We develop a new approach to finding corners in images that combines foveated edge detection and curvature calculation with saccadic placement of foveal fixations. Each saccade moves the fovea to a location of high curvature combined with high edge gradient. Edges are located using a foveated Canny edge detector with spatial constant that increases with eccentricity. Next, we calculate a measure of local corner strength, based on a product of curvature and gradient. An inhibition factor based on previous visits to a region of the image prevents the system from repeatedly returning to the same locale. A long saccade is move thes fovea to previously unexplored areas of the image. Subsequent short saccades improve the accuracy of the location of the corner approximated by the long saccade. The system is tested on two natural scenes and the results compared against subjects observing the same test images through an eyetracker. Results show that the algorithm is a good locator of corners.
A novel filtering technique based on local information and Fuzzy Logic is proposed. The performance of the proposed method is evaluated through different criteria. Preliminary experimental results show that proposed method is effective for different filtering tasks.
A model of human visual detection performance has been developed, based on available anatomical and physiological data for the primate visual system. The inhomogeneous retino- cortical (IRC) model computes detection thresholds by comparing simulated neural responses to target patterns with responses to a uniform background of the same luminance. The model incorporates human ganglion cell sampling distributions; macaque monkey ganglion cell receptive field properties; macaque cortical cell contrast nonlinearities; and a optical decision rule based on ideal observer theory. Spatial receptive field properties of cortical neurons were not included. Two parameters were allowed to vary while minimizing the squared error between predicted and observed thresholds. One parameter was decision efficiency, the other was the relative strength of the ganglion-cell center and surround. The latter was only allowed to vary within a small range consistent with known physiology. Contrast sensitivity was measured for sinewave gratings as a function of spatial frequency, target size and eccentricity. Contrast sensitivity was also measured for an airplane target as a function of target size, with and without artificial scotomas. The results of these experiments, as well as contrast sensitivity data from the literature were compared to predictions of the IRC model. Predictions were reasonably good for grating and airplane targets.
A round florescent bulb placed around the lens of a CCD camera illuminates a group of carbon resistors. The pictures are passed to a series of feature detectors the outputs of which are sent to a back propagation neural network. Two identical boards were each made with 112 resistors of various colors. Resistors on one board were burned. The network was trained with a test set of about half the good and bad resistors. Results showed 93.8 percent correct when recalling on the untrained half.
In image processing the solution is often unique to the problem. To be more specific, the importance of the filter window and sampling pattern chosen to filter, pass, or enhance a specific shape is very specific to the problem at hand. We detect suspect tumors in mammograms using a weighted majority minimum range filter and different sampling patterns and windows as a demonstration of this fact. Several methods have been developed to automate the process of detecting tumors in mammograms. We show that traditional windowing or sampling methods may be replaced by a hexagonal method that more accurately reflects the geometry of the problem and could improve the techniques already in existence. Several theorems involving a hexagonal filter window are presented, followed by the results of our application to mammograms.
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