Preliminary evaluation of infrared and radar imagery, Washington and Oregon coasts

Airborne infrared and radar photography of Oregon and Washington coastal region

Des normes pour les compétences dans l\u27usage de l\u27information dans l\u27enseignement supérieur : une perspective internationale

Communication faite lors du 67e congrès annuel de l\u27IFLA, 16 au 25 août 2001, Boston, Etats-Uni

Shading Annotations in the Wild

Understanding shading effects in images is critical for a variety of vision and graphics problems, including intrinsic image decomposition, shadow removal, image relighting, and inverse rendering. As is the case with other vision tasks, machine learning is a promising approach to understanding shading - but there is little ground truth shading data available for real-world images. We introduce Shading Annotations in the Wild (SAW), a new large-scale, public dataset of shading annotations in indoor scenes, comprised of multiple forms of shading judgments obtained via crowdsourcing, along with shading annotations automatically generated from RGB-D imagery. We use this data to train a convolutional neural network to predict per-pixel shading information in an image. We demonstrate the value of our data and network in an application to intrinsic images, where we can reduce decomposition artifacts produced by existing algorithms. Our database is available at http://opensurfaces.cs.cornell.edu/saw/.Comment: CVPR 201

Unsupervised Learning of Depth and Ego-Motion from Video

We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. We achieve this by simultaneously training depth and camera pose estimation networks using the task of view synthesis as the supervisory signal. The networks are thus coupled via the view synthesis objective during training, but can be applied independently at test time. Empirical evaluation on the KITTI dataset demonstrates the effectiveness of our approach: 1) monocular depth performing comparably with supervised methods that use either ground-truth pose or depth for training, and 2) pose estimation performing favorably with established SLAM systems under comparable input settings.Comment: Accepted to CVPR 2017. Project webpage: https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner

What Does It Take for a Family to Afford to Pay for Health Care?

Addresses how much Californians can afford to pay for health care based on their current insurance premiums and out-of-pocket expenses as well as other basic necessities, such as housing, child care, transportation, food, and taxes