Learning Human Pose Estimation Features with Convolutional Networks

This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level features and higher-level weak spatial models. Unconstrained human pose estimation is one of the hardest problems in computer vision, and our new architecture and learning schema shows significant improvement over the current state-of-the-art results. The main contribution of this paper is showing, for the first time, that a specific variation of deep learning is able to outperform all existing traditional architectures on this task. The paper also discusses several lessons learned while researching alternatives, most notably, that it is possible to learn strong low-level feature detectors on features that might even just cover a few pixels in the image. Higher-level spatial models improve somewhat the overall result, but to a much lesser extent then expected. Many researchers previously argued that the kinematic structure and top-down information is crucial for this domain, but with our purely bottom up, and weak spatial model, we could improve other more complicated architectures that currently produce the best results. This mirrors what many other researchers, like those in the speech recognition, object recognition, and other domains have experienced

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Monocular tracking of the human arm in 3D: real-time implementation and experiments

We have developed a system capable of tracking a human arm in 3D and in real time. The system is based on a previously developed algorithm for 3D tracking which requires only a monocular view and no special markers on the body. In this paper we describe our real-time system and the insights gained from real-time experimentation

A stabilized adaptive appearance changes model for 3D head tracking

A simple method is presented for 3D head pose estimation and tracking in monocular image sequences. A generic geometric model is used. The initialization consists of aligning the perspective projection of the geometric model with the subjects head in the initial image. After the initialization, the gray levels from the initial image are mapped onto the visible side of the head model to form a textured object. Only a limited number of points on the object is used allowing real-time performance even on low-end computers. The appearance changes caused by movement in the complex light conditions of a real scene present a big problem for fitting the textured model to the data from new images. Having in mind real human-computer interfaces we propose a simple adaptive appearance changes model that is updated by the measurements from the new images. To stabilize the model we constrain it to some neighborhood of the initial gray values. The neighborhood is defined using some simple heuristic