In this paper, a novel apple Physalospora recognition approach based on the Gabor feature-based principal component analysis (GBPCA) is proposed. In this method, the principal component analysis (PCA) is a powerful technique for finding patterns in data of high dimensionality and can reduce the high dimensionality of the data space to the low dimensionality of feature space effectively. Gabor filter is an effective tool because of its accurate time-frequency localization and robustness against variations caused by illumination and rotation. Three main steps are taken in the proposed GBPCA: Firstly, Gabor features of different scales and orientations are extracted by convoluting the Gabor filter bank and the original gray images. Then eigenvectors in the direction of the largest variance of the training vectors is computed by PCA. An eigenspace is composed of these eigenvectors. Thirdly, we project the testing images into the constructed eigenspace and the Euclidean distance and nearest neighbor classifier are adopted for classification. Therefore, the proposed method is not only insensitive to illumination and rotation, but also efficient in feature matching. Experimental results demonstrate the effectiveness of the proposed GBPCA.

Date Published: 10 July 2009
PDF: 7 pages
Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890P (10 July 2009); doi: 10.1117/12.836886

Published in SPIE Proceedings Vol. 7489:
PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering
Honghua Tan; Qi Luo, Editor(s)