Abstract-Relaxation labeling processes have been widely used in many different domains including image processing, pattern recognition, and artificial intelligence. They are iterative procedures that aim at reducing local ambiguities and achieving global consistency through a parallel exploitation of contextual information, which is quantitatively expressed in terms of a set of “compatibility coefficients. ” The problem of determining compatibility coefficients has received a considerable attention in the past and many heuristic, statistical-based methods have been suggested. In this paper, we propose a rather different viewpoint to solve this problem: we derive them attempting to optimize the performance of the relaxation algorithm over a sample of training data; no statistical interpretation is given: compatibility coefficients are simply interpreted as real numbers, for which performance is optimal. Experimental results over a novel application of relaxation are given, which prove the effectiveness of the proposed approach. Index Terms- Compatibility coefficients, constraint satisfaction, gradient projection, learning, neural networks, nonlinear

We summarize our work on the parallel complexity of local consistency in constraint networks, and present several basic techniques for achieving parallel execution of constraint networks. We are interested primarily in developing a classification of constraint networks according to whether they admit massively parallel execution. The major result supported by our investigations is that the parallel complexity of constraint networks is critically dependent on subtle properties of the network that do not influence its sequential complexity. 1 Introduction In this position paper we summarize our work on the parallel complexity of local consistency in constraint networks [Kas90, Kas86, Kas89, KRS87, KD90]. Our research is aimed at deriving a precise characterization of the utility of parallelism in such networks. We are interested primarily in developing a classification of constraint networks according to whether they admit massively parallel execution. We have analyzed parallel executio...

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this paper we present a discrete relaxation algorithm for reducing the intrinsic combinatorial complexity by pruning the decision tree based on unreliable prior information from independent 2D-tracking for each view. The algorithm improves the reliability of spatio-temporal correspondence by simultaneous optimisation over multiple views in the case where 2D-tracking in one or more views are ambiguous. Application to the 3D reconstruction of human movement, based on tracking of skin-coloured regions in three views, demonstrates considerable improvement in reliability and performance. Results demonstrate that the optimisation over multiple views gives correct 3D reconstruction and object labeling in the presence of incorrect 2D-tracking whilst maintaining real-time performance

The ‘LeRP’ algorithm approximates subgraph isomorphism for attributed graphs based on counts of Length-R Paths. The algorithm provides a good approximation to the maximal isomorphic subgraph. The basic approach of the LeRP algorithm differs fundamentally from other methods. When comparing structural similarity LeRP uses a neighborhood of nodes that varies in size dynamically. This approach provides sufficient evidence of similarity to permit LeRP to form a node-to-node mapping and can be computed with polynomial effort in the worst-case. Results from over 32,000 simulated cases are reported. We demonstrate that LeRP does not need a high dynamic range of node and edge coloring to perform well. For example, LeRP can input 50-node and 100-node graphs that contain a common 50-node subgraph, and then compute a matching subgraph having 49.74 +/- 0.46 nodes (mean +/- one standard deviation). This takes from 0.4 to 0.5 seconds. In this example, 100 trials were evaluated and graphs had discrete coloring for nodes and edges with a dynamic range of four. Test conditions are varied and include strongly regular graphs as well as Model A.

The Rapidly Deployable Radio Network (RDRN) is an architecture and experimental system to develop and evaluate hardware and software components suitable for implementing mobile, rapidly deployable, and adaptive wireless communications systems. The driving application for the RDRN is the need to quickly establish a communications infrastructure following a natural disaster, during a law enforcement activity, or rapid deployment of military force. The RDRN project incorporates digitally controlled antenna beams, programmable radios, adaptive protocols at the link layer, and mobile node management. This paper describes the architecture for the Rapidly Deployable Radio Network and a prototype system built to evaluate key system components.

The LeRP algorithm determines subgraph isomorphism for attributed graphs based on counts of Length-R Paths. The algorithm provides a good approximation to the maximal isomorphic subgraph. The paradigm associated with the LeRP algorithm differs fundamentally from other approaches. When comparing structural similarity it uses a neighborhood of nodes, which varies in size dynamically. This approach provides sufficient evidence of similarity to permit LeRP to form a node-to-node mapping in just a few iterations. LeRP requires polynomial effort for each of these iterations. And, just three iterations were used for all of the 32,000 simulated trials reported herein. Results from an image registration application are also presented.

n 11.1 and Figure 11.1 show an invertible mapping between points of a model M and points of an image I . Actually, M and I can each be any 2D coordinate space and can each represent a map, model, or image. M [x; y] = I [g(x; y); h(x; y)] (11.1) I [r; c] = M [g 1 (r; c); h 1 (r; c)] 357 358 Computer Vision: Mar 2000 ( r , c ) ( x , y ) M I Figure 11.1: A mapping between 2D spaces M and I. M may be a model and I an image, but in general any 2D spaces are possible. 78 Definition The mapping from one 2D coordinate space to another as dened in Equation 1

The Rapidly Deployable Radio Network #RDRN# is an Advanced Research Projects Agency #ARPA# grantawarded to the University of Kansas's Telecommunications and Information Sciences Laboratory #TISL#. The goal of this project is to develop a portable high speed wireless communications network that could easily be deployed in a time of battle or emergency.

This paper considers an application of computer vision notions for document recognition problem. The well developed in computer vision labeling technique is applied for interpretation of black-and-white layers of geographical maps. The knowledge base and main relations between cartographical objects and segments are extracted and described. Map-drawing interpretation process by using labeling technique is proposed. The suggested approach allows to increase a level of automatic object recognition.