Estimating 3D Hand Pose from a Cluttered Image
نویسندگان
چکیده
A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclidean space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this cluttertolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.
منابع مشابه
Hand Shape and 3D Pose Estimation Using Depth Data from a Single Cluttered Frame
This paper describes a method that, given an input image of a person signing a gesture in a cluttered scene, locates the gesturing arm, automatically detects and segments the hand and finally creates a ranked list of possible shape class, 3D pose orientation and full hand configuration parameters. The clutter-tolerant hand segmentation algorithm is based on depth data from a single image captur...
متن کاملRelevant Feature Selection for Human Pose Estimation and Localization in Cluttered Images
We address the problem of estimating human body pose from a single image with cluttered background. We train multiple local linear regressors for estimating the 3D pose from a feature vector of gradient orientation histograms. Each linear regressor is capable of selecting relevant components of the feature vector depending on pose by training it on a pose cluster which is a subset of the traini...
متن کامل3D Human Pose Estimation from Monocular Image Sequences
Automatic 3D reconstruction of human poses from monocular images is a challenging and popular topic in the computer vision community, which provides a wide range of applications in multiple areas. Solutions for 3D pose estimation involve various learning approaches, such as Support Vector Machines and Gaussian processes, but many encounter difficulties in cluttered scenarios and require additio...
متن کاملSpatio-temporal 3D Pose Estimation of Objects in Stereo Images
In this contribution we describe a vision system for modelbased 3D detection and spatio-temporal pose estimation of objects in cluttered scenes. As low-level features, our approach requires 3D depth points along with information about their motion and the direction of the local intensity gradient. We extract these features by spacetime stereo based on local image intensity modelling. After appl...
متن کاملObject Recognition and Full Pose Registration in Cluttered Environments
Robust perception is a vital capability for robotic manipulation in unstructured scenes. In this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. In this paper, we present an approach for building metric 3D models of objects using local descriptors from several images. Each model is optimized to fit a...
متن کامل