995 resultados para Cohn, Moron
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H-n.
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Will Pleß
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Berta Lask
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von J. Guttmann
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Signatur des Originals: S 36/F03369
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von Bernhard Wachstein
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Mode of access: Internet.
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Includes indexes.
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Separate, from Programm-Elste städtische Realschule zu Berlin.
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O uso de probióticos na alimentação dos peixes pode estimular o sistema imunológico e agir na prevenção de enfermidades. O presente estudo avaliou a fisiologia de pirarucu Arapaima gigas após alimentação por 30 dias com dietas enriquecidas com probiótico Bacillus subtilis em 3 níveis de inclusão: 0 (Controle); 0,02 (BS0,02%) e 0,05% (BS0,05%) (triplicata), sendo realizada biometria e coleta de amostras de sangue. Os grupos foram comparados entre si (P<0,05). O peso, comprimento e o índice hepatossomático (IHS) dos pirarucus BS0,05% foram menores que os do Controle. As análises hematológicas e bioquímicas indicaram que BS0,02% promoveu aumento do hematócrito e da albumina, com relação aos demais grupos; e diminuição da concentração de hemoglobina corpuscular média (CHCM) e da glicose plasmática com relação ao Controle. A hemoglobina corpuscular média (HCM) e atividade respiratória dos leucócitos do BS0,05% diminuíram com relação ao Controle. BS0,02% apresentaram redução do número de leucócitos, com relação ao Controle, que refletiu na diminuição de monócitos, neutrófilos, LG-PAS e eosinófilos. Assim, os níveis de B. subtilis avaliados não promoveram crescimento ou alterações fisiológicas nos pirarucus, que indiquem melhoria do seu sistema imune.
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This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing ‘salient’ Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using ‘salient’ Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach.
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Gabor representations have been widely used in facial analysis (face recognition, face detection and facial expression detection) due to their biological relevance and computational properties. Two popular Gabor representations used in literature are: 1) Log-Gabor and 2) Gabor energy filters. Even though these representations are somewhat similar, they also have distinct differences as the Log-Gabor filters mimic the simple cells in the visual cortex while the Gabor energy filters emulate the complex cells, which causes subtle differences in the responses. In this paper, we analyze the difference between these two Gabor representations and quantify these differences on the task of facial action unit (AU) detection. In our experiments conducted on the Cohn-Kanade dataset, we report an average area underneath the ROC curve (A`) of 92.60% across 17 AUs for the Gabor energy filters, while the Log-Gabor representation achieved an average A` of 96.11%. This result suggests that small spatial differences that the Log-Gabor filters pick up on are more useful for AU detection than the differences in contours and edges that the Gabor energy filters extract.
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When classifying a signal, ideally we want our classifier to trigger a large response when it encounters a positive example and have little to no response for all other examples. Unfortunately in practice this does not occur with responses fluctuating, often causing false alarms. There exists a myriad of reasons why this is the case, most notably not incorporating the dynamics of the signal into the classification. In facial expression recognition, this has been highlighted as one major research question. In this paper we present a novel technique which incorporates the dynamics of the signal which can produce a strong response when the peak expression is found and essentially suppresses all other responses as much as possible. We conducted preliminary experiments on the extended Cohn-Kanade (CK+) database which shows its benefits. The ability to automatically and accurately recognize facial expressions of drivers is highly relevant to the automobile. For example, the early recognition of “surprise” could indicate that an accident is about to occur; and various safeguards could immediately be deployed to avoid or minimize injury and damage. In this paper, we conducted initial experiments on the extended Cohn-Kanade (CK+) database which shows its benefits.