Ilustraciones de telar vertical y su uso entre los indígenas Guambianos y Paeces Cauca.

13 hojas : ilustraciones, fotografías a color.

Color Region Grouping and Shape Recognition with Deformable Models

A new deformable shape-based method for color region segmentation is described. The method includes two stages: over-segmentation using a traditional color region segmentation algorithm, followed by deformable model-based region merging via grouping and hypothesis selection. During the second stage, region merging and object identification are executed simultaneously. A statistical shape model is used to estimate the likelihood of region groupings and model hypotheses. The prior distribution on deformation parameters is precomputed using principal component analysis over a training set of region groupings. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with similarly colored adjacent objects. Furthermore, the recovered parametric shape model can be used directly in object recognition and comparison. Experiments in segmentation and image retrieval are reported.

Deformable Shape Detection and Description via Model-Based Region Grouping

A method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color, or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported.

MosaicShape: Stochastic Region Grouping with Shape Prior

A novel method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multi-scale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework and solved by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. By this means, object segmentation and recognition are accomplished simultaneously. Within each sampling move during the simulation process,probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized parallel algorithm by Barbu and Zhu,combined with a large sampling jump and other implementation improvements, greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images.

Fast Synchronization of Perpetual Grouping in Laminar Visual Cortical Circuits

Perceptual grouping is well-known to be a fundamental process during visual perception, notably grouping across scenic regions that do not receive contrastive visual inputs. Illusory contours are a classical example of such groupings. Recent psychophysical and neurophysiological evidence have shown that the grouping process can facilitate rapid synchronization of the cells that are bound together by a grouping, even when the grouping must be completed across regions that receive no contrastive inputs. Synchronous grouping can hereby bind together different object parts that may have become desynchronized due to a variety of factors, and can enhance the efficiency of cortical transmission. Neural models of perceptual grouping have clarified how such fast synchronization may occur by using bipole grouping cells, whose predicted properties have been supported by psychophysical, anatomical, and neurophysiological experiments. These models have not, however, incorporated some of the realistic constraints on which groupings in the brain are conditioned, notably the measured spatial extent of long-range interactions in layer 2/3 of a grouping network, and realistic synaptic and axonal signaling delays within and across cells in different cortical layers. This work addresses the question: Can long-range interactions that obey the bipole constraint achieve fast synchronization under realistic anatomical and neurophysiological constraints that initially desynchronize grouping signals? Can the cells that synchronize retain their analog sensitivity to changing input amplitudes? Can the grouping process complete and synchronize illusory contours across gaps in bottom-up inputs? Our simulations show that the answer to these questions is Yes.

Spiking Dynamics during Perceptual Grouping in the Laminar Circuits of Visual Cortex

Grouping of collinear boundary contours is a fundamental process during visual perception. Illusory contour completion vividly illustrates how stable perceptual boundaries interpolate between pairs of contour inducers, but do not extrapolate from a single inducer. Neural models have simulated how perceptual grouping occurs in laminar visual cortical circuits. These models predicted the existence of grouping cells that obey a bipole property whereby grouping can occur inwardly between pairs or greater numbers of similarly oriented and co-axial inducers, but not outwardly from individual inducers. These models have not, however, incorporated spiking dynamics. Perceptual grouping is a challenge for spiking cells because its properties of collinear facilitation and analog sensitivity to inducer configurations occur despite irregularities in spike timing across all the interacting cells. Other models have demonstrated spiking dynamics in laminar neocortical circuits, but not how perceptual grouping occurs. The current model begins to unify these two modeling streams by implementing a laminar cortical network of spiking cells whose intracellular temporal dynamics interact with recurrent intercellular spiking interactions to quantitatively simulate data from neurophysiological experiments about perceptual grouping, the structure of non-classical visual receptive fields, and gamma oscillations.

A Neural Model of 3D Shape-From-Texture: Multiple-Scale Filtering, Boundary Grouping, and Surface Filling-In

A neural model is presented of how cortical areas V1, V2, and V4 interact to convert a textured 2D image into a representation of curved 3D shape. Two basic problems are solved to achieve this: (1) Patterns of spatially discrete 2D texture elements are transformed into a spatially smooth surface representation of 3D shape. (2) Changes in the statistical properties of texture elements across space induce the perceived 3D shape of this surface representation. This is achieved in the model through multiple-scale filtering of a 2D image, followed by a cooperative-competitive grouping network that coherently binds texture elements into boundary webs at the appropriate depths using a scale-to-depth map and a subsequent depth competition stage. These boundary webs then gate filling-in of surface lightness signals in order to form a smooth 3D surface percept. The model quantitatively simulates challenging psychophysical data about perception of prolate ellipsoids (Todd and Akerstrom, 1987, J. Exp. Psych., 13, 242). In particular, the model represents a high degree of 3D curvature for a certain class of images, all of whose texture elements have the same degree of optical compression, in accordance with percepts of human observers. Simulations of 3D percepts of an elliptical cylinder, a slanted plane, and a photo of a golf ball are also presented.

Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning

A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.

Preattentive Texture Segmentation and Grouping by the Boundary Contour System

An improved Boundary Contour System (BCS) neural network model of preattentive vision is applied to two images that produce strong "pop-out" of emergent groupings in humans. In humans these images generate groupings collinear with or perpendicular to image contrasts. Analogous groupings occur in computer simulations of the model. Long-range cooperative and short-range competitive processes of the BCS dynamically form the stable groupings of texture regions in response to the images.

Networks versus vertical integration

We construct a theory to compare vertically integrated firms to networks of manufacturers and suppliers. Vertically integrated firms make their own specialized inputs. In networks, manufacturers procure specialized inputs from suppliers that, in turn, sell to several manufacturers. The analysis shows that networks can yield greater social welfare when manufacturers experience large idiosyncratic demand shocks. Individual firms may also have the incentive to form networks, despite the lack of long-term contracts. The analysis is supported by existing evidence and provides predictions as to the shape of different industries.

Placement of Small Vertical Axis Wind Turbine to Maximize Power Generation Influenced by Architectural and Geographic Interfaces in Urban Areas

Current methods for large-scale wind collection are unviable in urban areas. In order to investigate the feasibility of generating power from winds in these environments, we sought to optimize placements of small vertical-axis wind turbines in areas of artificially-generated winds. We explored both vehicular transportation and architecture as sources of artificial wind, using a combination of anemometer arrays, global positioning system (GPS), and weather report data. We determined that transportation-generated winds were not significant enough for turbine implementation. In addition, safety and administrative concerns restricted the implementation of said wind turbines along roadways for transportation-generated wind collection. Wind measurements from our architecture collection were applied in models that can help predict other similar areas with artificial wind, as well as the optimal placement of a wind turbine in those areas.

Integración vertical en las empresas yerbateras y tealeras argentinas : variables que afectan el comportamiento de las firmas en sus decisiones de integración vertical

El sector de la yerba mate y el té en la Argentina tiene una larga historia en las provincias de Misiones y Corrientes. Esta historia siempre tiene como protagonista a la gran cantidad de productores, la industria y las autoridades políticas. Hace años entre estos actores se observa un fenómeno que es la integración vertical hacia la producción primaria por parte de las industrias. Dicho fenómeno genera preocupación en los eslabones inferiores y a la vez genera una interesante área para estudiar el comportamiento de la organización vertical de las empresas. El objetivo del presente trabajo es determinar las variables que explican la integración vertical de las empresas. Para esto se realizó un análisis de los aportes teóricos de los costos de transacción, costos de agencia e incertidumbre como principales fuentes que explicarían este fenómeno. En base a datos de 82 encuestas realizadas a empresas se estimaron modelos econométricos mediante el modelo Tobit, con el fin de cuantificar los efectos de las variables. Los resultados comprobaron que las empresas realizan una comparación entre costos de agencia vs. costos de transacción; costos de producción interna vs. precio pagado por comprar; incertidumbre de producción interna vs. la externa. Además, los factores de incertidumbre en las ventas y abastecimiento juegan otro rol importante en el nivel de integración. Se esperaba que la incertidumbre en las ventas disminuyera la integración de las empresas y generara una integración parcial, lográndose resultados opuestos. Esto último deja una interesante área de investigación posterior: el efecto del riesgo en la integración parcial de las empresas. Finalmente la integración actual de las empresas y la tendencia a aumentarla, deja en claro las diferencias en la eficiencia entre el sector primario y la industria en el desarrollo de la actividad agrícola en común

Circulación vertical del agua y su relación con la vegetación en zonas áridas y semiáridas

Los cambios de vegetación, especialmente aquellos que involucran transiciones de vegetación leñosa a herbácea afectan el ciclo hidrológico, modificando los flujos de agua y sales. En esta tesis se explora cómo la conversión de bosques semiáridos a tierras agrícolas afecta la dinámica de agua y sales en las planicies semiáridas intensamente deforestadas del centro-oeste de la región del Espinal (San Luis, Argentina). El estudio abarcó diferentes escalas espaciales (stand, paisaje y cuenca) y diferentes aproximaciones metodológicas: muestreos de la zona no saturada/saturada, modelización hidrológica, obtención de perfiles geoeléctricos, medición de caudales y análisis de imágenes e información histórica. A escala de stand, los análisis de suelo no saturado y los resultados obtenidos de los modelos hidrológicos confirmaron que los bosques semiáridos evapotranspiran prácticamente la totalidad de la precipitación anual, generando recarga nula y una elevada acumulación de sales en sus perfiles (0.15 a 9 kg/m2 hasta 6 m de profundidad), a pesar del aumento regional de las precipitaciones registrado en los últimos 50 años. En parcelas agrícolas, más de un 4 por ciento de la precipitación anual escapa del alcance y absorción de las raíces, generando recarga y lixiviación de más del 75 por ciento del stock de cloruro existente originalmente bajo vegetación natural. Estas diferencias en las tasas de recarga y acumulación de sales entre ambos tipos de vegetación se incrementaron en suelos con mayor contenido de arena (a recarga es hasta dos órdenes de magnitud superior bajo agricultura y llega hasta un 99 por ciento de lixiviación de sales). Además, la modelización hidrológica sugiere que la generación de recarga bajo agricultura durante el periodo de estudio se asoció a eventos muy intensos o años especialmente húmedos. La caracterización de la resistividad en suelos, mediante técnicas geoeléctricas a escala de paisaje, confirmaron los patrones de acumulación de agua y sales descriptos a nivel de stand para bosques y agricultura, demostrándose la utilidad de estas técnicas para el estudio de la dinámica espacial del agua y las sales, con continuidad horizontal. Finalmente, a escala de cuenca, se han registrado grandes transformaciones hidrogeomorfológicas, con fuertes disecciones en el paisaje y aparición repentina de cursos de agua, como resultado de la modificación de la condición de recarga nula y el ascenso continuado de los niveles freáticos. Estos cambios se asocian principalmente al reemplazo de los bosques nativos por cultivos anuales, acompañado por el aumento regional de las precipitaciones; mientras que la actividad sísmica se ha demostrado despreciable como agente causal de los excesos hídricos en la cuenca en estudio. Paralelamente, se ha descrito el inicio de un proceso de salinización secundaria, similar al referido como dryland salinity en paisajes agrícolas del suroeste y sur de Australia. Las estrategias de uso y/o recuperación de estas tierras incluirían la aplicación de sistemas mixtos que conserven parches de bosque natural, pasturas perennes y cultivos anuales, así como la optimización de estrategias de producción agrícola en concordancia con las condiciones climáticas imperantes en el corto plazo.

Impact of the vertical potshell deformation on the MHD cell stability behavior of a 500 kA aluminium electrolysis cell

In previous publications [1,2], it was rationalized that a large vertical potshell deformation may have a negative impact on the operations of very high amperage cells. The MHD-Valdis non-linear Magneto-Hydro-Dynamic model was therefore extended to take into account the displacement of the potshell. The MHD cell stability behavior of a 500 kA cell with a 17.3 meters long potshell was then studied.