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Explore various thinning algorithms used to convert thick binary images to thin ones, with examples and rules for effective image processing.
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Binary Thinning Algorithms Thick images Thin images Color images Character Recognition (OCR)
Thinning of thick binary images Thinning: from many pixels width to just one • Much work has been done on the thinning of ``thick'' binary images, • where attempts are made to reduce shape outlines which are many pixels thick to outlines which are only one pixel thick. • Skeletonization
Thinning using Zhang and Suen algorithm [1984].) (b) is slightly increased image Point just removed 7 8 26 25 results of the first pass results of the second pass final results
Example 1 of Rules for Thinning Algorithm Rule 1 All four rules can be illustrated like that New and old one Old one Don’t care Rule 2 Rule 3 Rule 4 Rule 1
Applying thinning to fault detection in PCB All lines are thinned to one pixel width Now you can check connectivity
Thinning Algorithm Correct background shows desired shape of letter T image • Thinning algorithm is sensitive to corrupted image segments Noise leads to lack of connectivity. BAD
Thinning of thin binary images Rules of binary thinning • We will present the rules used for the ``binary thinning'' which is applied to the edge images (found using the edge detector). • The rules are simple and quick to carry out, requiring only one pass through the image.
The SUSAN Thinning Algorithm • It follows a few simple rules • remove spurious or unwanted edge points • add in edge points where they should be reported but have not been. • The rules fall into three categories; • removing spurious or unwanted edge points • adding new edge points • shifting edge points to new positions. • Note that the new edge points will only be created if the edge response allows this. These all can be called “local improving” rules
The SUSAN Thinning Algorithm 0 neighbors • The rules are listed according to the number of edge point neighbours which an edge point has (in the eight pixel neighbourhood) 1 neighbor 2 neighbors 2 neighbors 3 neighbors Discuss size of window and direction of movement
The SUSAN Thinning Algorithm • 0 neighbors. • Remove the edge point. • 1 neighbor. • Search for the neighbor with the maximum (non-zero) edge response, to continue the edge, and to fill in gaps in edges. • The responses used are those found by the initial stage of the SUSAN edge detector, before non-maximum suppression. • They are slightly weighted according to the existing edge orientation so that the edge will prefer to continue in a straight line. • An edge can be extended by a maximum of three pixels. Filling gaps by adding new edge points
The SUSAN Thinning Algorithm “Edge response” is a measure of neighborhood • 2 neighbours. • There are three possible cases: • 1. If the point is ``sticking out'' of an otherwise straight line, then compare its edge response to that of the corresponding point within the line. • If the potential point within the straight edge has an edge response greater than 0.7 of the current point's response, move the current point into line with the edge. • 2. If the point is adjoining a diagonal edge then remove it. • 3. Otherwise, the point is a valid edge point. My point has two neighbors My point has two neighbors
The SUSAN Thinning Algorithm • More than 2 neighbours. • If the point is not a link between multiple edgesthen thin the edge. • This will involve a choice between the current point and one of its neighbours. • If this choice is made in a logical consistent way then a ``clean'' looking thinned edge will result.
The SUSAN Thinning Algorithm How rules are applied? • These rules are applied to every pixel in the image sequentially left to right and top to bottom. • If a change is made to the edge image then the current search point is moved backwards up to two pixelsleftwards and upwards. • This means that iterative alterations to the image can be achieved using only one pass of the algorithm.
Correct and Incorrect Thinning Examples • X correct • V misread as Y • 8 has noise added and not removed, wrong semantic network will be created
Good thinning examples • Here every symbol correctly thinned
Another set of Rules for Thinning Algorithm Thinning Rules new Old and new Down rules • Examples of rules for shifting up and down algorithm Up rules
Tracing Direction from left to right Tracing direction • Notation for points in window • Rules based on point replacements
Tracing Direction This pixed changed to white
Example of bad thinning • We would like to have one pixel width everywhere
Encoding to discrete angles • Image after thinning
Replacement of blocks with points Select the closest point Coding in 8 directions Also, coding in 4 directions or more directions
Polygon Approximation -Encoding We start with the set of rectangles with points inside • Two Methods are used: • Included objects • Minimal objects • Included objects Line Segments make minimum change to the line
start • (a) original figure, (b) computation of distances,(c) connection of vertices, (d) resultant polygon Draw straight angles Method of minimal objects
Encoding of figures • (a) completion of a figure • (b) partitioning to segments
영문자 숫자 한글 한자
Feature segmentation A Dist(A,B) vs Dist(A,C) C B
Distortion at Crossing Point Thinning OverlappingStroke @99 Daru 김진형
Erosion at end point Noise Sensitivity Thinning @99 Daru 김진형
Gray-scale Image @99 Daru 김진형
1:N Stroke Matching reference Model input character @99 Daru 김진형
Processing Whole Image • original
