Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

Arquivo documental: relativo à Lei do Cinema e do Audiovisual, criação do FICA e transformação do ICAM em ICA (2003-2007)

O projecto “Principais tendências no cinema português contemporâneo” nasceu no Departamento de Cinema da ESTC, com o objectivo de desenvolver investigação especializada a partir de um núcleo formado por alunos da Licenciatura em Cinema e do Mestrado em Desenvolvimento de Projecto Cinematográfico, a que se juntaram professores-investigadores membros do CIAC e convidados. O que agora se divulga corresponde a dois anos e meio de trabalho desenvolvido pela equipa de investigação, entre Abril de 2009 e Novembro de 2011. Dada a forma que ele foi adquirindo, preferimos renomeá-lo, para efeitos de divulgação, “Novas & velhas tendências no cinema português contemporâneo”.

Processamento de imagens BOLD de Ressonância Magnética do músculo através da técnica ICA

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Structural analysis of the masonry envelope of Ica Cathedral, Peru

Dissertação de mestrado em Structural Analysis of Monuments and Historical Constructions

Structural analysis of the timber structure of Ica Cathedral, Peru

Dissertação de mestrado em Structural Analysis of Monuments and Historical Constructions

Prospección y pesquería de caracol Stramonita chocolata (Duclos) en el litoral de Ica y norte de Arequipa. Diciembre 2000

Contiene los resultados de la prospección de caracol en los puertos de Pisco, San Juan de Marcona y Lomas realizada entre el 26 de diciembre de 2000 al 05 de enero de 2001. Se analizan los aspectos biométricos, relaciones biométricas, índices de abundancia relativa y el desarrollo de su pesquería, teniendo en cuenta que el caracol Stramonita chocolata (Duclos) se caracteriza por su abundancia lo cual lo ayuda a ubicarse como el tercer recurso en importancia por sus niveles de extracción.

Evaluación del recurso macha (Mesodesma donacium) en el litoral de Ica y Arequipa. Marzo - abril 1998

Presenta los resultados de la evaluación de los bancos naturales del recurso en las playas del litoral costero norte del departamento de Arequipa (provincia de Caravelí) y playas del departamento de Ica, con el fin de determinar la situación biológica pesquera del recurso macha. El elevado porcentaje de ejemplares menores a la talla mínima comercial en las playas, fue uno de los resultados negativos de la influencia del Fenómeno El Niño que se sintió a finales de enero de 1998, obteniendo como consecuencia la ausencia total de extractores artesanales del recurso para el mes de febrero de 1998.

Prospección del caracol Thais chocolata en el litoral de Ica y norte de Arequipa, mayo 1998

Detalla los resultados de la prospección de caracol en los puertos de Pisco, San Juan de Marcona y Lomas, realizada del 21 de mayo al 02 de junio de 1998. Los aspectos considerados son los índices de abundancia, estructura por tallas, madurez sexual, relaciones biométricas, aspectos medio ambientales y el desarrollo de su pesquería.

Informe general de la segunda Encuesta estructural de la Pesquería Artesanal Peruana 2003-2005. Regiones Tumbes, Piura, Lambayeque, La Libertad, Áncash, Lima, Ica, Arequipa, Moquegua, Tacna.

Se presentan los resultados de la Segunda Encuesta Estructural de la Pesquería Artesanal Peruana en el Litoral (II ENEPA), ejecutada por el IMARPE entre octubre del 2003 y mayo del 2005, y los resultados del Análisis de Cobertura de embarcaciones realizado en abril del 2009.

Comparing ICA-based and single-trial topographic ERP analyses.

Single-trial analysis of human electroencephalography (EEG) has been recently proposed for better understanding the contribution of individual subjects to a group-analysis effect as well as for investigating single-subject mechanisms. Independent Component Analysis (ICA) has been repeatedly applied to concatenated single-trial responses and at a single-subject level in order to extract those components that resemble activities of interest. More recently we have proposed a single-trial method based on topographic maps that determines which voltage configurations are reliably observed at the event-related potential (ERP) level taking advantage of repetitions across trials. Here, we investigated the correspondence between the maps obtained by ICA versus the topographies that we obtained by the single-trial clustering algorithm that best explained the variance of the ERP. To do this, we used exemplar data provided from the EEGLAB website that are based on a dataset from a visual target detection task. We show there to be robust correspondence both at the level of the activation time courses and at the level of voltage configurations of a subset of relevant maps. We additionally show the estimated inverse solution (based on low-resolution electromagnetic tomography) of two corresponding maps occurring at approximately 300 ms post-stimulus onset, as estimated by the two aforementioned approaches. The spatial distribution of the estimated sources significantly correlated and had in common a right parietal activation within Brodmann's Area (BA) 40. Despite their differences in terms of theoretical bases, the consistency between the results of these two approaches shows that their underlying assumptions are indeed compatible.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Improving the Quality of EEG Data in Patients With Alzheimers Disease Using ICA

Does Independent Component Analysis (ICA) denature EEG signals? We applied ICA to two groups of subjects (mild Alzheimer patients and control subjects). The aim of this study was to examine whether or not the ICA method can reduce both group di®erences and within-subject variability. We found that ICA diminished Leave-One- Out root mean square error (RMSE) of validation (from 0.32 to 0.28), indicative of the reduction of group di®erence. More interestingly, ICA reduced the inter-subject variability within each group (¾ = 2:54 in the ± range before ICA, ¾ = 1:56 after, Bartlett p = 0.046 after Bonfer- roni correction). Additionally, we present a method to limit the impact of human error (' 13:8%, with 75.6% inter-cleaner agreement) during ICA cleaning, and reduce human bias. These ¯ndings suggests the novel usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of subject variability.