1 / 31

Image Segmentation & Template Matching

Image Segmentation & Template Matching. Multimedia Signal Processing lecture on 6.3.2007 Petri Hirvonen. Image Segmentation. Tracking Rolling Leukocytes With Shape and Size Constrained Active Contours Image segmentation based on maximum-likelihood

kami
Download Presentation

Image Segmentation & Template Matching

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Image Segmentation&Template Matching Multimedia Signal Processing lecture on 6.3.2007 Petri Hirvonen

  2. Image Segmentation

  3. Tracking Rolling Leukocytes With Shape and Size Constrained Active Contours Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE–OED) Terminology Image processing tools Examples Details of the Assignment

  4. Image segmentation problem is basically one of psychophysical perception, and therefore not susceptible to a purely analytical solution.

  5. Motivation: Image content representation Requirements: object definition & extraction Mathematical morphology is very useful for analyzing shapes in images. Basic tools: dilation A+B and erosion A–B Application: boundary detection Internal boundary: A - (A–B) External boundary: (A+B) - A Morphological gradient: (A+B) - (A–B) Assignment: object edges A - (A–B) (A+B) - (A–B) (A+B) - A

  6. Dilation: Replace every point (x,y) in A with a copy of B centered at B(0,0) The result D is the union of all translations. Erosion: The resulting set of points E consists of all points for which B is in A. 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 11 1 0 0 0 1 1 0 0 0 0 0 0 B B 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 A D E A D E Structuring element, kernel = B Minkowski addition / subtraction

  7. Image information

  8. Segmenting SEM-images

  9. Dilation of the thresholded block contains the thresholded gradient completely at the optimal threshold. (k and p are thresholds, D is dilation with a structuring element of radius r)

  10. Information & colour

  11. Histogram-based thresholding Otsu’s method • Nobuyuki Otsu, A Threshold Selection Method from Gray-Level Histograms, 1979 • For bimodal distributions minimized maximized

  12. Mean of group @ k Probability of intensity k

  13. Hough transform Region Of Interest Histogram

  14. Perimeter is computed by the Chain Code algorithm. Length and width are the perpendicular distances on the original (thresholded) target area.

  15. Template Matching

  16. We have first created a DATABASE that contains the elements in the table. FOR-loop is executed for all templates Font_images{index} And the result is visualized in colours: Scale ? Rotation ?

  17. Object perimeter

  18. Object perimeter

More Related