Non-linear Image-Based Regression of Body Segment Parameters

Le, Sang N.; Lee, M. K.; Fang, Anthony

doi:10.1007/978-3-540-92841-6_508

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Recent developments in computer vision and machine learning have opened the door to explore the possibility of estimating the weight of an object using visual information obtained from 2D or 3D cameras. Weight estimation for specific materials is already explored, such as detecting the weight of Alaskan salmon [7], beef [8], pigs [9], and human body parts [10]. A broader list of food classes such as banana or bread are considered for computing the mass in [11].…”

Section: Related Workmentioning

confidence: 99%

Estimating Weight of Unknown Objects Using Active Thermography

Aujeszky¹,

Κορρές²,

Eid³

et al. 2019

Robotics

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Successful manipulation of unknown objects requires an understanding of their physical properties. Infrared thermography has the potential to provide real-time, contactless material characterization for unknown objects. In this paper, we propose an approach that utilizes active thermography and custom multi-channel neural networks to perform classification between samples and regression towards the density property. With the help of an off-the-shelf technology to estimate the volume of the object, the proposed approach is capable of estimating the weight of the unknown object. We show the efficacy of the infrared thermography approach to a set of ten commonly used materials to achieve a 99.1% R 2 -fit for predicted versus actual density values. The system can be used with tele-operated or autonomous robots to optimize grasping techniques for unknown objects without touching them.

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Section: Related Workmentioning

confidence: 99%

Estimating Weight of Unknown Objects Using Active Thermography

Aujeszky¹,

Κορρές²,

Eid³

et al. 2019

Robotics

View full text Add to dashboard Cite

show abstract

“…Center of mass was the average coordinates of voxels center belonging to an organ. [15][16][17] We assumed the coordinate center at the end of the spine.…”

Section: Iia Phantommentioning

confidence: 99%