.
Bias-Corrected Optical Flow Estimation for Road Vehicle Tracking
Authors: Hans-Hellmut Nagel, Michael Haag
Venue: ICCV
Year: 1998

Model-based vehicle tracking in traffic image sequences can be made more robust by matching expected displacement rates of vehicle surface points to optical flow (OF) vectors computed from an image sequence. The capability to track vehicles uninterruptedly in this manner over extended image sequences results in the ability to investigate even small errors in OF estimation. It turns out that the OF magnitudes are systematically underestimated. The-albeit small-bias can be corrected by analyzing the influence of explicitly modeled grey value noise on the precision of OF values estimated by means of the neighborhood sampling method.
A Cubist Approach to Object Recognition
Authors: Randal C. Nelson, Andrea Selinger
Venue: ICCV
Year: 1998

We describe an appearance-based object recognition system using a keyed, multi-level contest representation reminiscent of certain aspects of cubist art. Specifically, we utilize distinctive intermediate-level features in this case automatically extracted 2-D boundary fragments, as keys, which are then verified within a local contest, and assembled within a loose global contest to evoke an overall percept. This system demonstrates good recognition of a variety of 3-D shapes, ranging from sports cars and fighter planes to snakes and lizards with full orthographic invariance. We report the results of large-scale tests, involving over 2000 separate test images, that evaluate performance with increasing number of items in the database, in the presence of clutter, background change, and occlusion, and also the results of some generic classification experiments where the system is tested on objects never previously seen or modeled. To our knowledge, the results we report are the best in the literature for full-sphere tests of general shapes with occlusion and clutter resistance.
Stereo with Mirrors
Authors: Sameer A. Nene, Shree K. Nayar
Venue: ICCV
Year: 1998

In this paper, we propose the use of mirrors and a single camera for computational stereo. When compared to conventional stereo systems that use two cameras, our method has a number of significant advantages such as wide field of view, single viewpoint projection, identical camera parameters and ease of calibration. We propose four stereo systems that use a single camera pointed towards planar, ellipsoidal, hyperboloidal, and paraboloidal mirrors. In each case, we present a derivation of the epipolar constraints. Next, we attempt to understand what can be seen by each system, and formalize the notion of field of view. We conclude with two experiments to obtain 3-D structure. In the first we use a pair of planar mirrors, and in the second a pair of paraboloidal mirrors. The results of our experiments demonstrate the viability of stereo using mirrors.
Three Dimensional MR Brain Segmentation
Authors: Wiro J. Niessen, Koen L. Vincken, Joachim Weickert, Max A. Viergever
Venue: ICCV
Year: 1998

In MR brain images, segmentation using intensity values is severely limited owing to field inhomogeneities, susceptibility artifacts and partial volume effects. Edge based segmentation methods suffer from spurious edges and gaps in boundaries. A method is presented which combines the advantages of edge based and region based segmentation. First a multiscale image representation, is constructed which favors intratissue diffusion over inter-tissue diffusion by exploiting local contrast. Subsequently a multiscale linking model (the hyperstack) is used to group voxels into a number of segments. This facilitates segmentation of grey matter, white matter and cerebrospinal fluid with minimal user interaction. Using a supervised segmentation, technique and MR simulations of a brain phantom as validation it is shown that the errors are in the order of or smaller than reported in literature.
Learning Dynamical Models Using Expectation-Maximisation
Authors: Ben North, Andrew Blake 0001
Venue: ICCV
Year: 1998

Tracking with deformable contours in a filtering framework requires a dynamical model for prediction. For any given application, tracking is improved by having an accurate model, learned from training data. We develop a method for learning dynamical models from training sequences, explicitly taking account of the fact that training data are noisy measurements and not true states. By introducing an 'augmented-state smoothing filter' we show how the technique of Expectation-Maximisation can be applied to this problem, and show that the resulting algorithm produces more robust and accurate tracking.
A Cooperative Framework for Segmentation Using 2D Active Contours and 3D Hybrid Models as Applied to Branching Cylindrical Structures
Authors: Thomas O&apos, Donnell, Marie-Pierre Dubuisson-Jolly, Alok Gupta
Venue: ICCV
Year: 1998

Hybrid models are powerful tools for recovery in that they simultaneously provide a gross parametric as well as a detailed description of an object. However, it is difficult to directly employ hybrid models in the segmentation process since they are not guaranteed to locate the optimal boundaries in cross-sectional slices. Propagating 2D active contours from slice to slice, on the other hand, to delineate an object's boundaries is often effective, but may run into problems when the object's topology changes, such as at bifurcations or even in areas of high curvature. Here, we present a cooperative framework to exploit the positive aspects of both 3D hybrid model and 2D active contour approaches for segmentation and recovery. In this framework the user-defined parametric component of a 3D hybrid model provides constraints for a set of 2D segmentations performed by active contours. The same hybrid model is then fit both parametrically and locally to this segmentation. For the hybrid model fit we employ several new variations on the physically-motivated paradigm which seek to speed recovery while guaranteeing stability. A by-product of these variations is an increased generality of the method via the elimination, of some of its ad hoc parameters. We apply our cooperative framework to the recovery of branching cylindrical structures from 3D image volumes. The hybrid model we employ has a novel parametric component which is a fusion of individual cylinders. These cylinders have spines that are arbitrary space curves and cross-sections which may be any star shaped planar curve.
A Curvature-Based Approach to Contour Motion Estimation
Authors: Jong Seung Park, Joon Hee Han
Venue: ICCV
Year: 1998

We present a novel method of velocity field estimation for points on moving contours in an image sequence. The method determines the corresponding point in the next image frame by considering curvature changes at each point on a contour. In previous methods, there are errors in estimation for the points which have low curvature variations since those methods compute the solutions by approximating the normal component of optical flow. The proposed method computes optical flow vectors of contour points by minimizing the curvature changes. As a first step, snakes are used to locate smooth curves in 2D imagery. Then, the extracted curves are tracked continuously. We excluded the rearranging process in snakes and allowed the snaxel distance to vary. Each point on a contour has a unique corresponding point in the nest frame. Experimental results showed that the proposed method computes accurate optical flow vectors for various moving contours.