Color a* b* Brightness L* Texture Original Image Features Feature combination E D 22 Boundary Processing Textons A B C A B C 22 Region Processing.

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Presentation transcript:

Color a* b* Brightness L* Texture Original Image Features Feature combination E D 22 Boundary Processing Textons A B C A B C 22 Region Processing

RegionTexture : Histogram of oriented edge response Boundary and Edge: Edge detection-> lines Interests points: Corner detection

Texture Feature (preview) Texture Gradient TG(x,y,r,  )  2 difference of texton histograms Textons are vector-quantized filter outputs Texton Map

Texture boundary Canny2MMUsHumanImage

Texture boundary Canny2MMUsHumanImage

Texture boundary Canny2MMUsHumanImage

Corner Detections (preview)

Matching with Features Detect feature points in both images Find corresponding pairs

Matching with Features Detect feature points in both images Find corresponding pairs Use these pairs to align images

Boundary and Edge: Edge detection-> lines

An example: S.F. in fogS.F. in Canny

An example: S.F. in fogS.F. with Hough lines

Hough Transform image edges needs to be grouped into lines and junctions Hough transform: Detect lines in an edge image

Line Representation is the distance from the origin to the line is the norm direction of the line Image space : Hough space : point in image space ==> a curve in hough space

Line Representation is the distance from the origin to the line is the norm direction of the line Image space : Hough space : point in image space ==> a curve in hough space For every theta, set:

Hough Space point in hough space ==> line in image space

Intersection of the curves Each pixel in the image => One curve in Hough space What is the intersection of the curves?

Hough Transform Points in the line : In hough space, all the curves pass: So the intersection of the curves is the parameters of the line! Next question: How to find the intersection ?

Voting Scheme Each edge pixel in the image votes in Hough space for a series of Choose the of maximum votes

Basic Hough Transform

Example

Extension Choose the sampling of Use gradient of the image voting for specific Iteratively find the maximum votes and remove corresponding edge pixels Suppress edge pixels close to the detected lines

Example of Using Estimated Edge Orientation+Iterative line removal