Radar track segmentation with cubic splines for collision risk models in high density terminal areas

This paper presents a method to segment airplane radar tracks in high density terminal areas where the air traffic follows trajectories with several changes in heading, speed and altitude. The radar tracks are modelled with different types of segments, straight lines, cubic spline function and shape preserving cubic function. The longitudinal, lateral and vertical deviations are calculated for terminal manoeuvring area scenarios. The most promising model of the radar tracks resulted from a mixed interpolation using straight lines for linear segments and spline cubic functions for curved segments. A sensitivity analysis is used to optimise the size of the window for the segmentation process.

High-quality region-based foreground segmentation using a spatial grid of SVM classifiers

This paper presents a novel background modeling system that uses a spatial grid of Support Vector Machines classifiers for segmenting moving objects, which is a key step in many video-based consumer applications. The system is able to adapt to a large range of dynamic background situations since no parametric model or statistical distribution are assumed. This is achieved by using a different classifier per image region that learns the specific appearance of that scene region and its variations (illumination changes, dynamic backgrounds, etc.). The proposed system has been tested with a recent public database, outperforming other state-of-the-art algorithms.

Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications

Low-cost systems that can obtain a high-quality foreground segmentation almostindependently of the existing illumination conditions for indoor environments are verydesirable, especially for security and surveillance applications. In this paper, a novelforeground segmentation algorithm that uses only a Kinect depth sensor is proposedto satisfy the aforementioned system characteristics. This is achieved by combininga mixture of Gaussians-based background subtraction algorithm with a new Bayesiannetwork that robustly predicts the foreground/background regions between consecutivetime steps. The Bayesian network explicitly exploits the intrinsic characteristics ofthe depth data by means of two dynamic models that estimate the spatial and depthevolution of the foreground/background regions. The most remarkable contribution is thedepth-based dynamic model that predicts the changes in the foreground depth distributionbetween consecutive time steps. This is a key difference with regard to visible imagery,where the color/gray distribution of the foreground is typically assumed to be constant.Experiments carried out on two different depth-based databases demonstrate that theproposed combination of algorithms is able to obtain a more accurate segmentation of theforeground/background than other state-of-the art approaches.

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.

Seamless, Static Multi-Texturing of 3D Meshes

In the context of 3D reconstruction, we present a static multi-texturing system yielding a seamless texture atlas calculated by combining the colour information from several photos from the same subject covering most of its surface. These pictures can be provided by shooting just one camera several times when reconstructing a static object, or a set of synchronized cameras, when dealing with a human or any other moving object. We suppress the colour seams due to image misalignments and irregular lighting conditions that multi-texturing approaches typically suffer from, while minimizing the blurring effect introduced by colour blending techniques. Our system is robust enough to compensate for the almost inevitable inaccuracies of 3D meshes obtained with visual hull–based techniques: errors in silhouette segmentation, inherently bad handling of concavities, etc.

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.

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.

A new marketing segmentation approach based on marginal individual utilities: applying CRM is not a chimera anymore

In the advent of Customer Relationship Management, a more accurate profile of the consumer is needed. The objective of this paper is to show the usefulness of knowing consumer’s complete utility function through his/her marginal utilities. This approach allows one to form groups of individuals with similar preferences (as traditional segmentation methods do) and to treat them individually (which represents an advance). The empirical application is carried out, on a sample of 2,127 individuals, in the context of tourism, where the customer relationship management philosophy is gaining more and more relevance.

Snakes based tracking and texture analysis of microscopic images

Comunicación presentada en el VII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, SNRFAI, Barcelona, abril 1997.