This paper explores statistical pose estimation in SAR ATR using a recently proposed training method based on information theory. The theory of training with information theoretic learning is briefly summarized. Different pose estimator topologies and training criteria are employed. Experimental results in the MSTAR I and II show that our proposed method is capable of producing 1-DOF and 2-DOF ...

Figure 1: Illustration of a depth sweep regression forest for 3D pose estimation from a 2D image. Top left. Patches sampled from different depths project onto the image with different scale. Top right. The projected patches traverse the tree evaluating splitting functions in the intermediate nodes (black and red) until they reach a leaf node (blue). A leaf node contains 3D offsets that point to...

In the recent past, object recognition and localization based on correspondences of local point features in 2D views has become very popular in the robotics community. For grasping and manipulation with robotic systems, in addition accurate 6-DoF pose estimation of the object of interest is necessary. Now there are two substantially different approaches to computing a 6-DoF pose: monocular and ...

Correct registration of virtual objects into real scenes requires robust estimation of camera pose. Since most augmented reality applications also require real-time performance in potentially restricted environments with no a priori motion model, we seek pose estimation algorithms which are fast, perform well with few reference objects and require no initialization. In this paper, we present a ...

Benchmarking methods for 3d hand tracking is still an open problem due to the difficulty of acquiring ground truth data. We introduce a new dataset and benchmarking protocol that is insensitive to the accumulative error of other protocols. To this end, we create testing frame pairs of increasing difficulty and measure the pose estimation error separately for each of them. This approach gives ne...

Pose estimation in the context of human motion analysis is the process of approximating the body configuration in each frame of a motion sequence. We propose a novel pose estimation method based on fitting a skeletal model to tree structures built from skeletonised visual hulls reconstructed from multi-view video. The pose is estimated independently in each frame, hence the method can recover f...

Exemplar-based models have achieved great success on localizing the parts of semi-rigid objects. However, their efficacy on highly articulated objects such as humans is yet to be explored. Inspired by hierarchical object representation and recent application of Deep Convolutional Neural Networks (DCNNs) on human pose estimation, we propose a novel formulation that incorporates both hierarchical...

This paper tackles the problem of accurately matching a 3D deformable face model to images in challenging real-world scenarios with large amounts of head movement, occlusion, and difficult lighting conditions (Figure 2). A baseline system [1] based on the ASM [2] involves searching with a set of view-dependent local patches to locate image features and using their positions to update the face s...

This paper describes a system that uses a camera and a point light source to track a user's hand in three dimensions. Using depth cues obtained from projections of the hand and its shadow, the system computes the 3D position and orientation of two ngers (thumb and pointing nger). The system recognizes one dynamic and two static gestures. Recognition and pose estimation are user independent and ...

We address the problem of determining where a photo was taken by estimating a full 6-DOF-plus-intrincs camera pose with respect to a large geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose estimation. Our method scales to datasets with hundreds of thousands of images and tens of millions of 3D points through the use of two new tec...