Titlebook: Computer Vision - ACCV 2012 Workshops; ACCV 2012 Internatio Jong-Il Park,Junmo Kim Conference proceedings 2013 Springer-Verlag Berlin Heide

CREST

Delude

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创新

KinectAvatar: Fully Automatic Body Capture Using a Single Kinect data captured from a single Kinect is sufficient to produce a good quality full 3D human model. In this setting, the challenges we face are the sensor’s low resolution with random noise and the subject’s non-rigid movement when capturing the data. To overcome these challenges, we develop an improve

极小

Essential Body-Joint and Atomic Action Detection for Human Activity Recognition Using Longest Commoncient human activity recognition/classification. Our human activity data is captured by a RGB-D camera, i.e. Kinect, where human skeletons are detected and provided by the Kinect SDK. Unique in our approach is the novel encoding that can effectively convert skeleton data into a symbolic sequence rep

Dysplasia

Exploiting Depth and Intensity Information for Head Pose Estimation with Random Forests and Tensor Mse measurements due to large head pose variations and illumination changes. Robust and accurate head pose estimation can be achieved by integrating intensity and depth information. In this paper we introduce a head pose estimation system that employs random forests and tensor regression algorithms.

可用

Dynamic Hand Shape Manifold Embedding and Tracking from Depth Mapsd shape variations. This paper presents a new manifold embedding method that models hand shape variations in different hand configurations and in different views due to hand rotation. Instead of traditional silhouette images, the hand shapes are modeled using depth map images, which provides rich sh

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摘要