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
Normalizing counts and cerebral blood flow intensity in functional imaging studies of the human brain.
TL;DR: Three normalization procedures were evaluated on their ability to remove extraneous error variation, induce homogeneity of intersubject variation, and remove unwanted dependencies, and all worked well at removing the dependency of rCBF on gCBF in count and flow images.
Proceedings ArticleDOI
Individual recognition from periodic activity using hidden Markov models
Qiang He,Christian H. Debrunner +1 more
TL;DR: A method is presented for recognizing individuals from their walking and running gait using hidden Markov models based on Hu moments of the motion segmentation in each frame that detects periodicity in a sequence of feature vectors.
Journal ArticleDOI
Automatic skin cancer images classification
TL;DR: Two hybrid techniques for the classification of the skin images to predict it if exists are presented, consisting of three stages, namely, feature extraction, dimensionality reduction, and classification.
Journal ArticleDOI
Review of computer vision education
TL;DR: The status of computer vision education today is reviewed to see if there is a strong demand for educating students to become knowledgeable in computer imaging and vision.
Journal ArticleDOI
The Detection of Buried Pipes From Time-of-Flight Radar Data
TL;DR: The Hough transform is extended by introducing a weighting factor depending on the differentials of the unknown parameters with respect to the experimental errors, namely, the probe position error and the time-of-flight error to enable optimally placed sets of data pairs to be given greater weight than "ill-conditioned" sets.