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This framework outlines methods for primal sketch and object recognition through advanced edge detection techniques. It covers local edges, corners, T-junctions, and blobs, emphasizing the significance of first and second derivatives in detecting sharp changes in image intensity. Multi-scale filtering using Laplacian of Gaussian filters aids in identifying edges at various scales through zero-crossings. Additionally, methods are proposed to improve edge detection by leveraging spatial structures, directional derivatives, and local oriented contrast, thereby enhancing the accuracy and effectiveness of early image processing.
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Marr’s framework for vision 2-1/2D sketch Primal sketch Object Recognition Early processing 3D estimation Image
Primal sketch • Local edges • Corners • T-junctions • Blobs • Groups of features
Derivatives as edge finders • Edges are sharp changes in image intensity. • 1st derivative of the image intensity peaks at an edge • 2nd derivative is zero at edges • Edges are at zero-crossings of the second derivative
Multi-scale filtering • Find zero-crossings at multiple scales • Filter with Laplacian of Gaussian filters that have different sizes • Edges = zero-crossing sat all scales • Find spatial coincidence of zero-crossings across scales
Mirage • Filter with three Laplacian of Gaussians (different sizes) • Seperately sum negative and positive parts • Mark zero (Z) regions, positive response regions (R+) and negative response regions (R-) • Rules • Z region = luminance plateau • R region with only one Z on a side = edge • R region with Z on both sides = bar
Ways to improve edge detection • Take advantage of the spatial structure of edges • Edges are oriented • Use directional derivatives • Simple cells as directional derivatives • Compute local oriented contrast “energy”
