Digital Image Processing with C++

David Tschumperle is a permanent CNRS Research Scientist, heading the IMAGE team at the GREYC Laboratory, in Caen / France. He’s particularly interested in partial differential equations and variational methods for processing multi-valued images in a local or non-local way. He has authored more than 40 papers in journals or conferences and is the project leader of CImg and G'MIC, two open-source software/libraries.Christophe Tilmant is Associate Professor in Computer Science at Clermont-Auvergne University. His research activities include image processing, artificial intelligence, where he has authored more than 30 papers. His teaching includes deep learning, image processing and network security. He participates or leads several French research programs.Vincent Barra is a full Professor in Computer Science at Clermont-Auvergne University, and Associate Director of the LIMOS Lab. He teaches artificial intelligence and image processing in engineering schools and in master's programs. His research activities focus on n-dimensional data analysis, with methodological and application aspects in various fields. He has authored more than 90 papers in journal or conferences, and participates or leads several French and European research programs.

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• I INTRODUCTION TO Clmg1. 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 CImg3. Point Processing Transformations. 3.1 Image operations. 3.2 Histogram operations. 4. Mathematical Morphology. 4.1 Binary images. 4.2 Gray-level images. 4.3 Some applications. 5. Filtering. 5.1 Spatial filtering. 5.2 Recursive filtering. 5.3 Frequency filtering. 5.4 Diffusion filtering. 6. Feature Extraction. 6.1 Points of interest. 6.2 Hough transform. 6.3 Texture features. 7. Segmentation. 7.1 Edge-based approaches. 7.2 Region-based approaches. 8. Motion Estimation. 8.1 Optical flow: dense motion estimation. 8.2 Sparse estimation. 9. Multispectral Approaches. 9.1 Dimension reduction. 9.2 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. 10.4 Visualization of a cardiac segmentation in MRI. 11. And So Many Other Things. 11.1 Compression by transform (JPEG). 11.2 Tomographic reconstruction. 11.3 Stereovision. 11.4 Interactive deformation using RBF. List of CImg Codes.References.Index.