Abstract not found

Humans have a well developed ability to detect curvilinear structure in noisy images. Good algorithms for performing this process would be very useful in machine vision for image segmentation and object recognition. Previous approaches to this problem such as those due to Parent and Zucker and Sha’shua and Ullman have been based on relaxation. We have developed a simple feedforward and parallel approach to this problem based on the idea of developing filters tuned to local oriented circular arcs. This provides a natural second order generalization of the idea of directional operators popular for edge detection. Curve detection can then be done by methods very similar to those used for edge detection. Experimental results are shown on both synthetic and natural images. We also review data from an experiment investigating human preattentive line segregation and present predictions from our model that agree with this data. „�™���™— � ‚���� � …gfGgƒh WIGTIWD g������ � ƒ™ � ��™ �

Transformational apparent motion (TAM) occurs when a figure changes discretely from one configuration to another overlapping configuration. Rather than an abrupt shape change, the initial shape is perceived to transform smoothly into the final shape as if animated by a series of intermediate shapes. We find that TAM follows an analysis of form that takes 80–140 msec. Form analysis can function both at and away from equiluminance and can occur over contours defined by uniform regions as well as outlines. Moreover, the forms analyzed can be 3-D, resulting in motion paths that appear to smoothly project out from or into the stimulus plane. The perceived transformation is generally the one that involves the least change in the shape or location of the initial figure in a 3-D sense. We conclude that perception of TAM follows an analysis of 3-D form that takes,100 msec. This stage of form analysis may be common to both TAM and second-order motion. When two nonoverlapping figures are flickered in succession within a certain range of spatiotemporal offsets (Korte, 1915), they appear to comprise a single object jumping rigidly back and forth in translational apparent motion. Because no object actually moves in the world, the

of the resulting images we computed C(x; y; 0 ; 0), and C(x; y; 90 ; 0) using oe of 6.5 pixels. To measure the errors predicted by our algorithm we used the negative of the ratio of the total response in the central vertical strip of C(x; y; 90 ; 0) to the total response in the central horizontal strip of C(x; y; 0 ; 0). The strips were 8 pixels wide. The resulting predictions are plotted in Figures 8b. Note, that the algorithm predicts the trend evident from the psychophysical data, where the lines made of collinear blobs or bars are detected more reliably then lines made of orthogonal elements. 15 Figure 7: The stimuli for experiment 1. The target line is vertical. In the first and second columns are the collinear bars and blobs, respectively. In the third and forth columns are the corresponding orthogonal elements. Aspect ratios are, from top to bottom, 1,1.8, and 2.3. 1.00 bars colinear blobs colinear Aspect ratio Long/Short Axis 1.8 2.3 blobs orthogonal bars

We test the proposition that the appearance and detection of visual contours is based on an increase in the perceived contrast of contour elements. First we show that detection of contours is quite possible in the presence of very high levels of variability in contrast. Second we show that inclusion in a contour does not induce Gabor patches to appear to be of higher contrast than patches outside of a contour. These results suggest that, contrary to a number of current models, contrast or its assumed physiological correlate (the mean firing rate of early cortical neurons) is not the

Abstract not found