OpenRaiser/Intern-Atlas huggingface dataset:
Image Description Via The Multiresolution Intensity Axis Of Symmetry
Authors: John M. Gauch, Stephen M. Pizer
Venue: ICCV
Year: 1988

A fundamental approach for providing an image description in terms of visually sensible image regions is described. It involves a) the representation of the image by a that captures image information and then b) the definition of a hierarchy of components of that by the order of annihilation of those components as the image is continuously simplified by lowering the scale. The information-capturing essential structure is chosen so that image regions are associated with each component during the image simplification. To guarantee image simplification, successive Gaussian blurring is chosen as the means of scale lowering. We argue that an that describes shape in both the spatial and intensity dimensions will produce an image description most likely to be useful for computer or human specification of image objects. In particular, we suggest that the intensity axis of symmetry (IAS) satisfies all desirable criteria for an structure. With such shape-based structures the approach of image description via annihilation under image simplification becomes a very attractive paradigm.
Efficiently Computing And Representing Aspect Graphs Of Polyhedral Objects
Authors: Ziv Gigus, John F. Canny, Raimund Seidel
Venue: ICCV
Year: 1988

The aspect graph is one of the approaches to representing of 3-D shape for the purposes of object recognition. In this approach, the viewing space of an object is partitioned into regions, such that in each region the topology of the line drawing of the object does not change. The viewing data of an object is the partition of the viewing space together with a representative view in each region. We present an efficient algorithm for computing the viewing data for line drawings of polyhedral objects under orthographic projection. For an object of size <i>O</i>(<i>n</i>) whose partition of size <i>O</i>(<i>m</i>), the algorithm runs <i>O</i>(<i>n</i>^4 log <i>n</i> + <i>m</i> log <i>m</i>) time. Using a novel data structure, we construct the set of all views in optimal <i>O</i>(<i>m</i>) time and space
Error Of Fit Measures For Recovering Parametric Solids
Authors: Ari D. Gross, Terrance E. Boult
Venue: ICCV
Year: 1988

Parametric models of objects are becoming increasingly more impor- tant in computer vision. In the past few years, a number of researchers have investigated the recovery of a class of parametric models by the minimization of an error of fit measure. The measures used have typ- ically been chosen in an ad hoc fashion. This paper looks at how these measures affect the performance of a recovery system. This research can be divided into two parts. The first studies the biases of the po- tential error-of-fit measures with respect to the parameters recovered and examines the cross-sectional shape of their respective error of fit surfaces. This study is done in simulation by holding all but one pa- rameter constant. The second part of the research compares two of the better error of fit measures by using them in a recovery system. Both the number of iterations and the quality of the reconstruction are considered.
Towards Real-time Trinocular Stereo
Authors: Charles D. Hansen, Nicholas Ayache, Francis Lustman
Venue: ICCV
Year: 1988

We present recent advancements in our passive trinocular stereo system. These include a technique for calibrating and rectifying in a very eficient and simple manner the triplets of images taken for trinocular stereovision systerns. After the rectification of images, epipolar lines are parallel to the axes of the image coordinate frames. Therefore, potential matches between the three images satisfy simpler relations, allowing for a less complicated and more efficient matching algorithm. We also describe a more robust and general control strategy now employed in our trinocular stereo system. We have also developed an innovative method for the reconstruction of 3-D seginents which provides better results and a new validation technique based on the observation that neighbors in the image should be neighbors in space. Experiments are presented demonstrating these advancements. 2 Rectification of the images
Towards The Automatic Generation Of Recognition Strategies
Authors: Charles D. Hansen, Thomas C. Henderson
Venue: ICCV
Year: 1988

This paper describes a method for the automatic generation of recognition strategies. This is accomplished using a technique developed for quantifying the following properties of 3-D features which compose models used in 3-D computer vision: robustness, completeness, consistency, cost, and uniqueness. By utilizing this inforniation, the automatic synthesis of a specialized recognition scheme, called a Strategy Tree, is accomplished. Strategy Trees describe, in a systematic and robust manner, the search process used for recognition and localization of particular objects in the given scene. System constraints are satisfied which lead to a set of features which guide the recognition process. Each feature has a Corroborating Evidence Subtrees which validate the initial hypothesis. Verification techniques, used to substantiate or refute these hypotheses, are explored. Experiments are presented.
A Color Reflectance Model And Its Use For Segmentation
Authors: Glenn Healey
Venue: ICCV
Year: 1988

This paper presents a color reflectance model and demonstrates its usefulness for segn~entation. I adopt general physical models which describe the interaction of light with matter. These models apply to both metal and dielectric materials. The models indicate that, in general, reflectance is a complicated function of waveleiigth and geometry. An analysis of the general reflectance models, however, shows that approximate reAectance models exist which preserve much of the structure of the more detailed models. The approximate color reflectance model is the basis of an algorithm which is used during segmentation. This algorithm uses normalized color to classify surfaces according to milr terial composition. Experimen ta1,results are presented. electrics, ACRM is equivalent to the dichromatic reflection model suggested by Shafer [14]. In this paper, I use the Reichman body scattering model [13] to show that the dichromatic reflection model is a reasonable approximation for a large class of inhomogeneous dielectrics. I also show from the Torrance-Sparrow [17] specular reflection model and the Fresnel equations [2] that a unichromatic reflection model is a reasonable approximation for metals. Thus, ACRM combines the dichromatic reflection model for inhomogeneous dielectrics with a unichromatic reflection model for metals. The analysis includes an estimate of the accuracy of ACRM for various materials. An algorithm is derived from ACRM which is used to classify image regions based on the material of the corresponding object surfaces.
Recognize The Similarity Between Shapes Under Affine Transformation
Authors: Jiawei Hong, Xiaonan Tan
Venue: ICCV
Year: 1988

This paper presents new techniques for recog:nition of similarity between shapes under affine transformation. We introduce the canonical form of a shape and give a linear algorithm for transforming a shape to its canonical form. The recognition of similarity between shapes under affine transforination can he done by computing the difference between their canonical forms. Our approach reduces the similarity recognition1 under affine transformation from a search problem in six dimensional space into one dimensional search problem. Furtherm'ore, we apply these techniques to recognition of objects under similarity transformation and problem of point matching. We obtain a linear algorithm for object recognition under similarily transformation and a linear average time algorithm for point matching under affine transformation. Some experimental results are reported.
Applying Sensor Models To Automatic Generation Of Object Recognition Programs
Authors: Katsushi Ikeuchi, Takeo Kanade
Venue: ICCV
Year: 1988

One of the most important and systematic methods to build modelbased vision systems is that to generate object recognition programs automatically from given geometric models. Automatic generation of object recognition programs requires several key components to be developed: object models to describe the geometric and photometric properties of an object to be recognized, sensor models to predict object appearances from the object model under a given sensor, strategy generation using the pred,icted appearances to produce an recognition strategy, and program generation converting the recognition strategy into an executable code. This paper concentrates on sensor modeling and its relationship with strategy generation, because we regard it as the bottle neck to automatic generation of object recognition programs. We consider two aspects of sensor characteristics: sensor detectability and sensor reliability. Sensor detectability specifies what kinds of features can be detected and in what condition the features are detected; sensor reliability is a confidence for the detected features. We define the configuration space to represent sensor characteristics. We propose a representation method for sensor detectability and rcliability in the configuration space. Finally, we investigate how to use the proposed sensor modcl in automatic generation of an objcct recognition program.
Polynomial Methods For Structure From Motion
Authors: Charles Jerian, Ramesh C. Jain
Venue: ICCV
Year: 1988

There have been many attempts to solve the SFM problem, yet few practical solutions. Previous work has used either linearizations that ignore vital constraints or non-linear equations with multiple solutions, where the existence of multiple solutions has been ignored. First, we classify and analyze many existing methods and find that they do not work well in the presence of noise, or without a good initial estimate. Then, we propose new polynomial systems solutions for both orthographic and perspective projection that work better than existing methods in the presence of noise. Finally, we examine the effect of such factors as the number of frames and the axis and angle of rotation on the ability to recover structure. We found that additional frames are of no value and that large amounts of rotation resulting in disparate views are very helpful for accurate structure recovery.
Reconstruction Of Consistent Shape From Inconsistent Data: Optimization Of 21/2d Sketches
Authors: Ken-ichi Kanatani
Venue: ICCV
Year: 1988

Although the 3D orientations of edges and surfaces are theoretically sufficient for reconstructing the 3D object shape, this does not mean that the 3D object shape can actually be reconstructed: Inconsistency may result if image data contain errors. We propose a scheme of oprimization to construct a consistent object shape from inconsistent data. Our optimization is achieved by solving a set of linear equations. This technique is first applied to the problem of shape from motion and then to the 3D recovery based on the rectangularity hypothesis and the parallelism hypothesis. 1. Constraints on 2%D Sketches In the past, various 3D shape recovery techniques called shape from ... have been proposed (shape from motion, shape from shading, shape from texture, etc.). Now, we must ask the following question: Do these techniques really enable us to recover the 3D object shape? The shape from ... paradigms usually present us with object images equipped with the following types of 3D information: (i) The surface gradient (p , q), or equivalently the unit surface nor
Color Image Analysis With An Intrinsic Reflection Model
Authors: Gudrun Klinker, Steven A. Shafer, Takeo Kanade
Venue: ICCV
Year: 1988

In this paper, we present an approach to color image understanding that can be used to segment and analyze surfaces with color variations due to highlights and shading. We begin with a theory - the Dichromatic Reflection Model - that relates the reflected light from dielectric materials, such as plastic, to fundamental physical reflection processes, and describes the color of the reflected light as a linear combination of the color of the light due to surface reflection (highlights) and body reflection (object color). This dichromatic theory is used in an algorithm that separates a color image into two parts: an image of just the highlights, and the original image with the highlights removed. In the past, we have applied this method to handsegmented images. This paper shows how to perform automatic segmentation by applying this theory in stages to identify the object and highlight colors. The result is a combination of segmentation and reflection analysis that is better than traditional heuristic segmentation methods and provides important physical information about the surface geometry and material properties at the same time. This line of research can lead to physics-based image understanding methods that are both more reliable and more useful than traditional methods.
A constraint-based system for interpretation of aerial imagery
Authors: Darwin Kuan, Hormoz Shariat, Kalyan Dutta, Pat Ransil
Venue: ICCV
Year: 1988

This paper describes a constraint-basled image understanding system (COBIUS) for aerial imagery interpretation. COBIUS consists of knowledge bases for domain objects, spatial and temporal constraints, control strategies, blackboard areas to instantiate hypotheses of objects and constraints, and an image feature database to fuse results from multiple image segmentation modules. Unlike previous efforts on knowledge-based image understanding where domain-specific rules are encoded for image interpretation, in COBIUS, domain-specific knowledge is represented in terms of generic temporal and spatial constraints. A set of g,eneric constraint manipulation and hypothesis formation rules are used to perform formation, evaluation, propagation, and satisfaction of constraints and hypotheses. More importantly, constraints are represented hierarchically and are no different from the representation of domain objects. This representation avoids time-consuming graph matching processes for image interpretation and provides greater representation modularity.
Geometric Hashing: A General And Efficient Model-based Recognition Scheme
Authors: Yehezkel Lamdan, Haim J. Wolfson
Venue: ICCV
Year: 1988

A general method for model-based object recognition in occluded scenes is presented. It is based on geometric hashing. The method stands out for its efficiency. We describe the general framework of the method and illustrate its applications for various recogni- tion problems both in 3-D and 2-D. Special attention is given to the recognition of 3-D objects in occluded scenes from 2-D gray scale images. New experimental results are included for this important case.
Structure And Motion From Two Perspective Views Via Planar Patch
Authors: Chia-Hoang Lee
Venue: ICCV
Year: 1988

We present a method which uses both a planar patch and points for an analysis of time-varying imagery. From our theory, two pieces of information are required for the unique recovery of structure from motion. One is the image motion of a plane and the other one is the image motion of points not on the plane. For the former, one needs four points while for the latter one need two points. Our technique has the following characteristics: (i) it is efficient, (ii) it appears to be very robust over a wide range of simulations, (iii) it sheds some new light on some previous work, and (iv) it has geometric meaning for each step of the derivation of the solution.
Coping With Discontinuities In Computer Vision: Their Detection, Classification, And Measurement
Authors: David Lee 0001
Venue: ICCV
Year: 1988

The general principles of detection, classification, and measurement of discontinuities are studied. The following issues are discussed: detecting the location of discontinuities; classifying discontinuities by their degrees; measuring the size of discontinuities; and coping with the random noise and designing optimal discontinuity detectors. An algorithm is proposed for discontinuity detection from an input signal S. For degree k discontinuity detection and measurement, a detector (P, Phi ) is used, where P is the pattern and Phi is the corresponding filter. If there is a degree k discontinuity at location t/sub 0/, then in the filter response there is a scaled pattern alpha P at t/sub 0/, where alpha is the size of the discontinuity. This reduces the problem to searching for the scaled pattern in the filter response. A statistical method is proposed for the approximate pattern matching. To cope with the random noise, a study is made of optimal detectors, which minimize the effects of noise. >
Computational Aspects Of Determining Optical Flow
Authors: David Lee 0001, Anargyros Papageorgiou, Grzegorz W. Wasilkowski
Venue: ICCV
Year: 1988

We study some computational aspects of determining optical flow. Necessary and sufficient con- ditions are investigated for the existence and uniqueness of the smoothing-spline from regularization. We discuss different boundary conditions: free, Neuman, and Dirich- let boundary conditions. We show that both free and Neuman boundary problems are ill-conditioned, and are not appropriate for optical flow computation. We discuss Dirichlet boundary problem in more details. As a com- mon practice in low-level vision, a continuous problem is formulated, and a discrete version of the problem is solved instead. We estimate the discretization errors, and com- pute the resulting discrete smoothing-splines. We study efficient iterative methods for solving the system of linear equations for the discrete smoothing-splines. We propose the Chebyshev method for the computation. The Cheby- shev method converges faster than the Gauss-Seidel and Jacobi methods, and is parallelizable.
Parallel Optical Flow Using Local Voting
Authors: James J. Little, Heinrich H. Bülthoff, Tomaso A. Poggio
Venue: ICCV
Year: 1988

We describe a parallel algorithm for computing optical flow from short-range motion. Regularizing optical flow computation leads to a forruulation which minimizes matching error and, at the same time, maximises smoothness of the optical flow. We develop an approximation to the full regularization computation in which corresponding points are found by comparing local patches of the images. Selection aniong competing matches is performed using a winner-take-all scheme. The algorithm accommodates many different image transformations uniformly, with siniilar results, from brightness to edges. The optical flow computed froni different image transformations, such as edge detection and direct brightness computation, can be simply combined. The algorithm is easily implemented using local operations on a finegrained computer, and has been implemented on a Connection Machine. Experiments with natural images show that the scheme is effective and robust against noise. The algorithm leads to dense optical flow fields ; in addition, inforniation from matching facilitates segmentation.
Perceiving Structure From Motion: Failure Of Shape Constancy
Authors: Jack M. Loomis, David W. Eby
Venue: ICCV
Year: 1988

Four experiments are reported on human perception of structure from motion that show systematic failures of shape consi:ancy as a function of object motion. Two of these experiments show that the perceived depth of an object undergoing sinusoidal oscillation about a vertical axis increases monotonically with the amplitude of oscillation. The other two experiments show how the shape of an object varies in the neighborhood of a degeneracy within the stimulus domain, at which shape information is absent. These systematic failures of shaps constancy are considered for their implications about the process underlying human perception of structure from motion.
Recovering Shape Deformation By An Extended Circular Image Representation
Authors: Evangelos E. Milios
Venue: ICCV
Year: 1988

Interpretation of deforming shapes involves the qualitative description of deformation. A new representation of a closed 2D shape is introduced as a cyclic sequence of the extended circular images of the convex and concave segments of its contour. A measure of similarity between two segments that accounts for both shape and orientation is defined that depends on the correlation coefficient of their extended circular images. Dynamic programming based on this similarity measure is used to establish correspondences between segments of the two contours. Finally, a qualitative description of the deformation between the two shapes is computed by accounting for unassociated segments via curvature processes. The result is shown of the application of the deformation recovery scheme to dynamic cloud silhouette analysis using hand-traced input from real satellite images.
Multiple Shape-from-texture Into Texture Analysis And Surface Segmentation
Authors: Mark L. Moerdler
Venue: ICCV
Year: 1988

This paper describes an approach to the classification and segmentation of textured imagery. The approach utilizes information derived during the recovery of surface shape parameters, which are generated by a system that integrates multiple shape-from cues. The robustness of this approach is illustrated by a system that integrates several shape-from-texture cues, recovering not only shape information but also segmenting images into surfaces and aiding in the classification of the surface textures. Two examples are given of the system operating on real, camera acquired imagery.
Reconstruction Of Surfaces Of 3-D Objects By M-array Pattern Projection Method
Authors: Hiroyoshi Morita, Kaanyasn Yajima, Shojiro Sakata
Venue: ICCV
Year: 1988

A common problem of Pattern projection methods to measure surfaces of 3-D objects is that an observed pattern possibly i ncludes disorders such as deficiency, d isplacement, and permutation of subpatterns. These disorders make it difficult to match observed patterns with its position on the projected one and cause wrong m easurements as a result. This paper proposes a new technique to correct pattern disorders by using a pattern made from an M-array which is a two-dimensional extension of a well-known M-sequence.
Using Flow Field Divergence For Obstacle Avoidance: Towards Qualitative Vision
Authors: Randal C. Nelson
Venue: ICCV
Year: 1988

The use of certain measures of flow field divergenee is investigated as a qualitative cue for obstacle avoidance during visual navigation. It is shown that a quantity termed the directional divergence of the 2-D motion field can be used as a reliable indicator of the presence of obstacles in the visual field of an observer undergoing generalized rotational and translational motion. Moreover, the necessary measurements can be robustly obtained from real image sequences. A procedure for robustly extracting divergence information from image sequences which can be performed using a highly parallel architecture is described. Experimental results are presented showing that the system responds as expected to divergence in real world image sequences, and the use of the system to navigate between obstacles is demonstrated.