62,320 research outputs found
Feature extraction and classification :a case study of classifying a simulated digital mammogram images using self-organizing maps (som)
Feature extraction is important in image processing and is a preliminary step to perform pattern classification. This project aims to propose a feature extraction technique. This feature extraction technique can be used to find five parameters
which are the size, intensity, centroid X, centroid Y and region distribution of segmented regions
. Several experiments have been conducted to verify the proposed algorithm and feature extraction results obtained will be used for the training of Neural Network classifier, Self-Organizing Maps (SOM).
A set of training input data is used to train SOM. The accuracy of classification performance was acquired. A case study of breast cancer has been
demonstrated in this study by using a simulated digital mammogram images. In this study, the results show that this system is able to perform the classification of mass with low intensity, mass with high intensity, cluster microcalcification, separate microcalcification and special case
to detect abnormality of the digital
mammogram images
Context-aware visual exploration of molecular databases
Facilitating the visual exploration of scientific data has
received increasing attention in the past decade or so. Especially
in life science related application areas the amount
of available data has grown at a breath taking pace. In this
paper we describe an approach that allows for visual inspection
of large collections of molecular compounds. In
contrast to classical visualizations of such spaces we incorporate
a specific focus of analysis, for example the outcome
of a biological experiment such as high throughout
screening results. The presented method uses this experimental
data to select molecular fragments of the underlying
molecules that have interesting properties and uses the
resulting space to generate a two dimensional map based
on a singular value decomposition algorithm and a self organizing
map. Experiments on real datasets show that
the resulting visual landscape groups molecules of similar
chemical properties in densely connected regions
Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map
This paper outlines the development of a multi-satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high-resolution, short-duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self-organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co-registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground-radar data in lieu of a dense constellation of polar-orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground-radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004-February 2005) at various temporal (daily and monthly) and spatial (0.04 and 0.25) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub-layers rather than a single layer. Furthermore, 2-year (2003-2004) satellite rainfall estimates generated by the current algorithm were compared with gauge-corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite-based rainfall estimations
Self-Organising Networks for Classification: developing Applications to Science Analysis for Astroparticle Physics
Physics analysis in astroparticle experiments requires the capability of
recognizing new phenomena; in order to establish what is new, it is important
to develop tools for automatic classification, able to compare the final result
with data from different detectors. A typical example is the problem of Gamma
Ray Burst detection, classification, and possible association to known sources:
for this task physicists will need in the next years tools to associate data
from optical databases, from satellite experiments (EGRET, GLAST), and from
Cherenkov telescopes (MAGIC, HESS, CANGAROO, VERITAS)
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