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
EURASIP Journal on Applied Signal Processing
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
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 morphological filtering using similarities based on geodesic time
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Overview of adaptive morphology: trends and perspectives
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
General adaptive neighborhood mathematical morphology
ICIP'09 Proceedings of the 16th IEEE international conference 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
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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 Adaptive Neighborhood (AN) paradigm allows the building of new image processing transformations using context-dependent analysis. Such operators are no longer spatially invariant, but vary over the whole image with ANs as adaptive operational windows, taking intrinsically into account the local image features. This AN concept is here largely extended, using well-defined mathematical concepts, to that General Adaptive Neighborhood (GAN) in two main ways. Firstly, an analyzing criterion is added within the definition of the ANs in order to consider the radiometric, morphological or geometrical characteristics of the image, allowing a more significant spatial analysis to be addressed. Secondly, general linear image processing frameworks are introduced in the GAN approach, using concepts of abstract linear algebra, so as to develop operators that are consistent with the physical and/or physiological settings of the image to be processed.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 transforms perform a relevant spatially-adaptive image processing, in an intrinsic manner, that is to say without a priori knowledge needed about the image structures. Moreover, in several important and practical cases, the adaptive morphological operators are connected, which is an overwhelming advantage compared to the usual ones that fail to this property.