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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 review of fruit and vegetable classification techniques

TL;DR: A critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable is presented.
Journal ArticleDOI

blobcat: software to catalogue flood‐filled blobs in radio images of total intensity and linear polarization

TL;DR: BLOBCAT as mentioned in this paper is a new source extraction software that uses the flood fill algorithm to detect and catalogue blobs, or islands of pixels representing sources, in twodimensional astronomical images.
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Vessel Boundary Delineation on Fundus Images Using Graph-Based Approach

TL;DR: An algorithm to measure the width of retinal vessels in fundus photographs using graph-based algorithm to segment both vessel edges simultaneously is proposed and is robust and estimates the vessel width with subpixel accuracy.
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Nontarget Image-Based Technique for Small Cable Vibration Measurement

TL;DR: In this paper, a proof-of-concept image-based technique is proposed for measuring small cable vibration, which analyzes an image sequence of a vibrating cable segment captured by a camera and calculates variation of optical intensity of an arbitrary selected region of interest ROI on the cable image sequence.
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Fast Localization of the Optic Disc Using Projection of Image Features

TL;DR: This work presents a fast technique that requires less than a second to localize the Optic Disc, based upon obtaining two projections of certain image features that encode the x- and y- coordinates of the OD.