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Pixel‐level snakes on the CNNUM: algorithm design, on‐chip implementation and applications

https://doi.org/10.1002/cta.302
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/(sici)1097-007x(199901/02)27:1<171::aid-cta47>3.0.co;2-xopenalex-snapshot
CNN-based spatio-temporal nonlinear filtering and endocardial boundary detection in echocardiography
inferredcites →https://doi.org/10.1002/cta.191openalex-snapshot
ACE4k: An analog I/O 64×64 visual microprocessor chip with 7‐bit analog accuracy
inferredcites →https://doi.org/10.1006/jcph.1995.1098openalex-snapshot
A Fast Level Set Method for Propagating Interfaces
inferredcites →https://doi.org/10.1007/978-1-4471-1555-7openalex-snapshot
Active Contours
inferredcites →https://doi.org/10.1007/bf00133570openalex-snapshot
Snakes: Active contour models
inferredcites →https://doi.org/10.1007/bf01385685openalex-snapshot
A geometric model for active contours in image processing
inferredcites →https://doi.org/10.1016/s0167-8655(98)00050-6openalex-snapshot
Discrete-time CNN for image segmentation by active contours
inferredcites →https://doi.org/10.1016/s0262-8856(02)00153-1openalex-snapshot
Cellular neural networks and active contours: a tool for image segmentation
inferredcites →https://doi.org/10.1016/s1361-8415(96)80007-7openalex-snapshot
Deformable models in medical image analysis: a survey
inferredcites →https://doi.org/10.1023/a:1008105404510openalex-snapshot
An Active Contour Algorithm for Continuous-Time Cellular Neural Networks
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/10.1073/pnas.93.4.1591openalex-snapshot
A fast marching level set method for monotonically advancing fronts.
inferredcites →https://doi.org/10.1109/31.55003openalex-snapshot
CNN cloning template: connected component detector
inferredcites →https://doi.org/10.1109/31.7600openalex-snapshot
Cellular neural networks: theory
inferredcites →https://doi.org/10.1109/34.244675openalex-snapshot
Finite-element methods for active contour models and balloons for 2-D and 3-D images
inferredcites →https://doi.org/10.1109/34.368173openalex-snapshot
Shape modeling with front propagation: a level set approach
inferredcites →https://doi.org/10.1109/34.841758openalex-snapshot
Geodesic active contours and level sets for the detection and tracking of moving objects
inferredcites →https://doi.org/10.1109/42.363106openalex-snapshot
The detection and quantification of retinopathy using digital angiograms
inferredcites →https://doi.org/10.1109/42.552055openalex-snapshot
Computerized tumor boundary detection using a Hopfield neural network
inferredcites →https://doi.org/10.1109/42.730407openalex-snapshot
Geodesic deformable models for medical image analysis
inferredcites →https://doi.org/10.1109/81.222795openalex-snapshot
The CNN paradigm
inferredcites →https://doi.org/10.1109/82.222815openalex-snapshot
The CNN universal machine: an analogic array computer
inferredcites →https://doi.org/10.1109/cnna.1998.685396openalex-snapshot
Image segmentation based on active contours using discrete time cellular neural networks
inferredcites →https://doi.org/10.1109/iccv.1995.466850openalex-snapshot
Topologically adaptable snakes
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/tpami.2003.1201824openalex-snapshot
Topology preserving level set method for geometric deformable models
inferredcites →https://doi.org/10.1137/s0036144598347059openalex-snapshot
Fast Marching Methods

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/cta.1798openalex-snapshot
Fast texture synthesis with cellular neural network‐based patch stitching
inferred← citeshttps://doi.org/10.1002/cta.2008openalex-snapshot
Efficient optimization‐oriented design methodology of high‐order 3‐D filters using 2‐D and 3‐D electromagnetic simulators
inferred← citeshttps://doi.org/10.1002/cta.337openalex-snapshot
Topographic cellular active contour techniques: theory, implementations and comparisons
inferred← citeshttps://doi.org/10.1002/cta.366openalex-snapshot
A binary‐based on‐chip CNN solution for pixel‐level snakes
inferred← citeshttps://doi.org/10.1002/cta.512openalex-snapshot
Fast retinal vessel tree extraction: A pixel parallel approach
inferred← citeshttps://doi.org/10.1007/11552499_19openalex-snapshot
On the Automatic 2D Retinal Vessel Extraction
inferred← citeshttps://doi.org/10.1007/978-3-030-03023-0_3openalex-snapshot
SOM-Like Neural Network and Differential Evolution for Multi-level Image Segmentation and Classification in Slit-Lamp Images
inferred← citeshttps://doi.org/10.1007/978-3-540-88464-4_9openalex-snapshot
Visual Algorithms for Cognition
inferred← citeshttps://doi.org/10.1007/s11554-014-0469-zopenalex-snapshot
GPU-based segmentation of retinal blood vessels
inferred← citeshttps://doi.org/10.1016/j.cmpb.2012.03.009openalex-snapshot
Blood vessel segmentation methodologies in retinal images – A survey
inferred← citeshttps://doi.org/10.1109/cnna.2005.1543161openalex-snapshot
CNN-based Automatic Retinal Vascular Tree Extraction
inferred← citeshttps://doi.org/10.1109/cnna.2006.341596openalex-snapshot
Testing Pixel Level Snakes
inferred← citeshttps://doi.org/10.1109/cnna.2006.341643openalex-snapshot
A Cellular Active Contours Algorithm Based on Region Evolution
inferred← citeshttps://doi.org/10.1109/cnna.2006.341651openalex-snapshot
CNNUM-Based Methods Using Deformable Contours on Smooth Boundaries
inferred← citeshttps://doi.org/10.1109/cnna.2008.4588655openalex-snapshot
Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach
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/cnna.2010.5430314openalex-snapshot
2D and 3D level-set algorithms on GPU
inferred← citeshttps://doi.org/10.1109/colcaci.2018.8484844openalex-snapshot
Multi-Level Image Segmentatión in Slit-Lamp Images: A Comparison Between two Machine Learning Techniques
inferred← citeshttps://doi.org/10.1109/ecctd.2007.4529645openalex-snapshot
On chip implementation of a pixel-parallel approach for retinal vessel tree extraction
inferred← citeshttps://doi.org/10.1109/ecctd.2009.5275113openalex-snapshot
A digital cellular-based system for retinal vessel-tree extraction
inferred← citeshttps://doi.org/10.1109/iscas.2005.1465956openalex-snapshot
A One-Quadrant Discrete-Time Cellular Neural Network CMOS Chip for Pixel-Level Snakes
inferred← citeshttps://doi.org/10.1109/iscas.2007.377965openalex-snapshot
Evolution of Pixel Level Snakes towards an efficient hardware implementation
inferred← citeshttps://doi.org/10.1109/iscas.2008.4541738openalex-snapshot
Pixel parallel vessel tree extraction for a personal authentication system
inferred← citeshttps://doi.org/10.1109/iscas.2008.4541739openalex-snapshot
Focal-plane moving object segmentation for realtime video surveillance
inferred← citeshttps://doi.org/10.1109/ispa.2009.5297659openalex-snapshot
A pixel-parallel moving object segmentation and tracking algorithm for video surveillance applications
inferred← citeshttps://doi.org/10.1109/newcas.2013.6573629openalex-snapshot
Evaluation of advanced pixel-level snakes on cellular hardware platform
inferred← citeshttps://doi.org/10.1109/newcas.2014.6934065openalex-snapshot
Cellular automaton based pixel-level snakes
inferred← citeshttps://doi.org/10.1109/tbme.2009.2024315openalex-snapshot
Online 3-D Reconstruction of the Right Atrium From Echocardiography Data via a Topographic Cellular Contour Extraction Algorithm
inferred← citeshttps://doi.org/10.1186/1687-5281-2011-10openalex-snapshot
Performance analysis of massively parallel embedded hardware architectures for retinal image processing
inferred← citeshttps://doi.org/10.2299/jsp.18.221openalex-snapshot
A Method Using CA-Based Pixel-Level Snakes for Multiple-Contour Expansion
inferred← citeshttps://doi.org/10.5772/7068openalex-snapshot
An FPGA-based Topographic Computer for Binary Image Processing
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