VLDB 2026 Research / reviewers in the wild / expert
Young-Chang Chang
dblp:31/176
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1996
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Image and video processing · 50% Computational photography and imaging · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational photography and imaging › color science › color management
color calibration |
0.0 | 1 | 1996 | RGB calibration for color image analysis in machine vision · IEEE Trans. Image Process. 1996 |
Image and video processing › color image processing
color image analysis |
0.0 | 1 | 1996 | RGB calibration for color image analysis in machine vision · IEEE Trans. Image Process. 1996 |
Methods — techniques the papers use, named apart from their topics
standardized color chart · 0.0illumination nonuniformity correction · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1996 | RGB calibration for color image analysis in machine visionabstractA color calibration method for correcting the variations in RGB color values caused by vision system components was developed and tested in this study. The calibration scheme concentrated on comprehensively estimating and removing the RGB errors without specifying error sources and their effects. The algorithm for color calibration was based upon the use of a standardized color chart and developed as a preprocessing tool for color image analysis. According to the theory of image formation, RGB errors in color images were categorized into multiplicative and additive errors. Multiplicative and additive errors contained various error sources-gray-level shift, a variation in amplification and quantization in camera electronics or frame grabber, the change of color temperature of illumination with time, and related factors. The RGB errors of arbitrary colors in an image were estimated from the RGB errors of standard colors contained in the image. The color calibration method also contained an algorithm for correcting the nonuniformity of illumination in the scene. The algorithm was tested under two different conditions-uniform and nonuniform illumination in the scene. The RGB errors of arbitrary colors in test images were almost completely removed after color calibration. The maximum residual error was seven gray levels under uniform illumination and 12 gray levels under nonuniform illumination. Most residual RGB errors were caused by residual nonuniformity of illumination in images, The test results showed that the developed method was effective in correcting the variations in RGB color values caused by vision system components. Young-Chang Chang, John F. Reid |
IEEE Trans. Image Process. | 1 |