Abstract
Due to the strong reflection property of materials with smooth surfaces like ceramic and metal, it will cause saturation and the highlight phenomenon in the image when taking pictures of those materials. In order to solve this problem, a new algorithm which is based on reflection component separation (RCS) and priority region filling theory is designed. Firstly, the specular pixels in the image are found by comparing the pixel parameters. Then, the reflection components are separated and processed. However, for ceramic, metal and other objects with strong specular highlight, RCS theory will change color information of highlight pixels due to larger specular reflection component. In this situation, priority region filling theory was used to restore the color information. Finally, we implement 3D experiments on objects with strong reflecting surfaces like ceramic plate, ceramic bottle, marble pot and yellow plate. Experimental results show that, with the proposed method, the highlight caused by the strong reflecting surface can be well suppressed. The highlight pixel number of ceramic plate, ceramic bottle, marble pot and yellow plate, is decreased by 43.8 times, 41.4 times, 33.0 times, and 10.1 times. Three-dimensional reconstruction results show that highlight areas were significantly reduced.
Highlights
Structured light (SL) vision measurement has drawn much attention due to its potential for three-dimensional (3D) applications to diverse areas, such as re-engineering, 3D games, industrial inspection, object recognition, and clothing design, to name only a few [1]
We proposed a specular highlight removal method based on reflection component separation (RCS) and priority pixel filling theory, which does not depend on either polarization or image segmentation
When only the diffuse reflection component is contained in the pixel β( x ) = 0, the chroma value is independent of the diffuse reflection weighting factor α( x )
Summary
Structured light (SL) vision measurement has drawn much attention due to its potential for three-dimensional (3D) applications to diverse areas, such as re-engineering, 3D games, industrial inspection, object recognition, and clothing design, to name only a few [1]. A single input image method requires complex color segmentation to deal with multi-colored images For this problem, we proposed a specular highlight removal method based on reflection component separation (RCS) and priority pixel filling theory, which does not depend on either polarization or image segmentation. We proposed a specular highlight removal method based on reflection component separation (RCS) and priority pixel filling theory, which does not depend on either polarization or image segmentation This method is based on color information completely without requiring any geometrical information of the object surface. For ceramic, metal and other objects with strong specular highlight, RCS theory will change color information of highlight pixel due to larger specular reflection component. The proposed method was applied to reconstruct ceramic surfaces with strong specular highlights
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
