JOURNAL ARTICLE

<title>Generic image matching system</title>

Zhongjie Liang

Year: 1992 Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Vol: 1670 Pages: 255-265   Publisher: SPIE

Abstract

The generic imaging matching system (GIMS) provides an optimal systematic solution to any problem of color image processing in printing and publishing that can be classified as or modeled to the generic image matching problem defined. Typical GIMS systems/processes include color matching from different output devices, color conversion, color correction, device calibration, colorimetric scanner, colorimetric printer, colorimetric color reproduction, and image interpolation from scattered data. GIMS makes color matching easy for the user and maximizes operational flexibility allowing the user to obtain the degree of match wanted while providing the capability to achieve the best balance with respect to the human perception of color, color fidelity, and preservation of image information and color contrast. Instead of controlling coefficients in a transformation formula, GIMS controls the mapping directly in a standard device-independent color space, so that color can be matched, conceptually, to the highest possible accuracy. An optimization algorithm called modified vector shading was developed to minimize the matching error and to perform a 'near-neighborhood' gamut compression. An automatic error correction algorithm with a multidirection searching procedure using correlated re-initialization was developed to avoid local minimum failures. Once the mapping for color matching is generated, it can be utilized by a multidimensional linear interpolator with a small look-up-table (LUT) implemented by either software, a hardware interpolator or a digital-signal-processor.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Keywords:
Computer science Computer vision Initialization Artificial intelligence ICC profile Color management Gamut Color depth Color balance Color image RGB color model Color space Lookup table Demosaicing Matching (statistics) Interpolation (computer graphics) Image processing Image (mathematics) Mathematics

Metrics

0
Cited By
0.00
FWCI (Field Weighted Citation Impact)
0
Refs
0.14
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Color Science and Applications
Physical Sciences →  Physics and Astronomy →  Atomic and Molecular Physics, and Optics
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image Enhancement Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

Related Documents

JOURNAL ARTICLE

<title>Color image matching</title>

Michael HahnClaus Brenner

Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Year: 1995 Vol: 2572 Pages: 92-101
JOURNAL ARTICLE

<title>Neural-net-based image matching</title>

Anna JerebkoNikita BarabanovVadim R. LucivN.M. Allinson

Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Year: 2000 Vol: 3962 Pages: 128-137
JOURNAL ARTICLE

<title>Wavelets for multiresolution image matching</title>

Su ZhangHanfeng ChenYuncai LiuPengfei Shi

Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Year: 2001 Vol: 4552 Pages: 57-62
JOURNAL ARTICLE

<title>Image Matching Using Structure Information</title>

Richard H. Hudgin

Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Year: 1977 Vol: 0117 Pages: 126-131
JOURNAL ARTICLE

<title>Image matching based on fractal image coding</title>

Xiaoping LiuJiaxiong PengMingyue DingJi Zhou

Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Year: 1995 Vol: 2620 Pages: 699-702
© 2026 ScienceGate Book Chapters — All rights reserved.