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Based on a local spatial/frequency representation,we employ a spectral histogram as a feature statistic for texture classification. The spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram stage.(More)
This paper investigates the use of range images of faces for recognizing people. 3D scans of faces lead to range images that are linearly projected to low-dimensional subspaces for use in a classifier, say a nearest neighbor classifier or a support vector machine, to label people. Learning of subspaces is performed using an optimal component analysis, i.e.(More)
Seeking universal probability models for image representations, we employ a spectral approach where the images are decomposed using several bandpass lters, and probability models on the lter outputs (or spectral components) are imposed. We apply a (two-parameter) analytical form, introduced in [9] and called a Bessel K form, for modeling the marginal(More)
Linear representations and linear dimension reduction techniques are very common in signal and image processing. Many such applications reduce to solving problems of stochastic optimizations or statistical inferences on the set of all subspaces, i.e. a Grassmann manifold. Central to solving them is the computation of an “exponential” map (for constructing(More)
Using a differential-geometric treatment of planar shapes, we present tools for: 1) hierarchical clustering of imaged objects according to the shapes of their boundaries, 2) learning of probability models for clusters of shapes, and 3) testing of newly observed shapes under competing probability models. Clustering at any level of hierarchy is performed(More)
Due to their light weight, low power, and practically unlimited identification capacity, radio frequency identification (RFID) tags and associated devices offer distinctive advantages and are widely recognized for their promising potential in context-aware computing; by tagging objects with RFID tags, the environment can be sensed in a costand(More)
This article presents a mathematical de nition of texture { the Julesz ensemble (h), which is the set of all images (de ned on Z) that share identical statistics h. Then texture modeling is posed as an inverse problem: given a set of images sampled from an unknown Julesz ensemble (h ), we search for the statistics h which de ne the ensemble. A Julesz(More)
We present a face detection method using spectral histograms and support vector machines (SVMs). Each image window is represented by its spectral histogram, which is a feature vector consisting of histograms of filtered images. Using statistical sampling, we show systematically the representation groups face images together; in comparison, commonly used(More)
We suggest a spectral histogram, defined as the marginal distribution of filter responses, as a quantitative definition for a texton pattern. By matching spectral histograms, an arbitrary image can be transformed to an image with similar textons to the observed. We use the chi(2)-statistic to measure the difference between two spectral histograms, which(More)
Within the glomerulus, the scaffolding protein nephrin bridges the actin-rich foot processes that extend from adjacent podocytes to form the slit diaphragm. Mutations affecting a number of slit diaphragm proteins, including nephrin, cause glomerular disease through rearrangement of the actin cytoskeleton and disruption of the filtration barrier. We recently(More)