23-03-2010, 04:12 PM
08-02-2012, 01:23 PM
send me a seminar report & ppt as soon as possible pleasss...
04-08-2012, 11:06 AM
Digital Image Processing with Matlab
[attachment=30153]
Introduction
Images and pictures
As we mentioned in the preface, human beings are predominantly visual creatures: we rely heavily
on our vision to make sense of the world around us. We not only look at things to identify and
classify them, but we can scan for dierences, and obtain an overall rough feeling for a scene with
a quick glance.
Humans have evolved very precise visual skills: we can identify a face in an instant; we can
dierentiate colours; we can process a large amount of visual information very quickly.
However, the world is in constant motion: stare at something for long enough and it will change
in some way. Even a large solid structure, like a building or a mountain, will change its appearance
depending on the time of day (day or night); amount of sunlight (clear or cloudy), or various shadows
falling upon it.
We are concerned with single images: snapshots, if you like, of a visual scene. Although image
processing can deal with changing scenes, we shall not discuss it in any detail in this text.
For our purposes, an image is a single picture which represents something. It may be a picture
of a person, of people or animals, or of an outdoor scene, or a microphotograph of an electronic
component, or the result of medical imaging. Even if the picture is not immediately recognizable,
it will not be just a random blur.
What is image processing?
Image processing involves changing the nature of an image in order to either
1. improve its pictorial information for human interpretation,
2. render it more suitable for autonomous machine perception.
We shall be concerned with digital image processing, which involves using a computer to change the
nature of a digital image (see below). It is necessary to realize that these two aspects represent two
separate but equally important aspects of image processing. A procedure which satis es condition
(1)a procedure which makes an image look bettermay be the very worst procedure for satisfying
condition (2). Humans like their images to be sharp, clear and detailed; machines prefer their
images to be simple and uncluttered.
Examples of (1) may include
Images and digital images
Suppose we take an image, a photo, say. For the moment, lets make things easy and suppose the
photo is black and white (that is, lots of shades of grey), so no colour. We may consider this image
as being a two dimensional function, where the function values give the brightness of the image at
any given point, as shown in gure 1.6. Usually
they take on only integer values, so the image shown in gure 1.6 will have and ranging from 1
to 256 each, and the brightness values also ranging from 0 (black) to 255 (white). A digital image
can be considered as a large array of discrete dots, each of which has a brightness associated with
it. These dots are called picture elements, or more simply pixels. The pixels surrounding a given
pixel constitute its neighbourhood. A neighbourhood can be characterized by its shape in the same
way as a matrix: we can speak of a neighbourhood, or of a neighbourhood. Except in
very special circumstances, neighbourhoods have odd numbers of rows and columns; this ensures
that the current pixel is in the centre of the neighbourhood.
[attachment=30153]
Introduction
Images and pictures
As we mentioned in the preface, human beings are predominantly visual creatures: we rely heavily
on our vision to make sense of the world around us. We not only look at things to identify and
classify them, but we can scan for dierences, and obtain an overall rough feeling for a scene with
a quick glance.
Humans have evolved very precise visual skills: we can identify a face in an instant; we can
dierentiate colours; we can process a large amount of visual information very quickly.
However, the world is in constant motion: stare at something for long enough and it will change
in some way. Even a large solid structure, like a building or a mountain, will change its appearance
depending on the time of day (day or night); amount of sunlight (clear or cloudy), or various shadows
falling upon it.
We are concerned with single images: snapshots, if you like, of a visual scene. Although image
processing can deal with changing scenes, we shall not discuss it in any detail in this text.
For our purposes, an image is a single picture which represents something. It may be a picture
of a person, of people or animals, or of an outdoor scene, or a microphotograph of an electronic
component, or the result of medical imaging. Even if the picture is not immediately recognizable,
it will not be just a random blur.
What is image processing?
Image processing involves changing the nature of an image in order to either
1. improve its pictorial information for human interpretation,
2. render it more suitable for autonomous machine perception.
We shall be concerned with digital image processing, which involves using a computer to change the
nature of a digital image (see below). It is necessary to realize that these two aspects represent two
separate but equally important aspects of image processing. A procedure which satis es condition
(1)a procedure which makes an image look bettermay be the very worst procedure for satisfying
condition (2). Humans like their images to be sharp, clear and detailed; machines prefer their
images to be simple and uncluttered.
Examples of (1) may include
Images and digital images
Suppose we take an image, a photo, say. For the moment, lets make things easy and suppose the
photo is black and white (that is, lots of shades of grey), so no colour. We may consider this image
as being a two dimensional function, where the function values give the brightness of the image at
any given point, as shown in gure 1.6. Usually
they take on only integer values, so the image shown in gure 1.6 will have and ranging from 1
to 256 each, and the brightness values also ranging from 0 (black) to 255 (white). A digital image
can be considered as a large array of discrete dots, each of which has a brightness associated with
it. These dots are called picture elements, or more simply pixels. The pixels surrounding a given
pixel constitute its neighbourhood. A neighbourhood can be characterized by its shape in the same
way as a matrix: we can speak of a neighbourhood, or of a neighbourhood. Except in
very special circumstances, neighbourhoods have odd numbers of rows and columns; this ensures
that the current pixel is in the centre of the neighbourhood.
07-08-2012, 03:02 PM
Image Processing Using Matlab
[attachment=30404]
What is the Image Processing Toolbox?
The Image Processing Toolbox is a collection of functions that extend the capability of the MATLAB ® numeric computing environment.
Many of the toolbox functions are MATLAB M-files, series of MATLAB statements that implement specialized image processing algorithms.
Geometric operations
Neighborhood and block operations
Linear filtering and filter design
Transforms
Image analysis and enhancement
Binary image operations
Region of interest operations
IP Toolbox
You can view the MATLAB code for these functions using the statement:
type function_name
You can extend the capabilities of the Image Processing Toolbox by writing your own M-files, or by using the toolbox in combination with with other toolboxes, such as the Signal Processing Toolbox and the Wavelet Toolbox.
Images in the Image Processing Toolbox
The basic data structure in MATLAB is the array, an ordered set of real or complex elements.
MATLAB stores most images as two-dimensional arrays (i.e., matrices), in which each element of the matrix corresponds to a single pixel in the displayed image.
For example, you can select a single pixel from an image matrix using normal matrix subscripting:
I(2,15)
Data Types Continued
In order to reduce memory requirements, MATLAB supports storing image data in arrays of class uint8. The data in these arrays is stored as 8-bit unsigned integers. Data stored in uint8 arrays requires one eighth as much memory as data in double arrays. (Noninteger values cannot be stored in uint8 arrays, for example, but they can be stored in double arrays.)
RGB Images
In MATLAB, the red, green, and blue components of an RGB image reside in a single m-by-n-by-3 array.
m and n are the numbers of rows and columns of pixels in the image, and the third dimension consists of three planes, containing red, green, and blue intensity values.
For each pixel in the image, the red, green, and blue elements combine to create the pixel’s actual color.
Working with Image Data
Reading in image data from files, and writing image data out to files
Converting images to other image types
Working with uint8 arrays in MATLAB and the Image Processing Toolbox
Writing Images
To write image data from MATLAB to a file, use the imwrite function. imwrite can write the same file formats that imread reads.
In addition, you can use the imfinfo function to return information about the image data in a file.
See the reference entries for imread, imwrite, and imfinfo for more information about these functions
[attachment=30404]
What is the Image Processing Toolbox?
The Image Processing Toolbox is a collection of functions that extend the capability of the MATLAB ® numeric computing environment.
Many of the toolbox functions are MATLAB M-files, series of MATLAB statements that implement specialized image processing algorithms.
Geometric operations
Neighborhood and block operations
Linear filtering and filter design
Transforms
Image analysis and enhancement
Binary image operations
Region of interest operations
IP Toolbox
You can view the MATLAB code for these functions using the statement:
type function_name
You can extend the capabilities of the Image Processing Toolbox by writing your own M-files, or by using the toolbox in combination with with other toolboxes, such as the Signal Processing Toolbox and the Wavelet Toolbox.
Images in the Image Processing Toolbox
The basic data structure in MATLAB is the array, an ordered set of real or complex elements.
MATLAB stores most images as two-dimensional arrays (i.e., matrices), in which each element of the matrix corresponds to a single pixel in the displayed image.
For example, you can select a single pixel from an image matrix using normal matrix subscripting:
I(2,15)
Data Types Continued
In order to reduce memory requirements, MATLAB supports storing image data in arrays of class uint8. The data in these arrays is stored as 8-bit unsigned integers. Data stored in uint8 arrays requires one eighth as much memory as data in double arrays. (Noninteger values cannot be stored in uint8 arrays, for example, but they can be stored in double arrays.)
RGB Images
In MATLAB, the red, green, and blue components of an RGB image reside in a single m-by-n-by-3 array.
m and n are the numbers of rows and columns of pixels in the image, and the third dimension consists of three planes, containing red, green, and blue intensity values.
For each pixel in the image, the red, green, and blue elements combine to create the pixel’s actual color.
Working with Image Data
Reading in image data from files, and writing image data out to files
Converting images to other image types
Working with uint8 arrays in MATLAB and the Image Processing Toolbox
Writing Images
To write image data from MATLAB to a file, use the imwrite function. imwrite can write the same file formats that imread reads.
In addition, you can use the imfinfo function to return information about the image data in a file.
See the reference entries for imread, imwrite, and imfinfo for more information about these functions