Search results for "RECOGNITION"
showing 10 items of 3607 documents
Analysis of Multi-Choice Questionnaires through Self-Organizing Maps
1998
This paper describes how Self-Organizing Maps can be used to analyse multi-choice gallups. In this method, the use of a single SOM for all available data is replaced with the use of multiple SOMs trained with subsets of gallup questions. The subgroupings located from these maps are then used to train a new concluding SOM that is more readable than any single SOM analysis would be.
RECURRENT SELF-ORGANIZATION OF SENSORY SIGNALS IN THE AUDITORY DOMAIN
2008
In this study, a psychoacoustical and connectionist modeling framework is proposed for the investigation of musical cognition. It is suggested that music perception involves the manipulation of 1) sensory representations that have correlations with psychoacoustical features of the stimulus, and 2) abstract representations of the statistical regularities underlying a particular musical syntax. In the implicit learning domain, sensory features have been shown to interact with the processes involved in the extraction of the regularities governing musical events combinations in a stream [e.g., 1]. Furthermore, in a more ecological context, it is well known that traditional Western tonal system …
Stable Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and a Modified Fuzzy C-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. Three features are extracted from the tested image. The features are scaled down by a factor of 2 and mapped into a Self-Organizing Map. A modified Fuzzy C-Means clustering algorithm is used to divide the neuron units of the map in 2 classes. The entire image is again input for the Self-Organizing Map and the class of each pixel will be the class of its best matching unit in the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the DRIVE database shows accurate ex…
Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A Self-Organizing Map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the Self-Organizing Map, and the class of each pixel will be the class of the best matching unit on the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the publicly available DRIVE database shows accurate extraction of vessels network and a good agreement between our segm…
Visualization of tonal content with self-organizing maps and self-similarity matrices
2005
This article presents a dynamic model of tonality perception based on a short-term memory model and a self-organizing map (SOM). The model can be used for dynamic visualization of perceived tonal content, making it possible to examine the clarity and locus of tonality at any given point of time. This article also presents a method for the visualization of tonal structure using self-similarity matrices. The methods are applied to compositions of J. S. Bach, S. Barber, and J. Pachelbel. Finally, a real-time application embracing the tonality perception model is presented.
A neural network approach to movement pattern analysis.
2004
Movements are time-dependent processes and so can be modelled by time-series of coordinates: E.g., each articulation has geometric coordinates; the set of the coordinates of the relevant articulations build a high-dimensional configuration. These configurations--or "patterns"--give reason for analysing movements by means of neural networks: The Kohonen Feature Map (KFM) is a special type of neural network, which (after having been coined by training with appropriate pattern samples) is able to recognize single patterns as members of pattern clusters. This way, for example, the particular configurations of a given movement can be identified as belonging to respective configuration clusters, …
Selective and Sensitive Chromogenic Detection of Cyanide and HCN in Solution and in Gas Phase
2013
Two triphenylmethane based chemodosimeters for selective and chromogenic sensing of cyanide anions in aqueous environments and of hydrogen cyanide in gas phase were prepared and studied.
Prior precision modulates the minimisation of prediction error in human auditory cortex
2018
AbstractThe predictive coding model of perception proposes that successful representation of the perceptual world depends upon cancelling out the discrepancy between prediction and sensory input (i.e., prediction error). Recent studies further suggest a distinction between prediction error associated with non-predicted stimuli of different prior precision (i.e., inverse variance). However, it is not fully understood how prediction error from different precision levels is minimised in the predictive process. The current research used magnetoencephalography (MEG) to examine whether prior precision modulates the cortical dynamics of the making of perceptual inferences. We presented participant…
Current bioinformatics tools in genomic biomedical research (Review).
2006
On the advent of a completely assembled human genome, modern biology and molecular medicine stepped into an era of increasingly rich sequence database information and high-throughput genomic analysis. However, as sequence entries in the major genomic databases currently rise exponentially, the gap between available, deposited sequence data and analysis by means of conventional molecular biology is rapidly widening, making new approaches of high-throughput genomic analysis necessary. At present, the only effective way to keep abreast of the dramatic increase in sequence and related information is to apply biocomputational approaches. Thus, over recent years, the field of bioinformatics has r…
Extraction of ERP from EEG data
2007
In this article, a simple but novel technique for extracting a linear subspace related to event related potentials (ERPs) from ElectroEncephaloGraphy (EEG) data is introduced. The technique consists of a sequence of basic linear operations applied to multidimensional EEG data in a problem-specific manner. The derivation of the proposed technique is given and results with real data are described together with overall conclusions.