Perceptual grouping is an important part of many computer vision systems. When inferring a new grouping from the primitive features there is always an uncertainty degree on this detection, that might be useful in further reasonings. In this paper, we present a fuzzy logic based system for the computation of the certainties assigned to pairwise line segment relations and introduce its applicatio...

Remote sensing research focusing on feature selection has long attracted the attention of the remote sensing community because feature selection is a prerequisite for image processing and various applications. Different feature selection methods have been proposed to improve the classification accuracy. They vary from basic search techniques to clonal selections, and various optimal criteria ha...

In speech recognition systems, information from multiple sources such as different feature streams can be combined in many different ways to yield better recognition accuracy. In general , information may be combined at the level of the incoming feature vectors, at the level of the decoding process, or after hypothesis generation. In this paper we focus on the specific case where parallel strea...

Computer vision has emerged as one of the most popular domains of embedded applications. The applications in this domain are characterized by complex, intensive computations along with very large memory requirements. Parallelization and multiprocessor implementations have become increasingly important for this domain, and various powerful new embedded platforms to support these applications hav...

This paper is a general overview of the Skipper project, led at Blaise Pascal University between 1997 and 2002. The main goal of the Skipper project was to demonstrate the applicability of skeleton-based parallel programming techniques to the fast prototyping of reactive vision applications. This project has produced several versions of a full-fledged integrated parallel programming environment...

Although the line-of-sight (LoS) is expected to be useful as input methodology for computer systems, the application area of the conventional LoS detection system composed of video camera and image processor is restricted in the specialized area, such as academic research, due to its large size and high cost. There is a rapid eye motion, so called ‘saccade’ in our eye motion, which is expected ...

Scale Invariant Feature Transform (SIFT) is a computer vision algorithm that is widely-used to extract features from images. We explored accelerating an existing implementation of this algorithm with message passing in order to analyze large data sets. We successfully tested two approaches to data decomposition in order to parallelize SIFT on a distributed memory cluster. Introduction In certai...

This paper considers the framework in which World-Wide-Web based online courseware can be developed. This framework is illustrated using courseware developed for parallel computing, C programming, X-Window programming and computer vision.

Only few problems in computer vision have been investigated more vigorously than stereo. Nevertheless, the main obstacle on the way to their practical application is the excessively long computation time needed to match stereo images. This paper presents parallel algorithms for edge-based stereo suitable for depth computation. Edge-based stereo techniques produce only sparse depth maps. Thus, w...

This paper introduces a new parallel programming model for situated multi-agent systems simulations and its parallel library implementation on shared memory MIMD parallel computers. The first goal is to allow users to easily implement situated multi-agent systems, following their natural paradigm: concurrent agent behavior definition and environment update programming. The second goal is to obt...