Journey of Image Processing Based on MatLab

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International Journal of Advanced Computer Engineering and Communication Technology (IJACECT) _______________________________________________________________________________________________

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ISSN (Print): 2278-5140, Volume-5, Issue-2, 2016 23

1Sunny Suman, ²Lawrence Borah

1,2Department of Computer Applications, Dayananda Sagar College of Arts Science and Commerce, Bangalore, India

Abstract— In the study of computer science, Matlab is the best platform in which an user can work in easily. In this research paper we are trying to discuss the basics image processing content using MatLab and also we are taking an normal image to test the inbuilt functions for comparing of the output.

Keyword- imhist, edge, imadd, imsubtract, immultyply, colorbar.

I. INTRODUCTION

MATLAB (MATrix LABoratory) integrates visualization, computation, and programming in an easy to use surroundings MATLAB provides inclusive collections of MATLAB functions (M-files) called toolboxes, each of them are helpful for particular class of problems. Areas in which toolboxes are available include control systems, signal processing, neural networks, wavelets, simulation, fuzzy logic, image processing and many others.

II. IMAGES IN MATLAB

MATLAB has the capabilities to stores most images as 2-D arrays, in which each element of the matrix correspond to a single pixel in the displayed image.

Figure 1:Input 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 three-dimensional array, where the first plane in the third dimension represents the red pixel intensities, the second plane represents the green pixel intensities, and the third plane represents the blue pixel intensities.

This principle makes working with images in MATLAB similar to working with any other type of matrix data, and renders the full power of MATLAB accessible for image processing applications. For example, a single pixel can be selected from an image matrix using normal matrix subscripting.

III. CONVERTING IMAGE STORAGE CLASSES

Using MATLAB commands, it is possible to transfer the data type in which the image is stored. For example, uint8 (unsigned integer, 8 bit) and uint16 (unsigned integer, 16 bit) data can be rehabilitated to double precision using the MATLAB function, double.

Nevertheless, changing in between storage classes changes the way MATLAB and the toolbox construe the data image. If it is preferred to interpret the consequential array properly as data image, the original data should be rescaled or offset to suit the conversion.

Figure 2:Output in unit8 Image

For easier alteration of storage classes, use one of these toolbox functions: im2double, im2uint8, and im2uint16.

These functions automatically toggle the rescaling and offsetting of the original data.

IV. CONVERTING GRAPHICS FILE FORMATS

MATLAB commands also can be used to convert the images saved in a format to another format. To change the graphics format of an image, use imread to read the image and then save the image with imwrite, specifying the appropriate format.

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V. IMAGE ARITHMETIC

Standard arithmetic operations, such as addition, subtraction, division and multiplication, when implemented on images are usually called Image Arithmetic. Image arithmetic has many uses in image processing both as a preliminary step and in more complex operations. It can be used to enhance or restrain the information, to detect the differences between two or more images of the same scene etc.

Figure 3: Two input Images with same size

VI. ADDING IMAGES

To add two images or add a steady value to an image, use the imadd function. imadd adds the significance of each pixel in one of the inserted images with the equivalent pixel in the other inserted image and returns the sum in the corresponding pixel of the output image.

Figure 4: Output of arithmetic operations of previous two images

VII. SUBTRACTING IMAGES

To subtract one image from an additional image, or subtract a constant value from an image, use the imsubtract function. imsubtract subtracts each pixel value in one of the input images from the corresponding pixel in the other input image and returns the result in the corresponding pixel in an output image.

Figure 5: Output of subtracting operations of previous two images

VIII. MULTIPLYING IMAGES

MATLAB command immultiply does an ingredient- byingredient multiplication of each corresponding pixel in a pair of inserted images and returns the product of these multiplications in the corresponding pixel in an output image. Image multiplication by a stable value, referred to as scaling, is a common image processing operation. When used with a scaling factor superior than one, scaling increases the brightness of an image; a factor less than one reduces the brightness of an image.

Image scaling preserves the relative contrast of the image and hence produces a much more natural brightening/ darkening effect.

Figure 6: Output of multiplying operations of previous two images

X. IMAGE CROPPING

To subset or to crop a rectangular portion of an image, the imcrop function can be used. The function requires two arguments as input viz., (i) The image can be cropped (ii) The coordinates of a rectangle that defines the crop area.

The coordinates of the rectangle may be specified manually or can be preferred from the image display window. If imcrop is called without specifying the coordinates of the rectangle, the cursor changes to a cross hair when it is over the image. Click on one corner of the region to be selected and while holding down the mouse button, drag across the image towards the diagonally opposite corner of the required rectangle.

Thus a rectangle is drawn around the selected area.

When the mouse button is released, imcrop extracts the corresponding coordinates and creates a new image of the selected region.

XI. IMAGE ANALYSIS

A range of standard image processing operations for image analysis are also supported by the MATLAB image processing toolbox. Two categories of operations accessible for image analysis and image development are mentioned here:

• Extracting pixel values and statistics

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• Analyzing images to extract information about their essential structure e.g contours, and edges

XII. EXTRACTING PIXEL VALUES

The toolbox includes two functions that provide information about the pixel values or the color data values of an image viz., pixval and impixel. The function pixval interactively displays the data values for pixels as the cursor is moved over the image. It can also display the Euclidean distance between two pixels.

The second function, impixel, on the other hand, returns the data values for a selected pixel or set of pixels. The coordinates of the pixel is used as the input argument.

The coordinates can be precise manually.

Figure 7: Output of color bar

XIII. IMAGE CONTOURS

Similar to the contour function in MATLAB, the toolbox function imcontour is used to display the contour plot of the data in an image. Contours bond with pixels of equal pixel values. The imcontour function automatically sets up the axes so their direction and aspect ratio match the image.

XIV. EDGE DETECTION

Edges are the spaces in an image corresponding to object limitations. Therefore, in an image edges generally correspond to rapid changes in the intensities or pixel values. The toolbox function edge looks for specific places in the image where the intensity changes hastily and hence detects the edges. Function edge returns a binary image containing 1's where edges are found and 0's elsewhere.

Any one of the following criteria is used to detect the rapid change in the intensity.

i Places where the first derivative of the intensity is larger in magnitude than some threshold

ii Places where the second derivative of the intensity is zero

For some of these estimators, it can be specified whether the operation should be responsive to horizontal or vertical edges, or both.

XV. IMAGE ENHANCEMENT

Image enhancement techniques are used to advance an image or to enhance the information contained in the image. For example, enhancement of the signal-to-noise ratio, enhancement of certain features by modifying the colors or intensities so than they can be easily identified or differentiated from others.

Figure 8: Rotation of input image

XVI. HISTOGRAM EQUALIZATION

Another approach for image development is through histogram equalization. In histogram equalization, for each input intensity level, corresponding output intensity levels are assigned based on the frequency ratio

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