We have proposed a novel method to separate diffuse and specular reflection components
Separating reflection components of textured surfaces using a single image.
IEEE Trans. Pattern Anal. Mach. Intell., no. 2 (2005): 178-193
In inhomogeneous objects, highlights are linear combinations of diffuse and specular reflection components. A number of methods have been proposed to separate or decompose these two components. To our knowledge, all methods that use a single input image require explicit color segmentation to deal with multicolored surfaces. Unfortunately,...更多
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- SEPARATING diffuse and specular reflection components is an essential subject in the field of computer vision.
- To properly acquire the diffuse only reflections, a method to separate the two components robustly and accurately is required.
- When a bundle of light rays enters an inhomogeneous opaque surface, some of the rays will immediately reflect back into the air, while the remainder will penetrate the body of the object.
- SEPARATING diffuse and specular reflection components is an essential subject in the field of computer vision
- Based on the dichromatic reflection model and chromaticities definitions derived above, we describe our goal: Given image intensities (IðxÞ) whose illumination chromaticity (À) is known, we intend to decompose them into their reflection components, mdðxÞÃðxÞ and msðxÞÀ
- We have proposed a novel method to separate diffuse and specular reflection components
- The main insight of the method is in the chromaticity-based iteration with regard to the logarithmic differentiation of the specular-free image
- In specular-free images, specular reflection disappear should equal to m d (m s 1⁄4 of the
- Given a normalized image, a specular-free image is generated
- Based on these two images, the “diffuse verification” verifies whether the normalized image has diffuse only pixels.
- If it has diffuse only, the processes terminate.
- The diffuse verification verifies once again whether the normalized image has diffuse-only pixels
- These two processes are done iteratively until there is no specularity in the normalized image.
- The following subsections will show the details of the two processes
- All images in the experiments were taken using a CCD camera: SONY DXC-9000 by setting the gamma correction off.
- The authors used convex-shaped objects to avoid interreflections and did not take account of saturated or blooming pixels in the experiments.
- The illumination chromaticities were estimated using a color constancy algorithm .
- This color constancy method requires crude highlight regions, which can be obtained using thresholding in both intensity and saturation
- The authors assumed that the estimated illumination chromaticity is exactly identical to the input image’s illumination chromaticity, À 1⁄4 Àest (10).
- The authors intend to describe the effect of illumination error in estimating the value of md by using the specular-to-diffuse mechanism.
- Md of error illumination becomes: The authors express the medrrWe have proposed a novel method to separate diffuse and specular reflection components.
- The main insight of the method is in the chromaticity-based iteration with regard to the logarithmic differentiation of the specular-free image.
- Without requiring explicit color segmentation
- It is possible because the authors base the method on local operation by utilizing the specular-free image.
- The experimental results on complex textured images show the effectiveness of the proposed method
- This research was, in part, supported by Japan Science and Technology (JST) under CREST Ikeuchi Project
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