Multifoveal imager for stereo applications

A multiresolution imager based on adaptive retinal structures, with data compressions above 85%, is presented in this article. The main goal of the imager platform is to speed up image processing with the use of selective data reductions to shorten the vision systems' tasks in stereo applications. Implemented on a field‐programmable gate array, the platform can be configured as the front end of active vision systems, with use of adaptive foveal sensing on uniresolution images to cover wide fields of view, being also a development tool for multiresolution applications with different image formats and interfaces. The multifoveal imager provides the hierarchical data structures related to multiresolution levels, following instructions to control sensor parameters or to perform adaptive fovea fixations in real time, adapting its operation to the constraints of the active vision systems. It also uses intermediate resolution data to implement in hardware an efficient background extractor to cooperate with image processors in motion detection tasks and attention mechanisms. Some platform configurations are explained and experimental results are discussed in relation to the advantages of the adaptive retinal structures. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 149–165, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10023

[1]  B. L. Zuber,et al.  Models of oculomotor behavior and control , 1981 .

[2]  K. P. Karmann,et al.  Moving object recognition using an adaptive background memory , 1990 .

[3]  Francisco Sandoval Hernández,et al.  Shifted fovea multiresolution geometries , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[4]  R. Bajcsy.  Active perception , 1988 .

[5]  Azriel Rosenfeld,et al.  A Pyramid Framework for Early Vision , 1994 .

[6]  Azriel Rosenfeld,et al.  Multiresolution image processing and analysis , 1984 .

[7]  Young Serk Shim,et al.  A fast hierarchical motion vector estimation algorithm using mean pyramid , 1995, IEEE Trans. Circuits Syst. Video Technol..

[8]  Liang-Gee Chen,et al.  Parallel architectures for 3-step hierarchical search block-matching algorithm , 1994, IEEE Trans. Circuits Syst. Video Technol..

[9]  Francisco Sandoval Hernández,et al.  Adaptive Fovea Structures for Space-Variant Sensors , 1997, ICIAP.

[10]  Mohammed Ismail,et al.  Analog VLSI Implementation of Neural Systems , 2011, The Kluwer International Series in Engineering and Computer Science.

[11]  Azriel Rosenfeld,et al.  Segmentation and Estimation of Image Region Properties through Cooperative Hierarchial Computation , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  Azriel Rosenfeld,et al.  Image Smoothing and Segmentation by Multiresolution Pixel Linking: Further Experiments and Extensions , 1982, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Giulio Sandini,et al.  A Foveated Retina-Like Sensor Using CCD Technology , 1989, Analog VLSI Implementation of Neural Systems.

[14]  Cesar Bandera,et al.  Foveal machine vision systems , 1989, Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.

[15]  J. Jonas,et al.  Retinal photoreceptor count, retinal surface area, and optic disc size in normal human eyes. , 1994, Ophthalmology.

[16]  C. Dyer,et al.  Node Linking Strategies in Pyramids for Image Segmentation , 1984 .

[17]  佐藤 孝紀,et al.  A Hierarchical Data Structure for Picture Processing , 1976 .

[18]  Thorne Shipley,et al.  The human texture visual field: Fovea-to-periphery pattern recognition , 1990, Pattern Recognit..