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
Robust iso-surface tracking for interactive character skinning
TL;DR: A novel approach to interactive character skinning is presented, which is robust to extreme character movements, handles skin contacts and produces the effect of skin elasticity (sliding), and includes new composition operators enabling blending effects and local self-contact between implicit surfaces.
Proceedings ArticleDOI
Maximally Stable Colour Regions for Recognition and Matching
TL;DR: A novel colour-based affine co-variant region detector based on a Poisson image noise model that performs better than the commonly used Euclidean distance and extends the state of the art in feature repeatability tests.
Journal ArticleDOI
Monitoring changing position of coastlines using Thematic Mapper imagery, an example from the Nile Delta
Kevin White,Hesham M El Asmar +1 more
TL;DR: In this paper, the synoptic capability of Landsat Thematic Mapper imagery enables monitoring of large sections of coastline at relatively coarse (30 m) spatial resolution, and areas of rapid change can be identified and targeted for more detailed monitoring in the field or using higher resolution images.
Journal ArticleDOI
Automated lung segmentation for thoracic CT impact on computer-aided diagnosis.
TL;DR: An automated lung segmentation method has been applied as preprocessing for automated lung nodule detection and as the foundation for computer-assisted measurements of pleural mesothelioma tumor thickness.
Journal ArticleDOI
Quantification of Pulmonary Emphysema from Lung Computed Tomography Images
TL;DR: A texture-based adaptive multiple feature method (AMFM) for evaluating pulmonary parenchyma from computed tomography (CT) images is described, which incorporates multiple statistical and fractal texture features and holds promise for the objective noninvasive evaluation of the pulmonary paretchyma.