Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
A color clustering technique for image segmentation
Computer Vision, Graphics, and Image Processing
A physical approach to color image understanding
International Journal of Computer Vision
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Statistical color models with application to skin detection
International Journal of Computer Vision
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffuse-Specular Separation and Depth Recovery from Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Detection of Specularity Using Color and Multiple Views
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Skin Detection in Video under Changing Illumination Conditions
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Local Non-Negative Matrix Factorization as a Visual Representation
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Properties of a Center/Surround Retinex: Part 2. Surround Design
Properties of a Center/Surround Retinex: Part 2. Surround Design
Properties of a Center/Surround Retinex: Part 1. Signal Processing Design
Properties of a Center/Surround Retinex: Part 1. Signal Processing Design
Highlight Removal by Illumination-Constrained Inpainting
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Extracting layers and analyzing their specular properties using epipolar-plane-image analysis
Computer Vision and Image Understanding
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Extraction of Illumination Effects from Natural Images with Color Transition Model
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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Specularities often confound algorithms designed to solve computer vision tasks such as image segmentation, object detection, and tracking. These tasks usually require color image segmentation to partition an image into regions, where each region corresponds to a particular material. Due to discontinuities resulting from shadows and specularities, a single material is often segmented into several sub-regions. In this paper, a specularity detection and removal technique is proposed that requires no camera calibration or other a priori information regarding the scene. The approach specifically addresses detecting and removing specularities in facial images. The image is first processed by the Luminance Multi-Scale Retinex [B.V. Funt, K. Barnard, M. Brockington, V. Cardei, Luminance-Based Multi-Scale Retinex, AIC'97, Kyoto, Japan, May 1997]. Second, potential specularities are detected and a wavefront is generated outwards from the peak of the specularity to its boundary or until a material boundary has been reached. Upon attaining the specularity boundary, the wavefront contracts inwards while coloring in the specularity until the latter no longer exists. The third step is discussed in a companion paper [M.D. Levine, J. Bhattacharyya, Removing shadows, Pattern Recognition Letters, 26 (2005) 251-265] where a method for detecting and removing shadows has also been introduced. The approach involves training Support Vector Machines to identify shadow boundaries based on their boundary properties. The latter are used to identify shadowed regions in the image and then assign to them the color of non-shadow neighbors of the same material as the shadow. Based on these three steps, we show that more meaningful color image segmentations can be achieved by compensating for illumination using the Illumination Compensation Method proposed in this paper. It is also demonstrated that the accuracy of facial skin detection improves significantly when this illumination compensation approach is used. Finally, we show how illumination compensation can increase the accuracy of face recognition. ition.