Titlebook: Intensive Care Medicine; Annual Update 2008 Jean-Louis Vincent (Head) Conference proceedings 2008 Springer-Verlag New York 2008 Internal me

grandiose

deep-learning based algorithms are designed for single image rain steak removal, deraining performance is still limited due to the insufficient utilization of multi-scale features, which either fails to remove rain steaks completely or damages the original image content. In our paper, a novel derain

不断的变动

自作多情

Deduct

R. Cartin-Ceba,M. N. Gong,O. Gajicdevelop a new metric in this paper to automatically assess the quality of stereoscopic images with the guidance of reference images. Visual saliency (VS) has been largely explored by researchers in the past decade to find out which areas of an image attract most attention of the viewers. We use the

Tracheotomy

F. B. Mayr,S. Yende,D. C. Angusive tool for medical education. It will be very interesting if we use the eye tracking to predict where pathologists or doctors and persons with no or little experience look at the pathological microscopic image. In the current work, we first establish a pathological microscopic image database with

adjacent

羽饰

P. E. Oishi,J. -H. Hsu,J. R. Fineman watching the lecture videos. 3D Convolutional Networks (3D ConvNet) has been regarded as an efficient approach to learn spatio-temporal features in videos. However, 3D ConvNet gives the same weight to all features in the image, and can’t focus on key feature information. We solve this problem by us

Functional

I. Cinel,R. Nanda,R. P. Dellingerirectional distribution features to represent nodules in different risk stages effectively. First, the reference map is constructed using integral image, and then K-Means approach is performed to clustering the reference map and calculate its label map. The density distribution map of lung nodule im

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H. Schmidt,U. Müller-Werdan,K. Werdanirectional distribution features to represent nodules in different risk stages effectively. First, the reference map is constructed using integral image, and then K-Means approach is performed to clustering the reference map and calculate its label map. The density distribution map of lung nodule im

burnish

B. Lamia,M. R. Pinskyever, due to the limitation of capturing devices, depth images usually suffer from noises. How to remove noises containing in depth images become an important problem, which will benefit many practical applications. Depth image denoising is ill-posed, whose performance largely relies on the prior kn