5 resultados para Nanling region

em Boston University Digital Common


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Background Chronic illness and premature mortality from malaria, water-borne diseases, and respiratory illnesses have long been known to diminish the welfare of individuals and households in developing countries. Previous research has also shown that chronic diseases among farming populations suppress labor productivity and agricultural output. As the illness and death toll from HIV/AIDS continues to climb in most of sub-Saharan Africa, concern has arisen that the loss of household labor it causes will reduce crop yields, impoverish farming households, intensify malnutrition, and suppress growth in the agricultural sector. If chronic morbidity and premature mortality among individuals in farming households have substantial impacts on household production, and if a large number of households are affected, it is possible that an increase in morbidity and mortality from HIV/AIDS or other diseases could affect national aggregate output and exports. If, on the other hand, the impact at the household farm level is modest, or if relatively few households are affected, there is likely to be little effect on aggregate production across an entire country. Which of these outcomes is more likely in West Africa is unknown. Little rigorous, quantitative research has been published on the impacts of AIDS on smallholder farm production, particularly in West Africa. The handful of studies that have been conducted have looked mainly at small populations in areas of very high HIV prevalence in southern and eastern Africa. Conclusions about how HIV/AIDS, and other causes of chronic morbidity and mortality, are affecting agriculture across the continent cannot be drawn from these studies. In view of the importance of agriculture, and particularly smallholder agriculture, in the economies of most African countries and the scarcity of resources for health interventions, it is valuable to identify, describe, and quantify the impact of chronic morbidity and mortality on smallholder production of important crops in West Africa. One such crop is cocoa. In Ghana, cocoa is a crop of national importance that is produced almost exclusively by smallholder households. In 2003, Ghana was the world’s second-largest producer of cocoa. Cocoa accounted for a quarter of Ghana’s export revenues that year and generated 15 percent of employment. The success and growth of the cocoa industry is thus vital to the country’s overall social and economic development. Study Objectives and Methods In February and March 2005, the Center for International Health and Development of Boston University (CIHD) and the Department of Agricultural Economics and Agribusiness (DAEA) of the University of Ghana, with financial support from the Africa Bureau of the U.S. Agency for International Development and from Mars, Inc., which is a major purchaser of West African cocoa, conducted a survey of a random sample of cocoa farming households in the Western Region of Ghana. The survey documented the extent of chronic morbidity and mortality in cocoa growing households in the Western Region of Ghana, the country’s largest cocoa growing region, and analyzed the impact of morbidity and mortality on cocoa production. It aimed to answer three specific research questions. (1) What is the baseline status of the study population in terms of household size and composition, acute and chronic morbidity, recent mortality, and cocoa production? (2) What is the relationship between household size and cocoa production, and how can this relationship be used to understand the impact of adult mortality and chronic morbidity on the production of cocoa at the household level? The study population was the approximately 42,000 cocoa farming households in the southern part of Ghana’s Western Region. A random sample of households was selected from a roster of eligible households developed from existing administrative information. Under the supervision of the University of Ghana field team, enumerators were graduate students of the Department of Agricultural Economics and Agribusiness or employees of the Cocoa Services Division. A total of 632 eligible farmers participated in the survey. Of these, 610 provided complete responses to all questions needed to complete the multivariate statistical analysis reported here.

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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.

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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.

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An improved method for deformable shape-based image segmentation is described. Image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. The quality of a candidate region merging is evaluated by a cost measure that includes: homogeneity of image properties within the combined region, degree of overlap with a deformed shape model, and a deformation likelihood term. Perceptually-motivated criteria are used to determine where/how to split regions, based on the local shape properties of the region group's bounding contour. A globally consistent interpretation is determined in part by the minimum description length principle. Experiments show that the model-based splitting strategy yields a significant improvement in segmention over a method that uses merging alone.

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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.