Innovative applications of associative morphological memories for image processing and pattern recognition

Morphological Associative Memories have been proposed for some image denoising applications. They can be applied to other less restricted domains, like image retrieval and hyper spectral image unsupervised segmentation. In this paper we present these applications. In both cases the key idea is that Autoassociative Morphological Memories selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. Linear unmixing based on the sets of morphological independent patterns define a feature extraction process that is the basis for the image processing applications. We discuss some experimental results on the fish shape data base and on a synthetic hyperspectral image, including the comparison with other linear feature extraction algorithms (ICA and CCA)

Interval-valued and intuitionistic fuzzy mathematical morphologies as special cases of L-fuzzy mathematical morphology

Mathematical morphology (MM) offers a wide range of tools for image processing and computer vision. MM was originally conceived for the processing of binary images and later extended to gray-scale morphology. Extensions of classical binary morphology to gray-scale morphology include approaches based on fuzzy set theory that give rise to fuzzy mathematical morphology (FMM). From a mathematical point of view, FMM relies on the fact that the class of all fuzzy sets over a certain universe forms a complete lattice. Recall that complete lattices provide for the most general framework in which MM can be conducted. The concept of L-fuzzy set generalizes not only the concept of fuzzy set but also the concepts of interval-valued fuzzy set and Atanassov’s intuitionistic fuzzy set. In addition, the class of L-fuzzy sets forms a complete lattice whenever the underlying set L constitutes a complete lattice. Based on these observations, we develop a general approach towards L-fuzzy mathematical morphology in this paper. Our focus is in particular on the construction of connectives for interval-valued and intuitionistic fuzzy mathematical morphologies that arise as special, isomorphic cases of L-fuzzy MM. As an application of these ideas, we generate a combination of some well-known medical image reconstruction techniques in terms of interval-valued fuzzy image processing

Detecao de Bordas baseada em Morfologia Matemática Fuzzy Intervalar e as Funcoes de Agregacao K

Edge detection is a digital image processing tool. It determines points in a digital image where light intensity suddenly changes. This process applies to a digital image which assumes some degree of uncertainty in the location and intensity of the pixel in the real image. In this work, we propose an edge detection model which consists in capturing this uncertainty in terms of interval images. Then we apply interval-valued fuzzy morphology to calculate the interval-valued erosion and dilation. Finally, we compute the convex combinations of the upper and lower bounds of the interval-valued erosion and dilation image, to obtain a morphological erosion and dilation respectively, and thus an edge image.A deteccao de bordas é uma ferramenta de processamento digital de imagenes. Ela determina pontos de uma imagem digital onde a intensidade da luz muda repentinamente. Esse processo aplica-se a uma imagem digital a qual supoe algum grau de incerteza na localizacao e na intensidade do pixel da imagem real. Neste trabalho, é proposto um modelo de detecao de bordas que consiste na captura dessa incerteza em termos de imagens intervalares, para depois aplicar a erosao e dilatacao intervalar fuzzy. Finalmente, por meio de uma combinacao convexa sobre os limites superiores e inferiores da erosao e a dilatacao intervalar, sao obtidas a erosao e a dilatacao morfológica respectivamente, com as quais se faz possível produzir uma imagem borda

Associative Morphological Memories Based On Variations Of The Kernel And Dual Kernel Methods.

Morphological associative memories (MAMs) belong to the class of morphological neural networks. The recording scheme used in the original MAM models is similar to the correlation recording recipe. Recording is achieved by means of a maximum (MXY model) or minimum (WXY model) of outer products. Notable features of autoassociative morphological memories (AMMs) include optimal absolute storage capacity and one-step convergence. Heteroassociative morphological memories (HMMs) do not have these properties and are not very well understood. The fixed points of AMMs can be characterized exactly in terms of the original patterns. Unfortunately, AMM fixed points include a large number of spurious memories. In this paper, we combine the MXX model and variations of the kernel method to produce new autoassociative and heteroassociative memories. We also introduce a dual kernel method. A new, dual model is given by a combination of the WXX model and a variation of the dual kernel method. The new MAM models exhibit better error correction capabilities than MXX and WXX and a reduced number of spurious memories which can be easily described in terms of the fundamental memories.16625-3

Kernels for Morphological Associative Memories

The ability of human beings to retrieve information on the basis of associated cues continues to elicit great interest among researchers. Investigations of how the brain is capable to make such associations from partial information have led to a variety of theoretical neural network models that act as associative memories. Recently, several researchers have had significant success in retrieving complete stored patterns from noisy or incomplete input pattern keys by using morphological associative memories. For certain types of noise in the input patterns, this new model of artificial associative memories can be successfully applied following a direct approach. If the input patterns contain both dilative and erosive noise, an indirect approach using kernel vectors is recommended, however the problem of how to select these kernel vectors has not yet been solved. In this paper, we provide sufficient conditions for kernel vectors which confirm the intuitive notion of kernel vectors as spar..

Morphological Perceptron Learning

During the last decade, researchers have applied neural networks to a multitude of difficult tasks which would normally require human intelligence. In particular, perceptrons are used to classify patterns into different classes. Recently, several researchers introduced a novel class of artificial neural networks, called morphological neural networks. In this new theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the nonlinear operation of adding neural values and their synaptic strengths followed by forming the maximum of the results. We have shown in previous papers that the properties of morphological neural networks differ drastically from those of traditional neural network models. In this paper, we introduce a learning algorithm for multilayer morphological perceptrons which is capable of solving arbitrary classification problems of patterns into two classes. Keywords: perceptrons, morphological neura..

An adaptive image filter based on the fuzzy transform for impulse noise reduction

CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOImpulse noise, also known as impulsive noise, is one of the most common types of noise occurring in digital images. The median filter and morphological filters are often used to remove impulsive noise, but these filters do not preserve image details. In this paper, we first apply the median filter in order to reduce the amount of impulsive noise in a corrupted image. After an application of the direct fuzzy transform (FT) to the resulting image, we restored the pixel values corresponding to locations flagged by the fuzzy rule-based noise detector by means of the inverse fuzzy transform. Finally, we obtained the output of our proposed image filter by combining the restored pixels with the ones marked as noiseless by the aforementioned noise fuzzy detector. We compared the results of our approach that we called adaptive FT-based image filter (AFT-IF) with the ones obtained by a number of other image filters for impulsive noise reduction.211336593672CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOsem informação , 311695/2014-

Associative memories based on fuzzy mathematical morphology and an application in prediction

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