OpenRaiser/Intern-Atlas huggingface dataset:
When is it Possible to Identify 3D Objects from Single Images Using Class Constraints?
Authors: Ronen Basri, Yael Moses
Venue: ICCV
Year: 1998

One approach to recognizing objects seen from arbitrary viewpoint is by extracting invariant properties of the objects from single images. Such properties are found in images of 3D objects only when the objects are constrained to belong to certain classes (e.g., bilaterally symmetric objects). Existing studies that follow this approach propose how to compute invariant representations for a handful of classes of objects. A fundamental question regarding the invariance approach is whether it call be applied to a wide range of classes. To answer this question it is essential to study the set of classes for which invariance exists. This paper introduces a new method for determining the existence of invariance for classes of objects together with the set of images from which these invariance can be computed. We develop algebraic tests that, given a class of objects undergoing affine projection, determine whether the objects in the class can be identified from single images. In addition, these tests allow us to determine the sell of views of the objects which are degenerate. We apply these tests to several classes of objects and determine which of them is identifiable and which of their views are degenerate.
Visual Homing: Surfing on the Epipoles
Authors: Ronen Basri, Ehud Rivlin, Ilan Shimshoni
Venue: ICCV
Year: 1998

We introduce a novel method for visual homing. Using this method a robot can be sent to desired positions and orientations in 3-D space specified by single images taken from these positions. Our method determines the path of the robot on-line. The starting position of the robot is not constrained, and a 3-D model of the environment is not required. The method is based on recovering the epipolar geometry relating the current image taken by the robot and the target image. Using the epipolar geometry, most of the parameters which specify the differences in position and orientation of the camera between the two images are recovered. However, since not all of the parameters can be recovered from two images, we have developed specific methods to bypass these missing parameters and resolve the ambiguities that exist. We present two homing algorithms for two standard projection models, weak and full perspective. We have performed simulations and real experiments which demonstrate the robustness of the method and that the algorithms always converge to the target pose.
Iterative Multi-Step Explicit Camera Calibration
Authors: Jorge Batista 0001, Helder Araújo, Aníbal T. de Almeida
Venue: ICCV
Year: 1998

Perspective camera calibration has been in the last decades a research subject for a large group of researchers and as a result several camera calibration methodologies can be found in the literature. However only a small number of those methods base their approaches on the use of monoplane calibration points. This paper describes one of those methodologies that uses monoplane calibration points to realize an explicit 3D camera calibration. To avoid the singularity obtained with the calibration equations when monoplane calibration points are used, this method computes the calibration parameters in a multi-step procedure and requires a first-guess solution for the intrinsic parameters. These parameters are updated and their accuracy increased through an iterative procedure. A stability analysis as a function of the pose of the camera is presented. Camera pose view strategies for accurate camera orientation computation can be extracted from the pose view stability analysis.
Using Algebraic Functions of Views for Indexing-Based Object Recognition
Authors: George Bebis, Michael Georgiopoulos, Mubarak Shah, Niels da Vitoria Lobo
Venue: ICCV
Year: 1998

Current indexing-based approaches build the hash table using either a large number of reference views or 3D models. In this paper, we propose building the hash table using algebraic functions of views. During preprocessing, we consider groups of model points and we represent all the views (i.e., images) that they can produce in a hash table. These views are computed using algebraic functions of a small number of reference views which contain the group. Fundamental to this procedure is a methodology based on singular value decomposition and interval arithmetic for estimating the ranges of values that the parameters of algebraic functions can assume. During recognition, scene groups are used to retrieve from the hash table the model groups that might have produced them. Using algebraic functions of views for indexing-based recognition offers a number of advantages. First of all, the hash table can be built easier without requiring 3D models or a large number of reference views. Second, recognition does not rely on the similarities between new and reference views. Third, verification becomes simpler. Finally, the approach is more general and extendible.
A Chromaticity Space for Specularity-, Illumination Color- and Illumination Pose-Invariant 3-D Object Recognition
Authors: Daniel Berwick, Sang Wook Lee
Venue: ICCV
Year: 1998

Most of the recent color recognition/indexing approaches concentrate on establishing invariance to illumination color to improve the utility of color recognition. However, other effects caused by illumination pose and specularity on three-dimensional object surfaces have not received notable attention. We present a chromaticity recognition method that discounts the effects of illumination pose, illumination color and specularity. It utilizes a chromaticity space based on log-ratio of sensor responses for illumination pose and color invariance. A model-based specularity detection/rejection algorithm can be used to improve the chromaticity recognition and illumination estimation for objects including specular reflections.
Information-Conserving Object Recognition
Authors: Margrit Betke, Nicholas C. Makris
Venue: ICCV
Year: 1998

Following the theory of statistical estimation, the problem of recognizing objects imaged in complex real-world scenes is examined from a parametric perspective. A scalar measure of an object's complexity, which is invariant under affine transformation and changes in image noise level, is extracted from the object's Fisher information. The volume of Fisher information is shown to provide an overall statistical measure of the object's recognizability in a particular image, while the complexity provides an intrinsically physical measure that characterizes the object in any image. An information-conserving method is then developed for recognizing an object imaged in a complex scene. Here the term information-conserving means that the method uses all the measured data pertinent to the object's recognizability, attains the theoretical lower bound on estimation error for any unbiased estimate, and therefore is statistically optimal. This method is then successfully applied to finding objects imaged in thousands of complex real-world scenes.
Self-Calibration from Image Derivatives
Authors: Tomás Brodský, Cornelia Fermüller, Yiannis Aloimonos
Venue: ICCV
Year: 1998

This study investigates the problem of estimating the calibration parameters from image motion fields induced by a rigidly moving camera with unknown calibration parameters, where the image formation is modeled with a linear pinhole-camera model. The equations obtained show the flow to be clearly separated into a component due to the translation and the calibration parameters and a component due to the rotation and the calibration parameters. A set of parameters encoding the latter component are linearly related to the flow, and from these parameters the calibration can be determined. However, as for discrete motion, in the general case it is not possible, to decouple image measurements from two frames only into their translational and rotational component. Geometrically, the ambiguity takes the form of a part of the rotational component being parallel to the translational component, and thus the scene can be reconstructed only up to a projective transformation. In general, for a full calibration at least four successive image frames are necessary with the 3D-rotation changing between the measurements. The geometric analysis gives rise to a direct self-calibration method that avoids computation of optical flow or point correspondences and uses only normal flow measurements. In this technique the direction of translation is estimated employing in a novel way smoothness constraints. Then the calibration parameters are estimated from the rotational components of several flow fields using Levenberg-Marquardt parameter estimation, iterative in the calibration parameters only. The technique proposed does not require calibration objects in the scene or special camera motions and it also avoids the computation of exact correspondence. This makes it suitable for the calibration of active vision systems which have to acquire knowledge about their intrinsic parameters while they perform other tasks, or as a tool for analyzing image sequences in large video databases.
Model Selection and Surface Merging in Reconstruction Algorithms
Authors: Kishore Bubna, Charles V. Stewart
Venue: ICCV
Year: 1998

The problem of model selection is relevant to many areas of computer vision. Model selection criteria have been used in the vision literature and many more have been proposed in statistics, but the relative strengths of these criteria have not been analyzed in vision. More importantly, suitable extensions to these criteria must be made to solve problems unique to computer vision. Using the problem of surface reconstruction as our context, we analyze existing criteria using simulations and sensor data, introduce new criteria from statistics, develop novel criteria capable of handling unknown error distributions and outliers, and extend model selection criteria to apply to the surface merging problem. The new surface merging rules improve upon previous results, and work well even at small step heights (h=3/spl sigma/) and crease discontinuities. Our results show that a Bayesian criteria and its bootstrapped variant perform the best, although for time-sensitive applications, a variant of the Akaike criterion may be a better choice. Unfortunately, none of the criteria work reliably for small region sizes, implying that model selection and surface merging should be avoided unless the region size is sufficiently large.
Color Recognition in Outdoor Images
Authors: Shashi D. Buluswar, Bruce A. Draper
Venue: ICCV
Year: 1998

The color associated with an object in machine vision images is not constant; under varying illuminating and viewing conditions (such as in outdoor images), the perceived color of an object can vary significantly, thus making color-based recognition difficult. Existing methods in color-based recognition have been applied mostly to indoor and/or constrained imagery, but not to realistic outdoor data. This work analyzes the variation of object color in outdoor images with respect to existing models of daylight illumination and surface reflectance. Two approaches for color recognition are then proposed: the first develops context-based models of daylight illumination and hybrid surface reflectance, and predicts the color of objects based on scene context. The second method shows that object color can be nonparametrically "learned" through classification methods such as Neural Networks and Multivariate Decision Trees. The methods have been successfully tested in domains such as road/highway scenes, off-road navigation and military target detection.
Automatic Tracking of Human Motion in Indoor Scenes Across Multiple Synchronized Video Streams
Authors: Quin Cai, Jake K. Aggarwal
Venue: ICCV
Year: 1998

This paper presents a comprehensive framework for tracking moving humans in an indoor environment from sequences of synchronized monocular grayscale images captured from multiple fixed cameras. The proposed framework consists of three main modules: Single View Tracking (SVT), Multiple View Transition Tracking (MVTT), and Automatic Camera Switching (ACS). Bayesian classification schemes based on motion analysis of human features are used to track (spatially and temporally) a subject image of interest between consecutive frames. The automatic camera switching module predicts the position of the subject along a spatial-temporal domain, and then, selects the camera which provides the best view and requires the least switching to continue tracking. Limited degrees of occlusion are tolerated within the system. Tracking is based upon the images of upper human, bodies captured from various viewing angles, and non-human moving objects are excluded using Principal Component Analysis (PCA). Experimental results are presented to evaluate the performance of the tracking system.
3D Shape Reconstruction Using Volume Intersection Techniques
Authors: Jonathan C. Carr, W. Richard Fright, Andrew H. Gee, Richard W. Prager, Kevin J. Dalton
Venue: ICCV
Year: 1998

Volume intersection algorithms are used to reconstruct incomplete objects from their silhouettes. An imagined light source is moved about the data and the cumulative amount of "light" seen at each point an space is interpreted as indicating the likelihood that the point is inside the object. The object data need not be uniformly distributed nor exclusively surface data. Explicit distinction between noise, surface and interior data is avoided. The novel concept of a localised viewing region is introduced to overcome the inherent inability of volume intersection algorithms to reconstruct concave surfaces. Algorithms for 2D pixel and 3D voxel data are described and applied to 3D ultrasound data.
3D Point Distribution Models of the Cortical Sulci
Authors: Angela Caunce, Christopher J. Taylor 0001
Venue: ICCV
Year: 1998

In this paper we present the first steps in the development of a statistical shape model, specifically a point distribution model (PDM), of the cortical surface of the brain. This will ultimately be used to locate, label, and describe the cortex, for visualisation, diagnosis, and quantification. In order to produce the model it was necessary to find and label the sulcal fissures on a series of MR images. Due to the complexity of the surface, an automated method was developed to facilitate development of a full surface model. Automating the marking process introduced the problem of identifying correspondences between examples, the knowledge of which is essential to the development of a PDM. Various methods were investigated to solve this problem including simple point matching and more complex curve matching. Each is outlined and discussed. The models obtained so far provide interesting insights into the shape and cortical pattern variations over a group of normal subjects.
A Fast and Robust Approach for Registration of Partially Overlapping Range Images
Authors: Chu-Song Chen, Yi-Ping Hung, Jen-Bo Cheng
Venue: ICCV
Year: 1998

A popular approach for 3D registration of partially-overlapping range images is the ICP (iterative closest point) method and many of its variations. The major drawback of this type of iterative approaches is that they require a good initial estimate to guarantee that the correct solution can always be found. In this paper, we propose a new method, the RANSAC-based DARCES (data-aligned rigidity-constrained exhaustive search) method, which can solve the partially-overlapping 3D registration problem efficiently and reliably without any initial estimation. Another important characteristic of our method is that it requires no local features in the 3D data set. An extra characteristic is that, for the noiseless case, the basic algorithm of our DARCES method can guarantee that the solution it finds is the true one, due to its exhaustive-search nature. Even with the nature of exhaustive search, its time complexity can be shown to be relatively low. Experiments have demonstrated that our method is efficient and reliable for registering partially-overlapping range images.
Quadric Surface Reconstruction from Dual-Space Geometry
Authors: Geoffrey Cross, Andrew Zisserman
Venue: ICCV
Year: 1998

We describe the recovery of a quadric surface from its image in two or more perspective views. The recovered quadric is used in 3D modeling and image registration applications. There are three novel contributions. First, it is shown that a one parameter family of quadrics is recovered from outlines in two views. The ambiguity is reduced to twofold given a point correspondence. There is no ambiguity from outlines in three or more views. Second, it is shown that degenerate quadrics reduce the ambiguity of reconstruction. Third, it is shown that surfaces can be piecewise quadric approximated from piecewise conic approximations of their outlines. All these cases are illustrated by examples with real images. Implementation details are given and the quality of the results is assessed.
Recognition of Plane Projective Symmetry
Authors: Rupert W. Curwen, Charles V. Stewart, Joseph L. Mundy
Venue: ICCV
Year: 1998

A novel approach to grouping symmetrical planar curves under a projective transform is described. Symmetric curves are important as a generic model for object recognition where an object class is defined by the set of symmetries that any object in the class obeys. In this paper, a new algorithm is presented for grouping curves based on their correspondence under a plane projectivity. The correspondence between curves is established from an initial correspondence between two pairs of distinguished lines, such as lines tangent to inflection points. This initial correspondence leads to a reduced dimensional form for the projective mapping between the curves and a natural method for establishing correspondence between all points on the curves. A saliency measure is introduced which permits grouping results to be ordered in terms of the degree of symmetry supported by each curve pair. This saliency measure provides a basis for recognition in the case of approximate symmetry.
Error-Tolerant Visual Planning of Planar Grasp
Authors: Colin Davidson, Andrew Blake 0001
Venue: ICCV
Year: 1998

This paper describes an efficient method to calculate, from an image of an object, configurations of a two-fingered robot gripper that form a "cage" to contain that object. Closing the fingers on the object from these configurations is guaranteed to reach a given desired grasp. This builds on the visual grasping theory of A. Blake et al. (1993), which describes how to find optimal grasps. It extends the results of E. Rimon and A. Blake (1996) which show how to construct such cages, in two ways. First, a more efficient algorithm for computing the cage is described. Second, a further development deals with occlusion by solving the caging problem within a restricted image window. The new methods greatly reduce the complexity of the visual caging problem, making it feasible in a real time computer vision system.
Deformable Model-Based Shape and Motion Analysis from Images using Motion Residual Error
Authors: Douglas DeCarlo, Dimitris N. Metaxas
Venue: ICCV
Year: 1998

We present a novel method for the shape and motion estimation of a deformable model using error residuals from model-based motion analysis. The motion of the model is first estimated using a model-based least squares method. Using the residuals from the least squares solution, the non-rigid structure of the model can be better estimated by computing how changes in the shape of the model affect its motion parameterization. This method is implemented as a component in a deformable model-based framework that uses optical flow information and edges. This general model-based framework is applied to human face shape and motion estimation. We present experiments that demonstrate that this framework is a considerable improvement over a framework that uses only optical flow information and edges.
Initialization of Deformable Models from 3D Data
Authors: Herve Delingette
Venue: ICCV
Year: 1998

The robustness of shape recovery based on deformable models depends in general, on the relative difference of position and topology of the initial model with respect to the data: a close initialization with correct topology guarantees a proper recovery of the object. Furthermore, the closeness of the initial model greatly influences the time of computation needed for the recovery. In this paper, we propose a method for initializing deformable models from range data or volumetric images. The proposed method solves two distinct problems. First, we use the topological segmentation of volumetric images in order to recover the approximate topology of the object. Second, we use an efficient mesh sampling algorithm to control the number of vertices of the initial model. The method takes into account missing data and outliers.
Illumination-Invariant Color Object Recognition via Compressed Chromaticity Histograms of Color-Channel-Normalized Images
Authors: Mark S. Drew, Jie Wei, Ze-Nian Li
Venue: ICCV
Year: 1998

Several color object recognition methods that are based on image retrieval algorithms attempt to discount changes of illumination in order to increase performance when test image illumination conditions differ from those that obtained when the image database was created. Here we extend the seminal method of Swain and Ballard to discount changing illumination. The new method is based on the first stage of the simplest color indexing method, which uses angular invariants between color image and edge image channels. That method first normalizes image channels, and then effectively discards much of the remaining information. Here we adopt the color-normalization stage as an adequate color constancy step. Further, we replace 3D color histograms by 2D chromaticity histograms. Treating these as images, we implement the method in a compressed histogram-image domain using a combination of wavelet compression and Discrete Cosine Transform (DCT) to fully exploit the technique of low-pass filtering for efficiency. Results are very encouraging, with substantially better performance than other methods tested. The method is also fast, in that the indexing process is entirely carried out in the compressed domain and uses a feature vector of only 36 or 72 values.
Optimal Polyline Tracking for Artery Motion Compensation in Coronary Angiography
Authors: Marie-Pierre Dubuisson-Jolly, Cheng-Chung Liang, Alok Gupta
Venue: ICCV
Year: 1998

We propose a novel solution to the problem of motion compensation of coronary angiographs. As the heart is beating, it is difficult for the physician to observe closely a particular point (e.g. stenosis) on the artery tree. We propose, to rigidly compensate the sequence so that the area around the point of interest appears stable. This is a difficult problem because the arteries deform in a non-rigid manner and only their 2D X-ray projection is observed. Also, the lack of features around the selected point makes the matching subject to the aperture problem. The algorithm automatically extracts a section of the artery of interest, models it as a polyline, and tracks it. The problem is formulated as an energy minimization problem which is solved using a shortest path in a graph algorithm. The motion compensated sequence can be obtained by translating every pixel so that the point of interest remains stable. We have applied this algorithm to many examples in two sets of angiography data and have obtained excellent results.