1 / 18

Demo Website

Demo Website. I highly recommend taking a look at this website: http://users.ecs.soton.ac.uk/msn/book/new_demo/ It has nice interactive demonstrations of the Fourier transform, the Hough transform, edge detection, and many other useful operations. Region Detection.

susan-waters
Download Presentation

Demo Website

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. Demo Website • I highly recommend taking a look at this website: • http://users.ecs.soton.ac.uk/msn/book/new_demo/ • It has nice interactive demonstrations of the Fourier transform, the Hough transform, edge detection, and many other useful operations. Computer Vision Lecture 13: Image Segmentation III

  2. Region Detection • There are two basic – and often complementary – approaches to segmenting an image into individual objects or parts of objects: region-based segmentation and boundary estimation. • Region-based segmentation is based on region detection, which we will discuss in this lecture. • Boundary estimation is based on edge detection, which we already discussed earlier. Computer Vision Lecture 13: Image Segmentation III

  3. Region Detection • We have already seen the simplest kind of region detection. • It is the labeling of connected components in binary images. • Of course, in general, region detection is not that simple. • Successful region detection through component labeling requires that we can determine an intensity threshold in such a way that all objects consist of 1-pixels and do not touch each other. Computer Vision Lecture 13: Image Segmentation III

  4. Region Detection • We will develop methods that can do a better job at finding regions in real-world images. • In our discussion we will first address the question of how to segment an image into regions. • Afterwards, we will look at different ways to represent the regions that we detected. Computer Vision Lecture 13: Image Segmentation III

  5. Region Detection • How shall we define regions? • The basic idea is that within the same region the intensity, texture, or other features do not change abruptly. • Between adjacent regions we do find such a change in at least one feature. • Let us now formalize the idea of partitioning an image into a set of regions. Computer Vision Lecture 13: Image Segmentation III

  6. Region Detection • A partition S divides an image I into a set of n regions Ri. Regions are sets of connected pixels meeting three requirements: • The union of regions includes all pixels in the image, • Each region Ri is homogeneous, i.e., satisfies a homogeneity predicate P so that P(Ri) = True. • The union of two adjacent regions Ri and Rj never satisfies the homogeneity predicate, i.e., P(Ri  Rj) = False. Computer Vision Lecture 13: Image Segmentation III

  7. Region Detection The homogeneity predicate could be defined as, for example, the maximum difference in intensity values between two pixels being no greater than a some threshold . Usually, however, the predicate will be more complex and include other features such as texture. Also, the parameters of the predicate such as  may be adapted to the properties of the image. Let us take a look at the split-and-merge algorithm of image segmentation. Computer Vision Lecture 13: Image Segmentation III

  8. The Split-and-Merge Algorithm • First, we perform splitting: • At the start of the algorithm, the entire image is considered as the candidate region. • If the candidate region does not meet the homogeneity criterion, we split it into four smaller candidate regions. • This is repeated until there are no candidate regions to be split anymore. • Then, we perform merging: • Check all pairs of neighboring regions and merge them if it does not violate the homogeneity criterion. Computer Vision Lecture 13: Image Segmentation III

  9. The Split-and-Merge Algorithm • Sample image to be segmented with  = 1 Computer Vision Lecture 13: Image Segmentation III

  10. The Split-and-Merge Algorithm • First split Computer Vision Lecture 13: Image Segmentation III

  11. The Split-and-Merge Algorithm • Second split Computer Vision Lecture 13: Image Segmentation III

  12. The Split-and-Merge Algorithm • Third split Computer Vision Lecture 13: Image Segmentation III

  13. The Split-and-Merge Algorithm • Merge Computer Vision Lecture 13: Image Segmentation III

  14. The Split-and-Merge Algorithm • Final result Computer Vision Lecture 13: Image Segmentation III

  15. A Better Criterion • The split-and-merge algorithm is a straightforward way of finding a segmentation of an image that provides homogeneity within regions and non-homogeneity of neighboring regions. • In practice, it is not a good idea to use a maximum intensity difference as the criterion. • A single outlier pixel (black or white) could decide about splitting or merging large regions. • Instead, we should use the standard deviation  of pixel intensities within a region. • Our criterion could be that   max. Computer Vision Lecture 13: Image Segmentation III

  16. Examples • Input image(512×512 pixels) • Image after splitting (max= 40) Computer Vision Lecture 13: Image Segmentation III

  17. Examples • Input image(512×512 pixels) • Image after split/merge (max= 40) Computer Vision Lecture 13: Image Segmentation III

  18. Examples • Input image(512×512 pixels) • Image after split/merge (max= 10) Computer Vision Lecture 13: Image Segmentation III

More Related