PRESENTED BY
M.VAMSI KRISHNA
S.BABAJAN

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ABSTRACT
In the era of multimedia and Internet, image processing is a key technology.
Image processing is any form of information processing for which the input is an image, such as photographs or frames of video; the output is not necessarily an image, but can be for instance a set of features of the image.
Image processing is of two types Analog image processing and digital image processing. Digital image processing has the same advantages over analog image processing as digital signal processing has over analog signal processing - it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing. But the cost of analog image processing was fairly high compared to digital image processing.
Analog image can be converted to a digital image which can be processed in greater aspects, having greater advantages affordably and the processes such as sampling, quantization, Image acquisition, Image Segmentation of converting analog image to a digital image is explained in this report.
Image processing has a very good scope in the fields of Signal-processing aspects of image processing, imaging systems, and image scanning, display and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, and color reproduction.
HISTORY
Many of the techniques of digital image processing, or digital picture processing as it was often called, were developed in the 1960s at the Jet Propulsion Laboratory, MIT, Bell Labs, University of Maryland, and a few other places, with application to satellite imagery, wirephoto standards conversion, medical imaging, videophone, character recognition, and photo enhancement. But the cost of processing was fairly high with the computing equipment of that era. In the 1970s, digital image processing proliferated, when cheaper computers and dedicated hardware became available. Images could then be processed in real time, for some dedicated problems such as television standards conversion. As general-purpose computers became faster, they started to take over the role of dedicated hardware for all but the most specialized and compute-intensive operations.
With the fast computers and signal processors available in the 2000s, digital image processing has become the most common form of image processing, and is generally used because it is not only the most versatile method, but also the cheapest
INTRODUCTION
Digital image processing is the use of computer algorithms to perform image processing on digital images. Digital image processing has the same advantages over analog image processing as digital signal processing has over analog signal processing — it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing.
We will restrict ourselves to two-dimensional (2D) image processing although most of the concepts and techniques that are to be described can be extended easily to three or more dimensions.
We begin with certain basic definitions. An image defined in the "real world" is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y). An image may be considered to contain sub-images sometimes referred to as regions-of-interest, ROIs, or simply regions. This concept reflects the fact that images frequently contain collections of objects each of which can be the basis for a region. In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition.
The amplitudes of a given image will almost always be either real numbers or integer numbers. The latter is usually a result of a quantization process that converts a continuous range (say, between 0 and 100%) to a discrete number of levels. In certain image-forming processes, however, the signal may involve photon counting which implies that the amplitude would be inherently quantized. In other image forming procedures, such as magnetic resonance imaging, the direct physical measurement yields a complex number in the form of a real magnitude and a real phase.
IMAGE
It is a 2D function f(x, y) where x and y are spatial co-ordinates and f (Amplitude of function) is the intensity of the image at x, y. Thus, an image is a 2-dimensional function of the co-ordinates x, y.
DIGITAL IMAGE
If x, y and amplitude of f are all discrete quantities, then the image is called Digital Image. Digital image is a collection of elements called pixels, where each pixel has a specific co-ordinate value and a particular gray-level. Processing of this image using a digital computer is called Digital Image Processing. E.g. Fingerprint Scanning Handwriting Recognition System Face recognition system Biometric scanning used for authentication in Modern pen drives. The effect of digitization is shown in Figure 1.
The 2D continuous image a(x, y) is divided into N rows and M columns. The intersection of a row and a column is termed a pixel. The value assigned to the integer coordinates [m,n] with {m=0,1,2,...,M-1} and {n=0,1,2,...,N-1} is a[m,n]. In fact, in most cases a(x, y)--which we might consider to be the physical signal that impinges on the face of a 2D sensor--is actually a function of many variables including depth (z), color ( ), and time (t). Unless otherwise stated, we will consider the case of 2D, monochromatic, static images in this