image features, hough transform image pyramid cse399b, spring 06 computer vision lecture 10
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Image Features, Hough Transform
Image Pyramid
CSE399b, Spring 06
Computer Vision
Lecture 10
http://www.quicktopic.com/35/H/NHVD8SZQQJHZ
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:
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
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
Image encoding-decoding
1) Image statistics: pixel in neighborhood are correlated, encode per pixel value is redundant
2) Predictive Coding:use raster scan, predict based on pass value, and store only the error in prediction. Simple and fast
1010 20 22 24 24
1010 15 21 23 24
signal
prediction
00 5 1 1 0Error-encoded
Non-causal prediction
non-causal involves typically transform, or solution to a large sets of equations. Encode block by block. Bigger compression but slower.
1010 20 22 24 24
1310 17 22 23 24
signal
prediction
20 3 0 1 0Error-encoded
Gaussian Pyramid for encoding
1) Prediction using weighted local Gaussian average2) Encode the difference as the Laplacian3) Both Laplacian and the Averaged image is easy to encode
[Burt & Adelson, 1983]
Gaussian pyramid is smooth=> can be subsampled
Laplacian pyramid has narrow band of frequency=> compressed