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
Weed detection in multi-spectral images of cotton fields
TL;DR: A means for automatic detection and evaluation of weeds in the field was developed and tested; it was based on an acousto-optic tunable hyperspectral sensor and a detection algorithm and showed a good detection ability.
73
Watermarking of Digital Images
Saraju P. Mohanty
- 01 Jan 1999
TL;DR: A Note to All Readers This is not an original electronic copy of the master's thesis, but a reproduced version of the authentic hardcopy of the thesis that was lost during transit from India to USA in December 1999.
73
Instrumental Evaluation of Fabric Wrinkle Recovery
Bugao Xu,Julia A Reed +1 more
TL;DR: In this paper, two variables, surface ratio and shade ratio, are defined to quantify wrinkled appearance, and two empirical equations are formulated to serve as mathematical models for grading fabric wrinkle recovery.
72
Contrast-Guided Image Interpolation
Zhe Wei,Kai-Kuang Ma +1 more
TL;DR: The proposed contrast-guided image interpolation method is superior to other state-of-the-art edge-guidedimage interpolation methods and the computational complexity is relatively low when compared with existing methods; hence, it is fairly attractive for real-time image applications.
72
Segmenting Brain Tumors From FLAIR MRI Using Fully Convolutional Neural Networks
Pablo Ribalta Lorenzo,Jakub Nalepa,Barbara Bobek-Billewicz,Pawel Wawrzyniak,Grzegorz Mrukwa,Michal Kawulok,Pawel Ulrych,Michael P. Hayball +7 more
TL;DR: A new deep learning technique for segmenting brain tumors from fluid attenuation inversion recovery MRI which can be trained from small and heterogeneous datasets annotated by a human reader and yields the better performance when compared with the state of the art method which utilizes hand-crafted features.
72
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