Segmentation. (template) matching

Transcription

1 Segmentation. (template) matching 1

2 Announcements Midterm March , in class Duration: 45 min Weight: 20% of final course mark Closed book, closed notes Calculator allowed Practice midterm posted. Solution will be posted on Sunday. Midterm Review: March 2 Office hours: March 2, 3-5 pm or by appointment 2

3 Reading 6.4 Adelson and Bergen, Image Pyramids 3

4 Template matching Assumes you know what you are looking for (supervised process) 4

5 Copyright 2008, Thomson Engineering, a division of Thomson Learning Ltd. 6-5

6 Comparing neighborhoods to templates By linear filtering Correlation can be considered as a dot product between two vectors: - the pattern and the considered image region. - The dot product is maximal (maximum correlation) when the pattern is very similar to the corresponding image region. 6

7 Optimality matching criterion evaluation 7

8 Challenge We need scaled representations because the details of interest can occur at various scales 8

9 A bar in the big images is a hair on the zebra s nose; in smaller images, a stripe; in the smallest, the animal s nose 9

10 Aliasing Can t shrink an image by taking every second pixel If we do, characteristic errors appear 10

11 11

12 Detecting a target pattern The target pattern may appear at any scale We want to use only convolutions Construct copies of the target at several expanded scales, and convolve them with the original image 12

13 Detecting a target pattern (cont d) Or maintain a fixed scale of the target and change the scale of the image 13

14 Detecting a target pattern Both approaches should give equivalent results The difference is in the computational complexity A convolution with the target pattern expanded in scale by a factor s requires s 2 more operations than the convolution with the image reduced in scale by s. s=2..32 A series of images at iteratively reduced scales will form a pyramid. 14

15 A Gaussian Pyramid 15

16 Levels of the Gaussian pyramid expanded to the size of the original image 16

17 How to construct a Gaussian pyramid At each iteration: Filtering with a low-pass filter (ex: Gaussian with constant σ or other) Subsampling G L =Reduce(G l-1 ) form the correlation kernel. The same kernel is used to produce all levels in the pyramid. Kernel should be small and separable 17

18 The Laplacian Pyramid series of band-pass images obtained by subtracting each Gaussian (low-pass) pyramid level from the next-lower level in the pyramid. 18

19 Flexible templates Target might not be exactly the same in every image Idea: break the template into pieces and try to match each piece Position the entire template over the neighborhood, then search around the position of each subtemplate for the best match Overall match is best combined match for all subtemplates From B. Morse, morse.cs.byu.edu/650/ 19

20 Evaluation issues in segmentation Reading

21 Evaluating segmentation techniques As in other areas of vision, evaluation is a problem We need to know what the correct result is We need some way to compare the result of each algorithm to the ideal situation From Tony Pridmore s Lecture Notes on Image Processing and Interpretation, University of Nottingham 21

22 Evaluating segmentation Possible approaches Ground truth get a correct segmentation and compare the results of the algorithm to it Evaluations based on region properties we want the regions to be uniform, and for adjacent regions to be different Evaluating robustness If we deliberately introduce noise or partially mask the object of interest, how will the segmentation result be affected? Adapted from Tony Pridmore s Lecture Notes on Image Processing and Interpretation, University of Nottingham 22

23 Ground truth segmentation Typically used in medical imaging applications Issue: human segmentations can vary significantly How do we build a ground truth segmentation from several human segmentations? 23

24 Copyright 2008, Thomson Engineering, a division of Thomson Learning Ltd. 6-24

25 Statistical ground truth 25

26 Ground truth in other applications Experiment: segmenting an image by hand Adapted from Tony Pridmore s Lecture Notes on Image Processing and Interpretation, University of Nottingham 26

27 Ground truth in other applications Experiment: segmenting an image by hand Adapted from Tony Pridmore s Lecture Notes on Image Processing and Interpretation, University of Nottingham 27

28 Ground truth in other applications Human segmentation of complex scenes is subjective; it depends on visual representation among many other things Are human segmentations consistent? Adapted from Tony Pridmore s Lecture Notes on Image Processing and Interpretation, University of Nottingham 28

29 Comparing image segmentations Suppose we have a agreed ground truth We need to compare two sets of regions What does it mean for two sets of regions to be similar? Is the number of regions important? Does it matter if two regions are merged or if one is split in two? Ground truth partition Which result is better? Adapted from Tony Pridmore s Lecture Notes on Image Processing and Interpretation, University of Nottingham 29

30 Segmentation of complex scenes 30

31 Current measures of similarity: region-based Applicable when only one region of interest in image Region-based: Mutual overlap Limits Does not give any information about boundaries Conceals quality differences between segmentations Assumes a closed contour Large errors for small objects 31

32 Current measures of similarity: border-based 32

33 Current measures of similarity: border-based Hausdorff distance Idea: consider the two contours as two finite sets of points h(a,b) = max min d(a,b) a A b B ( ) H (A,B) = max h(a,b),h(b, A) 33

34 Unsupervised evaluation Haralick and Shapiro: Regions should be uniform and homogeneous with respect to some characteristic(s) Adjacent regions should have significant differences with respect to the characteristic on which they are uniform Region interiors should be simple and without holes Boundaries should be simple, not ragged, and be spatially accurate 34