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Welcome to all:

Welcome to all

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SEMINAR ON IMAGE PROCESSING PRESENTED BY B.VEERANJINEYULU MCA third semester Roll no: 1011172

CONTENTS :

CONTENTS INTRODUCTION APPLICATIONS VARIOUS SOFTWARES OPERATIONS ON IMAGE PROCESSING TYPES OF IMAGES COMMANDS OPERATIONS CONVERSIONS ANALYSIS CONCLUSION REFERENCES

INTRODUCTION:

INTRODUCTION Image Processing is any form of signal processing for which our input is an image, such as parameters related to the image. Image Processing generally refers to processing of two dimensional picture and by two dimensional picture we implies a digital image. A digital image is an array of real or complex numbers represented by a finite number of bits. But now in these days optical and analog image processing is also possible.

APPLICATIONS:

APPLICATIONS Face detection Feature detection Non-photorealistic rendering Medical image processing Microscope image processing Morphological image processing Remote sensing Automated Sieving Procedures Finger print reorganization

SOFTWARES :

SOFTWARES 1. Mat lab 2. Adobe Photoshop 3. Irfan view

MATLAB:

MATLAB The name MATLAB stands for matrix laboratory. It is a high-performance language for technical computing . It is an interactive system whose basic data element is an array which does not require any dimensioning. This allows us to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time

IMAGE FORMATS SUPPORTED BY MAT LAB: :

IMAGE FORMATS SUPPORTED BY MAT LAB: BMP HDF JPEG PCX TIFF XWB

Types of images:

Types of images INTESITY IMAGE : BINARY IMAGE: INDEXED IMAGE RGB IMAGE:

COMMANDS FOR INTENSITY ADJUSTMENT:   :

COMMANDS FOR INTENSITY ADJUSTMENT : 1.HISTEQ(): To improve the contrast in the image before after Syntax: histeq(‘Image_name.format’);

IMADJUST() : :

IMADJUST () : This command is used to adjust the contrast of the image SYNTAX: Imadjust(‘ image_name.format’);

ADAPTHSTEQ( ):

ADAPTHSTEQ( ) This command is used to perform contrast-limited adaptive histogram equalization (CLAHE). Before After SYNTAX: adapthisteq(‘Image_name.format’);

OPERATIONS::

OPERATIONS : IMREAD ( ): IMVIEW ( ) IMWRITE ( ) IMINFO ( ) : IMSUBSTRACT ( ): IMROTATION ( ):

IMREAD ( ): AND IMVIEW :

IMREAD ( ): AND IMVIEW IMREAD ( ): This command is used to read that image on which the operation has to be done. Imread returns the image data in the array. SYNTAX Imread(‘ image_name.format ’) IMVIEW( ) : This command is used to view the image on the screen. And this command is always used with the imread and imwrite command, because, it views only that image which is under process. SYNTAX: Imview (‘image_name.format ’)’

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EXAMPLE IMAGE

IMWRITE ()AND IMINFO():

IMWRITE ()AND IMINFO() IMWRITE( ) : This command is used to change the format of read file. By using this command we can change the file formats from one to another . SYNTAX: Imwrite(‘image_name.format2’, ‘image_name.format1’) IMINFO ( ) : This command is used to obtain the information about the image under process. It is used to obtain information such as size, position of pixels, number of rows and columns etc . SYNTAX: Iminfo(‘ image_name.format);

IMSIZE() AND OMROTATE() :

IMSIZE() AND OMROTATE() IMRESIZE: This command is used to resize the processed image. To enlarge an image, specify factor between 0 and 1 in the command . Syntax: imresize(‘Image_name.format’, value); IMROTATION( ): This command is used to rotate the given image. This command accepts two primary arguments, one is the image to be rotated and other one is rotation angle. We have to specify the rotation angle in degrees. If a positive value is specified then imrotate rotates the image counterclockwise and if a negative value then imrotate rotates the image clockwise . SYNTAX: imrotate (‘Image_name.format’, angle_in_degree);

RESULT OF ABOVE USED COMMANDS:

RESULT OF ABOVE USED COMMANDS

IMCROP() : :

IMCROP() : This command is used to crop the particular image. It accepts two primary arguments : Before After SYNTAX: Imcrop (‘Image_name.format’ , [ret]);

IMAGE CONVERSION:

IMAGE CONVERSION We can convert images in any of formats/types described above using the following commands. Intensity format to Indexed format. gray2ind( )

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GRAY TO INDEX

ANALYSIS:

ANALYSIS Edge Detection Boundary Tracing Quadtree Decomposition

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EDGE DETECTION: We can use the edge function to detect edges, which are those places in an image that correspond to object boundaries. To find edges, this function looks for places in the image where the intensity changes rapidly. Edge takes an intensity image I as its input, and returns a binary image BW of the same size as I, with 1's where the function finds edges in I and 0's elsewhere BOUNDARY TRACING: The toolbox includes two functions you can use to find the boundaries of objects in a binary image:

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BWTRCEBOUNDARY: The bwtraceboundary function returns the row and column coordinates of all the pixels on the border of an object in an image. We must specify the location of a border pixel on the object as the starting point for the trace. SYNTAX: Boundary = bwtraceboundary(binary image ,[row, col],'N'); BWBOUNDARIES: The bwboundaries function returns the row and column coordinates of border pixels of all the objects in an image . SYNTAX: Binary image _filled = infill (binary_image,'holes'); Boundaries = bwboundaries (binary image _filled);

Examples for above explained commands:

Examples for above explained commands

QUADTREE DECOMPOSITION: :

QUADTREE DECOMPOSITION: Quadtree decomposition is an analysis technique that involves subdividing an image into blocks that are more homogeneous than the image itself. This technique reveals information about the structure of the image. SYNTAX: qtdecomp(‘Image_name.format’, threshol);

EXAMPLE FIGURE:

EXAMPLE FIGURE

CONCLUSION:

CONCLUSION You have seen a few of the features of a good introductory image processing program. There are many more complex modifications you can make to the images. For example, you can apply a variety of filters to the image. The filters use mathematical algorithms to modify the image. Some filters are easy to use, while others require a great deal of technical knowledge. The software also will calculate the ra, dec, and magnitude of all objects in the field if you have a star catalog such as the Hubble Guide Star Catalog (although this feature requires the purchase of an additional CD-ROM). The standard tricolor images produced by the SDSS are very good images. If you are looking for something specific, you can frequently make a picture that brings out other details. The "best" picture is a very relative term. A picture that is processed to show faint asteroids may be useless to study the bright core of a galaxy in the same field.

References:

References Ballard ,computer vision. Baxes,gregory a digital image processing. Foley,j.d. vandam “fundamental of interactive computer graphics. www.springs.com www.imageprocessing.com www.processing.com

Queries ?:

Queries ?

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THANK YOU