1. Adaptive Filter Based on Image Region Characteristics for Optimal Edge Detection Lussiana ETP STMIK JAKARTA STI&K Januari-2012

2. Presentation Plan Background Problems Objective Proposed Method Experiment ResultsExperiment Results Conclusion

3. Background The determination of an object’s edge for the need of image analysis is an important step in image processing This could be problematic in noisy image (difficult to detect the edge) Optimal filters have been developed: e.g. Canny edge detector, Deriche filter, Madenda filter, etc. ClearSharpBlurNoiseMix

4. Background Comparisons of the filters based on their parameters: Canny detector required one noise parameter input (  ) from the operator Deriche filter required one noise parameter input (  ) from the operator Madenda filter required two input: noise and blur parameters (  &  ) from the operator Those parameters are used for edge detection of the whole image

5. Problems The operator’s expertise is required to accurately estimate the parameter value manually. It is possible that a parameter value estimation is different from one operator to the next. An estimation is done repeatedly when inaccuracy occurs

6. Problems An acquired image might have many characteristics such as blurred, sharp, & noisy. The use of a single parameter value for the whole image has a negative effect on the quality of the detected edge.

7. Objective To develop an image edge detection method based on the image region characteristics

8. Proposed Method INPUT IMAGE OUTPUT IMAGE IMAGE REGION CHARACTERIZATION & SEGMENTATION REGIONAL SMOOTHING REGIONAL EDGE DETECTION

9. Details of Region Characterization & Segmentation REGION CHARACTERIZATION & SEGMENTATION CALCULATION OF REGION PARAMETER α & β ENTROPY & CONTRAST CALCULATION VALIDATING FORMING REGION & BINARY TREE

10. Image Region Characterization & Segmentation The purpose of segmentation is to obtain image region with similar characteristic. Image decomposition is conducted by dividing each image area into four regions N/2 (horizontal) N/2 (vertical) N N

11. Validating & Forming Binary Tree

12. Entropy & Contrast Calculation Entropy is a measure of an image intensity randomness level [Gonzales 2004] Contrast is relative smoothness [Gonzales 87] Contrast average:

13. Parameter  &  Formula Group I : Group II : Group III : E reg : Entropy region K reg : region contrast average

14. Experiment Results Segmentation region result of 16x16 pixels Segmentation region result of 64x64 pixels

15. Canny Vs Regionization

16. Deriche Vs Regionization

17. Madenda Vs Regionization

18. Performance Analysis With regionization Without regionization Parameter value Adaptive to region characteristics Not adaptive Noise reduction process Based on area characteristics Uniform for all characteristics

19. Performance Analysis With regionization Without regionization Image quality as a result of noise reduction Better, noise is reduced, based on region characteristic Not good, noise is reduced uniformly Edge detection quality Edge is more emphasized Some part of the edge is missing Execution time3405 milliseconds Canny : 3705 milliseconds Deriche : 1161 milliseconds Madenda : 761 milliseconds

20. Conclusions With regionization, noise reduction process is not implemented uniformly, but based on the image region characteristics With regionization,  and  are determined automatically, so that the whole process doesn’t have to go through the trial and error phase.

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