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PCA-Based Feature Reduction for Depth Estimation  

Shin, Sung-Sik (Division of Computer Science and Engineering, Chonbuk National University)
Gwun, Ou-Bong (Division of Computer Science and Engineering, Chonbuk National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.47, no.3, 2010 , pp. 29-35 More about this Journal
Abstract
This paper discusses a method that can enhance the exactness of depth estimation of an image by PCA(Principle Component Analysis) based on feature reduction through learning algorithm. In estimation of the depth of an image, hyphen such as energy of pixels and gradient of them are found, those selves and their relationship are used for depth estimation. In such a case, many features are obtained by various filter operations. If all of the obtained features are equally used without considering their contribution for depth estimation, The efficiency of depth estimation goes down. This paper proposes a method that can enhance the exactness of depth estimation of an image and its processing speed is considered as the contribution factor through PCA. The experiment shows that the proposed method(30% of an feature vector) is more exact(average 0.4%, maximum 2.5%) than using all of an image data in depth estimation.
Keywords
PCA; Principal Component Analysis; Feature Reduction; Depth Estimation;
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