Segmentation
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This document discusses image segmentation techniques. It describes two main approaches: discontinuity and similarity. Discontinuity partitions an image based on abrupt intensity changes like edges, while similarity partitions into regions that are similar according to criteria like thresholding. Specific techniques discussed include point, line, and edge detection. Point detection finds isolated high-contrast points. Line detection uses oriented masks to find lines. Edge detection finds where brightness changes abruptly using gradient and Laplacian operators to detect magnitude and direction of intensity changes.
Segmentation
- 1. Presented by Ramchandra Regmi Mizoram University
- 2. Introduction The objective is to subdivide an image into its constituent parts or objects for subsequent processing such as recognition. It is one of the most important steps leading to the analysis of processed image data. Two approach: 1. Discontinuity 2. Similarity Discontinuity Approach is to partition an image based on abrupt changes in intensity such as edge, lines, point. Similarity Approach is to partition an image into regions that are similar according to a set of predefined criteria such as thresholding , region growing and region splitting.
- 3. 1. Point Detection The detection of the isolated point has been detected at the location on which mask is centered if Where, T= non-negative threshold |R |>T
- 4. Cont..
- 5. Cont.. The idea is that the gray level of an isolated point will be quite different from the gray level of its neighbors.
- 6. 2. Line detection Line detection is an important step in image processing and analysis. If the first mask were moved around an image, it would respond more strongly to lines oriented horizontally. With constant background, the maximum response would result when the line passed through the middle row of the mask.
- 7. Cont.. This is easily verified by sketching a simple array of 1’s with a line of a different gray level running horizontally through the array. A similar experiment would reveal that the second mask in responds best to lines oriented at +45; the third mask to vertical lines; and the fourth mask to lines in the – 45 direction. Each mask is weighted with larger coefficient i.e., 2
- 8. Let R1, R2, R3 and R4 denote the responses of the masks in from left to right. Suppose that all masks are run through an image. If, at a certain point in the image, for all j ≠ I, that point is said to be more likely associated with a line in the direction of mask i. Figure: Line mask Cont..
- 9. 3. Edge Detection An edge is a set of connected pixels that lie on the boundary between two regions. Edges are pixels where brightness changes abruptly. A change of the image function can be described by a gradient that points in the direction of the largest growth of the image function. An edge is a property attached to an individual pixel and is calculated from the image function behavior in a neighborhood of the pixel.
- 10. Cont.. Magnitude of the first derivative detects the presence of the edge. Sign of the second derivative determines whether the edge pixel lies on the dark side or light side. Figure: Edge Detection by derivation operators, a- light stripe on a dark background. b- dark stripe on a light background.
- 11. Cont.. Two operator are- 1. Gradient & 2.Laplacian. 1)Gradient Operators: the gradient of f at coordinates (x', y') is defined as the vector- Magnitude of vector is-
- 12. Cont.. Direction of the vector is- Its magnitude can be approximated in the digital domain in the number of ways , which result in a number of operator such as Roberts, prewitt and sobel operator for computing its values: Fig: A 3*3 region of an image and various mask used to compute the gradient at point labeled Z5
- 13. b. The Laplacian The Laplacian of a 2D function f(x,y) is a 2nd-order derivative defined as Cont.. Figure: Laplacian masks
- 14. Cont.. Figure: using Laplacian Operator Figure: using Gradient operator