Open AccessBook
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.read more
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Journal ArticleDOI
The data explosion: tackling the taboo of automatic feature recognition in airborne survey data
TL;DR: Embracing the new generation of vast datasets requires reassessment of established workflows and greater understanding of the different types of information that may be generated using computer-aided methods.
Proceedings ArticleDOI
Image processing and neural computing used in the diagnosis of tuberculosis
TL;DR: The study presented in this paper indicates that machine-assisted diagnosis of tuberculosis is certainly feasible and should be more accurate due to the higher number of view-fields processed.
Journal ArticleDOI
Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding
TL;DR: The modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights, is put forward, which has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.
Proceedings ArticleDOI
On the effects of sensor noise in pixel-level image fusion performance
V. Petrovi,Costas Xydeas +1 more
TL;DR: The aim is to develop appropriate metrics which measure the effect of input sensor noise on the performance of a given pixel-level image fusion system and to employ these metrics in a comparative study of the robustness of typical image fusion schemes whose input is corrupted by sensor noise.
Proceedings ArticleDOI
Vessel Tracking in Peripheral CTA Datasets -- An Overview
TL;DR: The paper collects seven methods applicable for vessel segmentation acquired by computer tomography angiography (CTA) of the human leg that simultaneously preserves the vessel calcification and allows localization of vessel narrowings.