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)

Associative learning on imbalanced environments: An empirical study

Associative memories have emerged as a powerful computational neural network model for several pattern classification problems. Like most traditional classifiers, these models assume that the classes share similar prior probabilities. However, in many real-life applications the ratios of prior probabilities between classes are extremely skewed. Although the literature has provided numerous studies that examine the performance degradation of renowned classifiers on different imbalanced scenarios, so far this effect has not been supported by a thorough empirical study in the context of associative memories. In this paper, we fix our attention on the applicability of the associative neural networks to the classification of imbalanced data. The key questions here addressed are whether these models perform better, the same or worse than other popular classifiers, how the level of imbalance affects their performance, and whether distinct resampling strategies produce a different impact on the associative memories. In order to answer these questions and gain further insight into the feasibility and efficiency of the associative memories, a large-scale experimental evaluation with 31 databases, seven classification models and four resampling algorithms is carried out here, along with a non-parametric statistical test to discover any significant differences between each pair of classifiers.This work has partially been supported by the Mexican Science and Technology Council (CONACYT-Mexico) through the Postdoctoral Fellowship Program (232167), the Mexican PRODEP(DSA/103.5/15/7004), the Spanish Ministry of Economy(TIN2013-46522-P) and the Generalitat Valenciana (PROMETEOII/2014/062)

Contributions to the analysis and segmentation of remote sensing hyperspectral images

142 p.This PhD Thesis deals with the segmentation of hyperspectral images from the point of view of Lattice Computing. We have introduced the application of Associative Morphological Memories as a tool to detect strong lattice independence, which has been proven equivalent to affine independence. Therefore, sets of strong lattice independent vectors found using our algorithms correspond to the vertices of convex sets that cover most of the data. Unmixing the data relative to these endmembers provides a collection of abundance images which can be assumed either as unsupervised segmentations of the images or as features extracted from the hyperspectral image pixels. Besides, we have applied this feature extraction to propose a content based image retrieval approach based on the image spectral characterization provided by the endmembers. Finally, we extended our ideas to the proposal of Morphological Cellular Automata whose dynamics are guided by the morphological/lattice independence properties of the image pixels. Our works have also explored the applicability of Evolution Strategies to the endmember induction from the hyperspectral image data

A comparative study on associative memories with emphasis on morphological associative memories

Orientador: Peter SussnerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação CientificaResumo: Memórias associativas neurais são modelos do fenômeno biológico que permite o armazenamento de padrões e a recordação destes apos a apresentação de uma versão ruidosa ou incompleta de um padrão armazenado. Existem vários modelos de memórias associativas neurais na literatura, entretanto, existem poucos trabalhos comparando as varias propostas. Nesta dissertação comparamos sistematicamente o desempenho dos modelos mais influentes de memórias associativas neurais encontrados na literatura. Esta comparação está baseada nos seguintes critérios: capacidade de armazenamento, distribuição da informação nos pesos sinápticos, raio da bacia de atração, memórias espúrias e esforço computacional. Especial ênfase dado para as memórias associativas morfológicas cuja fundamentação matemática encontra-se na morfologia matemática e na álgebra de imagensAbstract: Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. There are several models of neural associative memories in the literature, however, there are few works relating them. In this thesis, we present a systematic comparison of the performances of some of the most widely known models of neural associative memories. This comparison is based on the following criteria: storage capacity, distribution of the information over the synaptic weights, basin of attraction, number of spurious memories, and computational effort. The thesis places a special emphasis on morphological associative memories whose mathematical foundations lie in mathematical morphology and image algebraMestradoMatematica AplicadaMestre em Matemática Aplicad

Ordem supervisionada baseada em valores fuzzy para morfologia matemática multivalorada

Orientador: Marcos Eduardo Ribeiro do Valle MesquitaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: Morfologia Matemática foi concebida como uma ferramenta para a análise e processamento de imagens binárias e foi subsequentemente generalizada para o uso em imagens em tons de cinza e imagens multivaloradas. Reticulados completos, que são conjuntos parcialmente ordenados em que todo subconjunto tem extremos bem definidos, servem como a base matemática para uma definição geral de morfologia matemática. Em contraste a imagens em tons de cinza, imagens multivaloradas não possuem uma ordem não-ambígua. Essa dissertação trata das chamadas ordens reduzidas para imagens multivaloradas. Ordens reduzidas são definidas por meio de uma relação binária que ordena os elementos de acordo com uma função h do conjunto de valores em um reticulado completo. Ordens reduzidas podem ser classificadas em ordens não-supervisionadas e ordens supervisionadas. Numa ordem supervisionada, o função de ordenação h depende de conjuntos de treinamento de valores de foreground e de background. Nesta dissertação, estudamos ordens supervisionadas da literatura. Também propomos uma ordem supervisionada baseada em valores fuzzy. Valores fuzzy generalizam cores fuzzy - conjuntos fuzzy que modelam o modo que humanos percebem as cores - para imagens multivaloradas. Em particular, revemos como construir o mapa de ordenação baseado em conjuntos fuzzy para o foreground e para o background. Também introduzimos uma função de pertinência baseada numa estrutura neuro-fuzzy e generalizamos a função de pertinência baseada no diagrama de Voronoi. Por fim, as ordens supervisionadas são avaliadas num experimento de segmentação de imagens hiperespectrais baseado num perfil morfológico modificadoAbstract: Mathematical morphology has been conceived initially as a tool for the analysis and processing of binary images and has been later generalized to grayscale and multivalued images. Complete lattices, which are partially ordered sets in whose every subset has well defined extrema, serve as the mathematical background for a general definition of mathematical morphology. In contrast to gray-scale images, however, there is no unambiguous ordering for multivalued images. This dissertation addresses the so-called reduced orderings for multi-valued images. Reduced orderings are defined by means of a binary relation which ranks elements according to a mapping h from the value set into a complete lattice. Reduced orderings can be classified as unsupervised and supervised ordering. In a supervised ordering, the mapping h depends on training sets of foreground and background values. In this dissertation, we study some relevant supervised orderings from the literature. We also propose a supervised ordering based on fuzzy values. Fuzzy values are a generalization of fuzzy colors - fuzzy sets that model how humans perceive colors - to multivalued images other than color images. In particular, we review how to construct the fuzzy ordering mapping based on fuzzy sets that model the foreground and the background. Also, we introduce a membership function based on a neuro-fuzzy framework and generalize the membership function based on Voronoi diagrams. The supervised orderings are evaluated in an experiment of hyperspectral image segmentation based on a modified morphological profileMestradoMatematica AplicadaMestre em Matemática Aplicada131635/2018-2CNP

Conceptual Representations for Computational Concept Creation

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.Peer reviewe

Contributions of synaptic filters to models of synaptically stored memory

The question of how neural systems encode memories in one-shot without immediately disrupting previously stored information has puzzled theoretical neuroscientists for years and it is the central topic of this thesis. Previous attempts on this topic, have proposed that synapses probabilistically update in response to plasticity inducing stimuli to effectively delay the degradation of old memories in the face of ongoing memory storage. Indeed, experiments have shown that synapses do not immediately respond to plasticity inducing stimuli, since these must be presented many times before synaptic plasticity is expressed. Such a delay could be due to the stochastic nature of synaptic plasticity or perhaps because induction signals are integrated before overt strength changes occur.The later approach has been previously applied to control fluctuations in neural development by low-pass filtering induction signals before plasticity is expressed. In this thesis we consider memory dynamics in a mathematical model with synapses that integrate plasticity induction signals to a threshold before expressing plasticity. We report novel recall dynamics and considerable improvements in memory lifetimes against a prominent model of synaptically stored memory. With integrating synapses the memory trace initially rises before reaching a maximum and then falls. The memory signal dissociates into separate oblivescence and reminiscence components, with reminiscence initially dominating recall. Furthermore, we find that integrating synapses possess natural timescales that can be used to consider the transition to late-phase plasticity under spaced repetition patterns known to lead to optimal storage conditions. We find that threshold crossing statistics differentiate between massed and spaced memory repetition patterns. However, isolated integrative synapses obtain an insufficient statistical sample to detect the stimulation pattern within a few memory repetitions. We extend the modelto consider the cooperation of well-known intracellular signalling pathways in detecting storage conditions by utilizing the profile of postsynaptic depolarization. We find that neuron wide signalling and local synaptic signals can be combined to detect optimal storage conditions that lead to stable forms of plasticity in a synapse specific manner.These models can be further extended to consider heterosynaptic and neuromodulatory interactions for late-phase plasticity.<br/

The mechanisms for pattern completion and pattern separation in the hippocampus

The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 together with a large number of independent memories stored in CA3 include a sparse distributed representation which is enhanced by the graded firing rates of CA3 neurons, representations that are independent due to the randomizing effect of the mossy fibers, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction. Recall of information from CA3 is implemented by the entorhinal cortex perforant path synapses to CA3 cells, which in acting as a pattern associator allow some pattern generalization. Pattern separation is performed in the dentate granule cells using competitive learning to convert grid-like entorhinal cortex firing to place-like fields. Pattern separation in CA3, which is important for completion of any one of the stored patterns from a fragment, is provided for by the randomizing effect of the mossy fiber synapses to which neurogenesis may contribute, by the large number of dentate granule cells each with a sparse representation, and by the sparse independent representations in CA3. Recall to the neocortex is achieved by a reverse hierarchical series of pattern association networks implemented by the hippocampo-cortical backprojections, each one of which performs some pattern generalization, to retrieve a complete pattern of cortical firing in higher-order cortical areas