PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
Various local descriptors have been used successfully in a variety of tasks including object recognition. Although different descriptors have been shown to have different strength...
Abstract. Probabilistic models with hidden variables such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have recently become popular for so...
Local invariant feature extraction methods are widely used for image-features matching. There exist a number of approaches aimed at the refinement of the matches between image-fe...
In this paper, we investigate the effect of substantial inter-image intensity changes and changes in modality on the performance of keypoint detection, description, and matching a...