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Image Processing: Analysis and Machine Vision

TLDR
The digitized image and its properties are studied, including shape representation and description, and linear discrete image transforms, and texture analysis.
Abstract
List of Algorithms. Preface. Possible Course Outlines. 1. Introduction. 2. The Image, Its Representations and Properties. 3. The Image, Its Mathematical and Physical Background. 4. Data Structures for Image Analysis. 5. Image Pre-Processing. 6. Segmentation I. 7. Segmentation II. 8. Shape Representation and Description. 9. Object Recognition. 10. Image Understanding. 11. 3d Geometry, Correspondence, 3d from Intensities. 12. Reconstruction from 3d. 13. Mathematical Morphology. 14. Image Data Compression. 15. Texture. 16. Motion Analysis. Index.

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Journal ArticleDOI

A comprehensive texture segmentation framework for segmentation of capillary non-perfusion regions in fundus fluorescein angiograms.

TL;DR: A comprehensive segmentation framework that can quantify capillary non-perfusion in retinopathy from two distinct etiologies, and has the potential to be adopted for wider applications is proposed.
Journal ArticleDOI

Automated aortic calcium scoring on low-dose chest computed tomography.

TL;DR: Automatic calcium scoring in the aorta appears feasible with good correlation between manual and automatic scoring in low-dose noncontrast-enhanced chest CT scans.
Journal ArticleDOI

Relevance feedback using generalized Bayesian framework with region-based optimization learning

TL;DR: A generalized Bayesian framework for relevance feedback in content-based image retrieval is presented and a time-varying user model is incorporated into the formulation, based on the Bayesian learning method.
Journal ArticleDOI

Numerical and experimental study of the plastic zone in cracked specimens

TL;DR: In this paper, a study of both bulk and surface behavior is presented, where the material behaviour is studied by powerful 3D ultrafine finite element analysis in terms of the crack tip plasticity for a range of different conditions.
Proceedings ArticleDOI

Comparing Shape and Texture Features for Pattern Recognition in Simulation Data

TL;DR: This paper investigates shape and texture features for pattern recognition in simulation data and explores which features are suitable for characterizing regions of interest in images resulting from fluid mixing simulations.