A two-class classification method of image patterns using principal component analysis (PCA) is proposed, in which classification is performed in the two-dimensional (2-D) space constructed by the reconstruction errors. The reconstruction error is computed using PCA for each assumed class. Training data sets are used to compute eigenvectors with which PCA reduces the dimensionality of the input vector space and reconstructs an input vector in the reduced space. The line equation with two parameters is defined as a linear decision boundary and these parameters are estimated by probabilistic approach. Also its application to face detection is experimented.
This paper presents a modified Hough transform (HT) by formulating the conjugate pair for line detection. By considering conjugate pair of the HT, a fast computing algorithm can be derived. The concept of conjugation is applied to the Radon transform, a generalized HT, and the Gabor transforms. Formulation of the conjugate pairs of 3D Hough transform is also presented.