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Image segmentation using a neural network
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Self-Organization and Associative Memory
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On problem solving with Hopfield neural networks
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“Neural” computation of decisions in optimization problems
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Neural networks and physical systems with emergent collective computational abilities.
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Neurons with graded response have collective computational properties like those of two-state neurons.
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Markov Random Fields and Gibbs Ensembles
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
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Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
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Spatial Interaction and the Statistical Analysis of Lattice Systems
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Real Time Textured-Image Segmentation Based On Noncausal Markovian Random Field Models
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Optimization by Simulated Annealing
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Parallel Distributed Processing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Oil Spill Detection and Characterization from Satellite Image Using Artificial Neural Network Algorithm
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