Image Analysis

The extraction of information from images using neural network methods.


Articles & Tech Transfers


Recognition of coloured and textured images through a multi-scale neural architecture with orientational filtering and chromatic diffusion
Abstract The aim of this paper is to outline a multiple scale neural model to recognise colour images of textured scenes. This model combines colour and textural information in order to recognise colour texture images through the ...

Critical Motion Detection of Nearby Moving Vehicles in a Vision-Based Driver-Assistance System
Abstract Driving always involves risk. Various means have been proposed to reduce the risk. Critical motion detection of nearby moving vehicles is one of the important means of preventing accidents. In this paper, a computational ...

Data Mining System for Biochemical Analysis in Experimental Physiology
Abstract We develop a Data Mining system to assist with the elucidation by graphical means of the biochemical changes in the brains of rodents. Manual analysis of such experiments is increasingly impractical because of the voluminous ...

TOWARDS AN ART BASED MATHEMATICAL EDITOR, THAT USES ONLINE HANDWRITTEN SYMBOL RECOGNITION
Abstract A new mathematical editor, based on the recognition of run-on discrete handwritten symbols, is proposed. The tested laboratory prototype of the system, modular and adaptable to the user habits and site requirements, uses a ...

On-line recognition of cursive Korean characters using neural networks
Abstract This paper proposes an efficient method for on-line recognition of cursive Korean characters. Since Korean characters are composed of two or three graphemes in two dimensions, strokes, primitive components of the characters, ...

Performance enhancement for fuzzy adaptive resonance theory (ART) neural networks
Abstract A modified fuzzy adaptive resonance theory neural network (ART) is used as a classifier for a texture recognition system. The system consists of a wavelet based low level feature detector and a high level ART classifier. The ...

On-line Chinese character recognition using ART-based stroke classification
Abstract This paper proposes an on-line Chinese character recognition method using Adaptive Resonance Theory (ART) based stroke classification. Strokes, primitive components of Chinese characters, are usually warped into a cursive ...

Using ART2 networks to deduce flow velocities
Abstract A novel algorithm for obtaining flow velocity vectors using ART2 networks (based on adaptive resonance theory) is presented. The method involves tracking the movement of groups of seeding particles in a fluid space through ...

A deployed engineering design retrieval system using neural networks
Abstract We describe a neural information retrieval system (NIRS), now in production within the Boeing Company, which has been developed for the identification and retrieval of engineering designs. Two-dimensional and ...

Feature recognition using ART2: A self-organizing neural network
Abstract A self-organizing neural network, ART2, based on adaptive resonance theory (ART), is applied to the problem of feature recognition from a boundary representation (B-rep) solid model. A modified face score vector calculation ...

Modular mART for 3D target recognition
Abstract A modified adaptive resonance theory (mART) neural network of modular structure is proposed. The similarity function and weight resolution of the ART neural networks are modified, and the cluster merging algorithm and ...

Wavelet-based feature-adaptive adaptive resonance theory neural network for texture identification
Abstract A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extraction based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The ...

Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images
Abstract The fuzzy ARTMAP has been applied to the supervised classification of multi-spectral remotely-sensed images. This method is found to be more efficient, in terms of classification accuracy, compared to the conventional ...

Intelligent tool wear identification based on optical scattering image and hybrid artificial intelligence techniques
Abstract Tool wear monitoring is crucial for an automated machining system to maintain consistent quality of machined parts and prevent damage to the parts during the machining operation. A vision-based approach is presented for tool ...

An efficient neural classification chain of SAR and optical urban images
Abstract In this paper a suitable neural classification algorithm, based on the use of Adaptive Resonance Theory (ART) networks, is applied to the fusion and classification of optical and SAR urban images. ART networks provide a ...

Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering
Abstract Most neural-network (NN) algorithms used for the purpose of vector quantization (VQ) focus on the mean squared error minimization within the reference- or code-vector space. This feature frequently causes increased entropy ...

Automatic change detection of driving environments in a vision-based driver assistance system
Abstract Detecting critical changes of environments while driving is an important task in driver assistance systems. In this paper, a computational model motivated by human cognitive processing and selective attention is proposed for ...

A clustering fuzzy approach for image segmentation
Abstract Segmentation is a fundamental step in image description or classification. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. ...

Trichromatic sorting of in vitro regenerated plants of gladiolus using adaptive resonance theory
Abstract A machine vision system is described to sort the regenerated plants of gladiolus into groups using trichromatic features of leaves. The machine vision system consisted of a scanner, image analysis software and an adaptive ...

An automatic road sign recognition system based on a computational model of human recognition processing
Abstract This paper presents an automatic road sign detection and recognition system that is based on a computational model of human visual recognition processing. Road signs are typically placed either by the roadside or above ...

ART-MMAP: A neural network approach to subpixel classification
Abstract Global or continental-scale land cover mapping with remote sensing data is limited by the spatial characteristics of satellites. Subpixel-level mapping is essential for the successful description of many land cover patterns ...

Colour image segmentation using the self-organizing map and adaptive resonance theory
Abstract We propose a new competitive-learning neural network model for colour image segmentation. The model, which is based on the adaptive resonance theory (ART) of Carpenter and Grossberg and on the self-organizing map (SOM) of ...

An intelligent video categorization engine
Abstract Purpose - We describe an intelligent video categorization engine (IVCE) that uses the learning capability of artificial neural networks (ANNs) to classify suitably preprocessed video segments into a predefined number of ...

Part family formation through fuzzy ART2 neural network
Abstract In order to overcome some unavoidable factors, like shift of the part, that influence the crisp neural networks recognition, the present study is dedicated in developing a novel fuzzy neural network (FNN), which integrates ...

A Gaussian adaptive resonance theory neural network classificationalgorithm applied to supervised land cover mapping using multitemporalvegetation index data
Abstract Neural network classifiers have been shown to provide supervised classification results that significantly improve on traditional classification algorithms such as the Bayesian (maximum likelihood [ML]) classifier. While the ...

POPART: partial optical implementation of adaptive resonance theory 2
Abstract Adaptive resonance architectures are neural nets that are capable of classifying arbitrary input patterns into stable category representations. A hybrid optoelectronic implementation utilizing an optical joint transform ...

An optoelectronic implementation of the adaptive resonance neural network
Abstract A solution to the problem of implementation of the adaptive resonance theory (ART) of neural networks that uses an optical correlator which allows the large body of correlator research to be leveraged in the implementation ...

Optoelectronic sensory neural network
Abstract A neural network for processing sensory information. The network comprise one or more layers including interconnecting cells having individual states. Each cell is connected to one or more neighboring cells. Sensory signals ...

Image processing in HSI color space using adaptive noise filtering
Abstract Adaptive noise filtering is applied to an image frame of HSI data to reduce and more uniformly distribute noise while preserving image feature edges. An adaptive spatial filter includes a plurality of averaging kernels. An ...

Motion estimation within a sequence of data frames using optical flow with adaptive gradients
Abstract The optical flow of an array of pixels in an image field is determined using adaptive spatial and temporal gradients. Artifacts are avoided for image objects which are moving smoothly relative to the image field background. ...

Color clustering for scene change detection and object tracking in video sequences
Abstract Pixel data forming an image is clustered into groups of data of similar color. Pixels of approximately the same color form one cluster. A vigilance parameter determines how many clusters are derived and how wide a range of ...

Machine vision approach for robotic assembly.
Abstract Purpose ? Outcome with a novel methodology for online recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. Design/methodology/approach ? The ...

Cortical dynamics of three-dimensional form, color, and brightness perception, II: Binocular theory
Abstract A real-time visual processing theory is developed to explain how three-dimensional form, color, and brightness perceptsa re coherently synthesized. The theory describeEhJo w several undamental uncertainty principles which ...

Cortical dynamics of three-dimensional form, color, and brightness perception, I: Monocular theory
Abstract A real-time visual processing theory is developed to explain how three-dimensional form, color, and brightness percept. aJ re coherently synthesized. The theory describesh ow several fundamental uncertainty principles which ...

Neural FACADES: Visual representations of static and moving form-and-color-and-depth
Abstract 1. Introduction: The Inadequacy of Visual Modules
This article discusses some implications for understanding vision of recent theoretical results concerning the neural architectures that subserve visual perception in humans ...

Invariant recognition of cluttered scenes by a self-organizing ART architecture: Figure-ground separation
Abstract A neural network model, called an FBF network, is proposed for automatic parallel separation of multiple image figures from each other and their backgrounds in noisy gray-scale or multi-colored images. The figures can then ...

A self-organizing neural system for learning to recognize textured scenes
Abstract A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices ...

Neural dynamics of motion processing and speed discrimination
Abstract A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the ...

A neural model of motion processing and visual navigation by cortical area MST
Abstract Cells in the dorsal medial superior temporal cortex (MSTd) process optic flow generated by self-motion during visually guided navigation. A neural model shows how interactions between well-known neural mechanisms (log polar ...

A quantitative evaluation of the AVITEWRITE model of handwriting learning
Abstract Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose ...

Depth perception from pairs of overlapping cues in pictorial displays
Abstract The experiments reported herein probe the visual cortical mechanisms that control near-far percepts in response to two-dimensional stimuli. Figural contrast is found to be a principal factor for the emergence of percepts of ...

A neural model of 3D shape-from-texture: Multiple-scale filtering, boundary grouping, and surface filling-in
Abstract A neural model is presented of how cortical areas V1, V2, and V4 interact to convert a textured 2D image into a representation of curved 3D shape. Two basic problems are solved to achieve this: (1) Patterns of spatially ...

Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation
Abstract A neural network model of boundary segmentation and surface representation is developed to process images containing range data gathered by a synthetic aperture radar (SAR) sensor. The boundary and surface processing are ...

Off-line signature verification, without a priori knowledge of class w2 A new approach
Abstract This work proposes a new approach to signature verification. It is inspired by the human learning and the approach adopted by the expert examiner of signatures, in which an a priori knowledge of the class of forgeries is not ...

3-D object recognition by the ART EMAP evidence accumulation network
Abstract ART-EMAP synthesizes adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). The network extends the capabilities of fuzzy ARTMAP in four incremental stages. ...

Comparative performance measures of fuzzy ARTMAP, learned vector quantization, and back propagation
Abstract The authors compare the performance of fuzzy ARTMAP with that of learned vector quantization and back propagation on a handwritten character recognition task. Training with fuzzy ARTMAP to a fixed criterion used many fewer ...

Invariant pattern recognition and recall by an attentive ART architecture in a nonstationary world
Abstract A neural network is described which can stably self-organize an invariant pattern recognition code in response to a sequence of analog or digital input patterns; be attentionally p[rimed to ignore all but a designated ...

CEDI:  A neural model of colour vision, with applications to image processing and classification
Abstract The CEDI (Contrast Enhance / Discount the Illuminant) system models a cascade of primate color vision cells: retinal ganglion, thalamic single opponent, and two types of cortical double opponents (Figure 1). A unified model ...

Unifying multiple knowledge domains using the ARTMAP information fusion system
Abstract Sensors working at different times, locations, and scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels that are reconciled by their implicit underlying ...

Temporal dynamics of decision-making during motion perception in the visual cortex
Abstract How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical ?decision neurons.? A ...

A neural network for enhancing boundaries and surfaces in synthetic aperture radar images
Abstract A neural network system for boundary segmentation and surface representation, inspired by a new local-circuit model of visual processing in the cerebral cortex, is used to enhance images of range data gathered by a synthetic ...

ARTSCENE: A Neural System for Natural Scene Classification
Abstract How do humans rapidly recognize a scene? How can neural models capture this biological competence to achieve state-of-the-art scene classification? The ARTSCENE neural system classifies natural scene photographs by using ...

Invariant recognition of cluttered scenes by a self-organizing ART architecture: CORT-X boundary segmentation
Abstract A neural network architecture is outlined that self-organizes invariant pattern recognition codes of noisy images. The processing stages are figure-ground separation, boundary segmentation, invariant filtering, and ...

Neural sensor fusion for spatial visualization on a mobile robot
Abstract An ARTMAP neural network is used to integrate visual information and ultrasonic sensory information on a B14 mobile robot. Training samples for the neural network are acquired without human intervention. Sensory snapshots ...

ART neural networks for remote sensing image analysis
Abstract ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing ...

A Neural Network Method for Mixture Estimation for Vegetation Mapping
Abstract While most forest maps identify only the dominant vegetation class in delineated stands, individual stands are often better characterized by a mix of vegetation types. Many land management applications, including wildlife ...

A neural network method for efficient vegetation mapping
Abstract This article describes the application of a neural network method designed to improve the efficiency of map production from remote sensing data. Specifically, the ARTMAP neural network produces vegetation maps of the Sierra ...

S-TREE: Self-organizing trees for data clustering and online vector quantization
Abstract This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers. The S-TREE1 model, which ...

Information fusion for image analysis: Geospatial foundations for higher-level fusion
Abstract In support of the AFOSR program in Information Fusion, the CNS Technology Laboratory at Boston University is developing and applying neural models of image and signal processing, pattern learning and recognition, associative ...

A neural network method for land use change classification, with application to the Nile River delta
Abstract Detecting and monitoring changes in conditions at the Earth?s surface are essential for understanding human impact on the environment and for assessing the sustainability of development. In the next decade, NASA will gather ...

DISCOV: A Neural Model of Colour Vision, with Applications to Image Processing and Classification
Abstract The DISCOV (DImensionless Shunting COlour Vision) system models a cascade of primate colour vision cells: retinal ganglion, thalamic single opponent, and two classes of cortical double opponents. A unified model formalism ...

Self-organizing hierarchical knowledge discovery by an ARTMAP information fusion system
Abstract Classifying terrain or objects may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from users with different goals and situations. Current fusion methods ...

Texture segregation by visual cortex: Perceptual grouping, attention, and learning
Abstract A neural model called dARTEX is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model unifies five interacting processes: region-based texture ...

CONFIGR: A vision-based model for long-range figure completion
Abstract CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial ...

How the Brain Sees:  Fundamentals and Recent Progress in Modeling Vision
Abstract This is a review of neural network based vision modeling techniques. The models discussed are: shunting and distance-dependent networks, boundary grouping and completion models, neon color spreading models, etc. ...

Off-line signature verification, without a priori knowledge of class. A new approach
Abstract This work proposes a new approach to signature verification. It is inspired by the human learning and the approach adopted by the expert examiner of signatures, in which an a priori knowledge of the class of forgeries is not ...

Off-line signature verification using Fuzzy ARTMAP neural network
Abstract This work presents a fuzzy ARTMAP based off-line signature verification system. Compared to the conventional systems proposed thus far, the presented system is trained with genuine signatures only. Six experiments have been ...

A Neural Network Structure for Detecting Straight Line Segments
Abstract A new method for detecting one-pixel wide vertical, horizontal and diagonal line segments in binary images, is presented. It is based on using four slabs of neural network each of which is composed of a set layers. Each ...

Recognition of Printed Arabic Words with the Fuzzy ARTMAP Neural Network
Abstract This paper presents a new method for the recognition of Arabic text using global features and fuzzy ARTMAP neural network. The method is divided into three major steps. The first step is digitization and pre-processing to ...

A Fuzzy ARTMAP Module for Graphics Symbol Recognition
Abstract This paper presents a method for recognizing graphics symbols of electronic components in a database of circuit layouts. The method is based on the one-class problem approach on our ability to recognize a 2D-objects without ...

How does the cerebral cortex work? Learning, attention and grouping by the laminar circuits of visual cortex
Abstract The organization of neocortex into layers is one of its most salient anatomical features. These layers include circuits that form functional columns in cortical maps. A major unsolved problem concerns how bottom-up, ...


Software


STARS
Description CONFIGR-STARS applies CONFIGR (CONtour FIgure and GRound) to solve the problem of star image registration. CONFIGR (CONtour FIgure and GRound) is a computational model based on principles of biological vision that completes ...

Directional transient cells
Description This microcircuit models how direction selectivity appears in the directional transient cells, which are found in layer 4Ca of area V1 for macaques.

Below are links to source article and zipped file that contains a ...

Non-directional transient cells
Description This microcircuit models how ON magnocellular cells in Retina and lateral geniculate nucleus transiently respond to temporal luminance changes in the visual input.

Below are links to source article and zipped file that ...

MOtion DEcision (MODE) model
Description MOtion DEcision (MODE) model is a neural model of perceptual decision-making that discriminates the direction of an ambiguous motion stimulus and simulates behavioral and physiological data obtained from macaques performing ...


Datasets


Boston Remote Sensing Testbed (preprocessed MAT files)
Description

These MATLAB-ready .mat data files contain pixel data for the Boston remote sensing testbed. This is the same data that is provided with Classer. However, all the Classer preprocessing scripts have been run, so the mat file

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