1. CS 376b Introduction to Computer Vision 03 / 21 / 2008 Instructor: Michael Eckmann

2. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Today’s Topics Comments/Questions Chapter 6 –color histograms

3. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Color Histograms To determine some match value between two image's histograms (say a largish input image and a smallish model image to try to determine if the model image is in the input image) we can do the following: compute the intersection of the histograms by –sum up over all bins the min histogram value in each bin then divide this sum by the number of pixels in the model image to get a match value –this value is not diminished due to background pixel colors in the input image that are not in the model the idea is that the higher the match value the more likely the model is contained within the image

4. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Color Histograms Other measures include distance measures where smaller values (little distance) implies similarity –sum of absolute value of differences (L1 distance)‏ –ssd = sum of squared differences –Euclidean distance = sqrt(ssd)‏ (L2 distance)‏ –... Let me give intuition into these measures by considering the 2d case on the board

5. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Color There is a section in our text 6.5, worth reading on color segmentation in relation to face detection. Read it, but it doesn't give enough details to understand the algorithm fully, however it does give a good overall discussion. The next section on shading is an important topic that I may cover if we end up covering a topic using shading, such as “shape from shading” in chapter 13.

6. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Segmentation Let's consider what ways you think we can segment images into separate regions.

7. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Segmentation Let's consider what ways you think we can segment images into separate regions. –similar intensities –similar colors –vs. –similar textures –vs. –similar motions

8. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Texture –can be used to segment images into regions –can be used to classify a region –can be used to compare regions What is texture? How do we quantify it?

9. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Texture Our text describes a few approaches –a) structural approach texels – book doesn't describe any detail on how they are determined but says that they are gotten from a simple thresholding procedure and then they are divided up into a pattern by a Voronoi tesselation (fig. 7.4 in “Computer Vision” by Shapiro and Stockman.)‏

10. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Texture Our text describes a few approaches –b) statistical approach compute values of textures from an image based on the intensities/colors it is an efficient approach that can be used to segment and classify (and then allow comparisons of textures)‏ several statistical approaches have been used/proposed

11. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Texture Edge detection and direction –number of edges in some (usually small) area –the directions of those edges Edgeness per unit area –# of edges / # pixels in region –can be grouped by magnitude of the edge into a histogram e.g. low medium and high magnitude edges can be computed separately into a 3 bin histogram The direction of edges could similarly be grouped into different histogram bins (e.g. 3 bins for horizontal, vertical and diagonal)‏

12. Texture We now have a quantitative description of the texture of a region by storing the two histograms (magnitude and direction of edges.)‏ in “Computer Vision” by Shapiro and Stockman Left = (0.24, 0.76) and (0.48, 0.52, 0)‏ Right = (0.0, 0.24) and (0.0, 0.0, 0.24)‏

13. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Texture These histograms can be compared using distance measures like we saw with the color histograms.

14. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Texture Local binary partitioning compute an eight bit binary number for each pixel –each bit represents the relationship between a pixel's intensities and one of it's 8-neighbors intensities –if the neighbor's intensity is <= the pixel's intensity then that bit is 0 –if the neighbor's intensity is > the pixel's intensity then that bit is 1 Use histograms of these binary numbers to describe the texture of an image region. Compare the histograms by a distance measure.

15. Michael Eckmann - Skidmore College - CS 376b - Spring 2008 Texture If you want to get a jump on the next programming assignment I am still working on the details but it will pull in quite a bit of the concepts we've covered –it will deal with finding edges in an image by convolving a few different masks –implementing a texture description scheme as just described and compare the textures with a few different distance measures –also create a color histogram matching scheme and compare the two –writeup of results and a discussion of the results