Body image rating scales to assess obesity

Obesity prevalence in the U.S. has increased during the last three decades with major impact on public health. Screening for obesity in a population with unknown weight status can be time- and resource-consuming, but the information is valuable for prioritizing and allocating scarce resources. The challenge remains to properly assess obesity with the available methods. Body Image Rating Scales (BIRS) have initially been developed to assess body image disturbances, but also seem useful as an alternative method in assessing obesity prevalence. Several different BIRS exists. In this project I reviewed the literature that exists regarding the use of BIRS, and its advantages and limitations for the assessment of obesity status with regards to BMI. The result yielded nine publications that examined eight different scales and their correlation with BMI, ranging from r=.59 for self-reported BMI to r=.94 for measured BMI. One concern is the lack of standardization of this method to assess obesity, given the range of different scales. While many methods for obesity assessment are available, the simplicity, ease of use and cost-effectiveness of BIRS make it very appealing. BIRS remain a potentially attractive option to assess the weight status of a large population with minimal requirements in assets and time, especially in situations where measuring instruments are not available, or when height or weight could not be recalled.^

Directional Geodesic Active Contours

We present a non-conformal metric that generalizes the geodesic active contours approach for image segmentation. The new metric is obtained by adding to the Euclidean metric an additional term that penalizes the misalignment of the curve with the image gradient and multiplying the resulting metric by a conformal factor that depends on the edge intensity. In this way, a closer fitting to the edge direction results. The provided experimental results address the computation of the geodesics of the new metric by applying a gradient descent to externally provided curves. The good performance of the proposed techniques is demonstrated in comparison with other active contours methods.

Efficient Model-based 3D Tracking by Using Direct Image Registration

This thesis deals with the problem of efficiently tracking 3D objects in sequences of images. We tackle the efficient 3D tracking problem by using direct image registration. This problem is posed as an iterative optimization procedure that minimizes a brightness error norm. We review the most popular iterative methods for image registration in the literature, turning our attention to those algorithms that use efficient optimization techniques. Two forms of efficient registration algorithms are investigated. The first type comprises the additive registration algorithms: these algorithms incrementally compute the motion parameters by linearly approximating the brightness error function. We centre our attention on Hager and Belhumeur’s factorization-based algorithm for image registration. We propose a fundamental requirement that factorization-based algorithms must satisfy to guarantee good convergence, and introduce a systematic procedure that automatically computes the factorization. Finally, we also bring out two warp functions to register rigid and nonrigid 3D targets that satisfy the requirement. The second type comprises the compositional registration algorithms, where the brightness function error is written by using function composition. We study the current approaches to compositional image alignment, and we emphasize the importance of the Inverse Compositional method, which is known to be the most efficient image registration algorithm. We introduce a new algorithm, the Efficient Forward Compositional image registration: this algorithm avoids the necessity of inverting the warping function, and provides a new interpretation of the working mechanisms of the inverse compositional alignment. By using this information, we propose two fundamental requirements that guarantee the convergence of compositional image registration methods. Finally, we support our claims by using extensive experimental testing with synthetic and real-world data. We propose a distinction between image registration and tracking when using efficient algorithms. We show that, depending whether the fundamental requirements are hold, some efficient algorithms are eligible for image registration but not for tracking.

Desing of an Airport Surface Routing Evaluation Tool

There are many studies related with airport surface routing algorithms, based on different approaches and with different evaluation methods and metrics. So, the need of performing a balanced analysis and comparison using a common framework is evident. This paper presents an implementation of an evaluation tool for airport surface routing algorithms. The routing evaluation tool presented here is based in three basic pillars composed by the airport model, the model and generation of traffic and a comprehensive figure of merit function. The paper includes some example evaluations performed over Barajas Airport with representative traffic samples using several simple routing methods.

Semantic Interoperability: Perspectives and Technology Evaluation

Interoperability between semantic technologies is a must because they need to be in communication to interchange ontologies and use them in the distributed and open environment of the SemanticWeb. However, such interoperability is not straightforward due to the high heterogeneity in such technologies. This chapter describes the problem of semantic technology interoperability from two different perspectives. First, from a theoretical perspective by presenting an overview of the different factors that affect interoperability and, second, from a practical perspective by reusing evaluation methods and applying them to six current semantic technologies in order to assess their interoperability.

Detección de patrones y anomalías espectrales del terreno mediante espectrometría de imagen de alta resolución : reconocimiento, optimización y evaluación multiescenario

El objeto de esta Tesis doctoral es el desarrollo de una metodologia para la deteccion automatica de anomalias a partir de datos hiperespectrales o espectrometria de imagen, y su cartografiado bajo diferentes condiciones tipologicas de superficie y terreno. La tecnologia hiperespectral o espectrometria de imagen ofrece la posibilidad potencial de caracterizar con precision el estado de los materiales que conforman las diversas superficies en base a su respuesta espectral. Este estado suele ser variable, mientras que las observaciones se producen en un numero limitado y para determinadas condiciones de iluminacion. Al aumentar el numero de bandas espectrales aumenta tambien el numero de muestras necesarias para definir espectralmente las clases en lo que se conoce como Maldicion de la Dimensionalidad o Efecto Hughes (Bellman, 1957), muestras habitualmente no disponibles y costosas de obtener, no hay mas que pensar en lo que ello implica en la Exploracion Planetaria. Bajo la definicion de anomalia en su sentido espectral como la respuesta significativamente diferente de un pixel de imagen respecto de su entorno, el objeto central abordado en la Tesis estriba primero en como reducir la dimensionalidad de la informacion en los datos hiperespectrales, discriminando la mas significativa para la deteccion de respuestas anomalas, y segundo, en establecer la relacion entre anomalias espectrales detectadas y lo que hemos denominado anomalias informacionales, es decir, anomalias que aportan algun tipo de informacion real de las superficies o materiales que las producen. En la deteccion de respuestas anomalas se asume un no conocimiento previo de los objetivos, de tal manera que los pixeles se separan automaticamente en funcion de su informacion espectral significativamente diferenciada respecto de un fondo que se estima, bien de manera global para toda la escena, bien localmente por segmentacion de la imagen. La metodologia desarrollada se ha centrado en la implicacion de la definicion estadistica del fondo espectral, proponiendo un nuevo enfoque que permite discriminar anomalias respecto fondos segmentados en diferentes grupos de longitudes de onda del espectro, explotando la potencialidad de separacion entre el espectro electromagnetico reflectivo y emisivo. Se ha estudiado la eficiencia de los principales algoritmos de deteccion de anomalias, contrastando los resultados del algoritmo RX (Reed and Xiaoli, 1990) adoptado como estandar por la comunidad cientifica, con el metodo UTD (Uniform Targets Detector), su variante RXD-UTD, metodos basados en subespacios SSRX (Subspace RX) y metodo basados en proyecciones de subespacios de imagen, como OSPRX (Orthogonal Subspace Projection RX) y PP (Projection Pursuit). Se ha desarrollado un nuevo metodo, evaluado y contrastado por los anteriores, que supone una variacion de PP y describe el fondo espectral mediante el analisis discriminante de bandas del espectro electromagnetico, separando las anomalias con el algortimo denominado Detector de Anomalias de Fondo Termico o DAFT aplicable a sensores que registran datos en el espectro emisivo. Se han evaluado los diferentes metodos de deteccion de anomalias en rangos del espectro electromagnetico del visible e infrarrojo cercano (Visible and Near Infrared-VNIR), infrarrojo de onda corta (Short Wavelenght Infrared-SWIR), infrarrojo medio (Meadle Infrared-MIR) e infrarrojo termico (Thermal Infrared-TIR). La respuesta de las superficies en las distintas longitudes de onda del espectro electromagnetico junto con su entorno, influyen en el tipo y frecuencia de las anomalias espectrales que puedan provocar. Es por ello que se han utilizado en la investigacion cubos de datos hiperepectrales procedentes de los sensores aeroportados cuya estrategia y diseno en la construccion espectrometrica de la imagen difiere. Se han evaluado conjuntos de datos de test de los sensores AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) y MASTER (MODIS/ASTER Simulator). Se han disenado experimentos sobre ambitos naturales, urbanos y semiurbanos de diferente complejidad. Se ha evaluado el comportamiento de los diferentes detectores de anomalias a traves de 23 tests correspondientes a 15 areas de estudio agrupados en 6 espacios o escenarios: Urbano - E1, Semiurbano/Industrial/Periferia Urbana - E2, Forestal - E3, Agricola - E4, Geologico/Volcanico - E5 y Otros Espacios Agua, Nubes y Sombras - E6. El tipo de sensores evaluados se caracteriza por registrar imagenes en un amplio rango de bandas, estrechas y contiguas, del espectro electromagnetico. La Tesis se ha centrado en el desarrollo de tecnicas que permiten separar y extraer automaticamente pixeles o grupos de pixeles cuya firma espectral difiere de manera discriminante de las que tiene alrededor, adoptando para ello como espacio muestral parte o el conjunto de las bandas espectrales en las que ha registrado radiancia el sensor hiperespectral. Un factor a tener en cuenta en la investigacion ha sido el propio instrumento de medida, es decir, la caracterizacion de los distintos subsistemas, sensores imagen y auxiliares, que intervienen en el proceso. Para poder emplear cuantitativamente los datos medidos ha sido necesario definir las relaciones espaciales y espectrales del sensor con la superficie observada y las potenciales anomalias y patrones objetivos de deteccion. Se ha analizado la repercusion que en la deteccion de anomalias tiene el tipo de sensor, tanto en su configuracion espectral como en las estrategias de diseno a la hora de registrar la radiacion prodecente de las superficies, siendo los dos tipos principales de sensores estudiados los barredores o escaneres de espejo giratorio (whiskbroom) y los barredores o escaneres de empuje (pushbroom). Se han definido distintos escenarios en la investigacion, lo que ha permitido abarcar una amplia variabilidad de entornos geomorfologicos y de tipos de coberturas, en ambientes mediterraneos, de latitudes medias y tropicales. En resumen, esta Tesis presenta una tecnica de deteccion de anomalias para datos hiperespectrales denominada DAFT en su variante de PP, basada en una reduccion de la dimensionalidad proyectando el fondo en un rango de longitudes de onda del espectro termico distinto de la proyeccion de las anomalias u objetivos sin firma espectral conocida. La metodologia propuesta ha sido probada con imagenes hiperespectrales reales de diferentes sensores y en diferentes escenarios o espacios, por lo tanto de diferente fondo espectral tambien, donde los resultados muestran los beneficios de la aproximacion en la deteccion de una gran variedad de objetos cuyas firmas espectrales tienen suficiente desviacion respecto del fondo. La tecnica resulta ser automatica en el sentido de que no hay necesidad de ajuste de parametros, dando resultados significativos en todos los casos. Incluso los objetos de tamano subpixel, que no pueden distinguirse a simple vista por el ojo humano en la imagen original, pueden ser detectados como anomalias. Ademas, se realiza una comparacion entre el enfoque propuesto, la popular tecnica RX y otros detectores tanto en su modalidad global como local. El metodo propuesto supera a los demas en determinados escenarios, demostrando su capacidad para reducir la proporcion de falsas alarmas. Los resultados del algoritmo automatico DAFT desarrollado, han demostrado la mejora en la definicion cualitativa de las anomalias espectrales que identifican a entidades diferentes en o bajo superficie, reemplazando para ello el modelo clasico de distribucion normal con un metodo robusto que contempla distintas alternativas desde el momento mismo de la adquisicion del dato hiperespectral. Para su consecucion ha sido necesario analizar la relacion entre parametros biofisicos, como la reflectancia y la emisividad de los materiales, y la distribucion espacial de entidades detectadas respecto de su entorno. Por ultimo, el algoritmo DAFT ha sido elegido como el mas adecuado para sensores que adquieren datos en el TIR, ya que presenta el mejor acuerdo con los datos de referencia, demostrando una gran eficacia computacional que facilita su implementacion en un sistema de cartografia que proyecte de forma automatica en un marco geografico de referencia las anomalias detectadas, lo que confirma un significativo avance hacia un sistema en lo que se denomina cartografia en tiempo real. The aim of this Thesis is to develop a specific methodology in order to be applied in automatic detection anomalies processes using hyperspectral data also called hyperspectral scenes, and to improve the classification processes. Several scenarios, areas and their relationship with surfaces and objects have been tested. The spectral characteristics of reflectance parameter and emissivity in the pattern recognition of urban materials in several hyperspectral scenes have also been tested. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) and MASTER (MODIS/ASTER Simulator) have been used in this research. It is assumed that there is not prior knowledge of the targets in anomaly detection. Thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by the image segmentation. Several experiments on different scenarios have been designed, analyzing the behavior of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. Results and their consequences in unsupervised classification processes are discussed. Detection of spectral anomalies aims at extracting automatically pixels that show significant responses in relation of their surroundings. This Thesis deals with the unsupervised technique of target detection, also called anomaly detection. Since this technique assumes no prior knowledge about the target or the statistical characteristics of the data, the only available option is to look for objects that are differentiated from the background. Several methods have been developed in the last decades, allowing a better understanding of the relationships between the image dimensionality and the optimization of search procedures as well as the subpixel differentiation of the spectral mixture and its implications in anomalous responses. In other sense, image spectrometry has proven to be efficient in the characterization of materials, based on statistical methods using a specific reflection and absorption bands. Spectral configurations in the VNIR, SWIR and TIR have been successfully used for mapping materials in different urban scenarios. There has been an increasing interest in the use of high resolution data (both spatial and spectral) to detect small objects and to discriminate surfaces in areas with urban complexity. This has come to be known as target detection which can be either supervised or unsupervised. In supervised target detection, algorithms lean on prior knowledge, such as the spectral signature. The detection process for matching signatures is not straightforward due to the complications of converting data airborne sensor with material spectra in the ground. This could be further complicated by the large number of possible objects of interest, as well as uncertainty as to the reflectance or emissivity of these objects and surfaces. An important objective in this research is to establish relationships that allow linking spectral anomalies with what can be called informational anomalies and, therefore, identify information related to anomalous responses in some places rather than simply spotting differences from the background. The development in recent years of new hyperspectral sensors and techniques, widen the possibilities for applications in remote sensing of the Earth. Remote sensing systems measure and record electromagnetic disturbances that the surveyed objects induce in their surroundings, by means of different sensors mounted on airborne or space platforms. Map updating is important for management and decisions making people, because of the fast changes that usually happen in natural, urban and semi urban areas. It is necessary to optimize the methodology for obtaining the best from remote sensing techniques from hyperspectral data. The first problem with hyperspectral data is to reduce the dimensionality, keeping the maximum amount of information. Hyperspectral sensors augment considerably the amount of information, this allows us to obtain a better precision on the separation of material but at the same time it is necessary to calculate a bigger number of parameters, and the precision lowers with the increase in the number of bands. This is known as the Hughes effects (Bellman, 1957) . Hyperspectral imagery allows us to discriminate between a huge number of different materials however some land and urban covers are made up with similar material and respond similarly which produces confusion in the classification. The training and the algorithm used for mapping are also important for the final result and some properties of thermal spectrum for detecting land cover will be studied. In summary, this Thesis presents a new technique for anomaly detection in hyperspectral data called DAFT, as a PP's variant, based on dimensionality reduction by projecting anomalies or targets with unknown spectral signature to the background, in a range thermal spectrum wavelengths. The proposed methodology has been tested with hyperspectral images from different imaging spectrometers corresponding to several places or scenarios, therefore with different spectral background. The results show the benefits of the approach to the detection of a variety of targets whose spectral signatures have sufficient deviation in relation to the background. DAFT is an automated technique in the sense that there is not necessary to adjust parameters, providing significant results in all cases. Subpixel anomalies which cannot be distinguished by the human eye, on the original image, however can be detected as outliers due to the projection of the VNIR end members with a very strong thermal contrast. Furthermore, a comparison between the proposed approach and the well-known RX detector is performed at both modes, global and local. The proposed method outperforms the existents in particular scenarios, demonstrating its performance to reduce the probability of false alarms. The results of the automatic algorithm DAFT have demonstrated improvement in the qualitative definition of the spectral anomalies by replacing the classical model by the normal distribution with a robust method. For their achievement has been necessary to analyze the relationship between biophysical parameters such as reflectance and emissivity, and the spatial distribution of detected entities with respect to their environment, as for example some buried or semi-buried materials, or building covers of asbestos, cellular polycarbonate-PVC or metal composites. Finally, the DAFT method has been chosen as the most suitable for anomaly detection using imaging spectrometers that acquire them in the thermal infrared spectrum, since it presents the best results in comparison with the reference data, demonstrating great computational efficiency that facilitates its implementation in a mapping system towards, what is called, Real-Time Mapping.

Evaluation of configurations of supply networks supported by it systems

Los sistemas transaccionales tales como los programas informáticos para la planificación de recursos empresariales (ERP software) se han implementado ampliamente mientras que los sistemas analíticos para la gestión de la cadena de suministro (SCM software) no han tenido el éxito deseado por la industria de tecnología de información (TI). Aunque se documentan beneficios importantes derivados de las implantaciones de SCM software, las empresas industriales son reacias a invertir en este tipo de sistemas. Por una parte esto es debido a la falta de métodos que son capaces de detectar los beneficios por emplear esos sistemas, y por otra parte porque el coste asociado no está identificado, detallado y cuantificado suficientemente. Los esquemas de coordinación basados únicamente en sistemas ERP son alternativas válidas en la práctica industrial siempre que la relación coste-beneficio esta favorable. Por lo tanto, la evaluación de formas organizativas teniendo en cuenta explícitamente el coste debido a procesos administrativos, en particular por ciclos iterativos, es de gran interés para la toma de decisiones en el ámbito de inversiones en TI. Con el fin de cerrar la brecha, el propósito de esta investigación es proporcionar métodos de evaluación que permitan la comparación de diferentes formas de organización y niveles de soporte por sistemas informáticos. La tesis proporciona una amplia introducción, analizando los retos a los que se enfrenta la industria. Concluye con las necesidades de la industria de SCM software: unas herramientas que facilitan la evaluación integral de diferentes propuestas de organización. A continuación, la terminología clave se detalla centrándose en la teoría de la organización, las peculiaridades de inversión en TI y la tipología de software de gestión de la cadena de suministro. La revisión de la literatura clasifica las contribuciones recientes sobre la gestión de la cadena de suministro, tratando ambos conceptos, el diseño de la organización y su soporte por las TI. La clasificación incluye criterios relacionados con la metodología de la investigación y su contenido. Los estudios empíricos en el ámbito de la administración de empresas se centran en tipologías de redes industriales. Nuevos algoritmos de planificación y esquemas de coordinación innovadoras se desarrollan principalmente en el campo de la investigación de operaciones con el fin de proponer nuevas funciones de software. Artículos procedentes del área de la gestión de la producción se centran en el análisis de coste y beneficio de las implantaciones de sistemas. La revisión de la literatura revela que el éxito de las TI para la coordinación de redes industriales depende en gran medida de características de tres dimensiones: la configuración de la red industrial, los esquemas de coordinación y las funcionalidades del software. La literatura disponible está enfocada sobre todo en los beneficios de las implantaciones de SCM software. Sin embargo, la coordinación de la cadena de suministro, basándose en el sistema ERP, sigue siendo la práctica industrial generalizada, pero el coste de coordinación asociado no ha sido abordado por los investigadores. Los fundamentos de diseño organizativo eficiente se explican en detalle en la medida necesaria para la comprensión de la síntesis de las diferentes formas de organización. Se han generado varios esquemas de coordinación variando los siguientes parámetros de diseño: la estructura organizativa, los mecanismos de coordinación y el soporte por TI. Las diferentes propuestas de organización desarrolladas son evaluadas por un método heurístico y otro basado en la simulación por eventos discretos. Para ambos métodos, se tienen en cuenta los principios de la teoría de la organización. La falta de rendimiento empresarial se debe a las dependencias entre actividades que no se gestionan adecuadamente. Dentro del método heurístico, se clasifican las dependencias y se mide su intensidad basándose en factores contextuales. A continuación, se valora la idoneidad de cada elemento de diseño organizativo para cada dependencia específica. Por último, cada forma de organización se evalúa basándose en la contribución de los elementos de diseño tanto al beneficio como al coste. El beneficio de coordinación se refiere a la mejora en el rendimiento logístico - este concepto es el objeto central en la mayoría de modelos de evaluación de la gestión de la cadena de suministro. Por el contrario, el coste de coordinación que se debe incurrir para lograr beneficios no se suele considerar en detalle. Procesos iterativos son costosos si se ejecutan manualmente. Este es el caso cuando SCM software no está implementada y el sistema ERP es el único instrumento de coordinación disponible. El modelo heurístico proporciona un procedimiento simplificado para la clasificación sistemática de las dependencias, la cuantificación de los factores de influencia y la identificación de configuraciones que indican el uso de formas organizativas y de soporte de TI más o menos complejas. La simulación de eventos discretos se aplica en el segundo modelo de evaluación utilizando el paquete de software ‘Plant Simulation’. Con respecto al rendimiento logístico, por un lado se mide el coste de fabricación, de inventario y de transporte y las penalizaciones por pérdida de ventas. Por otro lado, se cuantifica explícitamente el coste de la coordinación teniendo en cuenta los ciclos de coordinación iterativos. El método se aplica a una configuración de cadena de suministro ejemplar considerando diversos parámetros. Los resultados de la simulación confirman que, en la mayoría de los casos, el beneficio aumenta cuando se intensifica la coordinación. Sin embargo, en ciertas situaciones en las que se aplican ciclos de planificación manuales e iterativos el coste de coordinación adicional no siempre conduce a mejor rendimiento logístico. Estos resultados inesperados no se pueden atribuir a ningún parámetro particular. La investigación confirma la gran importancia de nuevas dimensiones hasta ahora ignoradas en la evaluación de propuestas organizativas y herramientas de TI. A través del método heurístico se puede comparar de forma rápida, pero sólo aproximada, la eficiencia de diferentes formas de organización. Por el contrario, el método de simulación es más complejo pero da resultados más detallados, teniendo en cuenta parámetros específicos del contexto del caso concreto y del diseño organizativo. ABSTRACT Transactional systems such as Enterprise Resource Planning (ERP) systems have been implemented widely while analytical software like Supply Chain Management (SCM) add-ons are adopted less by manufacturing companies. Although significant benefits are reported stemming from SCM software implementations, companies are reluctant to invest in such systems. On the one hand this is due to the lack of methods that are able to detect benefits from the use of SCM software and on the other hand associated costs are not identified, detailed and quantified sufficiently. Coordination schemes based only on ERP systems are valid alternatives in industrial practice because significant investment in IT can be avoided. Therefore, the evaluation of these coordination procedures, in particular the cost due to iterations, is of high managerial interest and corresponding methods are comprehensive tools for strategic IT decision making. The purpose of this research is to provide evaluation methods that allow the comparison of different organizational forms and software support levels. The research begins with a comprehensive introduction dealing with the business environment that industrial networks are facing and concludes highlighting the challenges for the supply chain software industry. Afterwards, the central terminology is addressed, focusing on organization theory, IT investment peculiarities and supply chain management software typology. The literature review classifies recent supply chain management research referring to organizational design and its software support. The classification encompasses criteria related to research methodology and content. Empirical studies from management science focus on network types and organizational fit. Novel planning algorithms and innovative coordination schemes are developed mostly in the field of operations research in order to propose new software features. Operations and production management researchers realize cost-benefit analysis of IT software implementations. The literature review reveals that the success of software solutions for network coordination depends strongly on the fit of three dimensions: network configuration, coordination scheme and software functionality. Reviewed literature is mostly centered on the benefits of SCM software implementations. However, ERP system based supply chain coordination is still widespread industrial practice but the associated coordination cost has not been addressed by researchers. Fundamentals of efficient organizational design are explained in detail as far as required for the understanding of the synthesis of different organizational forms. Several coordination schemes have been shaped through the variation of the following design parameters: organizational structuring, coordination mechanisms and software support. The different organizational proposals are evaluated using a heuristic approach and a simulation-based method. For both cases, the principles of organization theory are respected. A lack of performance is due to dependencies between activities which are not managed properly. Therefore, within the heuristic method, dependencies are classified and their intensity is measured based on contextual factors. Afterwards the suitability of each organizational design element for the management of a specific dependency is determined. Finally, each organizational form is evaluated based on the contribution of the sum of design elements to coordination benefit and to coordination cost. Coordination benefit refers to improvement in logistic performance – this is the core concept of most supply chain evaluation models. Unfortunately, coordination cost which must be incurred to achieve benefits is usually not considered in detail. Iterative processes are costly when manually executed. This is the case when SCM software is not implemented and the ERP system is the only available coordination instrument. The heuristic model provides a simplified procedure for the classification of dependencies, quantification of influence factors and systematic search for adequate organizational forms and IT support. Discrete event simulation is applied in the second evaluation model using the software package ‘Plant Simulation’. On the one hand logistic performance is measured by manufacturing, inventory and transportation cost and penalties for lost sales. On the other hand coordination cost is explicitly considered taking into account iterative coordination cycles. The method is applied to an exemplary supply chain configuration considering various parameter settings. The simulation results confirm that, in most cases, benefit increases when coordination is intensified. However, in some situations when manual, iterative planning cycles are applied, additional coordination cost does not always lead to improved logistic performance. These unexpected results cannot be attributed to any particular parameter. The research confirms the great importance of up to now disregarded dimensions when evaluating SCM concepts and IT tools. The heuristic method provides a quick, but only approximate comparison of coordination efficiency for different organizational forms. In contrast, the more complex simulation method delivers detailed results taking into consideration specific parameter settings of network context and organizational design.

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.

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.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Monitoring the condition of savanna riparian zones in Northern Australia using high spatial resolution imagery and a field based rapid assessment method

Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone condition. The objective of this work was to compare the Tropical Rapid Appraisal of Riparian Condition (TRARC) method to a satellite image based approach. TRARC was developed for rapid assessment of the environmental condition of savanna riparian zones. The comparison assessed mapping accuracy, representativeness of TRARC assessment, cost-effectiveness, and suitability for multi-temporal analysis. Two multi-spectral QuickBird images captured in 2004 and 2005 and coincident field data covering sections of the Daly River in the Northern Territory, Australia were used in this work. Both field and image data were processed to map riparian health indicators (RHIs) including percentage canopy cover, organic litter, canopy continuity, stream bank stability, and extent of tree clearing. Spectral vegetation indices, image segmentation and supervised classification were used to produce RHI maps. QuickBird image data were used to examine if the spatial distribution of TRARC transects provided a representative sample of ground based RHI measurements. Results showed that TRARC transects were required to cover at least 3% of the study area to obtain a representative sample. The mapping accuracy and costs of the image based approach were compared to those of the ground based TRARC approach. Results proved that TRARC was more cost-effective at smaller scales (1-100km), while image based assessment becomes more feasible at regional scales (100-1000km). Finally, the ability to use both the image and field based approaches for multi-temporal analysis of RHIs was assessed. Change detection analysis demonstrated that image data can provide detailed information on gradual change, while the TRARC method was only able to identify more gross scale changes. In conclusion, results from both methods were considered to complement each other if used at appropriate spatial scales.

A generative probabilistic oriented wavelet model for texture segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.

Priority evaluation in transportation policies and programmes

This work is concerned with the development of techniques for the evaluation of large-scale highway schemes with particular reference to the assessment of their costs and benefits in the context of the current transport planning (T.P.P.) process. It has been carried out in close cooperation with West Midlands County Council, although its application and results are applicable elsewhere. The background to highway evaluation and its development in recent years has been described and the emergence of a number of deficiencies in current planning practise noted. One deficiency in particular stood out, that stemming from inadequate methods of scheme generation and the research has concentrated upon improving this stage of appraisal, to ensure that subsequent stages of design, assessment and implementation are based upon a consistent and responsive foundation. Deficiencies of scheme evaluation were found to stem from inadequate development of appraisal methodologies suffering from difficulties of valuation, measurement and aggregation of the disparate variables that characterise highway evaluation. A failure to respond to local policy priorities was also noted. A 'problem' rather than 'goals' based approach to scheme generation was taken, as it represented the current and foreseeable resource allocation context more realistically. A review of techniques with potential for highway problem based scheme generation, which would work within a series of practical and theoretical constraints were assessed and that of multivariate analysis, and classical factor analysis in particular, was selected, because it offerred considerable application to the difficulties of valuation, measurement and aggregation that existed. Computer programs were written to adapt classical factor analysis to the requirements of T.P.P. highway evaluation, using it to derive a limited number of factors which described the extensive quantity of highway problem data. From this, a series of composite problem scores for 1979 were derived for a case study area of south Birmingham, based upon the factorial solutions, and used to assess highway sites in terms of local policy issues. The methodology was assessed in the light of its ability to describe highway problems in both aggregate and disaggregate terms, to guide scheme design, coordinate with current scheme evaluation methods, and in general to improve upon current appraisal. Analysis of the results was both in subjective, 'common-sense' terms and using statistical methods to assess the changes in problem definition, distribution and priorities that emerged. Overall, the technique was found to improve upon current scheme generation methods in all respects and in particular in overcoming the problems of valuation, measurement and aggregation without recourse to unsubstantiated and questionable assumptions. A number of deficiencies which remained have been outlined and a series of research priorities described which need to be reviewed in the light of current and future evaluation needs.

A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

Menu Analysis: Review and Evaluation

In the article - Menu Analysis: Review and Evaluation - by Lendal H. Kotschevar, Distinguished Professor School of Hospitality Management, Florida International University, Kotschevar’s initial statement reads: “Various methods are used to evaluate menus. Some have quite different approaches and give different information. Even those using quite similar methods vary in the information they give. The author attempts to describe the most frequently used methods and to indicate their value. A correlation calculation is made to see how well certain of these methods agree in the information they give.” There is more than one way to look at the word menu. The culinary selections decided upon by the head chef or owner of a restaurant, which ultimately define the type of restaurant is one way. The physical outline of the food, which a patron actually holds in his or her hand, is another. These descriptions are most common to the word, menu. The author primarily concentrates on the latter description, and uses the act of counting the number of items sold on a menu to measure the popularity of any particular item. This, along with a formula, allows Kotschevar to arrive at a specific value per item. Menu analysis would appear a difficult subject to broach. How does a person approach a menu analysis, how do you qualify and quantify a menu; it seems such a subjective exercise. The author offers methods and outlines on approaching menu analysis from empirical perspectives. “Menus are often examined visually through the evaluation of various factors. It is a subjective method but has the advantage of allowing scrutiny of a wide range of factors which other methods do not,” says Distinguished Professor, Kotschevar. “The method is also highly flexible. Factors can be given a score value and scores summed to give a total for a menu. This allows comparison between menus. If the one making the evaluations knows menu values, it is a good method of judgment,” he further offers. The author wants you to know that assigning values is fundamental to a pragmatic menu analysis; it is how the reviewer keeps score, so to speak. Value merit provides reliable criteria from which to gauge a particular menu item. In the final analysis, menu evaluation provides the mechanism for either keeping or rejecting selected items on a menu. Kotschevar provides at least three different matrix evaluation methods; they are defined as the Miller method, the Smith and Kasavana method, and the Pavesic method. He offers illustrated examples of each via a table format. These are helpful tools since trying to explain the theories behind the tables would be difficult at best. Kotschevar also references examples of analysis methods which aren’t matrix based. The Hayes and Huffman - Goal Value Analysis - is one such method. The author sees no one method better than another, and suggests that combining two or more of the methods to be a benefit.