Titlebook: Computer Vision –ACCV 2016; 13th Asian Conferenc Shang-Hong Lai,Vincent Lepetit,Yoichi Sato Conference proceedings 2017 Springer Internatio

Lacunar-Stroke

填料

Anticlimax

Anticipating Accidents in Dashcam Videosto (1) distribute soft-attention to candidate objects dynamically to gather subtle cues and (2) model the temporal dependencies of all cues to robustly anticipate an accident. Anticipating accidents is much less addressed than anticipating events such as changing a lane, making a turn, etc., since a

CHURL

Pano2Vid: Automatic Cinematography for Watching 360, Videosal-looking normal field-of-view (NFOV) video. By selecting “where to look” within the panorama at each time step, Pano2Vid aims to free both the videographer and the end viewer from the task of determining what to watch. Towards this goal, we first compile a dataset of 360. videos downloaded from th

Nutrient

PicMarker: Data-Driven Image Categorization Based on Iterative Clustering with personal preferences. As previous researches mainly focus on the accuracy of automatic classification within the pre-defined label space, they cannot be used directly for the personalized categorization. In this paper, we propose a data-driven classification method for personalized image class

杂役

Adaptive Direct RGB-D Registration and Mapping for Large Motionss a peculiar aspect which drastically limits the applicability of direct registration, namely the weakness of the convergence domain. First, we propose an activation function based on the conditioning of the RGB and ICP point-to-plane error terms. This function strengthens the geometric error influe

一个姐姐

cancellous-bone

Dense Motion Estimation for Smokehave difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation a

蒸发

赏钱

Combining Texture and Shape Cues for Object Recognition with Minimal Supervision information learned from freely available unlabeled web search results. The explosion of visual data on the web can potentially make visual examples of almost any object easily accessible via web search. Previous unsupervised methods have utilized either large scale sources of texture cues from the