Enhancing Image Edge Detection in Vision Processing
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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.
Enhancing Image Edge Detection in Vision Processing
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Presentation Transcript
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”
