Standard medical chest and abdominal computed tomography (CT) scans of 46 subjects were analyzed to characterize aspects of human ribcage geometry and bone density. A semi-automatic algorithm was developed to define framework curves for individual ribs. Measurements of this framework were taken to record anthropometric properties of the ribcage such as overall ribcage dimensions and individual rib lengths and angles. Furthermore, the ribcage framework was used to explore the voxel space of the CT images, recording local rib bone cross-sectional density properties. Proposals are made for the use of these measurement techniques to inform and improve human finite element (FE) chest models in terms of global geometry, material properties, and individuality.
The deployment of computer vision algorithms in mobile applications is growing at a rapid pace. A primary component of the computer vision software pipeline is feature extraction, which identifies and encodes relevant image features. We present an embedded heterogeneous multicore design named EFFEX that incorporates novel functional units and memory architecture support, making it capable of increasing mobile vision performance while balancing power and area. We demonstrate this architecture running three common feature extraction algorithms, and show that it is capable of providing significant speedups at low cost. Our simulations show a speedup of as much as 14× for feature extraction with a decrease in energy of 40x for memory accesses.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.