Sub-pattern texture recognition using intelligent focal-plane imaging sensor of small window-size

Pattern Recognition Letters

Volumn 20, Issue 11-13, 1999, Pages 1133-1139

  Szirányi, Tamás ^a   Hanis, Attila ^b  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE ANALYSIS; IMAGE QUALITY; IMAGE SENSORS; MICROPROCESSOR CHIPS; NONLINEAR NETWORKS; STATISTICAL METHODS; VLSI CIRCUITS;

CELLULAR NONLINEAR NETWORKS (CNN); INTELLIGENT FOCAL-PLANE IMAGING SENSORS; SUBPATTERN TEXTURE RECOGNITION;

OBJECT RECOGNITION;

EID: 17144471738     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(99)00080-X     Document Type: Article

References (10)

1. Barron, A.R., Györfi, L., Meulen, E., 1992. Distribution estimation in total variation and in informational divergence. IEEE Trans. Information Theory 38, 1437-1454.
2. Brodatz, P., 1996. Textures. A Photographic Album for Artists and Designers. Dover, New York.
3. Chua, L.O., Yang, L., 1988. Cellular neural networks. IEEE Trans. Circuit Syst. 35, 1257-1290.
4. Dominguez-Castro, R., Espejo, S.M., Rodriguez-Vazquez, A., Carmona, R., Földesy, P., Zarándy, Á., Szolgay, P., Szirányi, T., Roska, T., 1997. A 0.8 μm CMOS 2-D programmable mixed-signal focal-plane array processor with on-chip binary imaging and instructions storage. IEEE J. Solid State Circuits 32, 1013-1026.
5. Földesy, P., Szolgay, P., 1997. The CNN engine board. In: Proceedings of ECCTD'97. Budapest, Design and Automation Day, IEEE.
6. Roska, T., Chua, L.O., 1993. The CNN universal machine: an analogic array computer. IEEE Trans. Circuits Syst. 40 (II), 163-173.
7. Szirányi, T., 1994. Subpixel pattern recognition by image histograms. Pattern Recognition 27 (8), 1079-1092.
8. Szirányi, T., 1997. Texture recognition using a superfast cellular neural network VLSI chip in real experimental environment. Pattern Recognition Letters 18 (11-13), 1329-1334.
9. Szirányi, T., Böröczki, Á., 1991. Overall picture degradation error for scanned images and the efficiency of character recognition. Opt. Engrg. 30, 1878-1885.
10. Szirányi, T., Csapodi, M., 1998. Texture classification and segmentation by cellular neural network using genetic learning. Computer Vision and Image Understanding 71 (3), 255-270.