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
Detecting periods of eating during free-living by tracking wrist motion.
TL;DR: Results indicate that vigorous wrist motion is a useful indicator for identifying the boundaries of eating activities, and that the method should prove useful in the continued development of body-worn sensor tools for monitoring energy intake.
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A fast stereo matching algorithm suitable for embedded real-time systems
TL;DR: A Census-based stereo matching algorithm that handles difficult areas for stereo matching, such as areas with low texture, very well in comparison to state-of-the-art real-time methods and can successfully eliminate false positives to provide reliable 3D data.
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Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends
Awais Mansoor,Ulas Bagci,Brent Foster,Ziyue Xu,Georgios Z. Papadakis,Les R. Folio,Jayaram K. Udupa,Daniel J. Mollura +7 more
TL;DR: A critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings.
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Fast, accurate, and reproducible automatic segmentation of the brain in T1‐weighted volume MRI data
TL;DR: A new fast automated algorithm has been developed to segment the brain from T1‐weighted volume MR images using automated thresholding and morphological operations, which is fully three‐dimensional and therefore independent of scan orientation.
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Splitting touching cells based on concave points and ellipse fitting
TL;DR: A new touching cells splitting algorithm based on concave points and ellipse fitting to split the binary contour of touching cells is proposed and experimental results show that the algorithm is efficient.