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CNN architectures for constrained diffusion based locally adaptive image processing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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DYNAMIC ANALOGIC CNN ALGORITHMS FOR A COMPLEX RECOGNITION TASK—A FIRST STEP TOWARDS A BIONIC EYEGLASS
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A CNN UNIVERSAL CHIP IN CMOS TECHNOLOGY
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Spatial logic algorithms using basic morphological analogic CNN operations
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CNN‐based difference‐controlled adaptive non‐linear image filters
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CNN-based spatio-temporal nonlinear filtering and endocardial boundary detection in echocardiography
inferredcites →https://doi.org/10.1002/cta.4490230602openalex-snapshot
The analogic cellular neural network as a bionic eye
inferredcites →https://doi.org/10.1016/0167-2789(91)90213-sopenalex-snapshot
Autowave principles for parallel image processing
inferredcites →https://doi.org/10.1016/0262-8856(90)80008-hopenalex-snapshot
Biased anisotropic diffusion: a unified regularization and diffusion approach to edge detection
inferredcites →https://doi.org/10.1016/0734-189x(88)90022-9openalex-snapshot
A survey of thresholding techniques
inferredcites →https://doi.org/10.1016/s0262-8856(96)00004-2openalex-snapshot
Subjective image fidelity metric based on bit allocation of the human visual system in the DCT domain
inferredcites →https://doi.org/10.1023/a:1008153320440openalex-snapshot
Computing with Front Propagation: Active Contour And Skeleton Models In Continuous-Time CNN
inferredcites →https://doi.org/10.1023/a:1008278225960openalex-snapshot
A 16 × 16 Cellular Neural Network Universal Chip: The First Complete Single-Chip Dynamic Computer Array with Distributed Memory and with Gray-Scale Input-Output
inferredcites →https://doi.org/10.1109/31.7601openalex-snapshot
Cellular neural networks: applications
inferredcites →https://doi.org/10.1109/34.232073openalex-snapshot
Comparing images using the Hausdorff distance
inferredcites →https://doi.org/10.1109/34.56205openalex-snapshot
Scale-space and edge detection using anisotropic diffusion
inferredcites →https://doi.org/10.1109/34.868688openalex-snapshot
Normalized cuts and image segmentation
inferredcites →https://doi.org/10.1109/42.141646openalex-snapshot
Nonlinear anisotropic filtering of MRI data
inferredcites →https://doi.org/10.1109/72.883456openalex-snapshot
Morphology and autowave metric on CNN applied to bubble-debris classification
inferredcites →https://doi.org/10.1109/81.257286openalex-snapshot
Resistive grid image filtering: input/output analysis via the CNN framework
inferredcites →https://doi.org/10.1109/81.473566openalex-snapshot
Methods for image processing and pattern formation in Cellular Neural Networks: a tutorial
inferredcites →https://doi.org/10.1109/81.558448openalex-snapshot
Markov random field image segmentation using cellular neural network
inferredcites →https://doi.org/10.1109/81.563627openalex-snapshot
A fast fixed point learning method to implement associative memory on CNNs
inferredcites →https://doi.org/10.1109/81.661683openalex-snapshot
Implementation of binary and gray-scale mathematical morphology on the CNN universal machine
inferredcites →https://doi.org/10.1109/81.747197openalex-snapshot
The computational infrastructure of analogic CNN computing. I. The CNN-UM chip prototyping system
inferredcites →https://doi.org/10.1109/82.222815openalex-snapshot
The CNN universal machine: an analogic array computer
inferredcites →https://doi.org/10.1109/82.227372openalex-snapshot
Design of linear cellular neural networks for motion sensitive filtering
inferredcites →https://doi.org/10.1109/cnna.1994.381635openalex-snapshot
Order statistic filtering with cellular neural networks
inferredcites →https://doi.org/10.1109/cnna.1998.685386openalex-snapshot
A 0.5 μm CMOS CNN analog random access memory chip for massive image processing
inferredcites →https://doi.org/10.1109/iccv.1998.710861openalex-snapshot
Motion segmentation and tracking using normalized cuts
inferredcites →https://doi.org/10.1109/icecs.1998.813300openalex-snapshot
A 48 by 48 CNN chip operating with B/W images
inferredcites →https://doi.org/10.1109/icecs.1998.813304openalex-snapshot
A 64×64 CNN universal chip with analog and digital I/O
inferredcites →https://doi.org/10.1109/icnn.1996.549106openalex-snapshot
Spatio-temporal image filtering with cellular neural networks
inferredcites →https://doi.org/10.1109/tcom.1977.1093773openalex-snapshot
On the Role of the Observer and a Distortion Measure in Image Transmission
inferredcites →https://doi.org/10.1109/tit.1974.1055250openalex-snapshot
The effects of a visual fidelity criterion of the encoding of images
inferredcites →https://doi.org/10.1109/tpami.1986.4767851openalex-snapshot
A Computational Approach to Edge Detection
inferredcites →https://doi.org/10.1109/tsmc.1977.4309680openalex-snapshot
A Nonlinear Model for the Spatial Characteristics of the Human Visual System
inferredcites →https://doi.org/10.1137/0147038openalex-snapshot
Propagation and Its Failure in Coupled Systems of Discrete Excitable Cells
inferredcites →https://doi.org/10.1137/0729012openalex-snapshot
Image Selective Smoothing and Edge Detection by Nonlinear Diffusion
inferredcites →https://doi.org/10.1137/0729052openalex-snapshot
Image Selective Smoothing and Edge Detection by Nonlinear Diffusion. II
inferredcites →https://doi.org/10.1142/s0218127494000149openalex-snapshot
A WAVE APPROACH TO PATTERN RECOGNITION (WITH APPLICATION TO OPTICAL CHARACTER RECOGNITION)
inferredcites →https://doi.org/10.1142/s0218127497001618openalex-snapshot
CNN: A Vision of Complexity
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Non‐linear coupled CNN models for multiscale image analysis
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Mechanisms for Identification of Micro-objects Based on the Use of Characteristics in the Image Structure
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Image Edge Detection and Orientation Selection with Coupled Nonlinear Excitable Elements
inferred← citeshttps://doi.org/10.1007/978-3-642-37781-5_24openalex-snapshot
Image Edge Detection and Orientation Selection with Coupled Nonlinear Excitable Elements
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A method analysis for hail cloudy prediction based on CNN
inferred← citeshttps://doi.org/10.1016/j.jneumeth.2008.01.002openalex-snapshot
A cellular neural network based method for classification of magnetic resonance images: Towards an automated detection of hippocampal sclerosis
inferred← citeshttps://doi.org/10.1109/cnna.2006.341648openalex-snapshot
Detecting Structural Alterations in the Brain using a Cellular Neural Network based Classification of Magnetic Resonance Images
inferred← citeshttps://doi.org/10.1109/cnna.2010.5430256openalex-snapshot
Variational computing based segmentation methods for medical imaging by using CNN
inferred← citeshttps://doi.org/10.1109/icmlc.2002.1176711openalex-snapshot
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CNN-based algorithm for drusen identification
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Image Processing Using SETMOS-based CNN
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Moving object segmentation algorithm based on cellular neural networks in the H.264 compressed domain
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ON STABILITY OF CELLULAR NEURAL NETWORKS WITH POLYNOMIAL INTERACTIONS
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A multi-view approach to cDNA micro-array analysis
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Superpixel Segmentation Based Synthetic Classifications with Clear Boundary Information for a Legged Robot
inferred← citeshttps://doi.org/10.4218/etrij.12.0112.0107openalex-snapshot
Modular Cellular Neural Network Structure for Wave-Computing-Based Image Processing
inferred← citeshttps://doi.org/10.48550/arxiv.math-ph/0610037openalex-snapshot
Cellular Computing and Least Squares for partial differential problems parallel solving
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