what is Image Segmentation?

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Segmentation is a process of that divides the images into its regions or objects that have similar features or characteristics.

Some examples of image segmentation are

1. In automated inspection of electronic assemblies, presence or absence of specific objects can be determined by analyzing images.

2. Analyzing aerial photos to classify terrain into forests, water bodies etc.

3. Analyzing MRI and X-ray images in medicine for classify the body organs.

Segmentation has no single standard procedure and it is very difficult in non-trivial images. The extent to which segmentation is carried out depends on the problem Specification. Segmentation algorithms are based on two properties of intensity values- discontinuity and similarity. First category is to partition an image based on the abrupt changes in the intensity and the second method is to partition the image into regions that are similar according to a set of predefined criteria.

In this report some of the methods for determining the discontinuity will be discussed and also other segmentation methods will be attempted. Three basic techniques for detecting the gray level discontinuities in a digital images points, lines and edges.

The other segmentation technique is the thresholding.  It is based on the fact that different types of functions can be classified by using a range functions applied to the intensity value of image pixels. The main assumption of this technique is that different objects will have distinct frequency distribution and can be discriminated on the basis of the mean and standard deviation of each distribution.

 Segmentation on the third property is region processing. In this method an attempt is made to partition or group regions according to common image properties. These image properties consist of Intensity values from the original image, texture that are unique to each type of region and spectral profiles that provide multidimensional image data.

 A very brief introduction to morphological segmentation will also be given. This method combines most of the positive attributes of the other image segmentation methods.

 Segmentation  using discontinuities: 

 Several techniques for detecting the three basic gray level discontinuities in a digital image are points, lines and edges.  The most common way to look for discontinuities is by spatial filtering methods.

 Point detection idea is to isolate a point which has gray level significantly different form its background.

Line detection is next level of complexity to point detection and the lines could be vertical, horizontal or at +/- 45 degree angle.

Edge detection :

The edge is a regarded as the boundary between two objects (two dissimilar regions) or perhaps a boundary between light and shadow falling on a single surface.

To find the differences in pixel values between regions can be computed by considering gradients.

The edges of an image hold much information in that image. The edges tell where objects are, their shape and size, and something about their texture. An edge is where the intensity of an image moves from a low value to a high value or vice versa.

There are numerous applications for edge detection, which is often used for various special effects. Digital artists use it to create dazzling image outlines. The output of an edge detector can be added back to an original image to enhance the edges.

Edge detection is often the first step in image segmentation. Image segmentation, a field of image analysis, is used to group pixels into regions to determine an image's composition.

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