Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Low cost RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author’s knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.

A comparison of open-source segmentation architectures for dealing with imperfect data from the media in speech synthesis

Traditional Text-To-Speech (TTS) systems have been developed using especially-designed non-expressive scripted recordings. In order to develop a new generation of expressive TTS systems in the Simple4All project, real recordings from the media should be used for training new voices with a whole new range of speaking styles. However, for processing this more spontaneous material, the new systems must be able to deal with imperfect data (multi-speaker recordings, background and foreground music and noise), filtering out low-quality audio segments and creating mono-speaker clusters. In this paper we compare several architectures for combining speaker diarization and music and noise detection which improve the precision and overall quality of the segmentation.

Pulmonary Artery-Vein Segmentation in Real and Synthetic CT Images = Segmentación Arteria-Vena Pulmonares en Imágenes de CT Reales y Sintéticas

La tomografía axial computerizada (TAC) es la modalidad de imagen médica preferente para el estudio de enfermedades pulmonares y el análisis de su vasculatura. La segmentación general de vasos en pulmón ha sido abordada en profundidad a lo largo de los últimos años por la comunidad científica que trabaja en el campo de procesamiento de imagen; sin embargo, la diferenciación entre irrigaciones arterial y venosa es aún un problema abierto. De hecho, la separación automática de arterias y venas está considerado como uno de los grandes retos futuros del procesamiento de imágenes biomédicas. La segmentación arteria-vena (AV) permitiría el estudio de ambas irrigaciones por separado, lo cual tendría importantes consecuencias en diferentes escenarios médicos y múltiples enfermedades pulmonares o estados patológicos. Características como la densidad, geometría, topología y tamaño de los vasos sanguíneos podrían ser analizados en enfermedades que conllevan remodelación de la vasculatura pulmonar, haciendo incluso posible el descubrimiento de nuevos biomarcadores específicos que aún hoy en dípermanecen ocultos. Esta diferenciación entre arterias y venas también podría ayudar a la mejora y el desarrollo de métodos de procesamiento de las distintas estructuras pulmonares. Sin embargo, el estudio del efecto de las enfermedades en los árboles arterial y venoso ha sido inviable hasta ahora a pesar de su indudable utilidad. La extrema complejidad de los árboles vasculares del pulmón hace inabordable una separación manual de ambas estructuras en un tiempo realista, fomentando aún más la necesidad de diseñar herramientas automáticas o semiautomáticas para tal objetivo. Pero la ausencia de casos correctamente segmentados y etiquetados conlleva múltiples limitaciones en el desarrollo de sistemas de separación AV, en los cuales son necesarias imágenes de referencia tanto para entrenar como para validar los algoritmos. Por ello, el diseño de imágenes sintéticas de TAC pulmonar podría superar estas dificultades ofreciendo la posibilidad de acceso a una base de datos de casos pseudoreales bajo un entorno restringido y controlado donde cada parte de la imagen (incluyendo arterias y venas) está unívocamente diferenciada. En esta Tesis Doctoral abordamos ambos problemas, los cuales están fuertemente interrelacionados. Primero se describe el diseño de una estrategia para generar, automáticamente, fantomas computacionales de TAC de pulmón en humanos. Partiendo de conocimientos a priori, tanto biológicos como de características de imagen de CT, acerca de la topología y relación entre las distintas estructuras pulmonares, el sistema desarrollado es capaz de generar vías aéreas, arterias y venas pulmonares sintéticas usando métodos de crecimiento iterativo, que posteriormente se unen para formar un pulmón simulado con características realistas. Estos casos sintéticos, junto a imágenes reales de TAC sin contraste, han sido usados en el desarrollo de un método completamente automático de segmentación/separación AV. La estrategia comprende una primera extracción genérica de vasos pulmonares usando partículas espacio-escala, y una posterior clasificación AV de tales partículas mediante el uso de Graph-Cuts (GC) basados en la similitud con arteria o vena (obtenida con algoritmos de aprendizaje automático) y la inclusión de información de conectividad entre partículas. La validación de los fantomas pulmonares se ha llevado a cabo mediante inspección visual y medidas cuantitativas relacionadas con las distribuciones de intensidad, dispersión de estructuras y relación entre arterias y vías aéreas, los cuales muestran una buena correspondencia entre los pulmones reales y los generados sintéticamente. La evaluación del algoritmo de segmentación AV está basada en distintas estrategias de comprobación de la exactitud en la clasificación de vasos, las cuales revelan una adecuada diferenciación entre arterias y venas tanto en los casos reales como en los sintéticos, abriendo así un amplio abanico de posibilidades en el estudio clínico de enfermedades cardiopulmonares y en el desarrollo de metodologías y nuevos algoritmos para el análisis de imágenes pulmonares. ABSTRACT Computed tomography (CT) is the reference image modality for the study of lung diseases and pulmonary vasculature. Lung vessel segmentation has been widely explored by the biomedical image processing community, however, differentiation of arterial from venous irrigations is still an open problem. Indeed, automatic separation of arterial and venous trees has been considered during last years as one of the main future challenges in the field. Artery-Vein (AV) segmentation would be useful in different medical scenarios and multiple pulmonary diseases or pathological states, allowing the study of arterial and venous irrigations separately. Features such as density, geometry, topology and size of vessels could be analyzed in diseases that imply vasculature remodeling, making even possible the discovery of new specific biomarkers that remain hidden nowadays. Differentiation between arteries and veins could also enhance or improve methods processing pulmonary structures. Nevertheless, AV segmentation has been unfeasible until now in clinical routine despite its objective usefulness. The huge complexity of pulmonary vascular trees makes a manual segmentation of both structures unfeasible in realistic time, encouraging the design of automatic or semiautomatic tools to perform the task. However, this lack of proper labeled cases seriously limits in the development of AV segmentation systems, where reference standards are necessary in both algorithm training and validation stages. For that reason, the design of synthetic CT images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image (including arteries and veins) is differentiated unequivocally. In this Ph.D. Thesis we address both interrelated problems. First, the design of a complete framework to automatically generate computational CT phantoms of the human lung is described. Starting from biological and imagebased knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. These synthetic cases, together with labeled real CT datasets, have been used as reference for the development of a fully automatic pulmonary AV segmentation/separation method. The approach comprises a vessel extraction stage using scale-space particles and their posterior artery-vein classification using Graph-Cuts (GC) based on arterial/venous similarity scores obtained with a Machine Learning (ML) pre-classification step and particle connectivity information. Validation of pulmonary phantoms from visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems, show good correspondence between real and synthetic lungs. The evaluation of the Artery-Vein (AV) segmentation algorithm, based on different strategies to assess the accuracy of vessel particles classification, reveal accurate differentiation between arteries and vein in both real and synthetic cases that open a huge range of possibilities in the clinical study of cardiopulmonary diseases and the development of methodological approaches for the analysis of pulmonary images.

Methods and Techniques for Segmentation of Consumers in Social Media

Los medios sociales han revolucionado la manera en la que los consumidores se relacionan entre sí y con las marcas. Las opiniones publicadas en dichos medios tienen un poder de influencia en las decisiones de compra tan importante como las campañas de publicidad. En consecuencia, los profesionales del marketing cada vez dedican mayores esfuerzos e inversión a la obtención de indicadores que permitan medir el estado de salud de las marcas a partir de los contenidos digitales generados por sus consumidores. Dada la naturaleza no estructurada de los contenidos publicados en los medios sociales, la tecnología usada para procesar dichos contenidos ha menudo implementa técnicas de Inteligencia Artificial, tales como algoritmos de procesamiento de lenguaje natural, aprendizaje automático y análisis semántico. Esta tesis, contribuye al estado de la cuestión, con un modelo que permite estructurar e integrar la información publicada en medios sociales, y una serie de técnicas cuyos objetivos son la identificación de consumidores, así como la segmentación psicográfica y sociodemográfica de los mismos. La técnica de identificación de consumidores se basa en la huella digital de los dispositivos que utilizan para navegar por la Web y es tolerante a los cambios que se producen con frecuencia en dicha huella digital. Las técnicas de segmentación psicográfica descritas obtienen la posición en el embudo de compra de los consumidores y permiten clasificar las opiniones en función de una serie de atributos de marketing. Finalmente, las técnicas de segmentación sociodemográfica permiten obtener el lugar de residencia y el género de los consumidores. ABSTRACT Social media has revolutionised the way in which consumers relate to each other and with brands. The opinions published in social media have a power of influencing purchase decisions as important as advertising campaigns. Consequently, marketers are increasing efforts and investments for obtaining indicators to measure brand health from the digital content generated by consumers. Given the unstructured nature of social media contents, the technology used for processing such contents often implements Artificial Intelligence techniques, such as natural language processing, machine learning and semantic analysis algorithms. This thesis contributes to the State of the Art, with a model for structuring and integrating the information posted on social media, and a number of techniques whose objectives are the identification of consumers, as well as their socio-demographic and psychographic segmentation. The consumer identification technique is based on the fingerprint of the devices they use to surf the Web and is tolerant to the changes that occur frequently in such fingerprint. The psychographic profiling techniques described infer the position of consumer in the purchase funnel, and allow to classify the opinions based on a series of marketing attributes. Finally, the socio-demographic profiling techniques allow to obtain the residence and gender of consumers.

Automatic segmentation of rivers as a tool for assessing river responses. Case study: the Porma and Curueño rivers, nw Spain.

Automatic segmentation using univariate and multivariate techniques provides more objective and efficient segmentations of the river systems (Alber & Piégay, 2011) and can be complementary to the expert criteria traditionally used (Brenden et al., 2008) INTEREST: A powerful tool to objectively segment the continuity of rivers, which is required for diagnosing problems associated to human impacts OBJECTIVE: To evaluate the potentiality of univariate and multivariate methods in the assessment of river adjustments produced by flow regulation

A conserved mode of head segmentation in arthropods revealed by the expression pattern of Hox genes in a spider

Chelicerates constitute a basic arthropod group with fossil representatives from as early as the Cambrian period. Embryonic development and the subdivision of the segmented body region into a prosoma and an opisthosoma are very similar in all extant chelicerates. The mode of head segmentation, however, has long been controversial. Although all other arthropod groups show a subdivision of the head region into six segments, the chelicerates are thought to have the first antennal segment missing. To examine this problem on a molecular level, we have compared the expression pattern of Hox genes in the spider Cupiennius salei with the pattern known from insects. Surprisingly, we find that the anterior expression borders of the Hox genes are in the same register and the same relative segmental position as in Drosophila. This contradicts the view that the homologue of the first antennal segment is absent in the spider. Instead, our data suggest that the cheliceral segment is homologous to the first antennal segment and the pedipalpal segment is homologous to the second antennal (or intercalary) segment in arthropods. Our finding implies that chelicerates, myriapods, crustaceans, and insects share a single mode of head segmentation, reinforcing the argument for a monophyletic origin of the arthropods.

Assessment of Customer Service in Academic Health Care Libraries (ACSAHL): an instrument for measuring customer service*†

Objectives: In a pilot study, the library had good results using SERVQUAL, a respected and often-used instrument for measuring customer satisfaction. The SERVQUAL instrument itself, however, received some serious and well-founded criticism from the respondents to our survey. The purpose of this study was to test the comparability of the results of SERVQUAL with a revised and shortened instrument modeled on SERVQUAL. The revised instrument, the Assessment of Customer Service in Academic Health Care Libraries (ACSAHL), was designed to better assess customer service in academic health care libraries.

The conserved role of Krox-20 in directing Hox gene expression during vertebrate hindbrain segmentation.

Transient segmentation in the hindbrain is a fundamental morphogenetic phenomenon in the vertebrate embryo, and the restricted expression of subsets of Hox genes in the developing rhombomeric units and their derivatives is linked with regional specification. Here we show that patterning of the vertebrate hindbrain involves the direct upregulation of the chicken and pufferfish group 2 paralogous genes, Hoxb-2 and Hoxa-2, in rhombomeres 3 and 5 (r3 and r5) by the zinc finger gene Krox-20. We identified evolutionarily conserved r3/r5 enhancers that contain high affinity Krox-20. binding sites capable of mediating transactivation by Krox-20. In addition to conservation of binding sites critical for Krox-20 activity in the chicken Hoxa-2 and pufferfish Hoxb-2 genes, the r3/r5 enhancers are also characterized by the presence of a number of identical motifs likely to be involved in cooperative interactions with Krox-20 during the process of hindbrain patterning in vertebrates.

Psychophysical evidence for fast region-based segmentation processes in motion and color.

Theories of image segmentation suggest that the human visual system may use two distinct processes to segregate figure from background: a local process that uses local feature contrasts to mark borders of coherent regions and a global process that groups similar features over a larger spatial scale. We performed psychophysical experiments to determine whether and to what extent the global similarity process contributes to image segmentation by motion and color. Our results show that for color, as well as for motion, segmentation occurs first by an integrative process on a coarse spatial scale, demonstrating that for both modalities the global process is faster than one based on local feature contrasts. Segmentation by motion builds up over time, whereas segmentation by color does not, indicating a fundamental difference between the modalities. Our data suggest that segmentation by motion proceeds first via a cooperative linking over space of local motion signals, generating almost immediate perceptual coherence even of physically incoherent signals. This global segmentation process occurs faster than the detection of absolute motion, providing further evidence for the existence of two motion processes with distinct dynamic properties.

Drosophila 5-HT2 serotonin receptor: coexpression with fushi-tarazu during segmentation.

Serotonin, first described as a neurotransmitter in invertebrates, has been investigated mostly for its functions in the mature central nervous system of higher vertebrates. Serotonin receptor diversity has been described in the mammalian brain and in insects. We report the isolation of a cDNA coding for a Drosophila melanogaster serotonin receptor that displays a sequence, a gene organization, and pharmacological properties typical of the mammalian 5-HT2 serotonin receptor subtype. Its mRNA can be detected in the adult fly; moreover, a high level of expression occurs at 3 hr of Drosophila embryogenesis. This early embryonic expression is surprisingly organized in a seven-stripe pattern that appears at the cellular blastoderm stage. In addition, this pattern is in phase with that of the even-parasegment-expressed pair-rule gene fushi-tarazu and is similarly modified by mutations affecting segmentation genes. Simultaneously with this pair-rule expression, the complete machinery of serotonin synthesis is present and leads to a peak of ligand concomitant with a peak of 5-HT2-specific receptor sites in blastoderm embryos.

How Does the Denver Public Library System Respond to its Customer's Requests for Global Information?

The interaction between globally available information and public library users is a changing one. Global information is readily available yet provider and user struggle to find efficiencies of time and resources. As a primary resource of global information the Denver Public Library (DPL) is approaching this challenge by providing changing technology to a changing user and by providing a customized approach to immigrant populations. DPL provides global information to library users through collections, programs and Internet. Internet and collections global information usage cannot be directly measured due to privacy restrictions. Only 12.5% of general user programs focus on global information. Four percent of budget serves the immigrant users. This is greater than national averages.

New framework for score segmentation and analysis in OpenMusic

We present new tools for the segmentation and analysis of musical scores in the OpenMusic computer-aided composition environment. A modular object-oriented framework enables the creation of segmentations on score objects and the implementation of automatic or semi-automatic analysis processes. The analyses can be performed and displayed thanks to customizable classes and callbacks. Concrete examples are given, in particular with the implementation of a semi-automatic harmonic analysis system and a framework for rhythmic transcription.

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.

A tale of oversimplification and deregulation: the mainstream approach to labour market segmentation and the recent responses to the crisis in European countries. WP C.S.D.L.E. “Massimo D’Antona”.INT – 102/2013

After advocating flexibilization of non-standard work contracts for many years, some European and international institutions and several policy makers now indicate the standard employment relationship and its regulation as a cause of segmentation between the labour market of "guaranteed" insiders, employed under permanent contracts with effective protection against unfair dismissal, and the market of the “not-guaranteed” outsiders, working with non-standard contracts. Reforms of employment legislation are therefore being promoted and approved in different countries, allegedly aiming to balance the legal protection afforded to standard and non-standard workers. This article firstly argues that this approach is flawed as it oversimplifies reasons of segmentation as it concentrates on an “insiders-outsiders” discourse that cannot easily be transplanted in continental Europe. After reviewing current legislative changes in Italy, Spain and Portugal, it is then argued that lawmakers are focused on “deregulation” rather than “balancing protection” when approving recent reforms. Finally, the mainstream approach to segmentation and some of its derivative proposals, such as calls to introduce a “single permanent contract”, are called into question, as they seem to neglect the essential role of job protection in underpinning the effectiveness of fundamental and constitutional rights at the workplace.