Learning hierarchical face representation to enhance HCI among medical robots

2021 ◽  
Vol 118 ◽  
pp. 180-186
Author(s):  
Dianmin Sun ◽  
Honghua Zhao ◽  
Tao Song ◽  
Aiqin Liu ◽  
Jinling Cheng ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yunjun Nam ◽  
Takayuki Sato ◽  
Go Uchida ◽  
Ekaterina Malakhova ◽  
Shimon Ullman ◽  
...  

AbstractHumans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).


Author(s):  
Antonio Di Lallo ◽  
Robin Murphy ◽  
Axel Krieger ◽  
Junxi Zhu ◽  
Russell H. Taylor ◽  
...  

1998 ◽  
Vol 06 (03) ◽  
pp. 265-279 ◽  
Author(s):  
Shimon Edelman

The paper outlines a computational approach to face representation and recognition, inspired by two major features of biological perceptual systems: graded-profile overlapping receptive fields, and object-specific responses in the higher visual areas. This approach, according to which a face is ultimately represented by its similarities to a number of reference faces, led to the development of a comprehensive theory of object representation in biological vision, and to its subsequent psychophysical exploration and computational modeling.


2009 ◽  
Vol 18 (4) ◽  
pp. 193-216 ◽  
Author(s):  
Matthieu Alric ◽  
Frédéric Chapelle ◽  
Jean-Jacques Lemaire ◽  
Grigore Gogu

2021 ◽  
pp. 29-31
Author(s):  
Saumya Jaiswal ◽  
Shivangi Tiwari ◽  
Vivek Kumar Tripathi ◽  
Ajay Sharma

1. What are robots used in healthcare? Areas within healthcare which are starting to use robots include: telepresence, rehabilitation, medical transportation, sanitization and prescription dispensing. But we are most interested in collaborative robotics. We will be discussing the COBOT(Cordial Robot) applications. Most modern healthcare robots are especially designed for their target applications. 2. Is it possible to use robotics in medicine? Robotics in medicine can happen in many ways, here are some. Healthcare has been predicted as “a promising industry for robotics” for the past 45 years or more. Since as far back as 1974, researchers have been looking for ways to incorporate robotics into medical applications. 3. Is there a need for more surgery/telepresence/rehabilitation/medical transportation/sanitation and disinfection/medicine prescription dispensing robots? There is denitely a need for many more surgery robots, laparoscopic, endoscopic and nanorobots, as the technology allows more functionalities with miniature propulsion mechanisms. M.A. Zenati, M. Mahvash, from the science of medical robotics, 2012. 4. How are medical robots used to treat patients, reduce contact, and cure pain? Using the medical robots reduces the direct contact between the doctor and the patient, helps in reducing pain, by minimizing the need for more medication and longer hospital stays, allowing the person to return home by the therapy sooner without any spread of infection.


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