seminar

Introduction to digital image processing
  Mainly digital imaging processing methods are used for the improvement of pictorial information for human interpretation and processing of scene data for autonomous mission perception. One of the first application of image processing technique was in improving digitize newspaper pictures sent by submarine cable between London newyork. Successful application of image processing concept can be found in astronomy, biology , nuclear, medicine law enforcement , defense and industrial application.  
  The over all objective is to produce a result problem domain by means of image processing. For example if the problem domains consist of pieces of mail and the objective is to read the address on each piece. Thus the desired output in this case is a stream of alpha numeric characters. The first step in the process is image acquisition that is to acquire a digital image. To do so require a imaging sensor and the capability to digitize the signal produced by the sensor. After a digital image has been obtained, the next step deals with preprocessing that image. The key function of preprocessing is to improve the image in ways that increase the chances for success of the other processes. The next stage deals with segmentation. Broadly defined segmentation partitions an image into its constituent parts or objects. In terms of character recognition ,the key role of segmentation is to extract individual characters and words from the background. The output of the segmentation stage usually is raw pixel data , constituting either the boundary of a region or all the points in the region itself. Choosing the a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing g. a method must also be specified for describing data so that feature of interest are highlighted. Description also called feature selection, deals with extracting features that result in some quantity information of interest or features that are basic for differencing one class of object from another. In terms of character recognition descriptors such as lakes(holes) and bays are power full features that help difference one part of alphabet from another . 
  The last stage involves recognition and interpretation. Recognition is the process that assigns a label to an object based on the information provided by its descriptors. Interpretation involves assigning meaning to an ensample of recognized objects. In terms of our example, identifying a character as say a “C” requires associating the descriptor for that character with the label “C”. Interpretation attempts to assign meaning to a set of labels entities. For example, a string of five no: or of five no: followed by a hyphen and for more no: can be interpreted as a ZIP code. Knowledge about a problem domain is coded into an image processing system in the form of knowledge data base. This knowledge can be simple or complex. This knowledge may be as simple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information.  
   
Image segmentation 
Adjacent regions are significantly different with respect to some characteristics. The level to which this segmentation is carried depends on the problem being solved. That is, segmentation should stop when the object of interest in an application have been isolated. For example, in autonomous air to ground target acquisition application, interest lies in identifying vehicles on a road. The first step is to segment the road from image and then to segment the contents of road down to objects of a range of sizes that corresponds to potential vehicles. There is no point in carrying segmentation below this scale nor is there any need to attempt segmentation of image component that lie outside the boundaries of the road.