Titlebook: Computer Vision – ECCV 2016 Workshops; Amsterdam, The Nethe Gang Hua,Hervé Jégou Conference proceedings 2016 Springer International Publish

膽小懦夫

Road Segmentation for Classification of Road Weather Conditionsy. This is a challenging problem for uncalibrated cameras such as removable dash cams or cell phone cameras, where the location of the road in the image may vary considerably from image to image. Here we show that combining a spatial prior with vanishing point and horizon estimators can generate imp

Melodrama

Recognizing Text-Based Traffic Guide Panels with Cascaded Localization Network-mounted cameras from natural scene images. The proposed framework involves two contributions. First, a novel Cascaded Localization Network (CLN) joining two customized convolutional nets is proposed to detect the guide panels and the scene text on them in a coarse-to-fine manner. In this network, t

揭穿真相

CRP743

Extracting Driving Behavior: Global Metric Localization from Dashcam Videos in the Wildze these dashcam videos harvested in the wild to extract the driving behavior—global metric localization of 3D vehicle trajectories (Fig.?.). We propose a robust approach to (1) extract a relative vehicle 3D trajectory from a dashcam video, (2) create a global metric 3D map using geo-localized Googl

ANT

From On-Road to Off: Transfer Learning Within a Deep Convolutional Neural Network for Segmentation a that notably surpass prior traditional feature driven approaches. Here, we take an existing CNN architecture, pre-trained for urban road-scene understanding, and retrain it towards the task of classifying off-road scenes, assessing the network performance within the training cycle. Within the parad

美麗的寫

hallow

Fusing Convolutional Neural Networks with a Restoration Network for Increasing Accuracy and Stabilit and instead of restoring the image directly, it generates a pattern which is added with the noisy image for restoring the clean image. Our experiments shows that the Lipschitz constant of the proposed network is less than 1 and it is able to remove very strong as well as very slight noises. This ab

毀壞

Global Scale Integral Volumeslarge scale 3D datasets is challenging due to high memory requirements. The difficulties lie in efficiently computing, storing, querying and updating the integral volume values. In this work, we address the above problems and present a novel solution for processing integral volumes for large scale 3

Encoding

上釉彩