Huzefa Neemuchwala

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Matching a reference image to a secondary image extracted from a database of transformed exemplars constitutes an important image retrieval task. Two related problems are: specification of a general class of discriminatory image features and an appropriate similarity measure to rank the closeness of the query to the database. In this paper we present a(More)
Quantitative evaluation of similarity between feature densities of images is an important step in several computer vision and data-mining applications such as registration of two or more images and retrieval and clustering of images. Previously we had introduced a new class of similarity measures based on entropic graphs to estimate Rènyi’s a-entropy,(More)
In many applications, fusion of images acquired via two or more sensors requires image alignment to an identical pose, a process called image registration. Image registration methods select a sequence of transformations to maximize an image similarity measure. Recently a new class of entropic-graph similarity measures was introduced for image registration,(More)
Image registration is a difficult task especially when spurrious image intensity differences and spatial variations between the two images are present. To robustify image registration algorithms to such spurrious variations it can be useful to employ an image registration matching criteria on higher dimensional feature spaces. This paper will present an(More)
Registration of an image, the query or reference, to a database of rotated and translated exemplars constitutes an important image retrieval and indexing application which arises in biomedical imaging, digital libraries, georegistration, and other areas. Two important issues are the specification of a class of discriminatory and generalizable image features(More)
We present a general framework for image discrimination based on identifying small, localized differences between images. Our novel matching scheme is based on an alternate information divergence criterion, the Rényi -entropy. The minimum spanning tree (MST) is used to derive a direct estimate of -entropy over a feature set defined by basis features(More)