Stochastic Geometry for Image Analysis (Digital Signal and Image Processing Series)

Hardcover, 345 pages, NOT ex-library. Manufacturing fault: missing the title page and all the contents pages (pp iii-x); main text is complete, all the chapters are there (book opens on a half-title page followed directly by page 1; the missing pages haven't been removed, looks like they just have been omitted in the printing process - book is firmly bound and shows no signs of damage). Residue of a removed sticker inside the front board (covered with a blank sticker). Interior is clean and bright throughout with unmarked text. Boards show gentle handling wear, short corner creases, some tiny scuff-marks. Issued without a dust jacket. -- "This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed. Numerous applications, covering remote sensing images, biological and medical imaging, are detailed. This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling". -- Contents: 1. Introduction 2. Marked Point Processes for Object Detection [Principal definitions; Density of a point process; Marked point processes; Point processes and image analysis] 3. Random Sets for Texture Analysis [Introduction; Random sets; Some geostatistical aspects; Some morphological aspects; Appendix: demonstration of Miles' formulae for the Boolean model] 4. Simulation and Optimization [Discrete simulations: Markov chain Monte Carlo algorithms; Continuous simulations; Mixed simulations; Simulated annealing] 5. Parametric Inference for Marked Point Processes in Image Analysis [Introduction; First question: what and where are the objects in the image?; Second question: what are the parameters of the point process that models the objects observed in the image?; Conclusion and perspectives] 6. How to Set Up a Point Process? [From disks to polygons, via a discussion of segments; From no overlap to alignment; From the likelihood to a hypothesis test; From Metropolis–Hastings to multiple births and deaths] 7. Population Counting [Detection of Virchow–Robin spaces; Evaluation of forestry resources; Counting a population of flamingos; Counting the boats at a port] 8. Structure Extraction [Detection of the road network; Extraction of building footprints; Representation of natural textures - Simple model (Data term; Sampling by jump diffusion; Results) - Models with complex interactions] 9. Shape Recognition [Modeling of a LIDAR signal - Motivation - Model library (Energy formulation) - Sampling - Results (Simulated data; Satellite data: large footprint waveforms; Airborne data: small footprint waveforms; Application to the classification of 3D point clouds); 3D reconstruction of buildings - Library of 3D models - Bayesian formulation (Likelihood; A priori) -Optimization - Results and discussion]; Bibliography; List of Authors; Index.