Inversion of picture operators
Pattern Recognition Letters
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional signal and image processing
Two-dimensional signal and image processing
Theoretical aspects of morphological filters by reconstruction
Signal Processing
Journal of Mathematical Imaging and Vision
Differentiation-Based Edge DetectionUsing the Logarithmic Image Processing Model
Journal of Mathematical Imaging and Vision
An Introduction to Nonlinear Image Processing
An Introduction to Nonlinear Image Processing
Digital Image Processing
Handbook of Computer Vision Algorithms in Image Algebra
Handbook of Computer Vision Algorithms in Image Algebra
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
General Adaptive Neighborhood Image Processing
Journal of Mathematical Imaging and Vision
The study of logarithmic image processing model and its application to image enhancement
IEEE Transactions on Image Processing
Flat zones filtering, connected operators, and filters by reconstruction
IEEE Transactions on Image Processing
General Adaptive Neighborhood Image Processing
Journal of Mathematical Imaging and Vision
General Adaptive Neighborhood Image Processing
Journal of Mathematical Imaging and Vision
EURASIP Journal on Applied Signal Processing
General Adaptive Neighborhood Choquet Image Filtering
Journal of Mathematical Imaging and Vision
Binary morphology with spatially variant structuring elements: algorithm and architecture
IEEE Transactions on Image Processing
Adaptive mathematical morphology: a unified representation theory
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Study on nonlocal morphological operators
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
General adaptive neighborhood viscous mathematical morphology
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
General Adaptive Neighborhood-Based Pretopological Image Filtering
Journal of Mathematical Imaging and Vision
General adaptive neighborhood image restoration, enhancement and segmentation
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Adaptive Shape Diagrams for Multiscale Morphometrical Image Analysis
Journal of Mathematical Imaging and Vision
Hi-index | 0.00 |
The so-called General Adaptive Neighborhood Image Processing (GANIP) approach is presented in a two parts paper dealing respectively with its theoretical and practical aspects. The General Adaptive Neighborhood (GAN) paradigm, theoretically introduced in Part I [20], allows the building of new image processing transformations using context-dependent analysis. With the help of a specified analyzing criterion, such transformations perform a more significant spatial analysis, taking intrinsically into account the local radiometric, morphological or geometrical characteristics of the image. Moreover they are consistent with the physical and/or physiological settings of the image to be processed, using general linear image processing frameworks.In this paper, the GANIP approach is more particularly studied in the context of Mathematical Morphology (MM). The structuring elements, required for MM, are substituted by GAN-based structuring elements, fitting to the local contextual details of the studied image. The resulting morphological operators perform a really spatially-adaptive image processing and notably, in several important and practical cases, are connected, which is a great advantage compared to the usual ones that fail to this property.Several GANIP-based results are here exposed and discussed in image filtering, image segmentation, and image enhancement. In order to evaluate the proposed approach, a comparative study is as far as possible proposed between the adaptive and usual morphological operators. Moreover, the interests to work with the Logarithmic Image Processing framework and with the `contrast' criterion are shown through practical application examples.