The current CEO of Virtual Physicians Network, George England, will assume the same role for DMHI. Reportedly, England led the successful development and national launch of Virtual Physicians Network's new mobile app designed for surgeons, hospitals and medical vendors.
According to the company, its current CEO, Rik J Deitsch, will stay on with DMHI and serve as chairman of the board.
Large-scale experimental results disclose that in our proposed DMHI PHOG and RDMHIPHOG descriptors, the neighbor gradients information and pyramid layers are very useful for our task, whose accuracies are the best on both depth and RGB channels.
In DMHI image, they not only convey important shape and motion clues of a human movement, but also they can filter most of static pixels.
After obtaining the DMHI and RDMHI, we need to design some suitable descriptors to represent the motion.
In fact, DMHI and RDMHI images also can be considered as different characteristics scenes in which naturalness, openness, roughness, expansion and ruggedness are very different.
PHOG extraction in our task can be given as follows in detail: 1) DMHI and RDMHI motion maps are constructed and calculated, and then the rectangular bounding boxes of them are searched and obtained, and background noise pixel are filtered; 2) On the basic of the rectangular bounding box, PHOG feature are extracted for DMHI and RDMHI motion maps respectively, and we called them as DMHI_PHOG and RDMHI_PHOG respectively.
For example, the accuracies of DMHI BAHB and RDMHI GIST descriptors are 81% and 89.6% respectively in SRC model, but the accuracy of fusion descriptor DMHI_BAHB_ RDMHI_GIST is 92.6%.