Matlab Image Processing In South Africa

Matlab Image Processing Assignment Help In South Africa

Image processing is the technique to convert an image into digital format and perform operations on it to get an enhanced image or extract some useful information from it. Changes that take place in images are usually performed automatically and rely on carefully designed algorithms.

Image processing is a multidisciplinary field, with contributions from different branches of science including mathematics, physics, optical and electrical engineering. Moreover, it overlaps with other areas such as pattern recognition, machine learning, artificial intelligence and human vision research. Different steps involved in image processing include importing the image with an optical scanner or from a digital camera, analysing and manipulating the image (data compression, image enhancement and filtering), and generating the desired output image.

The need to extract information from images and interpret their content has been the driving factor in the development of image processing. Image processing finds use in numerous sectors, including medicine, industry, military, consumer electronics and so on.

Image processing includes the transforming any input image to digital format so we can perform different operations according to our requirements to get the useful information from it ,Image processing system usually treats images as two dimensional when applying certain predetermined signal processing methods.

Types of image processing:

• Visualization
• Recognition
• Sharpening and restoration
• Pattern recognition
• Retrieval

Image Processing Steps

Image acquisition is the first step in image processing. This step is also known as preprocessing in image processing. It involves retrieving the image from a source, usually a hardware-based source.

Image enhancement is the process of bringing out and highlighting certain features of interest in an image that has been obscured. This can involve changing the brightness, contrast, etc.

Image restoration is the process of improving the appearance of an image. However, unlike image enhancement, image restoration is done using certain mathematical or probabilistic models.

Color image processing includes a number of color modeling techniques in a digital domain. This step has gained prominence due to the significant use of digital images over the internet.

Wavelets are used to represent images in various degrees of resolution. The images are subdivided into wavelets or smaller regions for data compression and for pyramidal representation.

Compression is a process used to reduce the storage required to save an image or the bandwidth required to transmit it. This is done particularly when the image is for use on the Internet.

Morphological processing is a set of processing operations for morphing images based on their shapes.

Segmentation is one of the most difficult steps of image processing. It involves partitioning an image into its constituent parts or objects.

Representation and Description After an image is segmented into regions in the segmentation process, each region is represented and described in a form suitable for further computer processing. Representation deals with the image’s characteristics and regional properties. Description deals with extracting quantitative information that helps differentiate one class of objects from the other.

What is a digital image?

A digital image may be defined as a two-dimensional function f(x,y), where ‘x’ and ‘y’ are spatial coordinates and the amplitude of ‘f’ at any pair of coordinates is called the intensity of the image at that point. When ‘x,’ ‘y’ and amplitude values of ‘f’ are all finite discrete quantities, the image is referred to as a digital image.

Digitising the coordinate values is referred to as ‘sampling,’ while digitising the amplitude values is called ‘quantisation.’ The result of sampling and quantisation is a matrix of real numbers.

Basic image processing commands in MATLAB

Reading images

Images are read in MATLAB environment using the function ‘imread.’ Syntax of imread is:
imread(‘filename’);
where ‘filename’ is a string having the complete name of the image, including its extension.
For example,
>>F = imread(Penguins_grey.jpg);
>>G = imread(Penguins_RGB.jpg);

Please note that when no path information is included in ‘filename,’ ‘imread’ reads the file from the current directory. When an image from another directory has to be read, the path of the image has to be specified.

Semicolon (;) at the end of a statement is used to suppress the output. If it is not included, MATLAB displays on the screen the result of the operation specified in that line.

‘>>’ indicates the beginning of a command line as it appears in the MATLAB command window.

Image display

Images are displayed on the MATLAB desktop using the function imshow, which has the basic syntax:

imshow(f)

where ‘f’ is an image array of data type ‘uint8’ or double. Data type ‘uint8’ restricts the values of integers between 0 and 255.
It should be remembered that for a matrix of type ‘double,’ the imshow function expects the values to be between 0 and 1, where 0 is displayed as black and 1 as white. Any value between 0 and 1 is displayed as grayscale. Any value greater than 1 is displayed as white, and a value less than zero is displayed as black. To get the values within this range, a division factor can be used. The larger the division factor, the darker will be the image.

Writing Images

Images are written to the current directory using the imwrite function, which has the following syntax:

imwrite(f, ‘filename’);

This command writes image data ‘f’ to the file specified by ‘filename’ in your current folder. The imwrite function supports most of the popular graphic file formats including GIF, HDF, JPEG or JPG, PBM, BMP, PGM, PNG, PNM, PPM and TIFF and so on.
The following example writes a 100×100 array of grayscale values to a PNG file named random.png in the current folder:

>> F = rand(100);
>>imwrite(F, ‘random.png’)
When you open the folder in which you are working, an image file named ‘random.png’ is created.

For JPEG images, the imwrite syntax applicable is:
imwrite(f, ‘filename.jpg’, ‘quality’, q)
where ‘q’ is an integer between 0 and 100.

It can be used to reduce the image size. The quality parameter is used as a trade-off between the resulting image’s subjective quality and file size.

Image information

File details including file name, data, size, format, height and width can be obtained using the command:

imfinfo’filename’

As an example, the command imfinfo(‘Penguins_grey.jpg’) gives the following information:

ans=
Filename: [1×50 char]
FileModDate: ’02-Aug-2016 09:24:12′
FileSize: 100978
Format: ‘jpg’
FormatVersion: ”
Width: 1024
Height: 768
BitDepth: 8
ColorType: ‘grayscale’
FormatSignature: ”
NumberOfSamples: 1
CodingMethod: ‘Huffman’
CodingProcess: ‘Sequential’
Comment: {}

Information fields displayed by ‘imfinfo’ can be captured into a structural variable that can be used for subsequent computation.

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