Classes of powerful stimuli that reliably elicit wrong depth percepts are examined. The depth percepts in turn cause the stimuli to appear to move vividly as observers move in front of them. These illusory percepts highlight the issue of how we obtain a stable percept of the environment as we move about, despite considerable changes in the retinal images. Experimental results on the role of pictorial cues, and possible explanations for the illusory motion are presented. The interaction of data-driven mechanisms and schema-driven cognitive processes is examined.

This paper presents the theory, design principles, implementation and performance results of PicHunter, a prototype content-based image retrieval (CBIR) system. In addition, this document presents the rationale, design and results of psychophysical experiments that were conducted to address some key issues that arose during PicHunter's development. The PicHunter project makes four primary contributions to research on CBIR. First, PicHunter represents a simple instance of a general Bayesian framework which we describe for using relevance feedback to direct a search. With an explicit model of what users would do, given the target image they want, PicHunter uses Bayes's rule to predict the target they want, given their actions. This is done via a probability distribution over possible image targets, rather than by refining a query. Second, an entropy-minimizing display algorithm is described that attempts to maximize the information obtained from a user at each iteration of the search. Third, PicHunter makes use of hidden annotation rather than a possibly inaccurate/inconsistent annotation structure that the user must learn and make queries in. Finally, PicHunter introduces two experimental paradigms to quantitatively evaluate the performance of the system, and psychophysical experiments are presented that support the theoretical claims.

As the healthcare community has begun to rely increasingly upon digital technologies for acquisition, storage, and transmission of pictorial data, image compression has become an indispensable tool. We have investigated the feasibility of lossy compression in a well-defined task domain, the clinical assessment of digitized images of chromatic microscopic pathology specimens. The effect of compression was measured under two distinct perceptual criteria, just noticeable difference (j.n.d.) and largest tolerable distortion (l.t.d.), differing in the involvement required from subjects, who were experts in pathology. For standard JPEG compressed images it was found that when the experiment is performed under the l.t.d. criterion, a significantly larger compression ratio is reported as satisfactory. It is concluded that lossy compression holds promise for diagnostic telepathology.

We have developed a computational model for texture perception which has physiological relevance and correlates well with human performance. The model attempts to simulate the visual processing characteristics by incorporating mechanisms tuned to detect luminance-polarity, orientation, spatial frequency and color, which are characteristic features of any textural image. We obtained a very good correlation between the model's simulation results and data from psychophysical experiments with a systematically selected set of visual stimuli with texture patterns defined by spatial variations in color, luminance, and orientation. In addition, the model predicts correctly texture segregation performance with key benchmarks and natural textures. This represents a first effort to incorporate chromatic signals in texture segregation models of psychophysical relevance, most of which have treated grey-level images so far. Another novel feature of the model is the extension or the concept of spatial double opponency to domains beyond color, such as orientation and spatial frequency. The model has potential applications in the areas of image processing, machine vision and pattern recognition, and scientific visualization.

The potential of lossy image compression is investigated in a well defined task domain, specifically, clinical assessment of chromatic, surgical and hematopathology specimens. Two criteria were employed, just noticeable difference and largest tolerable distortion. Compression tolerances differed among pathologists, but conformed to well defined upper and lower limits. The level of tolerable compression was significantly larger for the second criterion. It is concluded that lossy image compression is feasible for diagnostic pathology and may hold promise for telepathology applications.