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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
A framework for evaluating the data-hiding capacity of image sources
TL;DR: An information-theoretic model for image watermarking and data hiding that considers autoregressive, block-DCT, and wavelet statistical models for images and compute data-hiding capacity for compressed and uncompressed host-image sources is presented.
202
Detection of shallowly buried objects using impulse radar
TL;DR: A signal model is introduced that exploits the different properties of the backscattered signals from target and ground surface that separates the target signal from the ground backscatter.
199
Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales
TL;DR: A statistical framework for monitoring grocery data to detect a large-scale but localized bioterrorism attack is described and the potential of data sources that may be more timely than traditional medical and public health data is illustrated.
199
Knowledge-based classification and tissue labeling of MR images of human brain
TL;DR: The presents a knowledge-based approach to automatic classification and tissue labeling of 2D magnetic resonance (MR) images of the human brain that provides an accurate complete labeling of all normal tissues in the absence of large amounts of data nonuniformity.
198
Connected Filtering and Segmentation Using Component Trees
TL;DR: The component tree is proposed as an efficient and accessible data structure used to implement nonflat gray-level connected filters and an application of nonflat component filters to the segmentation of wood micrographs is presented.
198
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