Introduction to Image processing
To create a digital image we need to convert the continuous sensed data into a digital form , this involves two processes : Sampling and quantization
Sample : the 2D space on a regular grid
Digitizing : the coordinate values is called sampling
Digitizing : the amplitude values is called Quantization
Digital Image Definitions
A digital image a[m,n] described in a 2D discrete space is derived from an analog image a(x,y) in a 2D continuous space through a sampling process that is frequently referred to as digitization.
The first step towards designing an image analysis system is digital image acquisition using sensors in optical or thermal wavelengths. A two dimensional image that is recorded by these sensors is the mapping of the three-dimensional visual world. The captured two dimensional signals are sampled and quantized to yield digital images.
Sometimes we receive noisy images that are degraded by some degrading mechanism. One common source of image degradation is the optical lens system in a digital camera that acquires the visual information. If the camera is not appropriately focused then we get blurred images.
Very often one may come across images of outdoor scenes that were procured in a foggy environment. Thus any outdoor scene captured on a foggy winter morning could invariably result into a blurred image. In this case the degradation is due to the fog and mist in the atmosphere, this type of degradation is known as atmospheric degradation. In some other cases there may be a relative motion between the object and the camera. Thus if the camera is given an impulsive displacement during the image capturing interval while the object is static, the resulting image will invariably be blurred and noisy. In some of the above cases, we need appropriate techniques of refining the images so that the resultant images are of better visual quality, free from aberrations and noises.
Image enhancement, filtering, and restoration have been some of the important applications of image processing.
Segmentation is the process that subdivides an image into a number of uniformly homogeneous regions. In other words, segmentation of an image is defined by a set of regions that are connected and nonoverlapping, so that each pixel in a segment in the image acquires a unique region label that indicates the region it belongs to. Segmentation is one of the most important elements
in automated image analysis, after extracting each segment; the next task is to extract a set of meaningful features such as texture, color, and shape. These are important measurable
entities which give measures of various properties of image segments.
Some of the texture properties are coarseness, smoothness, regularity, etc., while the common shape descriptors are length, breadth, aspect ratio, area, location, perimeter, compactness, etc. Each segmented region in a scene may be characterized by a set of such features.