Titlebook: Industrial Crops; Breeding for BioEner Von Mark V. Cruz,David A. Dierig Book 2015 Springer Science+Business Media New York 2015 BioEnergy.I

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Lizz Kezzy de Morais,Marcelo Sfeir de Aguiar,Paulo de Albuquerque e Silva,Tassiano Maxuell Marinho Cunderstandings. Data-driven approaches, such as deep neural networks, can deal with the ambiguity inherent in this task to some extent, but it is extremely expensive to acquire the temporal annotations of a large-scale video dataset. To leverage the plentiful web-crawled videos to improve the perfor

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Toshihiko Yamadaental problems of computer vision. Previous intrinsic decomposition approaches either address the problem in a fully supervised manner, or require multiple images of the same scene as input. These approaches are less desirable in practice, as ground truth intrinsic images are extremely difficult to

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Ian Shield,William Macalpine,Steve Hanley,Angela Karpral intervals in videos that are likely to contain an action. Previous methods can be divided to two groups: sliding window ranking and actionness score grouping. Sliding windows uniformly cover all segments in videos, but the temporal boundaries are imprecise; grouping based method may have more pr

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Freeborn G. Jewett, which specify classes that observations do . belong to. Let . and . be the true and complementary labels, respectively. We first model the annotation of complementary labels via transition probabilities ., where . is the number of classes. Previous methods implicitly assume that ., are identical,

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Brad M. Ostranderdestrian detection and occlusion estimation by regressing two bounding boxes to localize the full body as well as the visible part of a pedestrian respectively. For this purpose, we learn a deep convolutional neural network (CNN) consisting of two branches, one for full body estimation and the other