This paper discusses the development of algorithms for parallel interpretation-tree model matching for 3-D computer vision applications such as object recognition. The algorithms are developed with a prototyping approach using ProSet-Linda. ProSet is a procedural prototyping language based on the theory of nite sets. The coordination language Linda provides a distributed shared memory model, ca...

This paper describes the use of an embedded multiprocessor to speed up the computer vision tasks of an autonomous mobile robot. A brief overview over the robot’s vision system is given, identifying a signature segmentation algorithm as the most runtime-consuming task. A parallelization strategy for the segmentation algorithm is presented, based on the exploitation of data-parallelism. The paral...

The idea of Fixation introduced a direct method for general recovery of shape and motion from images without using either feature correspondence or optical flow [1,2]. There are some parameters which have important effects on the performance of fixation method. However, the theory of fixation does not say anything about the autonomous and correct choice of those parameters. This paper presents ...

While promising feasible solutions to several shortcomings of homogeneous parallel computing, Heterogeneous SuperComputing (HSC) poses new problems to be solved. In this paper, we outline the issues and problems arising in using heterogeneous environments for parallel solutions to various applications. Preliminary solutions to a set of problems in heterogeneous supercomputing are presented. AB ...

A robust parallel multichannel filtering method for modelling preattentive vision is presented in this paper. Our aim is to achieve an artificial perception such that its pertbrmance rather than its working be as close as possible to that of human vision. In establishing a rigorous theoretical basis tbr constructing original and efficient operators to be incorporated in a robust computer vision...

Parallelism can be exploited to handle the enormous computational requirements of many vision applications. However, the computational power oered by multiprocessor architectures cannot be fully harnessed to achieve the desired speedup. This is primarily due to the unbalanced distribution of computational load among the processors of a parallel architecture. Furthermore, in parallel implementat...

Parallelism can be exploited to handle the enormous computational requirements of many vision applications. However, the computational power oered by multiprocessor architectures cannot be fully harnessed to achieve the desired speedup. This is primarily due to the unbalanced distribution of computational load among the processors of a parallel architecture. Furthermore, in parallel implementat...