thesis on image processing using matlab pdf

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A Course on Digital Image Processing with MATLAB ®

Author P K Thiruvikraman Published November 2019

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Concentrating on the principles and techniques of image processing, this book provides an in-depth presentation of key topics, including many techniques not included in introductory texts. Practical implementation of the various image processing algorithms is an important step in learning the subject, and computer packages such as MATLAB facilitate this without the need to learn more complex programming languages. Whilst two chapters are devoted to the MATLAB programming environment and the image processing toolbox, the use of image processing algorithms using MATLAB is emphasised throughout the book, and every chapter is accompanied by a collection of exercises and programming assignments. Including coverage of colour and video image processing as well as object recognition, the book is augmented with supplementary MATLAB code and hints and solutions to problems are also provided.

Copyright © IOP Publishing Ltd 2020 Online ISBN: 978-0-7503-2604-9 • Print ISBN: 978-0-7503-2602-5

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Front matter

Introduction.

P K Thiruvikraman

Image enhancement in the spatial domain

Filtering in the fourier domain, image compression, image analysis and object recognition, image restoration, color image processing, introduction to matlab ®, the image processing toolbox, video processing, solutions to selected exercises, d o i.

https://doi.org/10.1088/978-0-7503-2604-9

Advanced students in physics and engineering.

Published November 2019

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Affiliations

Department of Physics, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, India

About the author

P K Thiruvikraman is currently Professor of Physics at Birla Institute of Technology and Science, Pilani, Hyderabad Campus. He has nearly two decades of experience in teaching courses from many areas of Physics apart from Digital Image Processing. The author has a PhD in Physics from Mangalore University and a Masters degree in physics from Indian Institute of Technology, Madras. During his teaching career he has come to realize that most subjects which are typically offered by different departments are intimately interrelated. He tries to convey this to students in his lectures by quoting examples from different areas of science and technology. An avid reader of both fiction and non-fiction and a sports and movie enthusiast, he tries to use these also to enliven his classes.

Supplementary material

The Research on Image Processing Based on Wavelet Analysis

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Research on Medical Image Processing Method Based on the MATLAB

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• First Online: 01 January 2013
• Cite this conference paper

• Shirui Gao 2  

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 217))

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This paper emphasizes the MATLAB-based medical image processing tools. It includes the theoretical background and examples. Through MATLAB, this paper makes the introduction of the post-imaging quality in medical technology and medical imaging. It also introduces the medical image processing technology and describes the image processing and processing technologies, including the organ contours, interpolation, filtering, and segmentation techniques. In medicine, the DICOM image data processing using MATLAB is also widely used in this type of image processing.

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Research on Medical Image Processing Method Based on the Matlab

Analysis of Algorithms in Medical Image Processing

Biomedical Image Segmentation: A Survey

Li F, Ju L, YunHua D (2005) VB and matlab mixed programming method. Softw Technol 05:110–112

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Xiong F (2003) Feisi technology R&D center. Matlab 6.5 application programming interface. Electronic Industry Press, Beijing vol 02, pp 1132–1134

Fang YY, Sheng YZ, Shuang ZY (2005) VB programming based on COM mixed with matlab. Comput Eng Des 01:61–65

Hao F, Liang W, wang D (2006) VB and matlab simulation of mixing programming application. Comput Simul 12:104–106

Mao LS, Peng DD, Hong LG (2009) Visual basic 6.0 advanced programming skills. Electronic Industry Press, Beijing, vol 09, pp 412–415

Heng W (2007) DICOM medical image file information extraction and the realization of the images show. Med Equip Inf 9:1–3

Zhaolin (2002) Matlab 6. X image processing. Tsinghua University Press, Beijing vol 02, pp 555–557

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School of Information Science and Technology, Northwest University, 710127, Xi’an, China

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Correspondence to Shirui Gao .

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Zhicai Zhong

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Gao, S. (2013). Research on Medical Image Processing Method Based on the MATLAB. In: Zhong, Z. (eds) Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012. Lecture Notes in Electrical Engineering, vol 217. Springer, London. https://doi.org/10.1007/978-1-4471-4850-0_35

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DOI : https://doi.org/10.1007/978-1-4471-4850-0_35

Published : 05 April 2013

Publisher Name : Springer, London

Print ISBN : 978-1-4471-4849-4

Online ISBN : 978-1-4471-4850-0

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Medical Image Processing Using Matlab

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THESIS ON IMAGE PROCESSING

Most of the research scholars to choose their thesis work under the domain of image processing. Operates on images and results in images which improve the visibility of features and to facilitate subsequent analysis. To enhance raw images received from sources such as cameras and sensors. Image processing thesis must depend on the implementation and paper preparation.

Image Processing operations:

• Enhancement of important image particulars by the way suppressing other information’s.
• Image imperfections and defects are corrected.
• Create binary representation.
• Done performance measurements of features.

Applications of Image Processing:

• Forensic Studies.
• Military Applications.
• Document Processing.
• Non-Destructive Evaluation.
• Graphic Arts.

Thesis Topics for Image Processing:

• Content based Image Retrieval
• Intelligent Transportation System
• Satellite Imaging
• Forensic Image Processing

Content based Image Retreival:- Thesis Topics for Image Processing

• Search analyzes contents according to the image not in the meta data such as keywords, tags or description associated with the image
• Contents are may be in the form of shape, color and texture

Recent CBIR issues:-Thesis Topics for Image Processing

• Image Blocking
• Color and Texture Feature Derivation
• Color Indexing

Security:-Thesis Topics for Image Processing

• Security to be maintained by the techniques of cryptography, steganography and watermarking

Intelligent Transportation System:-Thesis Topics for Image Processing

• Video retrieval process to be done onto the intelligent transportation system

Satellite Imaging:-Thesis Topics for Image Processing

• Geographic Information System based concepts are developed under matlab simulation
• Research problems taken into remote sensing input images such as SAR, RADAR, SONAR, ASTER

Forensic Image Processing:-Thesis Topics for Image Processing

• Involves the computer restoration and enhancement of surveillance imagery
• To maximize information extraction from surveillance imagery

Methods of Image Processing:-Thesis Topics for Image Processing

• Adaptive Transformations
• Watermarking
• Sparse Representation
• Dictionary learning

Adaptive Transformation:-Thesis Topics for Image Processing

• Adaptive transformation method works based on classical methods and adaptive methods
• Adaptive method constituents are stepping rule, stopping criterion and threshold estimate
• Performances are measured by bias, precision and efficiency

Watermarking:-Thesis Topics for Image Processing

• Embedding data into an image
• Data must be stored in the actual pixels
• Format may change

Sparse Representation:-Thesis Topics for Image Processing

• Sparse signals can be exactly reconstructed from a small number of elementary signals
• Collaborative sparse coding defined by a single point, it have more than one sparse representation with similar data fitting errors
• Sparse representations are classified into linear representation and non-linear representation

Dictionary Learning:-Thesis Topics for Image Processing

• Dictionary learning is to be solved by a class dependent supervised simultaneous orthogonal matching
• NP-hard but greedy optimizations are possible
• Compute redundant dictionary size which minimizes average non-linear approximation error

MATLAB THESIS PHD  Sample Code for Curve Fitting:-

x = [1 2 3 4 5 6]; y = [5.5 43.1 128 290.7 498.4 978.67]; %data p = polyfit(x,y,4) %get the polynomial % Compute the values of the polyfit estimate over a finer range, % and plot the estimate over the real data values for comparison: x2 = 1:.1:6; y2 = polyval(p,x2); plot(x,y,’o’,x2,y2) grid on

MATLAB THESIS PHD  Sample Code for Image Retrieval using bag of features:-

Select image features for retrieval: % Display a few of the flower images helperDisplayImageMontage(flowerImageSet.ImageLocation(1:50:1000)); Create bag of Features: % Load pre-trained bagOfFeatures load(‘savedColorBagOfFeatures.mat’,’colorBag’); Index Images: % Create a search index % flowerImageIndex = indexImages(flowerImageSet, colorBag, ‘SaveFeatureLocations’, false); % Load the pre-saved index load(‘savedColorBagOfFeatures.mat’, ‘flowerImageIndex’);

Search similar images: % Define a query image queryImage = read(flowerImageSet, 502);

figure imshow(queryImage)

% Search for the top 20 images with similar color content[imageIDs, scores] = retrieveImages(queryImage, flowerImageIndex);

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