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
Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels on chest CT images
TL;DR: A fast and fully automatic scheme based on iterative weighted averaging and adaptive curvature threshold is proposed in this study to facilitate accurate lung segmentation for inclusion of juxtapleural nodules and pulmonary vessels and ensure the smoothness of the lung boundary.
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Automated Planning and Optimization of Lumber Production Using Machine Vision and Computed Tomography
TL;DR: An automated system for planning and optimization of lumber production using Machine Vision and Computed Tomography and a prototype implementation shows significant gains in value yield recovery when compared with lumber processing strategies that use only the information derived from the external log structure.
Object Tracking by Particle Filtering Techniques in Video Sequences
TL;DR: This work reviews particle filtering techniques for tracking single and multiple moving objects in video sequences, by using different features such as colour, shape, motion, edge and sound, along with pros and cons.
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Spatial kernel K-harmonic means clustering for multi-spectral image segmentation
TL;DR: The use of a spatial kernel-based KHM (SKKHM) algorithm on the problem of image segmentation has been investigated, and instead of the original Euclidean intensity distance, a robust kernel- based KHM metric is employed to reduce the effect of outliers and noise.
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Segmentation algorithms for ear image data towards biomechanical studies
TL;DR: A review of the algorithms used in ear segmentation is presented, and their specificities and difficulties as well as their advantages and disadvantages are identified and analysed using experimental examples.