[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Rajapakse et al., 1990 - Google Patents

Medical image segmentation with MARA

Rajapakse et al., 1990

Document ID
3591311089302699126
Author
Rajapakse J
Acharya R
Publication year
Publication venue
1990 IJCNN International Joint Conference on Neural Networks

External Links

Snippet

The multilayer adaptive resonance architecture (MARA) is a highly stable and plastic self- organizing neural network which is capable of recognizing, reconstructing, and segmenting the traces of previously learned binary patterns. The recognition and reconstruction …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
    • G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
    • G06K9/4619—Biologically-inspired filters, e.g. receptive fields
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Nattkemper et al. A neural network architecture for automatic segmentation of fluorescence micrographs
Fukushima Recent advances in the deep CNN neocognitron
Costa et al. Estimating the number of clusters in multivariate data by self-organizing maps
Fukushima Artificial vision by deep CNN neocognitron
Boubez et al. Wavelet neural networks and receptive field partitioning
Wann et al. A Comparative study of self-organizing clustering algorithms Dignet and ART2
Bock et al. Gray-scale ALIAS
Rajapakse et al. Medical image segmentation with MARA
Ramírez-Quintana et al. Self-organizing retinotopic maps applied to background modeling for dynamic object segmentation in video sequences
Choi et al. Biologically motivated visual attention system using bottom-up saliency map and top-down inhibition
Krzyzak et al. Classification of large set of handwritten characters using modified back propagation model
Ozimek et al. A space-variant visual pathway model for data efficient deep learning
Srinivas et al. Convolution neural networks for binary classification
Rajapakse et al. Multi sensor data fusion within hierarchical neural networks
Sabisch et al. Identification of complex shapes using a self organizing neural system
Minnix et al. A multilayered self-organizing artificial neural network for invariant pattern recognition
Lu et al. Artificial neural networks for boundary extraction
Wersing Learning lateral interactions for feature binding and sensory segmentation
Lepage et al. Extraction of texture features with a multiresolution neural network
Horváth Sorted pooling in convolutional networks for one-shot learning
Rajapakse et al. Hierarchical neural architecture for visual pattern recognition and reconstruction
Makfuzova et al. Experience of Using Neural Network Neocognitron in Solving the Inverse Problem of Geophysics
Eglen et al. Unsupervised discovery of invariances
Herpers et al. Evaluation of local image structures applying a DCS network
Yogeswaran et al. Biasing restricted Boltzmann machines using Gaussian filters to learn invariant visual features