2 resultados para Convex Operator
em Dalarna University College Electronic Archive
Resumo:
BACKGROUND AND OBJECTIVE: To a large extent, people who have suffered a stroke report unmet needs for rehabilitation. The purpose of this study was to explore aspects of rehabilitation provision that potentially contribute to self-reported met needs for rehabilitation 12 months after stroke with consideration also to severity of stroke. METHODS: The participants (n = 173) received care at the stroke units at the Karolinska University Hospital, Sweden. Using a questionnaire, the dependent variable, self-reported met needs for rehabilitation, was collected at 12 months after stroke. The independent variables were four aspects of rehabilitation provision based on data retrieved from registers and structured according to four aspects: amount of rehabilitation, service level (day care rehabilitation, primary care rehabilitation and home-based rehabilitation), operator level (physiotherapist, occupational therapist, speech therapist) and time after stroke onset. Multivariate logistic regression analyses regarding the aspects of rehabilitation were performed for the participants who were divided into three groups based on stroke severity at onset. RESULTS: Participants with moderate/severe stroke who had seen a physiotherapist at least once during each of the 1st, 2nd and 3rd-4th quarters of the first year (OR 8.36, CI 1.40-49.88 P = 0.020) were more likely to report met rehabilitation needs. CONCLUSION: For people with moderate/severe stroke, continuity in rehabilitation (preferably physiotherapy) during the first year after stroke seems to be associated with self-reported met needs for rehabilitation.
Resumo:
The demands of image processing related systems are robustness, high recognition rates, capability to handle incomplete digital information, and magnanimous flexibility in capturing shape of an object in an image. It is exactly here that, the role of convex hulls comes to play. The objective of this paper is twofold. First, we summarize the state of the art in computational convex hull development for researchers interested in using convex hull image processing to build their intuition, or generate nontrivial models. Secondly, we present several applications involving convex hulls in image processing related tasks. By this, we have striven to show researchers the rich and varied set of applications they can contribute to. This paper also makes a humble effort to enthuse prospective researchers in this area. We hope that the resulting awareness will result in new advances for specific image recognition applications.