A Time Efficient Approach for Decision-Making Style Recognition in Lane-Change Behavior

Fast recognizing driver's decision-making style of changing lanes plays a pivotal role in safety-oriented and personalized vehicle control system design. This paper presents a time-efficient recognition method by integrating k-means clustering (k-MC) with K-nearest neighbor (KNN), called kMC-KNN. The mathematical morphology is implemented to automatically label the decision-making data into three styles (moderate, vague, and aggressive), while the integration of kMC and KNN helps to improve the recognition speed and accuracy. Our developed mathematical morphology-based clustering algorithm is then validated by comparing to agglomerative hierarchical clustering. Experimental results demonstrate that the developed kMC-KNN method, in comparison to the traditional KNN, can shorten the recognition time by over 72.67% with recognition accuracy of 90%-98%. In addition, our developed kMC-KNN method also outperforms the support vector machine (SVM) in recognition accuracy and stability. The developed time-efficient recognition approach would have great application potential to the in-vehicle embedded solutions with restricted design specifications

Complete lattice projection autoassociative memories

Orientador: Marcos Eduardo Ribeiro do Valle MesquitaTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: A capacidade do cérebro humano de armazenar e recordar informações por associação tem inspirado o desenvolvimento de modelos matemáticos referidos na literatura como memórias associativas. Em primeiro lugar, esta tese apresenta um conjunto de memórias autoassociativas (AMs) que pertecem à ampla classe das memórias morfológicas autoassociativas (AMMs). Especificamente, as memórias morfológicas autoassociativas de projeção max-plus e min-plus (max-plus e min-plus PAMMs), bem como suas composições, são introduzidas nesta tese. Tais modelos podem ser vistos como versões não distribuídas das AMMs propostas por Ritter e Sussner. Em suma, a max-plus PAMM produz a maior combinação max-plus das memórias fundamentais que é menor ou igual ao padrão de entrada. Dualmente, a min-plus PAMM projeta o padrão de entrada no conjunto de todas combinações min-plus. Em segundo, no contexto da teoria dos conjuntos fuzzy, esta tese propõe novas memórias autoassociativas fuzzy, referidas como classe das max-C e min-D FPAMMs. Uma FPAMM representa uma rede neural morfológica fuzzy com uma camada oculta de neurônios que é concebida para o armazenamento e recordação de conjuntos fuzzy ou vetores num hipercubo. Experimentos computacionais relacionados à classificação de padrões e reconhecimento de faces indicam possíveis aplicações dos novos modelos acima mencionadosAbstract: The human brain¿s ability to store and recall information by association has inspired the development various mathematical models referred to in the literature as associative memories. Firstly, this thesis presents a set of autoassociative memories (AMs) that belong to the broad class of autoassociative morphological memories (AMMs). Specifically, the max-plus and min-plus projection autoassociative morphological memories (max-plus and min-plus PAMMs), as well as their compositions, are introduced in this thesis. These models are non-distributed versions of the AMM models developed by Ritter and Sussner. Briefly, the max-plus PAMM yields the largest max-plus combination of the stored vectors which is less than or equal to the input pattern. Dually, the min-plus PAMM projects the input pattern into the set of all min-plus combinations. In second, in the context of fuzzy set theory, this thesis proposes new fuzzy autoassociative memories mentioned as class of the max-C and min-D FPAMMs. A FPAMM represents a fuzzy morphological neural network with a hidden layer of neurons that is designed for the storage and retrieval of fuzzy sets or vectors on a hypercube. Computational experiments concerning pattern classification and face recognition indicate possible applications of the aforementioned new AM modelsDoutoradoMatematica AplicadaDoutor em Matemática AplicadaCAPE

Characterization and statistics of distance-based morphological operators using Voronoi diagram with application for edge detection in color images

Orientador: Marcos Eduardo Ribeiro do Valle MesquitaTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: A morfologia matemática (MM) é uma teoria que utiliza conceitos geométricos e topológicos para processamento e análise de imagens. Aplicações da MM incluem, por exemplo, detecção de bordas, segmentação e reconstrução automática de imagens, reconhecimento de padrões e decomposição de sinais e imagens. Nesta tese, estudamos os operadores morfológicos para imagens em tons de cinza e coloridas segundo a abordagem baseada em distância proposta por Angulo. Este tipo de abordagem geralmente se depara com a difícil tarefa de escolher uma referência apropriada. Nesta tese, estabelecemos uma relação direta entre a escolha da referência e o diagrama de Voronoi. Além disso, utilizamos conceitos de estatística descritiva para superar a dificuldade de escolher uma referência e, com isso, definimos novos operadores, chamados pseudo-morfológicos. Por exemplo, a média de dilatações ou a média de erosões, o desvio padrão do gradiente, entre outros. Experimentos computacionais mostraram que alguns dos novos operadores pseudo-morfológicos, por exemplo o desvio padrão do gradiente, apresentaram um bom desempenho quando aplicados em problemas de detecção de bordas em imagens coloridasAbstract: Mathematical morphology (MM) is a theory that uses geometric and topological concepts for image processing and analysis. Applications MM include boundary detection, automatic image segmentation and reconstruction, pattern recognition, and signal and image decomposition. In this thesis, we study morphological operators for grayscale and color images defined according to the distance-based approach proposed by Angulo. This type of approach usually involves the difficult task of choosing an appropriate reference. In this thesis, we establish a direct relationship between the choice of reference and the Voronoi diagram. In addition, we use descriptive statistics concepts to overcome the hard task of choosing a reference, and thus we define new pseudo-morphological operators. Such as the average of dilations and the average of erosions, the standard deviation of the gradient. Computational experiments show that some of the new pseudo-morphological operators, for example the standard deviation of the gradient are suitable for edge detection of color imagesDoutoradoMatematica AplicadaDoutor em Matemática Aplicad

Mathematical Morphology on the Spherical CIELab Quantale with an Application in Color Image Boundary Detection

CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOMathematical morphology is a theory with applications in image processing and analysis. This paper presents a quantale-based approach to color morphology based on the CIELab color space in spherical coordinates. The novel morphological operations take into account the perceptual difference between color elements by using a distance-based ordering scheme. Furthermore, the novel approach allows for the use of non-flat structuring elements. An illustrative example reveals that non-flat dilations and erosions may preserve more features of a color image than their corresponding flat operations. Furthermore, the novel non-flat morphological operators yielded promising results on experiments concerning the detection of the boundaries of objects on color images.Mathematical morphology is a theory with applications in image processing and analysis. This paper presents a quantale-based approach to color morphology based on the CIELab color space in spherical coordinates. The novel morphological operations take into account the perceptual difference between color elements by using a distance-based ordering scheme. Furthermore, the novel approach allows for the use of non-flat structuring elements. An illustrative example reveals that non-flat dilations and erosions may preserve more features of a color image than their corresponding flat operations. Furthermore, the novel non-flat morphological operators yielded promising results on experiments concerning the detection of the boundaries of objects on color images.572183201CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO305486/2014-42013/12310-