The Glimpse.3D is a body-worn camera that captures, processes, stores, and transmits 3D visual information of a real-world environment using a low cost camera-based sensor system that is constrained by its limited processing capability, storage, and battery life. The 3D content is viewed on a mobile device such as a smartphone or a virtual reality headset. This system can be used in applications such as capturing and sharing 3D content in the social media, training people in different professions, and post-facto analysis of an event. Glimpse.3D uses off-the-shelf hardware and standard computer vision algorithms. Its novelty lies in the ability to optimally control camera data acquisition and processing stages to guarantee the desired quality of captured information and battery life. The design of the controller is based on extensive measurements and modeling of the relationships between the linear and angular motion of a body-worn camera and the quality of generated 3D point clouds as well as the battery life of the system. To achieve this, we 1) devise a new metric to quantify the quality of generated 3D point clouds, 2) formulate an optimization problem to find an optimal trigger point for the camera system that prolongs its battery life while maximizing the quality of captured 3D environment, and 3) make the model adaptive so that the system evolves and its performance improves over time.
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A Framework for Analyzing the Whole Body Surface Area from a Single View
We present a virtual reality (VR) framework for the analysis of whole human body surface area. Usual methods for determining the whole body surface area (WBSA) are based on well known formulae, characterized by large errors when the subject is obese, or belongs to certain subgroups. For these situations, we believe that a computer vision approach can overcome these problems and provide a better estimate of this important body indicator. Unfortunately, using machine learning techniques to design a computer vision system able to provide a new body indicator that goes beyond the use of only body weight and height, entails a long and expensive data acquisition process. A more viable solution is to use a dataset composed of virtual subjects. Generating a virtual dataset allowed us to build a pop- ulation with different characteristics (obese, underweight, age, gender). However, synthetic data might differ from a real scenario, typical of the physician’s clinic. For this reason we develop a new virtual environment to facilitate the analysis of human subjects in 3D. This framework can simulate the acquisition process of a real camera, making it easy to analyze and to create training data for machine learning algorithms. With this virtual environment, we can easily simulate the real setup of a clinic, where a subject is standing in front of a cam- era, or may assume a different pose with respect to the camera. We use this newly desig- nated environment to analyze the whole body surface area (WBSA). In particular, we show that we can obtain accurate WBSA estimations with just one view, virtually enabling the pos- sibility to use inexpensive depth sensors (e.g., the Kinect) for large scale quantification of the WBSA from a single view 3D map.
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- NSF-PAR ID:
- 10053838
- Date Published:
- Journal Name:
- PloS one
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0166749
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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