IMAGE SEGMENTATION.

Editor's Notes

• #3: An holistic,comprehensive,introductory approach
• #5: An image is a 2-D light intensity function f(x,y)A digital image f(x,y) is discretized both in spatial coordinates and brightnessIt can be considered as a matrix whose row, column indices specify a point in the image and the element value identifies gray level at that pointThese elements are referred to as pixels or pels
• #8: Image Acquisition :- An imaging sensor and the capability to digitize the signal produced by the sensorPreprocessing :- Enhances the image quality, filtering, contrast enhancement etc.Segmentation :- Partitions an input image into constituent parts of objectsDescription / Feature Selection :- Extracts description of image objects suitable for further computer processing.Recognition and Interpretation :- Assigning a label to the object based on the information provided by its descriptor. Interpretation assigns meaning to a set of labeled objects.Knowledge Base :- Knowledge Base helps for efficient processing as well as inter module cooperation.
• #13: Segmentation algorithms have been used for a variety of applications. Some examples are :Optical character recognition(OCR)Automatic Target AcquisitionColorization of Motion PicturesDetection and measurement of bone, tissue, etc., in medical images.
• #24: It is sufficient and necessary for an image to undergo pre-processing to correct image defects
• #31: Permit me to say threshold works like a compiler.the binary image contain all of the essential information about the position and shape of the objects of interest(foreground)
• #32: Thresholding works well when a grey level histogram of the image groups separates the pixels of the object and the background into two dominant modes. Then a threshold T can be easily chosen between the modes.The threshold operator T,is the widely used image-to-image transformation.it follows that the threshold operator maps any gray-tone image into a binary image.
• #33: Histogram are constructed by splitting the range of the data into equal-sized bins (called classes). Then for each bin, the number of points from the data set that fall into each bin are counted. Vertical axis: Frequency (i.e., counts for each bin) Horizontal axis: Response variableIn image histograms the pixels form the horizontal axis In Matlab histograms for images can be constructed using the imhist command.Horizontal axis: Response variable/pixel intensityVerticalaxis: pixel count
• #34: Thresholding may be viewed as an operation that involves tests against a function T of the form:T = T[x,y,p(x,y),f(x,y)]Where f(x,y) is the gray level , and p(x,y) is some local property.Simple thresholding schemes compare each pixels gray level with a single global threshold. This is referred to as Global Thresholding.If T depends on both f(x,y) and p(x,y) then this is referred to a Local Thresholding.Thresholding is also used to filter the output of or input to other operators. For instance, in the former case, an edge detector like Sobel will highlight regions of the image that have high spatial gradients. If we are only interested in gradients above a certain value (i.e. sharp edges), then thresholding can be used to just select the strongest edges and set everything else to black. As an example,
• #38: Suppose that the gray-level histogram corresponds to an image, f(x,y), composed of dark objects in a light background, in such a way that object and background pixels have gray levels grouped into two dominant modes. One obvious way to extract the objects from the background is to select a threshold ‘T’ that separates these modes. Then any point (x,y) for which f(x,y) > T is called an object point, otherwise, the point is called a background point.
• #39: Basic Adaptive Thresholding: Images having uneven illumination makes it difficult to segment using histogram, this approach is to divide the original image into sub images and use the above said thresholding process to each of the sub images.
• #40: If for example an image is composed of two types of light objects on a dark background, three or more dominant modes characterize the image histogram.In such a case the histogram has to be partitioned by multiple thresholds.Multilevel thresholding classifies a point (x,y) as belonging to one object class if T1 < (x,y) <= T2, to the other object class if f(x,y) > T2 and to the background if f(x,y) <= T1.
• #49: Edge is the boundary between two homogeneous regions.The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges.Edge detection refers to the process of identifying and locating sharp discontinuities in an image. These methods exploit the fact that the pixel intensity values change rapidly at the boundary(edge) of two regions.
• #51: Edge-based approachApplying edge detector on the image. Then linking detected edges to generate boundaries. Makes use of only local information, so difficult to guarantee continuous and closed boundaries.
• #56: Zero out any pixel response  the two neighboring pixels on either side of it, along the direction of the gradient.Trackhigh-magnitude contours.Keep only pixels along these contours, so weak little segments go away.