Embedded Vision System

Call for Papers

Vision systems allow computers to understand images, and to take appropriate actions, often under hard real-time constraints.

Most vision systems need high computer performance. The decisive constraint to develop pattern recognition or monitoring systems was therefore to consider computer hardware with excellent key features to fulfill the high requirements. This causality has several disadvantages. The costs of the final products are high, the size of the hardware becomes voluminous, the electromagnetic capability is reduced, and the energy consumption is often a problem. Therefore, the pressure to realize vision systems on the base of Embedded Systems was and is still increasing dramatically. Meanwhile, the number of possible applications has exploded since several disadvantages of classic systems can be avoided. The history of mobile phones evolution is one of the best examples. It would not have been possible without Embedded Systems, and especially not in such an affordable way. However, it is not necessary to consider only the mass market where Embedded Vision Systems can improve the current situation dramatically. If many cameras are installed to watch a scene, one is able to define a virtual camera, which always shows the most important angle of a view. If a bank note should be checked under the conditions of high accuracy, high probability of error recognition, and high throughput, the realization is only feasible, if the computer is assisted by a network of special parallelized chips. Usually, the algorithms can be divided into three areas, the prestage, where data is compressed, the specialized computational phase, and the interpretation stage. With this setup, the bandwidth and the data throughput may be improved in an amazing way.

Many other ideas could be presented. The main issues are the parallelization of processes, as well as the communications between them, which are based on networked chip sets. The challenge for the research work is to find optimal structures concerning real-time problems, energy consumption, low-price solutions, and so forth. However, not all algorithms for vision systems are suitable to be implemented in Embedded Systems; better solutions have to be discovered. In this sense many tasks and problems in the research field have to be solved, and many application areas are concerned.

This special issue focuses on new results of research work in the field of Embedded Vision Systems. Several main keywords are:

  • Innovative architectures for embedded vision systems
  • Innovative sensor systems for embedded vision applications
  • Architectural considerations in complex image-processing programs in an embedded environment
  • FPGA designs for image processing applications
  • DSP and FPGA: alternative and/or complement
  • Networking for distributed embedded vision systems
  • Performance bottlenecks/solutions for high-performance vision systems
  • Smart camera systems
  • Virtual camera systems
  • Object tracking
  • Automotive applications
  • Traffic flow measurement systems
  • Robot and machine vision
  • Bioinspired vision systems
  • Verification methods for mission-critical embedded computer vision systems

Authors should follow the EURASIP JES manuscript format described at the journal site http://www.hindawi.com/journals/es/. Prospective authors should submit an electronic copy of their complete manuscript through the EURASIP JES manuscript tracking system at http://www.hindawi.com/mts/, according to the following timetable:

Manuscript DueMay 1, 2006
First Round of ReviewsSeptember 1, 2006
Publication DateJanuary 1, 2007

Guest Editors

  • Dietmar Dietrich, Vienna University of Technology, Gusshausstrasse 25-27/E384, 1040 Vienna, Austria
  • Heinrich Garn, ARC Seibersdorf research GmbH, 2444 Seibersdorf, Austria
  • Udo Kebschull, Universität Heidelberg, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany
  • Christoph Grimm, Institute of Microelectronic Systems, University of Hannover, Appelstrasse 4, 30167 Hannover, Germany
  • Moshe Ben-Ezra, Real-Time Vision and Modeling Department, Siemens Corporate Research, 755 College Road East, 08540 Princeton NJ, USA