65,493 research outputs found
Morphological Network: How Far Can We Go with Morphological Neurons?
In recent years, the idea of using morphological operations as networks has
received much attention. Mathematical morphology provides very efficient and
useful image processing and image analysis tools based on basic operators like
dilation and erosion, defined in terms of kernels. Many other morphological
operations are built up using the dilation and erosion operations. Although the
learning of structuring elements such as dilation or erosion using the
backpropagation algorithm is not new, the order and the way these morphological
operations are used is not standard. In this paper, we have theoretically
analyzed the use of morphological operations for processing 1D feature vectors
and shown that this gets extended to the 2D case in a simple manner. Our
theoretical results show that a morphological block represents a sum of hinge
functions. Hinge functions are used in many places for classification and
regression tasks (Breiman (1993)). We have also proved a universal
approximation theorem -- a stack of two morphological blocks can approximate
any continuous function over arbitrary compact sets. To experimentally validate
the efficacy of this network in real-life applications, we have evaluated its
performance on satellite image classification datasets since morphological
operations are very sensitive to geometrical shapes and structures. We have
also shown results on a few tasks like segmentation of blood vessels from
fundus images, segmentation of lungs from chest x-ray and image dehazing. The
results are encouraging and further establishes the potential of morphological
networks.Comment: 35 pages, 19 figures, 7 table
Neural computation as a tool for galaxy classification : methods and examples
We apply and compare various Artificial Neural Network (ANN) and other
algorithms for automatic morphological classification of galaxies. The ANNs are
presented here mathematically, as non-linear extensions of conventional
statistical methods in Astronomy. The methods are illustrated using different
subsets Artificial Neural Network (ANN) and other algorithms for automatic
morphological classification of galaxies. The ANNs are presented here
mathematically, as non-linear extensions of conventional statistical methods in
Astronomy. The methods are illustrated using different subsets from the ESO-LV
catalogue, for which both machine parameters and human classification are
available. The main methods we explore are: (i) Principal Component Analysis
(PCA) which tells how independent and informative the input parameters are.
(ii) Encoder Neural Network which allows us to find both linear (PCA-like) and
non-linear combinations of the input, illustrating an example of unsupervised
ANN. (iii) Supervised ANN (using the Backpropagation or Quasi-Newton
algorithms) based on a training set for which the human classification is
known. Here the output for previously unclassified galaxies can be interpreted
as either a continuous (analog) output (e.g. -type) or a Bayesian {\it a
posteriori} probability for each class. Although the ESO-LV parameters are
sub-optimal, the success of the ANN in reproducing the human classification is
2 -type units, similar to the degree of agreement between two human experts
who classify the same galaxy images on plate material. We also examine the
aspects of ANN configurations, reproducibility, scaling of input parameters and
redshift information.Comment: uuencoded compressed postscript. The preprint is also available at
http://www.ast.cam.ac.uk/preprint/PrePrint.htm
A broad-coverage distributed connectionist model of visual word recognition
In this study we describe a distributed connectionist model of morphological processing, covering a realistically sized sample of the English language. The purpose of this model is to explore how effects of discrete, hierarchically structured morphological paradigms, can arise as a result of the statistical sub-regularities in the mapping between
word forms and word meanings. We present a model that learns to produce at its output a realistic semantic representation of a word, on presentation of a distributed representation of its orthography. After training, in three experiments, we compare the outputs of the model with the lexical decision latencies for large sets of English nouns and verbs. We show that the model has developed detailed representations of morphological structure, giving rise to effects analogous to those observed in visual lexical decision experiments. In addition, we show how the association between word form and word meaning also
give rise to recently reported differences between regular and irregular verbs, even in their completely regular present-tense forms. We interpret these results as underlining the key importance for lexical processing of the statistical regularities in the mappings between form and meaning
A Learning Framework for Morphological Operators using Counter-Harmonic Mean
We present a novel framework for learning morphological operators using
counter-harmonic mean. It combines concepts from morphology and convolutional
neural networks. A thorough experimental validation analyzes basic
morphological operators dilation and erosion, opening and closing, as well as
the much more complex top-hat transform, for which we report a real-world
application from the steel industry. Using online learning and stochastic
gradient descent, our system learns both the structuring element and the
composition of operators. It scales well to large datasets and online settings.Comment: Submitted to ISMM'1
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