Image segmentation 2


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Image segmentation 2

  1. 1. Segmentation Objective:Student will learn on how to findregions that represent objects or meaningful part of objects. 1
  2. 2.  Download di 2
  3. 3. Introduction Segmentation is generally the first stage in any attempt to analyze or interpret an image automatically. Image segmentation is important in many computer vision and image processing applications. Segmentation partitions an image into distinct regions that are meant to correlate strongly with objects or features of interest in the image. Segmentation can also be regarded as a process of grouping together pixels that have similar attributes. For segmentation to be useful, the regions or groups of pixels that we generate should be meaningful. 3
  4. 4.  Segmentation bridges the gap between low-level image processing, which concerns itself with manipulation of pixel grey level or color to correct defects or enhance certain characteristics of the image, and high-level processing, which involves the manipulation and analysis of groups of pixel that represent particular features of interest. 4
  5. 5.  Some kind of segmentation technique will be found in any application involving the detection, recognition and measurement of objects in image. Examples  Industrial inspection  Optical character recognition (OCR)  Tracking of objects in a sequence of images  Classification of terrains visible in satellite images  Detection and measurement of bone, tissue, etc., in medical images. 5
  6. 6.  The goal of image segmentation is to find regions that represent objects or meaningful parts of objects. Division of the image into regions corresponding to objects of interest is necessary before any processing can be done at a level higher that that of the pixel. Identifying real objects, pseudo objects and shadows or actually finding anything of interest within the image requires some form of segmentation. 6
  7. 7.  The role of segmentation is crucial in most tasks requiring image analysis. The success or failure of the task is often a direct consequence of the success or failure of segmentation. Segmentation techniques can be classified as either contextual or non-contextual. Non-contextual technique ignore the relationships that exist between features in an image.  Pixels are simply grouped together on the basis of some global attribute, such as grey level. Contextual technique exploit the relationships between grey image features.  Group together pixels that have similar grey levels and are close to one another. 7
  8. 8. Overview Image segmentation methods will look for objects that either have some measure of homogeneity within themselves or have some measure of contrast with the objects on their border. Most image segmentation algorithm are modifications, extensions or combinations of these two basic concepts. 8
  9. 9.  The homogeneity and contrast measures can include features such as grey level, color and texture. After performed some preliminary segmentation, we may incorporate higher-level object properties, such as perimeter and shape, into the segmentation process. The major problems are a result of noise in the image and digitization of a continuous image. 9
  10. 10.  Noise is typically caused by the camera, the lenses, the lighting, or the signal path and can be reduced by the use of the pre-processing methods. Spatial digitization can cause problems regarding connectivity of objects. These problems can be resolved with careful connectivity definitions and heuristics applicable to the specific domain. 10
  11. 11. Connectivity Connectivity refers to the way in which we define an object. After we have segmented an image, which segments should be connected to form an object? Or at lower level, when searching the image for homogeneous regions, how do we define which pixels are connected? 11
  12. 12.  We can define connectivity in three different ways: 1. 4-connectivity 2. 8-connectivity, and 3. 6-connectivity Which is which? 12
  13. 13. 6-connectivity NW/SE 6-connectivity NE/SW•Which definition is chosen depends on the application,but the key to avoiding problems is to be consistent. 13
  14. 14.  We can divide image segmentation techniques into 3 main categories: 1. Region growing and shrinking 2. Clustering methods, and 3. Boundary detection. The region growing and shrinking methods use the row and column or x and y based image space. Clustering techniques can be applied to any domain (spatial domain, color, space, feature space, etc.) The boundary detection methods are extensions of the edge detection techniques. 14
  15. 15. Region Growing and Shrinking Segment the image into regions by operating principally in rc/xy-based image space. Some are local, others are global, and combine split and merge. 15
  16. 16.  Split and merge technique  1. Define a homogeneity test. A measurement which incorporate brightness, color, texture, or other application-specific information, and determining a criterion the region must meet to pass the homogeneity test. 2. Split the image into equally sized regions. 3. It the homogeneity test is passed for a region, then merge is attempted with its neighbour (s). If the criterion is not met, the region is split. 4. Continue this process until all regions pass the homogeneity test. There are many variations of this algorithm. 16
  17. 17.  The user defined homogeneity test is largely application dependent. The general idea is to look for features that will be similar within an object and different from the surrounding objects. In the simplest case  use grey level as feature of interest. Could use the grey level variance as homogeneity measure and define a homogeneity test that required the grey level variance within a region to be less than some threshold. 17
  18. 18.  We can define grey-level variance as  1 2 f ( x, y ) I N 1 ( x, y ) region 1 where I f ( x, y ) N ( x, y ) region•The variance is basically a measure of howwidely the grey level within a region vary.•Higher order statistic can be used for featuressuch as texture. 18
  19. 19. Clustering Technique Clustering techniques are image segmentation methods which individual elements are placed into groups based on some measure of similarity within the groups. The simplest method is to divide the space of interest into regions by selecting the centre or median along each dimension and splitting it. Can be done iteratively until the space is divided into specific number of regions needed.  used in the SCT/Center and PCT/Median segmentation algorithms.  will be effective only if the space and the entire algorithm is designed intelligently. 19
  20. 20.  Recursive region splitting is a clustering method that has become a standard technique. One of the 1st algorithms based on recursive region splitting 1. Consider the entire image as one region and computer histograms for each component of interest (red, green and blue for a color image). 2. Apply a peak finding test to each histogram. Select the best peak and put thresholds on either side of the peak. Segment the image into two regions based on this peak. 3. Smooth the binary threshold image so that only a single connected sub-region is left. 4. Repeat step 1-3 for each region until no new sub-regions can be created  no histograms have significant peaks. 20
  21. 21. 2 threshold are selected, one on each side of the bestpeak. The image is then split into two regions. Region 1corresponds to those pixels with feature values betweenthe selected thresholds. Region 2 consists of those pixelswith feature values outside the threshold. 21
  22. 22. Many of the parameters of this algorithm are applicationspecific. What peak-finding test do we use? And what isa significant peak? 22
  23. 23.  Other Clustering Technique 1. SCT/Center segmentation, and 2. PCT/Median segmentation. 23
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  25. 25. Boundary Detection Performed by finding the boundaries between object defining the objects. Other segmentation technique include Combined approaches and Morphological Filtering. 25