CS292 Computational Vision and Language Segmentation and Region Detection

Introduction All pixels belong to a region, which can be –an object –part of object –background Find region –By finding constituent pixels in a region –By finding boundary pixels

Image Segmentation Task To divide the image into regions or segments, each of which is in some sense homogeneous, but the union of adjacent segments is not homogeneous in the same sense. Homogeneity here is characterized by some properties like –smoothly varying intensity, similar statistics, or colour.

Region Detection A set of pixels P An homogeneity predicate H(P) Partition P into regions {R}, such that

Image Segmentation Many techniques including –Non-contextual technique: thresholding –Contextual techniques: region-based connectivity-preserving relaxation methods. –Other methods: Image primitive based Mixture of all these

Threshold techniques make decisions based on local pixel information –are effective when the intensity levels of the objects fall squarely outside the range of levels in the background.

Global thresholds Compute threshold from whole image Incorrect in some regions

Local thresholds Divide image into regions Compute threshold per region Merge thresholds across region boundaries

Contextual techniques Contextual techniques take into account the fact that pixels belonging to a single object are close to one another. Approaches to contextual segmentation are based on the concept of discontinuity or concept of similarity. –detecting abrupt changes- edge detection techniques, –or to create uniform regions directly, Discontinuity and similarity approaches mirror one another, in the sense that completion of boundary is equivalent to breaking one region into two.

Region Growing All pixels belong to a region Select a pixel Grow the surrounding region (we will practise this in lab class)

Slow Algorithm If a pixel is –not assigned to a region –adjacent to region –has colour properties not different to region’s Then –Add to region –Update region properties

Split and Merge Initialise image as a region While region is not homogeneous –split into quadrants and examine homogeneity

Recursive Splitting Split(P) { If (!H(P)) { P  subregions 1 … 4; Split (subregion 1); Split (subregion 2); Split (subregion 3); Split (subregion 4); }

Recursive Merging If adjacent regions are –weakly split weak edge, depending on defined criteria –similar similar greyscale/colour properties Merge them

Edge Following Detection –finds candidate edge pixels Following –links candidates to form boundaries

Representing Regions Constituent pixels Boundary pixels

Based on both regions and edges

Based on the combination of colour and texture

Active Contour Model- Snake A connectivity-preserving relaxation-based segmentation method, - active contour model – snake –The main idea is to start with some initial boundary shape represented in the form of spline curves, and iteratively modify it by applying various shrink/expansion operations according to some energy function. Concepts involved –Image gradient –Smooth operation –Histogram equalization –Energy functions

Snakes, Active/Dynamic Contours Borders follow outline of object Outline obscured? Snake provides a solution

Algorithm Snake computes smooth, continuous border Minimises –length of border –curvature of border Against an image property –gradient?

Minimisation Initialise snake Integrate energy along it Iteratively move snake to global energy minimum

Active Contour Method Case study next week, notes will be given during the lecture

Summary Image segmentation Region detection –growing –edge following