Open AccessBook
Fundamentals of digital image processing
Anil K. Jain
- 03 Oct 1988
8.9K
TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
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Abstract: Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.
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Citations
Hierarchical face clustering on polygonal surfaces
Michael Garland,Andrew Willmott,Paul S. Heckbert +2 more
- 01 Mar 2001
TL;DR: A new method for representing a hierarchy of regions on a polygonal surface which partition that surface dinto a set of face clusters, which represent the aggregate properties of the original surface at different scales rather than providing geometric approximations of varying complexity.
462
Ordered Upwind Methods for Static Hamilton--Jacobi Equations: Theory and Algorithms
TL;DR: A family of fast ordered upwind methods for approximating solutions to a wide class of static Hamilton-Jacobi equations with Dirichlet boundary conditions with complexity O(M log M), where M is the total number of points in the domain.
460
•Posted Content
A Comprehensive Review of Image Enhancement Techniques
Raman Maini,Himanshu Aggarwal +1 more
TL;DR: Underlying concepts of underlying concepts, along with algorithms commonly used for image enhancement, are provided, with particular reference to point processing methods and histogram processing.
Graph Learning From Data Under Laplacian and Structural Constraints
TL;DR: This paper proposes a novel framework for learning/estimating graphs from data, which includes formulation of various graph learning problems, their probabilistic interpretations, and associated algorithms.
441
Image interpolation and resampling
Philippe Thévenaz,Thierry Blu,Michael Unser +2 more
- 15 Oct 2000
TL;DR: This chapter presents a survey of interpolation and resampling techniques in the context of exact, separable interpolation of regularly sampled data, and explains why the approximation order inherent in the synthesis function is important to limit these interpolation artifacts.
References
A New Approach to Linear Filtering and Prediction Problems
Tamer Basar
- 01 Jan 2001
TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
22.7K
Linear prediction: A tutorial review
John Makhoul
- 01 Apr 1975
TL;DR: This paper gives an exposition of linear prediction in the analysis of discrete signals as a linear combination of its past values and present and past values of a hypothetical input to a system whose output is the given signal.
4.4K
•Proceedings Article
Image Processing
E.E. Pissaloux
- 01 Jan 1994
TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.
2.5K