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
Image watermarking based on invariant regions of scale-space representation
Jin S. Seo,Chang D. Yoo +1 more
TL;DR: Experimental results show that the proposed method is robust against various image processing steps, including geometric transformations, cropping, filtering, and JPEG compression.
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
V. Boskovitz,Hugo Guterman +1 more
TL;DR: An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented that consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique.
185
An image enhancement technique combining sharpening and noise reduction
TL;DR: A new approach to contrast enhancement of image data is presented, based on a multiple-output system that adopts fuzzy models in order to prevent the noise increase during the sharpening of the image details.
185
Applied Digital Signal Processing: Theory and Practice
Dimitris G. Manolakis,Vinay K. Ingle +1 more
- 21 Nov 2011
TL;DR: A focus on algorithms that are of theoretical importance or useful in real-world applications ensures that students cover material relevant to engineering practice, and equips students and practitioners alike with the basic principles necessary to apply DSP techniques to a variety of applications.
185
A study of cloud classification with neural networks using spectral and textural features
TL;DR: The performance of the PNN when used in conjunction with these feature extraction and postprocessing schemes showed the potential of this neural-network-based cloud classification system.
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