引起 发表于 2025-3-23 13:34:21
Classemes: A Compact Image Descriptor for Efficient Novel-Class Recognition and Search,e recognized are not known in advance. The motivating application is “object-class search by example” where a user provides at query time a small set of training images defining an arbitrary novel category and the system must retrieve images belonging to this class from a large database. This settinBother 发表于 2025-3-23 14:29:49
http://reply.papertrans.cn/83/8255/825494/825494_12.png轻触 发表于 2025-3-23 20:01:58
Registration and Segmentation in Medical Imaging,ntary anatomical information about the underlying tissues such as the X-ray attenuation coefficients from X-ray computed tomography (CT), and proton density or proton relaxation times from magnetic resonance (MR) imaging. The images allow clinicians to gather information about the size, shape and spCampaign 发表于 2025-3-24 00:03:07
http://reply.papertrans.cn/83/8255/825494/825494_14.pngdebase 发表于 2025-3-24 06:17:38
http://reply.papertrans.cn/83/8255/825494/825494_15.pngAsparagus 发表于 2025-3-24 07:57:32
http://reply.papertrans.cn/83/8255/825494/825494_16.pngRedundant 发表于 2025-3-24 13:24:09
Mobile Computational Photography with FCam,ible programming of cameras, especially of camera phones and tablets. We cover the API and several example programs that run on the NVIDIA Tegra 3 prototype tablet and the Nokia N900 and N9 Linux-based phones. We discuss the implementation and porting of FCam to different platforms. We also describeGoblet-Cells 发表于 2025-3-24 16:31:12
http://reply.papertrans.cn/83/8255/825494/825494_18.pngorganism 发表于 2025-3-24 20:33:53
Visual Correspondence, the Lambert-Ambient Shape Space and the Systematic Design of Feature Descrip modeled, insensitive to other nuisances that are not explicitly modeled, and maximally discriminative, relative to the chosen family of classifiers. Existing descriptors are interpreted in this framework, where their limitations are illustrated, together with pointers on how to improve them.碌碌之人 发表于 2025-3-25 00:35:47
Socially-Driven Computer Vision for Group Behavior Analysis,stances in the space and the like. This paper will discuss recent advancements in video analytics, most of them related to the modelling of group activities. By adopting SSP principles, an age-old problem -what is a group of people?- is effectively faced, and the characterization of human activities in different respects is improved.