Determining the Camera Response from Images: What Is Knowable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Space of Camera Response Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Simple Self-Calibration Method To Infer A Non-Parametric Model Of The Imaging System Noise
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
The Journal of Supercomputing - Special issue: Parallel and distributed processing and applications
A camera-based mobile data channel: capacity and analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A biologically inspired technique for sampling of color images
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
Vision-based production of personalized video
Image Communication
Wavelet based denoising by correlation analysis for high dynamic range imaging
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An optimized tongue image color correction scheme
IEEE Transactions on Information Technology in Biomedicine
Collaborative color calibration for multi-camera systems
Image Communication
Exploiting image collections for recovering photometric properties
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
A theoretical analysis of camera response functions in image deblurring
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Estimating Photometric Properties from Image Collections
Journal of Mathematical Imaging and Vision
| Hi-index | 0.01 |
A 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