Local invariant feature based methods have been proven to be effective in computer vision for object recognition and learning. But for an image, the number of points detected and ...
Abstract. In this article, scale and orientation invariant object detection is performed by matching intensity level histograms. Unlike other global measurement methods, the presen...
Michael Villamizar, Alberto Sanfeliu, Juan Andrade...
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and com...
LBP (Local Binary Pattern) as an image operator is used to extract LBPH (LBP histogram) features for texture description. In this paper, we present a novel method to use LBPH featu...
This chapter presents an approach for texture and object recognition that uses scale- or affine-invariant local image features in combination with a discriminative classifier. Text...