Abstract. The development and evaluation of texture synthesis algorithms is discussed. We present texture synthesis algorithms based on the gray-level co-occurrence (GLC) model of a texture field. These algorithms use a texture similarity metric, which is shown to have high correlation with human perception of textures. Synthesis algorithms are evaluated using extensive experimental analysis. These experiments are designed to compare various iterative algorithms for synthesizing a random texture possessing a given set of second-order probabilities as characterized by a GLC model. Three texture test cases are selected to serve as the targets for the synthesis process in the experiments. The three texture test cases are selected so as to represent three different types of primitive texture: disordered, weakly ordered, and strongly ordered. For each experiment, we judge the relative quality of the algorithms by two criteria. First, we consider the quality of the final synthesized result in terms of the visual similarity to the target texture as well as a numerical measure of the error between the GLC models of the synthesized texture and the target texture. Second, we consider the relative computational efficiency of an algorithm, in terms of how quickly the algorithm converges to the final result. We conclude that a multiresolution version of the ''spin flip'' algorithm, where an individual pixel's gray level is changed to the gray level that most reduces the weighted error between the images second order probabilities and the target probabilities, performs the best for all of the texture test cases considered. Finally, with the help of psychophysical experiments, we demonstrate that the results for the texture synthesis algorithms have high correlation with the texture similarities perceived by human observers.
We present an experimental framework for evaluating metrics for the search and discrimination of a natural texture pattern from its background. Such metrics could help identify preattentive cues and underlying models of search and discrimination, and evaluate and design camouflage patterns and automatic target recognition systems. Human observers were asked to view image stimuli consisting of various target patterns embedded within various background patterns. These psychophysical experiments provided a quantitative basis for comparison of human judgments to the computed values of target distinctness metrics. Two different experimental methodologies were utilized. The first methodology consisted of paired comparisons of a set of stimuli containing targets in a fixed location known to the observers. The observers were asked to judge the relative target distinctness for each pair of stimuli. The second methodology involved stimuli in which the targets were placed in random locations unknown to the observer. The observers were asked to search each image scene and identify suspected target locations. Using a prototype eye tracking testbed, the integrated testbed for eye movement studies, the observers' fixation points during the experiment were recorded and analyzed. For both experiments, the level of correlation with the psychophysical data was used as the basis for evaluating target distinctness metrics. Overall, of the set of target distinctness metrics considered, a metric based on a model of image texture was the most strongly correlated with the psychophysical data.
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