Chest expansion measurement in 3-dimension by using accelerometers
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Publication Details
Author list: Arthittayapiwat K., Pirompol P., Samanpiboon P.
Publisher: Society of Photo-optical Instrumentation Engineers
Publication year: 2019
Volume number: 23
Issue number: 2
Start page: 71
End page: 84
Number of pages: 14
ISBN: 9781510627734
ISSN: 0277-786X
eISSN: 1996-756X
Languages: English-Great Britain (EN-GB)
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Abstract
A computer vision computation requires high number of multiplications causing a bottleneck. Based on the work of Zhenhong Liu, the multiplications in these algorithms do not always require high precision provided by the processors. As a result, we can reduce computation redundancy by means of multiplication approximation. Following this approach, in this paper, we investigate two major algorithms namely convolutional neural network (CNN) and scale-invariant features transform (SIFT) to find their error tolerances due to multiplication approximation. A multiplication approximation is done by injecting a random value to each of precise multiplication value. The INRIA and OXFORD datasets were used in the SIFT algorithm analysis while the CIFAR-10 and MNIST datasets were applied for the CNN experiments. The results showed that SIFT can withstand only small percents of multiplication approximation while CNN can tolerate over 30% of multiplication approximation. ฉ COPYRIGHT SPIE.
Keywords
Approximate Algorithm, Approximate Computing, CNN, Multiplication Approximation
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