10-08-2012, 04:28 PM
DIGITAL IMAGE PROCESSING
[attachment=31015]
INTRODUCTION
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image 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. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of Multidimensional Systems.
Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s at the Jet Propulsion Laboratory, Massachusetts Institute of Technology, Bell Laboratories, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement. The cost of processing was fairly high, however, with the computing equipment of that era. That changed in the 1970s, when digital image processing proliferated as cheaper computers and dedicated hardware became available. Images then could 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 computer-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 generally, is used because it is not only the most versatile method, but also the cheapest.
Digital image processing technology for medical applications was inducted into the Space Foundation Space Technology Hall of Fame in 1994.
Digital image processing allows the use of much more complex algorithms for image processing, and hence, can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analog means.
Digital image processing focuses on two major tasks
– Improvement of pictorial information for human interpretation
– Processing of image data for storage, transmission and representation for autonomous machine perception
FUNDAMENTAL OF IMAGE
IMAGES
An image is a 2D function f(x, y) where (x, y) are spatial co-ordinates and amplitude of a function at any point (x, y) is called intensity or gray level of the image at that point .A digital image is a representation of a 2D image as a finite set of digit values, called picture elements or pixels.Pixel value typically represents gray level, colors, etc.Digitization implies that a digital image is an approximation of a real scene.
An image processing task
We will look in some detail at a particular real-world task, and see how the above classes may beused to describe the various stages in performing this task. The job is to obtain, by an automaticprocess, the postcodes from envelopes. Here is how this may be accomplished:
Acquiring the image.
First we need to produce a digital image from a paper envelope. This anbe done using either a CCD camera, or a scanner.
Preprocessing.
This is the step taken before the _major_ image processing task. The problem hereis to perform some basic tasks in order to render the resulting image more suitable for the job
to follow. In this case it may involve enhancing the contrast, removing noise, or identifying
regions likely to contain the postcode.
S[b]egmentation.
Here is where we actually _get_ the postcode; in other words we extract from the
image that part of it which contains just the postcode.
Representation and description.
These terms refer to extracting the particular features which allow us to di_erentiate between objects. Here we will be looking for curves, holes and corners which allow us to distinguish the di_erent digits which constitute a postcode.
This means assigning labels to objects based on their descriptors from the previous step), and assigning meanings to those labels. So we identify particular digits,and we interpret a string of four digits at the end of the address as the postcode.
[attachment=31015]
INTRODUCTION
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image 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. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of Multidimensional Systems.
Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s at the Jet Propulsion Laboratory, Massachusetts Institute of Technology, Bell Laboratories, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement. The cost of processing was fairly high, however, with the computing equipment of that era. That changed in the 1970s, when digital image processing proliferated as cheaper computers and dedicated hardware became available. Images then could 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 computer-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 generally, is used because it is not only the most versatile method, but also the cheapest.
Digital image processing technology for medical applications was inducted into the Space Foundation Space Technology Hall of Fame in 1994.
Digital image processing allows the use of much more complex algorithms for image processing, and hence, can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analog means.
Digital image processing focuses on two major tasks
– Improvement of pictorial information for human interpretation
– Processing of image data for storage, transmission and representation for autonomous machine perception
FUNDAMENTAL OF IMAGE
IMAGES
An image is a 2D function f(x, y) where (x, y) are spatial co-ordinates and amplitude of a function at any point (x, y) is called intensity or gray level of the image at that point .A digital image is a representation of a 2D image as a finite set of digit values, called picture elements or pixels.Pixel value typically represents gray level, colors, etc.Digitization implies that a digital image is an approximation of a real scene.
An image processing task
We will look in some detail at a particular real-world task, and see how the above classes may beused to describe the various stages in performing this task. The job is to obtain, by an automaticprocess, the postcodes from envelopes. Here is how this may be accomplished:
Acquiring the image.
First we need to produce a digital image from a paper envelope. This anbe done using either a CCD camera, or a scanner.
Preprocessing.
This is the step taken before the _major_ image processing task. The problem hereis to perform some basic tasks in order to render the resulting image more suitable for the job
to follow. In this case it may involve enhancing the contrast, removing noise, or identifying
regions likely to contain the postcode.
S[b]egmentation.
Here is where we actually _get_ the postcode; in other words we extract from the
image that part of it which contains just the postcode.
Representation and description.
These terms refer to extracting the particular features which allow us to di_erentiate between objects. Here we will be looking for curves, holes and corners which allow us to distinguish the di_erent digits which constitute a postcode.
This means assigning labels to objects based on their descriptors from the previous step), and assigning meanings to those labels. So we identify particular digits,and we interpret a string of four digits at the end of the address as the postcode.