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...
Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. In multi-stage Optima...
In this paper, we propose Local Binary Pattern Histogram Fourier features (LBP-HF), a novel rotation invariant image descriptor computed from discrete Fourier transforms of local b...
Timo Ahonen, Jiri Matas, Chu He, Matti Pietikä...
Registration of facial features is a significant step towards a complete solution of the face recognition problem. We have built a general framework for detecting a set of indivi...
Camera phones present new opportunities and challenges for mobile informationassociation and retrieval. The visual input in the real environment is a new and rich interaction moda...