1. Line Detection Based on Chain Code Detection Guang-quan Lu, Hong-guo Xu, Yi-bing Li Presented by Xinyu Chang

2. Introduction A new method for line detection based on chain code detection. Key features: 1.Chain codes tracked. 2.Corners in chain codes can be detected. 3.Line segments are linked based on the linking criterions.

3. Relevant work --- Hough Transform Three classic approached for line detection: 1)The Hough Transform is a global method for finding straight lines hidden in larger amounts of other data.

4. Relevant work --- Hough Transform A problem happens when we have a vertical line will make the value of m infinity So an alternative approach has been used

5. Relevant work --- Hough Transform The way of calculate rho and theta

6. Relevant work --- Hough Transform The peak in the curvature chart represents the straight line in the original image Drawbacks: Huge memory consumption, high time complexity and quantization complexity

7. Relevant work --- Grouping Segments Elementary line segments can be obtained by linking edge pixels and approximating them to piecewise straight line segments. These elementary line segments are used as an input to group. Adjacent line segments are grouped based on some grouping criteria and replaced by a new line segment. This process is repeated until no new line segment occurs.

8. Relevant work --- Grouping Segments Disadvantages: The approach does not work when most of the edge pixels are isolated or when elementary line segments are perturbed severely by noises rendering the data almost useless. Its process is purely local. Repetition of locally optimal grouping of line segments does not guarantee their globally optimal grouping

9. Relevant work --- Gradient magnitute The Sobel operator

10. Implementation Four steps （ I ） Chain code detection （ II ） Corner detection （ III ） determine whether a chain code is a line based on histograms of chain code. （ IV ） estimate parameters of lines and link piecewise lines segments according to the criterion.

11. Detect Chain Codes

12. Elementary Chain Code Detection and Linking Connected Chain Codes

13. CORNER DETECTION AND RECOGNIZING CHAIN CODES OF LINE SEGMENTS n is the number of codes that ith code of chain is their center

14. Recognizing Chain Codes of Line Segments f(i) and g(i) that represent the global orientation of the segments on both sides of the element. Local histogram of a chain code can be defined as

15. Recognizing Chain Codes of Line Segments The three criterions to detect chain codes of straight line segments can be represented as follow To judge the two segments whether it is a line The second criterion is about frequency of two important directions in chain. can be set as 0.85-0.95.

16. Recognizing Chain Codes of Line Segments The third criterion is concerned for coherence off forward and backward local histogram. It can be set as 0.1-0.3.

17. ESTIMATE PARAMETERS OF LINE SEGMENTS AND LINK[NG BROKEN LINE SEGMENTS Because of noise and corner detection, some line segments may be broken. So the linking processing must be done. The key problem is to set criterions according to angles and relative positions of line segments. The linking process is based on the similarity of orientation, adjacency and the gap size between the two line segments.

18. The criterions of linking processing are proposed as the followings: ESTIMATE PARAMETERS OF LINE SEGMENTS AND LINK[NG BROKEN LINE SEGMENTS

19. Criterion 1 is used to Judge whether two line segments are co-linear. If one of line segments is shorter, criterion 1 can be relaxed by power parameter. Criterion 2 is used to judge the related positions of two line segments. If the difference of directions is less, the criterion can be relaxed by power parameter ESTIMATE PARAMETERS OF LINE SEGMENTS AND LINK[NG BROKEN LINE SEGMENTS

20. Criterion 3 is used to judge the gap of two line segments, and it is also used to judge whether the merged line longer than Li and Lj. ESTIMATE PARAMETERS OF LINE SEGMENTS AND LINK[NG BROKEN LINE SEGMENTS

21. Test and Result

22. Thank you