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
Gabor filters and phase portraits for the detection of architectural distortion in mammograms
TL;DR: This work presents a new method to detect and localise architectural distortion by analysing the oriented texture in mammograms, using a bank of Gabor filters to obtain the orientation field of the given mammogram.
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Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models.
TL;DR: A new technique for sputum image analysis is presented that combines invariant shape features and chromatic channel thresholding, which constitutes a step towards automating the process and providing a high specificity.
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Filament Recognition and Image Cleaning on Meudon Hα Spectroheliograms
TL;DR: In this article, a region growing method is used to segment the filament regions in the Meudon Hα spectroheliograms and then the skeleton of the filament is described by means of their pruned skeleton.
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Nasal architecture: form and flow
TL;DR: This work outlined means to compare complex airway geometries and demonstrated the effects of rational geometric simplification on the flow structure, offering a fresh approach to studies of how natural conduits guide and control flow.
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Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images
TL;DR: A complete segmentation system is developed for segmenting DR, including hard exudates, cotton wool spots, MAs, and HEMs, and a Naïve Bayes method is applied for segmentation of DR.