Loading [a11y]/accessibility-menu.js
Shadow Boundaries Identification in Single Natural Images via Multiple Kernels Learning | IEEE Conference Publication | IEEE Xplore

Shadow Boundaries Identification in Single Natural Images via Multiple Kernels Learning


Abstract:

The identification of shadow and shading boundaries is a key step towards reducing the imaging effects that are caused by direct illumination of the light source in the s...Show More

Abstract:

The identification of shadow and shading boundaries is a key step towards reducing the imaging effects that are caused by direct illumination of the light source in the scene. Discriminating shadow boundaries from images of natural scenes has been widely applied in the field of computer vision such as object recognition, intelligent monitoring and image understanding. In this paper, we propose a method to identify shadow boundaries based on multiple kernel learning. We first extract all possible candidate boundaries and then analyze their properties. Unlike the previous proposed methods which simply combine features as a vector, we choose the optimal kernel function for every feature and learn the correct weights of different features from training database. At last, we link shadow boundaries fragments together to get longer and complete shadow boundaries. The experiment results show that the method we propose works well in shadow boundaries identification.
Date of Conference: 26-28 July 2013
Date Added to IEEE Xplore: 24 October 2013
Electronic ISBN:978-0-7695-5050-3
Conference Location: Qingdao, China

References

References is not available for this document.