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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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Dissertation

衛星影像中飛機機型之辨識; Aircraft Type Recognition in Satellite Images

陳建銘, +1 more
TL;DR: A hierarchical classification algorithm to accurately recognise aircrafts in satellite images by using the area feature first for a rough categorisation on which detailed classifications are achieved using several suggested features.
DissertationDOI

Optical Tracking - From User Motion To 3D Interaction

TL;DR: This work is focused on stereoscopic tracking that provides an effective way to enhance the accuracy of optical based trackers that overcomes many of the drawbacks of conventional tracking systems.
Journal ArticleDOI

Riverbed: A Novel User-Steered Image Segmentation Method Based on Optimum Boundary Tracking

TL;DR: An optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed and its theoretical relation with other popular methods such as live wire and graph cuts is discussed.
Journal ArticleDOI

ImageParser: a tool for finite element generation from three-dimensional medical images.

TL;DR: The ImageParser can successfully exact the geometry of ROIs from a complex medical image and generate the FEM mesh with customer-defined segmentation information.
Journal ArticleDOI

An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images

TL;DR: In this paper, a flexible mathematical morphological (MM) driven approach was proposed for the extraction of water bodies from several satellite images with different spatial resolutions, which preserves the actual size and shape of the water bodies since it is based on morphological operators based on geodesic reconstruction.