Abstract not found

We propose a computational model for detecting and localizing instances from an object class in static grey level images. We divide detection into visual selection and final classification, concentrating on the former: Drastically reducing the number of candidate regions which require further, usually more intensive, processing, but with a minimum of computation and missed detections. Bottom-up processing is based on local groupings of edge fragments constrained by loose geometrical relationships. They have no a priori semantic or geometric interpretation. The role of training is to select special groupings which are moderately likely at certain places on the object but rare in the background. We show that the statistics in both populations are stable. The candidate regions are those which contain global arrangements of several local groupings. Whereas our model was not conceived to explain brain functions, it does cohere with evidence about the functions of neurons in V1 and V2, such ...

Shape matching is an important ingredient in shape retrieval, recognition and classification, alignment and registration, and approximation and simplification. This paper treats various aspects that are needed to solve shape matching problems: choosing the precise problem, selecting the properties of the similarity measure that are needed for the problem, choosing the specific similarity measure, and constructing the algorithm to compute the similarity. The focus is on methods that lie close to the field of computational geometry.

We study visual selection: Detect and roughly localize all instances of a generic object class, such as a face, in a greyscale scene, measuring performance in terms of computation and false alarms. Our approach is sequential testing which is coarse-to-fine in both in the exploration of poses and the representation of objects. All the tests are binary and indicate the presence or absence of loose spatial arrangements of oriented edge fragments. Starting from training examples, we recursively find larger and larger arrangements which are “decomposable,” which implies the probability of an arrangement appearing on an object decays slowly with its size. Detection means finding a sufficient number of arrangements of each size along a decreasing sequence of pose cells. At the beginning, the tests are simple and universal, accommodating many poses simultaneously, but the false alarm rate is relatively high. Eventually, the tests are more discriminating, but also more complex and dedicated to specific poses. As a result, the spatial distribution of processing is highly skewed and detection is rapid, but at the expense of (isolated) false alarms which, presumably, could be eliminated with localized, more intensive, processing.

This paper describes techniques and tools for shape representation and matching, developed in the context of MPEG-7 standardisation. The application domains for each descriptor are considered, and the contour-based shape descriptor is presented in some detail. Example applications are also shown.

The ability of a normal human listener to recognize objects in the environment from only the sounds they produce is extraordinarily robust with regard to characteristics of the acoustic environment and of other competing sound sources. In contrast, computer systems designed to recognize sound sources function precariously, breaking down whenever the target sound is degraded by reverberation, noise, or competing sounds. Robust listening requires extensive contextual knowledge, but the potential contribution of sound-source recognition to the process of auditory scene analysis has largely been neglected by researchers building computational models of the scene analysis process. This thesis proposes a theory of sound-source recognition, casting recognition as a process of gathering information to enable the listener to make inferences about

Theories of visual object recognition must solve the problem of recognizing 3D objects given that perceivers only receive 2D patterns of light on their retinae. Recent findings from human psychophysics, neurophysiology and machine vision provide converging evidence for `image-based' models in which objects are represented as collections of viewpoint-specific local features. This approach is contrasted with `structural-description' models in which objects are represented as configurations of 3D volumes or parts. We then review recent behavioral results that address the biological plausibility of both approaches, as well as some of their computational advantages and limitations. We conclude that, although the image-based approach holds great promise, it has potential pitfalls that may be best overcome by including structural information. Thus, the most viable model of object recognition may be one that incorporates the most appealing aspects of both image-based and structural-description theories. 1998 Elsevier Science B.V. All rights reserved Keywords: Object recognition; Image-based model; Structural description 1.

Abstract not found

One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations could be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. Reinforcement learning means learning a policy---a mapping of observations into actions---based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies being searched is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate various architectures for controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multi-agent system. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience re-use. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.

This paper examines four current theoretical approaches to the representation and recognition of visual objects: structural descriptions, geometric constraints, multidimensional feature spaces, and shape-space approximation. The strengths and the weaknesses of the theories are considered, with a special focus on their approach to categorization --- a computationally challenging task which is not widely addressed in computer vision (where the stress is rather on the generalization of recognition across changes of viewpoint).