Imge processing

Quick introduction:

The first step towards designing an image analysis system is digital image

acquisition using sensors in optical or thermal wavelengths. a twodimensional

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. Here the blurring

mechanism is the defocused camera. 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, and 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 since the early days of the field.

Segmentation is the process that subdivides an image into a number of

uniformly homogeneous regions. Each homogeneous region is a constituent

part or object in the entire scene. 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, mainly because a t this step the objects or other

entities of interest are extracted from an image for subsequent processing,

such as description and recognition. For example, in case of an aerial image

containing the ocean and land, the problem is to segment the image initially

into two parts-land segment and water body or ocean segment. Thereafter

the objects on the land part of the scene need to be appropriately segmented

and subsequently classified.

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.

Finally based on the set of these extracted features, each segmented object

is classified to one of a set of meaningful classes. In a digital image of ocean,

these classes may be ships or small boats or even naval vessels and a large class

of water body. The problems of scene segmentation and object classification

are two integrated areas of studies in machine vision. Expert systems, semantic

networks, and neural network-based systems have been found to perform

such higher-level vision tasks quite efficiently.

Another aspect of image processing involves compression and coding of

the visual information. With growing demand of various imaging applications,

storage requirements of digital imagery are growing explosively. Compact

representation of image data and their storage and transmission through

communication bandwidth is a crucial and active area of development today.

Interestingly enough, image data generally contain a significant amount of superfluous

and redundant information in their canonical representation. Image

compression techniques helps to reduce the redundancies in raw image data

in order to reduce the storage and communication bandwidth.