A SPATIAL SEGMENTATION METHOD

The problem of partitioning images into homogenous regions or semantic entities is a basic problem for identifying relevant objects. Visual segmentation is related to some semantic concepts because certain parts of a scene are pre-attentively distinctive and have a greater significance than other parts. However, even if image segmentation is a heavily researched field, extending the algorithms to spatial has been proven not to be an easy task. A true spatial segmentation remains a difficult problem to tackle due to the complex nature of the topology of spatial objects, the huge amount of data to be processed and the complexity of the algorithms that scale with the new added dimension. Unfortunately there are huge amount of papers for planar images and segmentation methods and most of them are graph-based for planar images. There are very few papers for spatial segmentation methods. The major concept used in graph-based spatial segmentation algorithms is the concept of homogeneity of regions. For color spatial segmentation algorithms the homogeneity of regions is color-based, and thus the edge weights are based on color distance. Early graph-based methods use fixed thresholds and local measures in finding a spatial segmentation. Complex grouping phenomena can emerge from simple computation on these local cues. As a consequence we consider that a spatial segmentation method can detect visual objects from images if it can detect at least the most objects. The aim in this paper is to present a new and efficient method to detect visual objects from color spatial images and to extract their color and geometric features, in order to determine later the contours of the visual objects and to perform syntactic analysis of the determined shapes. In this paper we extend our previous work for planar segmentation by adding a new step in the spatial segmentation algorithm that allows us to determine regions closer to it. The key to the whole algorithm of spatial segmentation is the honeycomb cells

ALGORITHMS FOR HIGH LEVEL PETRI NETS SIMULATION AND RULE-BASED SYSTEMS

This paper presents a way for implementing the dynamic semantics of High Level Petri Nets by using a modified version of RETE algorithm. To do this, a rule-based language for High Level Petri Nets specifications is described. First a connection between rule-based systems and High Level Petri Nets is presented, and it is sown then that for every Petri net, a rule-based system exists, which is semantic equivalent. The proposed algorithms are algorithms for simulation, and the High Level Petri Nets can be used for discrete event system modelling. The algorithms presented in this paper are based on the RETE algorithm for rule-based systems, and avoid the testing of all transitions for enabling determination. The tokens are propagated in the RETE graph, as much as possible; when tokens reach an output node, they activate the transition associated to that node. Keywords: Petri nets, rule-based systems, system modelling, discrete event systems, algebraical specifications

THE DESIGN AND THE IMPLEMENTATION OF A DOSTP- A DISTRIBUTED OBJECT ORIENTED SYSTEM BASED ON TRANSACTIONS PROCESSING

Abstract. This paper presents an experimental object oriented system that provides operating system level support for distributed transactions that operate on share objects. Transactions are a common construct that distributed programming environments provide in order to maintain the consistency of distributed information in the presence of partial failure and concurrency. The goal of constructing a transactions support in a distributed object oriented system is to make transactions available as a fundamental programming constructs for reliable distributed computing � on the other hand, the distributed environment can be used to exploit the parallelism in some computation within transactions. This paper presents the basic components of a distributed object oriented system based on transactions processing and its implementation on a local network of DEC Stations 3000 under OSF/1 vers 1.32 and Zenith PC under Linux 1.2.8 and on a NOVELL network under NetWare 3.11. Basic transport{level communication is performed by TCP/IP, UDP/IP via an Ethernet and by IPX, respectively. 1