Color

a*b*

Brightness

L*

TextureOriginal Image

Features

Feature combination

ED

2

Boundary Processing

Textons

A

B

C

A

B

C

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

TextonMap

Texture boundary

Canny 2MM Us HumanImage

Texture boundary

Canny 2MM Us HumanImage

Texture boundary

Canny 2MM Us HumanImage

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 fog S.F. in Canny

An example:

S.F. in fog S.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 fora series of

• Choose the of maximum votes

Basic Hough Transform

Example

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

•