Skeletons for parallel image processing:: an overview of the SKIPPER project

被引:35
作者
Sérot, J
Ginhac, D
机构
[1] Univ Clermont Ferrand, LASMEA, UMR 6602 CNRS, F-63177 Clermont Ferrand, France
[2] Univ Burgundy, FRE 2309 CNRS, LE2I, F-21078 Dijon, France
关键词
parallelism; skeleton; computer vision; fast prototyping; data-flow;
D O I
10.1016/S0167-8191(02)00189-8
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper is a general overview of the SKIPPER project, run at Blaise Pascal University between 1996 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 (PPE). These PPEs have been used to implement realistic vision applications, such as road following or vehicle tracking for assisted driving, on embedded parallel platforms embarked on semi-autonomous vehicles. All versions Of SKIPPER share a common front-end and repertoire of skeletons-presented in previous papers-but differ in the techniques used for implementing skeletons. This paper focuses on these implementation issues, by making a comparative survey, according to a set of four criteria (efficiency, expressivity, portability, predictability), of these implementation techniques. It also gives an account of the lessons we have learned, both when dealing with these implementation issues and when using the resulting tools for prototyping vision applications. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:1685 / 1708
页数:24
相关论文
共 50 条
  • [41] Image Acquisition and Processing Based on OpenCV
    王望
    校园英语, 2018, (14) : 255 - 256
  • [42] Image processing for precise geometry determination
    Belgacem, I
    Jonniaux, G.
    Schmidt, F.
    PLANETARY AND SPACE SCIENCE, 2020, 193
  • [43] Deep Learning for Whole Slide Image Analysis: An Overview
    Dimitriou, Neofytos
    Arandjelovic, Ognjen
    Caie, Peter D.
    FRONTIERS IN MEDICINE, 2019, 6
  • [44] Tool Wear in a Ceramic Microdrilling Processing using Image Processing Methods
    Chang, Dar-Yuan
    Su, Kuo-Ho
    Deng, Chyn-Shu
    ADVANCED MANUFACTURING FOCUSING ON MULTI-DISCIPLINARY TECHNOLOGIES, 2012, 579 : 227 - 234
  • [45] Seam tracking with texture based image processing for laser materials processing
    Kraemer, S.
    Fiedler, W.
    Drenker, A.
    Abels, P.
    HIGH-POWER LASER MATERIALS PROCESSING: LASERS, BEAM DELIVERY, DIAGNOSTICS, AND APPLICATIONS III, 2014, 8963
  • [46] Image processing processing based quality control of coated paper folding
    Pal, Magdolna
    Novakovic, Dragoljub
    Dedijer, Sandra
    Koltai, Laszlo
    Juric, Ivana
    Vladic, Gojko
    Kasikovic, Nemanja
    MEASUREMENT, 2017, 100 : 99 - 109
  • [47] Parallel processing of minimization algorithm for determination Finite Automata
    Sun, Yu-Qiang
    Lu, Hai-Lian
    Li, Yu-Ping
    Wang, Hai-Yan
    ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES, 2007, 2 : 73 - +
  • [48] Improving a solution's quality through parallel processing
    Akl, SG
    Bruda, SD
    JOURNAL OF SUPERCOMPUTING, 2001, 19 (02) : 221 - 233
  • [49] Multicore Parallel Processing Concepts for Effective Sorting and Searching
    Sujatha, Kota
    Rao, A. Arjuna
    Rao, P. V. Nageswara
    Sastry, V. G.
    Praneeta, V.
    Bharat, Raj Kumar
    2015 INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION ENGINEERING SYSTEMS (SPACES), 2015, : 162 - 166
  • [50] Improving A Solution's Quality Through Parallel Processing
    Selim G. Akl
    Stefan D. Bruda
    The Journal of Supercomputing, 2001, 19 : 221 - 233