Marr’s framework for vision

2. Marr’s framework for vision 2-1/2D sketch Primal sketch Object Recognition Early processing 3D estimation Image

3. Primal sketch • Local edges • Corners • T-junctions • Blobs • Groups of features

4. 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

5. 2-D second derivative operator: the laplacian

11. 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

12. 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

15. 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”