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Introduction to Grouping: The Hough Transform
CS 510 Lecture #18
March 30, 2015
Recall - Hierarchy of Features (e.g. Szeliski’s book)
• Edges • Corners • Chains • Line segments • Parameterized curves • Regions • Surface patches • Closed Polygons
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Hierarchical Feature Extraction • Most features are extracted by combining a
small set of primitive features (edges, corners, regions) – Grouping: which pixels should group? – Model Fitting: what structure describes group?
• Simple example: The Hough Transform – Groups points into lines – (patented in 1962)
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Hough Transform: Grouping • The idea of the Hough transform is that a change in
representation converts a point grouping problem into a peak detection problem.
• Standard line representations: – y = mx + b -- compact, but problems with vertical
lines – (x0, y0) + t(x1, y1) -- your raytracer used this form, but
it is highly redundant (4 free parameters) – ax + by + c = 0 -- Bresenham’s uses this form. Still
redundant (3 free parameters) • How else might you represent a line?
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Hough Grouping (cont.) • Represent infinite lines as (φ,ρ):
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ρ
φ
Hough Grouping (III) • Why? This representation is:
– Small: only two free parameters (like y=mx+b) – Finite in all parameters : 0 <= ρ< √(row2+col2), 0 <= φ
< 2π – Unique: only one representation per line
• General Idea: – Hough space (φ,ρ) represents all possible lines – Next step - use discrete Hough space – Let every point “vote for” any line is might belong to.
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• Every edge has a location and position, so it can be part of only one (infinitely extended) line.
• Co-linear edges map to one bucket in Hough space.
Hough Grouping: Directed Edges
φ
ρ
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Hough Grouping: Edges • Reduces line grouping to peak detection
– Each edge votes for a bucket (line) – # of votes equates to support
• The # of participating edges. – Position of bucket provides the φ, ρ parameters
• Problem: if “true” line parameters are on the boundary of a bucket, supporting data may be split
• Solution: smooth the histogram (Hough image) before selecting peaks.
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Basic Hough – Infinite Lines • The Hough Transform in pure form … • Does not return end-points • Instead, it returns a rho and theta pairs.
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Example 1
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Example 2
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Example 3
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Hough Fitting
• After finding the peaks in the Hough Transform - still two potential problems: – Resolution limited by bucket size. – Infinite lines, not line segments
• Both of these problems can be fixed, – If you kept a linked list of edges (not just #) – Of course, this is more expensive...
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Hough Fitting (II) • Sort your edges
– rotate edge points according to ρ – sort them by (rotated) x coordinate
• Look for gaps – have the user provide a “max gap” threshold – if two edges (in the sorted list) are more than
max gap apart, break the line into segments – if there are enough edges in a given segment,
fit a straight line to the points
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Open CV Hough Version 2
• Second Hough algorithm in OpenCV • Returns segments – based on work below
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Building Example
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hGp://docs.opencv.org/modules/imgproc/doc/feature_detec4on.html