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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
An Analysis of Edge Detection by Using the Jensen-Shannon Divergence
Juan Francisco Gómez-Lopera,José Martínez-Aroza,Aureliano M. Robles-Pérez,Ramón Román-Roldán +3 more
TL;DR: The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation, by means of the Jensen-Shannon divergence.
Rate control for MPEG video coding
TL;DR: A new rate control approach which addresses the problems associated with degradation in picture quality at scene cuts and nonuniform picture quality due to buffer-dependent variations of the quantization parameter is presented.
59
Assessing Changes in Texture Periodicity Due to Appearance Loss in Carpets: Gray Level Co-occurrence Analysis
TL;DR: In this article, the periodic components of texture in wool carpets for the purpose of assessing the effects of wear on carpet textures were identified and evaluated by computing gray level differences and second-order gray level statistics.
59
Segmentation of T1 MR scans for reconstruction of resistive head models.
TL;DR: This paper describes a segmentation method primarily developed for reconstructing resistive head models for electroencephalographic modelling purposes by combining several image processing techniques, such as amplitude segmentation, region growing, and image fusion.
59
Compressive data hiding: an unconventional approach for improved color image coding
TL;DR: This paper takes an unconventional approach and considers "piggy-backing" the color information on the luminance component of an image for improved color image coding and transforms a given color image into the YIQ color space where the chrominance information is subsampled and embedded in the wavelet domain of the Luminance component.
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