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YUN-CHUNG CHUNG, SHEN CHERNG*, ROBERT R. BAILEY AND SEI-WANG CHEN
Department of Computer Science and Information Engineering
National Taiwan Normal University
Taipei, 116 Taiwan
*Department of Computer Science and Information Engineering
Cheng Shin University
Kaohsiung, 833 Taiwan
An image is often modeled as a product of two principal components: illumination
and reflectance components. The former is related to the amount of light incident on the
scene and the latter is associated with the scene characteristics. The images formed from
the two components are referred to as the illumination and the reflectance images; both
are called the intrinsic images of the original image. The illumination components of the
images of a fixed scene vary from image to image, while the reflectance components of
the images in principle remain constant. Both reflectance and illumination images have
their own applications. Intrinsic image extraction has long been an important task for
computer vision applications. However, this task is not at all simple because it is an illconditioned
problem. The proposed approach convolves an input image with a prescribed
set of derivative filters. The pixels of the derivative images are next classified as
being reflectance or illumination according to three measures: chromatic, intensity contrast
and edge sharpness, which are calculated in advance for each pixel from the input
image. Finally, a de-convolution process is applied to the classified derivative images to
obtain the intrinsic images. The results reveal the feasibility of the proposed technique in
both rapidly and effectively decomposing intrinsic images from one single image.
Received December 24, 2007; revised June 27, 2008; accepted August 22, 2008.
Communicated by Tong-Yee Lee.