Digital Image Processing with C++: Implementing Reference Algorithms with the CImg Library

Length: 312 pages
Edition: 1
Language: English
Publisher: CRC Press
Publication Date: 2023-03-23
ISBN-10: 1032347538
ISBN-13: 9781032347530
Sales Rank: #0 (See Top 100 Books)

Digital Image Processing with C++ presents the theory of digital image processing, and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language, using the free and easy-to-use CImg library.

Chapters cover in a broad way the field of digital image processing and proposes practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization.

Students or developers wishing to discover or specialize in this discipline, teachers and researchers wishing to quickly prototype new algorithms, or develop courses, will all find in this book material to discover image processing or deepen their knowledge in this field.

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Preamble
I. Introduction to CImg
	1. Introduction
	2. Getting Started with the CImg Library
		2.1. Objective: subdivide an image into blocks
		2.2. Setup and first program
		2.3. Computing the variations
		2.4. Computing the block decomposition
		2.5. Rendering of the decomposition
		2.6. Interactive visualization
		2.7. Final source code
II. Image Processing Using CImg
	3. Point Processing Transformations
		3.1. Image operations
			3.1.1. Mathematical transformations
			3.1.2. Bitwise transformations
			3.1.3. Contrast enhancement
		3.2. Histogram operations
			3.2.1. Definition
			3.2.2. Histogram specification
			3.2.3. Local histogram specification
	4. Mathematical Morphology
		4.1. Binary images
			4.1.1. Dilation and erosion
			4.1.2. Opening and closing
		4.2. Gray-level images
		4.3. Some applications
			4.3.1. Kramer-Bruckner filter
			4.3.2. Alternating sequential filters
			4.3.3. Morphological gradients
			4.3.4. Skeletonization
	5. Filtering
		5.1. Spatial filtering
			5.1.1. Introduction
			5.1.2. Low-pass filters
			5.1.3. High-pass filters
			5.1.4. Adaptive filters
			5.1.5. Adaptive window filters
		5.2. Recursive filtering
			5.2.1. Optimal edge detection
			5.2.2. Deriche filter
		5.3. Frequency filtering
			5.3.1. Introduction
			5.3.2. The Fourier transform
			5.3.3. Frequency filtering
			5.3.4. Processing a Moiré image
		5.4. Diffusion filtering
			5.4.1. Introduction
			5.4.2. Physical basis of diffusion
			5.4.3. Linear diffusion filter
			5.4.4. Non-linear diffusion filter in two dimensions
			5.4.5. Non-linear diffusion filter on a video sequence
	6. Feature Extraction
		6.1. Points of interest
			6.1.1. Introduction
			6.1.2. Harris and Stephens detector
			6.1.3. Shi and Tomasi algorithm
			6.1.4. Points of interest with sub-pixel accuracy
		6.2. Hough transform
			6.2.1. Introduction
			6.2.2. Line detection
			6.2.3. Circle and ellipse detection
		6.3. Texture features
			6.3.1. Texture spectrum
			6.3.2. Tamura coefficients
			6.3.3. Local binary pattern
			6.3.4. Application
	7. Segmentation
		7.1. Edge-based approaches
			7.1.1. Introduction to implicit active contours
			7.1.2. Implicit representation of a contour
			7.1.3. Evolution equation
			7.1.4. Discretization of the evolution equation
			7.1.5. Geodesic model propagation algorithm
		7.2. Region-based approaches
			7.2.1. Introduction
			7.2.2. Histogram-based methods
			7.2.3. Thresholding by clustering
			7.2.4. Transformation of regions
			7.2.5. Super-pixels partitioning
	8. Motion Estimation
		8.1. Optical flow: dense motion estimation
			8.1.1. Variational methods
			8.1.2. Lucas and Kanade differential method
			8.1.3. Affine flow
		8.2. Sparse estimation
			8.2.1. Displacement field using spatial correlation
			8.2.2. Displacement field using phase correlation
			8.2.3. Kalman filtering
	9. Multispectral Approaches
		9.1. Dimension reduction
			9.1.1. Principal component analysis
			9.1.2. Example
		9.2. Color imaging
			9.2.1. Colorimetric spaces
			9.2.2. Median filtering in color imaging
			9.2.3. Edge detection in color imaging
	10. 3D Visualisation
		10.1. Structuring of 3D mesh objects
		10.2. 3D plot of a function z = f (x, y)
		10.3. Creating complex 3D objects
			10.3.1. Details on vertex structuring
			10.3.2. Details on primitive structuring
			10.3.3. Details on material structuring
			10.3.4. Details on opacity structuring
		10.4. Visualization of a cardiac segmentation in MRI
			10.4.1. Description of the input data
			10.4.2. Extraction of the 3D surface of the ventricle
			10.4.3. Adding 3D motion vectors
			10.4.4. Adding cutting planes
			10.4.5. Final result
	11. And So Many Other Things...
		11.1. Compression by transform (JPEG)
			11.1.1. Introduction - Compression by transform
			11.1.2. JPEG Algorithm
			11.1.3. Discrete cosine transform and quantization
			11.1.4. Simplified JPEG algorithm
		11.2. Tomographic reconstruction
			11.2.1. Introduction
			11.2.2. Analytical tomographic reconstruction
			11.2.3. Algebraic tomographic reconstruction
		11.3. Stereovision
			11.3.1. Epipolar geometry
			11.3.2. Depth estimation
		11.4. Interactive deformation using RBF
			11.4.1. Goal of the application
			11.4.2. The RBF interpolation
			11.4.3. RBF for image warping
			11.4.4. User interface for keypoint management
List of CImg Codes
Bibliography
Index