By Oge Marques
Up-to-date, technically exact assurance of crucial issues in photograph and video processing
This is the 1st publication to mix picture and video processing with a pragmatic MATLAB®-oriented strategy so that it will exhibit crucial picture and video thoughts and algorithms. using minimum math, the contents are provided in a transparent, target demeanour, emphasizing and inspiring experimentation.
The ebook has been equipped into components. half I: picture Processing starts with an outline of the sphere, then introduces the elemental suggestions, notation, and terminology linked to photograph illustration and simple photograph processing operations. subsequent, it discusses MATLAB® and its snapshot Processing Toolbox with the beginning of a chain of chapters with hands-on actions and step by step tutorials. those chapters disguise photo acquisition and digitization; mathematics, good judgment, and geometric operations; point-based, histogram-based, and neighborhood-based photo enhancement recommendations; the Fourier rework and suitable frequency-domain photo filtering options; photograph recovery; mathematical morphology; aspect detection concepts; photo segmentation; picture compression and coding; and have extraction and representation.
Part II: Video Processing provides the most innovations and terminology linked to analog video indications and structures, in addition to electronic video codecs and criteria. It then describes the technically concerned challenge of criteria conversion, discusses movement estimation and reimbursement concepts, indicates how video sequences will be filtered, and concludes with an instance of an answer to item detection and monitoring in video sequences utilizing MATLAB®.
Extra good points of this publication include:
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More than 30 MATLAB® tutorials, which encompass step by step courses to exploring snapshot and video processing thoughts utilizing MATLAB®
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Chapters supported via figures, examples, illustrative difficulties, and exercises
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Useful web content and an in depth checklist of bibliographical references
This available textual content is perfect for upper-level undergraduate and graduate scholars in electronic picture and video processing classes, in addition to for engineers, researchers, software program builders, practitioners, and a person who needs to profit approximately those more and more well known themes on their own.
Supplemental assets for readers and teachers are available at http://www.ogemarques.com
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Additional resources for Practical Image and Video Processing Using MATLAB
Sample text
Adapted and redrawn from [Umb05]. The eye: a cross-sectional view. 2 5, optical axis; 6, line of sight; 7, cornea; 8, crystalline lens; 9, choroid; 10, optic nerve; 11, optic disk; 12, fovea; 13, retina; 14, vitreous humor. The eye-camera analogy. Adapted and redrawn from [Pal99]. Dark adaptation. Adapted and redrawn from [Pal99]. Distribution of rods and cones in the human retina for the right eye (seen from the bottom). Adapted and redrawn from [Ost35]. EIA 1956 standard test pattern. Courtesy of http://www.
Example of color histogram equalization. (a) Original image and its Y channel histogram; (b) output image and its equalized Y channel histogram. Original image: courtesy of MathWorks. 29 Spatial convolution masks for grayscale and RGB color images. Thresholding in RGB space. Defining spherical (ellipsoidal) regions in RGB space. Example of color segmentation using requantization. Another example of color segmentation using requantization: (a) original image; (b) requantized image with two color levels; (c) requantized image with five color levels.
Edge detection algorithms and techniques are discussed in Chapter 14. 1 Image sharpening: (a) original image; (b) after sharpening. 5. 5): In many image analysis applications, it is often necessary to reduce the number of gray levels in a monochrome image to simplify and speed up its interpretation. Reducing a grayscale image to only two levels of gray (black and white) is usually referred to as binarization, a process that will be discussed in more detail in Chapter 15. 2 Noise removal: (a) original (noisy) image; (b) after removing noise.