14-02-2013, 03:13 PM
Image Processing with MATLAB
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• What is digital image processing? Transforming digital information
representing images
• Motivating problems:
o Improve pictorial information for human interpretation
Remove noise
Correct for motion, camera position, distortion
Enhance by changing contrast, color
o Process pictorial information by machine.
Segmentation - dividing an image up into constituent parts
Representation - representing an image by some more abstract
models
Classification
o Reduce the size of image information for efficient handling.
Compression with loss of digital information that minimizes loss
of "perceptual" information. JPEG and GIF, MPEG,
Multiresolution representations versus quality of service
• How do we see?
o Lens focuses an image on the retina (like a camera).
o Pattern is affected by distribution of light receptors (rods and cones) --see
Figure 2.2 of CIP.
o The (6-7 million) cones are in the center of the retina (fovea) and are
sensitive to color - each connected to own neuron.
o The (75-150 million) rods are distributed everywhere, connected in
clusters to a neuron
o Unlike ordinary camera, the eye is flexible.
o Range of intensity levels supported by the human visual system is 1010.
o Uses brightness adaptation to set sensitivity.
Color Vision
The color-responsive chemicals in the cones are called cone pigments and are very
similar to the chemicals in the rods. The retinal portion of the chemical is the same,
however the scotopsin is replaced with photopsins. Therefore, the color-responsive
pigments are made of retinal and photopsins. There are three kinds of color-sensitive
pigments:
• Red-sensitive pigment
• Green-sensitive pigment
• Blue-sensitive pigment
Pixels:
Pixel based images are created by painting programs such as Adobe Photoshop and
Microsoft's Paintbrush. These are the most common of digital images. Photographs, for
example, are described by breaking an image up into a mosaic of colour squares (pixels).
Depending on their final destination, the number of pixels used per inch varies (PPI or
DPI). On the web where big file sizes mean long downloads, only 72 pixels per inch are
required since monitors only display 72 ppi. For publishing, anywhere from 200-1200 ppi
is required depending on the press and desired quality. Laser printers usually print at
anywhere from 300 to 600 dpi. That is why they produce such sharp images.
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,
an image composed of 200 rows and 300 columns of different colored dots would be
stored in MATLAB as a 200-by-300 matrix. Some images, such as RGB, require a threedimensional
array, where the first plane in the 3rd dimension represents the red pixel
intensities, the second plane represents the green pixel intensities, and the third plane
represents the blue pixel intensities.
Indexed Images
An indexed image consists of a data matrix, X, and a colormap matrix, map. map is an mby-
3 array of class double containing floating-point values in the range [0, 1]. Each row
of map specifies the red, green, and blue components of a single color. An indexed image
uses "direct mapping" of pixel values to colormap values. The color of each image pixel
is determined by using the corresponding value of X as an index into map. The value 1
points to the first row in map, the value 2 points to the second row, and so on. You can
display an indexed image with the statements:
image(X); colormap(map)
A colormap is often stored with an indexed image and is automatically loaded with the
image when you use the imread function. However, you are not limited to using the
default colormap--you can use any colormap that you choose. The description for the
property CDataMapping describes how to alter the type of mapping used.
The next figure illustrates the structure of an indexed image. The pixels in the image are
represented by integers, which are pointers (indices) to color values stored in the
colormap.