]> BibSonomy publications for/author/Ogawa SIGGRAPH Postersconf/siggraph/2008posters108Interactive deformation using volumetric constraints.2008dblp 2008-08-18 00:00:00.0 IBM Journal of Research and Development3517-526Micropitch connection using anisotropic conductive materials for driver IC attachment to a liquid crystal display.421998dblp 2008-08-13 00:00:00.0 J. Bioinformatics and Computational Biology51103-1122Stem Kernels for RNA Sequence Analyses.52007dblp 2008-08-08 00:00:00.0 \\http://vis.cs.ucdavis.edu/$\sim$ogawa/eclipse/JanuaryVisualizing the {Eclipse} Bug Data2007imported 2008-07-07 16:45:32.0 MVAconf/mva/1998220-223A Map-Based Approach to Extracting Object Information from Aerial Images.1998dblp 2008-06-30 00:00:00.0 MVAconf/mva/2002302-305Extraction of Face Regions and Eye Windows from a Set of Gray-scale Images with Different Face Orientations.2002dblp 2008-06-30 00:00:00.0 Gutzwiller en el Hubbard con orden magnetico y coexistenciaProg. Theor. Phys.614Untitled531975high-tc, htsct, theory 2008-06-25 19:30:29.0 ICIP (3)conf/icip/2007101-104POCS-Based Iterative Reconstruction Algorithm of Missing Textures.2007dblp 2008-06-25 00:00:00.0 IEEE Transactions on Industrial ElectronicsApril2878--886Onboard Evolutionary Risk Recognition System for Automobiles Toward the Risk Map System542007algorithms, analysis automobiles, board camera detection, driving genetic human image map on pedestrian processing, programming, recognition recognition, risk system, vehicle 2008-06-19 17:46:40.0To achieve a system that improves the safety and comfort of the vehicle driving, a recognition system equivalent to the human recognition ability should be developed. However, the vehicle environment is complicated and involves situations so diverse that a uniform recognition processing approach cannot function sufficiently. For a solution to this problem, we have been studying a comprehensive risk recognition system, which we call the risk map system, with learning capability. As part of this paper, a system has been developed that autonomously obtains the image recognition processing. This paper presents a system as an example that automatically learns through genetic programming to obtain the image processing of pedestrians and vehicles taken by an onboard camera system ACM Conference on Computer Scienceconf/acm/198442-49Knowledge representation and inference control of SPERIL-II.1984dblp 2008-06-18 00:00:00.0