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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

Machine vision system: a tool for quality inspection of food and agricultural products

TL;DR: The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on food and agricultural produce.
Journal ArticleDOI

Computational methods for the image segmentation of pigmented skin lesions

TL;DR: A review of the current methods for the segmentation of pigmented skin lesions in images, and a comparative analysis with regards to several of the fundamental steps of image processing, such as image acquisition, pre-processing and segmentation.
Journal ArticleDOI

Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach

TL;DR: A novel robust active shape model (RASM) matching method is utilized to roughly segment the outline of the lungs through an optimal surface finding approach, which delivered statistically significant better segmentation results, compared to two commercially available lung segmentation approaches.
Posted Content

Stochastic Block Models and Reconstruction

TL;DR: Following Decelle et al, this work establishes a rigorous connection between the clustering problem, spin-glass models on the Bethe lattice and the so called reconstruction problem.
Journal ArticleDOI

Obstructive lung diseases: texture classification for differentiation at CT.

TL;DR: The proposed technique discriminates well between patterns of obstructive lung disease on the basis of parenchymal texture alone and was tested with a new cohort of subjects with a similar spectrum of diseases.