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Kamal Kornilov
Kamal Kornilov

Poornima Thangam's Digital Image Processing: How to Apply Theory to Practice with MATLAB Examples (Free 28-Day Trial)


# Digital Image Processing Book By Poornima Thangam Free 28 ## Introduction - What is digital image processing and why it is important - Who is Poornima Thangam and what are her credentials - What is the main objective and scope of the book - How to download the book for free ## Chapter 1: Fundamentals of Digital Image Processing - What are digital images and how they are represented - What are the basic operations and transformations on digital images - What are the advantages and disadvantages of different image formats - How to use MATLAB for image processing ## Chapter 2: Image Enhancement in Spatial Domain - What are the objectives and methods of image enhancement - What are the basic techniques of point processing, histogram processing, spatial filtering and sharpening - How to apply these techniques to improve image quality and contrast - How to evaluate the performance of image enhancement methods ## Chapter 3: Image Enhancement in Frequency Domain - What are the concepts and properties of Fourier transform and frequency domain - What are the benefits and limitations of frequency domain image enhancement - What are the common filters and techniques for frequency domain image enhancement - How to implement these filters and techniques using MATLAB ## Chapter 4: Image Restoration and Reconstruction - What are the sources and types of noise and degradation in digital images - What are the models and methods of image restoration and reconstruction - What are the differences between inverse filtering, Wiener filtering, constrained least squares filtering and blind deconvolution - How to use these methods to restore noisy or blurred images using MATLAB ## Chapter 5: Color Image Processing - What are the characteristics and models of color images - What are the challenges and applications of color image processing - What are the techniques for color image enhancement, segmentation, representation and description - How to perform these techniques using MATLAB ## Chapter 6: Wavelets and Multiresolution Processing - What are wavelets and multiresolution processing and why they are useful for image processing - What are the basic concepts and properties of wavelet transform and subband coding - What are the types and applications of wavelet transform for image processing - How to use wavelet transform for image compression, denoising, edge detection and feature extraction using MATLAB ## Chapter 7: Image Compression - What is image compression and why it is necessary for efficient storage and transmission of digital images - What are the criteria and measures of image compression performance - What are the principles and methods of lossless and lossy image compression - How to use Huffman coding, run-length coding, arithmetic coding, LZW coding, JPEG, JPEG2000, MPEG and other standards for image compression using MATLAB ## Chapter 8: Morphological Image Processing - What is morphological image processing and what are its advantages over linear image processing - What are the basic concepts and operations of morphological image processing such as erosion, dilation, opening, closing, hit-or-miss transform, thinning, thickening, skeletonization and pruning - What are the applications of morphological image processing for binary and gray-scale images such as boundary extraction, region filling, noise removal, shape analysis and recognition - How to perform morphological image processing using MATLAB ## Chapter 9: Image Segmentation - What is image segmentation and what are its goals and challenges - What are the basic approaches and techniques for image segmentation such as thresholding, region-based methods, edge-based methods, clustering methods, graph-based methods and active contour models - How to evaluate the quality and accuracy of image segmentation results - How to implement these methods for image segmentation using MATLAB ## Chapter 10: Feature Extraction and Description - What are features and why they are important for image analysis and recognition - What are the types and characteristics of features such as points, lines, edges, corners, blobs, regions, shapes, textures, colors and moments - What are the methods for feature extraction such as edge detection, corner detection, blob detection, region growing, watershed transform, Hough transform and scale-invariant feature transform (SIFT) - How to use these methods for feature extraction using MATLAB ## Chapter 11: Object Recognition - What is object recognition and what are its applications and challenges - What are the stages and components of object recognition such as preprocessing, segmentation, feature extraction, feature matching, classification and verification - What are the methods and techniques for object recognition such as template matching, statistical methods, structural methods, neural networks, support vector machines, decision trees, k-nearest neighbors and deep learning - How to use these methods for object recognition using MATLAB ## Chapter 12: Image Registration - What is image registration and what are its applications and challenges - What are the steps and components of image registration such as feature detection, feature matching, transformation estimation and resampling - What are the types and methods of image registration such as intensity-based methods, feature-based methods, area-based methods and frequency-based methods - How to use these methods for image registration using MATLAB ## Chapter 13: Video Processing - What is video processing and what are its applications and challenges - What are the characteristics and formats of video data - What are the basic operations and techniques for video processing such as frame extraction, frame difference, motion estimation, motion compensation, optical flow, background subtraction, video stabilization, video enhancement, video segmentation, video compression, video analysis and video synthesis - How to use these techniques for video processing using MATLAB ## Conclusion - Summarize the main points and contributions of the book - Highlight the benefits and limitations of digital image processing - Provide some suggestions and directions for future research and development in digital image processing ## FAQs - Q: Who is the target audience of this book? - A: This book is intended for undergraduate and graduate students, researchers, engineers and practitioners who are interested in learning the theory and practice of digital image processing. - Q: What are the prerequisites for reading this book? - A: The readers should have some basic knowledge of mathematics, linear algebra, calculus, probability, statistics and programming. Familiarity with MATLAB is also helpful but not essential. - Q: How can I download this book for free? - A: You can download this book for free from the following link: https://www.ebooknetworking.net/ebooks/poornima-thangam-digital-image-processing.html - Q: How can I contact the author of this book? - A: You can contact the author of this book by sending an email to poornimathangam@gmail.com - Q: Where can I find more resources and references on digital image processing? - A: You can find more resources and references on digital image processing from the following sources: - Digital Image Processing by Rafael C. Gonzalez and Richard E. Woods (4th edition, Pearson, 2018) - Digital Image Processing Using MATLAB by Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins (3rd edition, Gatesmark Publishing, 2020) - Image Processing Toolbox User's Guide by MathWorks (https://www.mathworks.com/help/images/index.html) - Image Processing Learning Resources by MathWorks (https://www.mathworks.com/discovery/image-processing.html)




Digital Image Processing Book By Poornima Thangam Free 28

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