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Schedule. F5: Texture and segmentation F6: Energy and graph based segmentation F7: Active contours, snakes and level sets F8: Fitting, Hough transform F9: Recognition and classification …. A Vision Application. Binary Image Segmentation. How ?. Cost function. - PowerPoint PPT Presentation
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Schedule
• F5: Texture and segmentation
• F6: Energy and graph based segmentation
• F7: Active contours, snakes and level sets
• F8: Fitting, Hough transform
• F9: Recognition and classification
• …
A Vision ApplicationBinary Image Segmentation
How ?
Cost function Models our knowledge about natural images
Optimize cost function to obtain the segmentation
Why do these tokens belong together?
TRAFFIC RESEARCH
• Increase traffic safety• Increase traffic flow• Together with Traffic
Dept in Lund.• Automatic detection
and analysis of objects and events in traffic environment
K-means clustering using intensity alone and color alone
Image Clusters on intensity Clusters on color
K-means using color alone, 11 segments
Image Clusters on color
K-means usingcolor alone,11 segments.
K-means using colour andposition, 20 segments
Graph theoretic clustering
• Represent tokens using a weighted graph.– affinity matrix
• Cut up this graph to get subgraphs with strong interior links
Example eigenvector
points
matrix
eigenvector
More than two segments
• Two options– Recursively split each side to get a tree,
continuing till the eigenvalues are too small– Use the other eigenvectors