Gustavo Carneiro

TL;DR: A framework for QoS support in NGNs, where multi-interface terminals are given end-to-end QoS guarantees regardless of their point of attachment, and integrates layer two and layer three handovers by exploiting minimal additions to existing IETF and IEEE standards.

TL;DR: In this article , a preclinical evaluation of a deep learning model intended to detect proximal femoral fractures in frontal x-ray films in emergency department patients, trained on films from the Royal Adelaide Hospital (Adelaide, SA, Australia).

TL;DR: A novel approach to improve the distinctiveness of local image features without significantly affecting their robustness with respect to image deformations is proposed by combining typical local features with shape context, and it is shown that in wide baseline stereo matching, and non-rigid motion applications, it produces a higher inlier/outlier ratio than the standard Hough clustering of the global spatial transform of parameters.

TL;DR: In this article, two pretrained encoder-decoder networks are sequentially stacked: the second network takes as input the concatenation of the original frame and the initial prediction generated by the first network, which acts as an attention mechanism enabling the second networks to focus on interesting areas within the image, thereby improving the quality of its predictions.

TL;DR: The experiments show that the use of sparse manifolds and deep belief networks for the rigid detection stage leads to segmentation results that are as accurate as the current state of the art, but with lower search complexity and training processes that require a small amount of annotated training data.