In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarilydistributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm, called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) build photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their effects on arbitrary views of a 3D scene. 1.

This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems.

. The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computer-aided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, computer architecture design, and computer network design. Traditional methods treat SAT as a discrete, constrained decision problem. In recent years, many optimization methods, parallel algorithms, and practical techniques have been developed for solving SAT. In this survey, we present a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective. We describe sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms. We give performance evaluation of some existing SAT algorithms. Finally, we provide a set of practical applications of the sat...

This article presents the uncertainty management system used for the execution activity of the Sensor Fusion Effects (SFX) architecture. The SFX architecture is a generic sensor fusion system for autonomous mobile robots, suitable for a wide variety of sensors and environments. The execution activity uses the belief generated for a percept to either proceed with a task safely (e.g., navigate to a specific location), terminate the task (e.g., can't recognize the location), or investigate the situation further in the hopes of obtaining sufficient belief (e.g., what has changed?). Dempster-Shafer (DS) theory serves as the foundation for uncertainty management. The SFX implementation of DS theory incorporates evidence from sensor observations and domain knowledge into three levels of perceptual abstraction. It also makes use of the DS weight of conflict metric to prevent the robot from acting on faulty observations. Experiments with four types of sensor data collected by a mobil...

A new cognitive architecture for artificial vision is proposed. The architecture, aimed at an autonomous intelligent system, is cognitive in the sense that several cognitive hypotheses have been postulated as guidelines for its design. The first one is the existence of a conceptual representation level between the subsymbolic level, that processes sensory data, and the linguistic level, that describes scenes by means of a high-level language. The conceptual level plays the role of the interpretation domain for the symbols at the linguistic levels. A second cognitive hypothesis concerns the active role of a focus of attention mechanism in the link between the conceptual and the linguistic level: the exploration process of the perceived scene is driven by linguistic and associative expectations. This link is modeled as a timedelay attractor neural network. Results are reported obtained by an experimental implementation of the architecture.

This paper addresses the problem of deducing the surface shape of an object given just the surface normals. Many shape measurement algorithms such as shape from shading and shape from texture only return the surface normals of an object, often with an ambiguity of π in the surface tilt. The surface shape has to be inferred from these normals, typically via some integration process. However, reconstruction through the integration of surface gradients is sensitive to noise and the choice of integration paths across the surface. In addition, existing techniques cannot accommodate ambiguities in tilt. This paper presents a new approach to the reconstruction of surfaces from surface normals using basis functions, referred to here as shapelets. The surface gradients of the shapelets are correlated with the gradients of the surface and the correlations summed to form the reconstruction. This results in a simple reconstruction process that is very robust to noise. Where there is an ambiguity of π in the surface tilt, reconstructions of reduced quality are still possible up to a positive/negative shape ambiguity. Intriguingly, some form of reconstruction is also possible using just slant information. 1.

To date, the overwhelming use of motion in computational vision has been to recover the three-dimensional structure of the scene. We propose that there are other, more powerful, uses for motion. Toward this end, we define dynamic perceptual organization as an extension of the traditional (static) perceptual organization approach. Just as static perceptual organization groups coherent features in an image, dynamic perceptual organization groups coherent motions through an image sequence. Using dynamic perceptual organization, we propose a new paradigm for motion understanding and show why it can be done independently of the recovery of scene structure and scene motion. The paradigm starts with a spatiotemporal cube of image data and organizes the paths of points so that interactions between the paths and perceptual motions such as common, relative and cyclic are made explicit. The results of this can then be used for high-level motion recognition tasks.

Recovering a hierarchical motion description of a long image sequence is one way to recognize objects and their motions. Intermediate-level and high-level motion analysis, i.e., recognizing a coordinated sequence of events such as walking and throwing, has been formulated previously as a process that follows high-level object recognition. This thesis develops an alternative approach to intermediate-level and high-level motion analysis. It does not depend on complex object descriptions and can therefore be computed prior to object recognition. Toward this end, a new computational framework for low and intermediate-level processing of long sequences of images is presented. Our new computational framework uses spatiotemporal (ST) surface flow and ST flow curves. As contours move, their projections into the image also move. Over time, these projections sweep out ST surfaces. Thus, these surfaces are direct representations of object motion. ST surface flow is defined as the natural extensio...

A novel shape description method based on the Morphological Signature Transform (MST) is presented in this dissertation. The MST uses multiresolution morphological image processing by non-convex multiple structuring elements. A binary image which contains the object shape to be described is represented by means of a multiresolution pyramid. The method is based on the successive morphological erosions of the input image at di erent resolutions by primary and rotated structuring elements. The areas of successively eroded images are computed for each structuring element at each pyramid level. The obtained set of numbers is arranged into vectors, ordered, and used as a shape descriptor. Experimental results demonstrate that the method is robust against noise and invariant to translation, rotation, and scale change. A new method for the selection of the optimal structuring element is presented in the second part of dissertation. For a given class of shapes the optimal structuring element for MST method is selected by means of a genetic algorithm. The optimization criteria is formulated to enable a robust shape matching. Experiments have been performed on a class of model shapes. The proposed optimal shape description method is applied to the problem of shape matching which

Multimedia involves the use of multiple forms of communication media in an interactive and integrated manner. At present, textual data is the media predominantly used to provide the interactivity due to the ease with which discrete semantic elements are identified. It is common practice to follow links from words or phrases within text to associated information elsewhere in the database. To achieve a similar degree of functionality with visual information typically requires that each image (or video sequence) be processed by hand, indicating the objects and locations within the image- a process that is excessively expensive and time-consuming for large databases. This paper describes the implementation of a simple object recognition system that allows the specification of 3-dimensional models that can then be used to recognise objects within any image, in an analogous fashion to words within text. This enables image data to be become a truly active media, within a multimedia database. It provides a significantly enhanced level of functionality while keeping the authoring effort to a minimum. The basic algorithms are described and then an example application is outlined, along with feedback from users of the system.