Optical flow can be thought of as a generalization of orientation selection to include time, and this is a first attempt to develop this generalization. This paper is informal, and begins with an overview of a computa...
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Optical flow can be thought of as a generalization of orientation selection to include time, and this is a first attempt to develop this generalization. This paper is informal, and begins with an overview of a computational theory of orientation selection that has been developed in our laboratory during the past few years. A theory of optical flow computations is then sketched on the basis of this background, and a number of consequences important for both computational modeling and psychophysics are discussed. We concentrate in particular on two notions—dimensionality and structure—and describe a series of experiments that demonstrate that sensitivity to discontinuities in optical flow patterns has significant similarities to those in orientation patterns. Such similarities lend strong support to the analogy.
We present an efficient method for detecting straight line segments in digital pictures using a hypothesis prediction/verification paradigm. In this paradigm, a straight line segment of predefined length is predicted ...
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We present an efficient method for detecting straight line segments in digital pictures using a hypothesis prediction/verification paradigm. In this paradigm, a straight line segment of predefined length is predicted to exist at some particular pixel location. The orientation of this predicted line segment is based on the edge orientation at the pixel location. This prediction is then verified against statistical tests performed on the line. As a result, the predicted line is either validated as being a line segment, or it is rejected. An extension of this algorithm for the detection of lines at different lengths is also presented, and a criterion is defined in order to evaluate the significance of the detected line segments.
Neurons in the visual cortex typically respond selectively to the orientation, and velocity and direction of movement, of moving-bar stimuli. These responses are generally thought to provide information about the orie...
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Neurons in the visual cortex typically respond selectively to the orientation, and velocity and direction of movement, of moving-bar stimuli. These responses are generally thought to provide information about the orientation and position of lines and edges in the visual field. Some cells are also endstopped, that is selective for bars of specific lengths. Hubel and Wiesel first observed that endstopped hypercomplex cells1 could respond to curved stimuli and suggested they might be involved in detection of curvature, but the exact relationship between endstopping and curvature has never been determined. We present here a mathematical model relating endstopping to curvature in which the difference in response of two simple cells gives rise to endstopping and varies in proportion to curvature. We also provide physiological evidence that endstopped cells in area 17 of the cat visual cortex are selective for curvature, whereas non-endstopped cells are not, and that some are selective for the sign of curvature. The prevailing view of edge and curve determination is that orientations are selected locally by the class of simple cortical cells3 and then integrated to form global curves. We have developed a computational theory of orientation selection4,5 which shows that measurements of orientation obtained by simple cells are not sufficient because there will be strong, incorrect responses from cells whose receptive fields (RFs) span distinct curves (Fig. 1). If estimates of curvature are available, however, these inappropriate responses can be eliminated. Curvature provides the key to structuring the network that underlies our theory and distinguishes it from previous lateral inhibition schemes6,7.
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