Temporal textures are textures with motion. Examples include wavy water, rising steam and fire. We model image sequences of temporal textures using the spatio-temporal autoregressive model (STAR). This model expresses each pixel as a linear combination of surrounding pixels lagged both in space and in time. The model provides a base for both recognition and synthesis. We show how the least squares method can accurately estimate model parameters for large, causal neighborhoods with more than 1000 parameters. Synthesis results show that the model can adequately capture the spatial and temporal characteristics of many temporal textures. A 95% recognition rate is achieved for a 135 element database with 15 texture classes. 1.

There is a growing need for the ability to query image databases based on similarity of image content rather than strict keyword search. As distance computations can be expensive, there is a need for indexing systems and algorithms that can eliminate candidate images without performing distance calculations. As user needs may change from session to session, there is also a need for runtime creation of distance measures. In this paper, we present FIDS, “flexible image database system. ” FIDS allows the user to query the database based on complex combinations of dozens of predefined distance measures. Using an indexing scheme and algorithms based on the triangle inequality, FIDS can often return matches to the query image without directly comparing the query image to more than a small percentage of the database. This paper describes the technical contributions of the FIDS approach to content-based image retrieval.

We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a Gaussian--Markov random fields (GMRF) model. Unlike a GMRFbased approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The second part is a two-dimensional (2--D) array of locally excitatory globally inhibitory oscillator networks (LEGION). After being filtered for noise suppression, features are used to determine the local couplings in the network. When LEGION runs, the oscillators corresponding to the same texture tend to synchronize, whereas different texture regions tend to correspond to distinct phases. In simulations, a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks. We provide results on real texture images to demonstrate the performance of our method. Index Terms---Dynamical systems, Gaussian Markov random fields, LEGION, neural networks, relaxation oscillators, texture segmentation. I.

| Based on a local spatial/frequency representation, we propose a spectral histogram as a feature statistic for characterizing texture appearance. The spectral histogram consists of marginal distributions of responses of a bank of lters and encodes implicitly the structure of images. The distance between two spectral histograms is measured using 2 -statistic. The spectral histogram with the associated distance measure exhibits several properties that are necessary for texture discrimination and classication. The spectral histogram provides a generic feature for texture as well as non-texture images, where the uniform image is a special case with a unique pattern. The spectral histogram is a nonlinear operator, consistent with the nonlinearity in human perception. Our classication experiments reveal that it can generalize well even with a small number of training samples and the classication result does not depend on a particular form of distance measure. We have obtained very g...

This paper shows how scale-based clustering can be done using the Radial Basis Function (RBF) Network, with the RBF width as the scale parameter and a dummy target as the desired output. The technique suggests the "right" scale at which the given data set should be clustered, thereby providing a solution to the problem of determining the number of RBF units and the widths required to get a good network solution. The network compares favorably with other standard techniques on benchmark clustering examples. Properties that are required of non-gaussian basis functions, if they are to serve in alternative clustering networks, are identified. The work on the whole points out an important role played by the width parameter in RBFN, when observed over several scales, and provides a fundamental link to the scale space theory developed in computational vision. The work described here is supported in part by the National Science Foundation under grant ECS-9307632 and in part by ONR Contract N...

The statistics of gray level differences have been successfully used in a number of texture analysis studies. In this paper we propose to use signed gray level differences and their multidimensional distributions for texture description. The present approach has important advantages compared to earlier related approaches based on gray level cooccurrence matrices or histograms of absolute gray level differences. Experiments with a difficult texture classification problem show that our approach provides a very good and robust classification performance in comparison to the mainstream paradigms such as cooccurrence matrices, Gaussian Markov Random Fields, or Gabor filtering.

Texture segmentation is one of the early steps towards identifying surfaces and objects in an image. In this paper a moment based texture segmentation algorithm is presented. The moments in small windows of the image are used as texture features which are then used to segment the textures. The algorithm has successfully segmented binary images containing textures with iso-second order statistics as well as a number of gray level texture images. 1. INTRODUCTION The natural world abounds with textured surfaces. Any realistic vision system that is expected to work successfully, therefore, must be able to handle such input. The process of identifying regions with similar texture and separating regions with different texture is one of the early steps towards identifying surfaces and objects. This process is called texture segmentation and is the major focus of this paper. Texture analysis has been studied for a long time using various approaches. Various methods perform texture anal...

Abstract — The operational segmentation of SAR sea ice imagery is a practical, challenging objective in the realm of applied pattern recognition. This research is in support of operational activities at the Canadian Ice Services (CIS), a government agency that monitors all ice-infested regions under Canadian jurisdiction. This paper uses a fusion of tone and texture to segment SAR sea ice images in an unsupervised manner. A novel Markov random field (MRF) segmentation technique is employed and produces improved results over K-means and the traditional MRF implementation. I.

Fingerprint Classification and Matching Using a Filterbank By Salil Prabhakar Accurate automatic personal identification is critical in a variety of applications in our electronically interconnected society. Biometrics, which refers to identification based on physical or behavioral characteristics, is being increasingly adopted to provide positive identification with a high degree of confidence. Among all the biometric techniques, fingerprint-based authentication systems have received the most attention because of the long history of fingerprints and their extensive use in forensics. However, the numerous fingerprint systems currently available still do not meet the stringent performance requirements of several important civilian applications. To assess the performance limitations of popular minutiae-based fingerprint verification system, we theoretically estimate the probability of a false correspondence between two fingerprints from di#erent fingers based on the minutiae representation of fingerprints. Due to the limited amount of information present in the minutiae-based representation, it is desirable to explore alternative representations of fingerprints. We present a novel filterbank-based representation of fingerprints. We have used this compact representation for fingerprint classification as well as fingerprint verification. Experimental results show that this algorithm competes well with the state-of-theart minutiae-based matchers. We have developed a decision level information fusion framework which improves the fingerprint verification accuracy when multiple matchers, multiple fingers of the user, or multiple impressions of the same finger are combined. A feature verification and purification scheme is proposed to improve the performance of the minutiae-...

This paper addresses the issue of selecting features from a given wavelet packet subband decomposition that are most useful for texture classification in an image. A functional measure based on Kullback-Leibler distance is proposed as a way to select most discriminant subbands. Experimental results show a superior performance in terms of classification error rates.