1,424 research outputs found
Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Network
Automatic sign language recognition is an open problem that has received a
lot of attention recently, not only because of its usefulness to signers, but
also due to the numerous applications a sign classifier can have. In this
article, we present a new feature extraction technique for hand pose
recognition using depth and intensity images captured from a Microsoft Kinect
sensor. We applied our technique to American Sign Language fingerspelling
classification using a Deep Belief Network, for which our feature extraction
technique is tailored. We evaluated our results on a multi-user data set with
two scenarios: one with all known users and one with an unseen user. We
achieved 99% recall and precision on the first, and 77% recall and 79%
precision on the second. Our method is also capable of real-time sign
classification and is adaptive to any environment or lightning intensity.Comment: Published in 2014 Canadian Conference on Computer and Robot Visio
New Bounds for Facial Nonrepetitive Colouring
We prove that the facial nonrepetitive chromatic number of any outerplanar
graph is at most 11 and of any planar graph is at most 22.Comment: 16 pages, 5 figure
Infrared face recognition: a comprehensive review of methodologies and databases
Automatic face recognition is an area with immense practical potential which
includes a wide range of commercial and law enforcement applications. Hence it
is unsurprising that it continues to be one of the most active research areas
of computer vision. Even after over three decades of intense research, the
state-of-the-art in face recognition continues to improve, benefitting from
advances in a range of different research fields such as image processing,
pattern recognition, computer graphics, and physiology. Systems based on
visible spectrum images, the most researched face recognition modality, have
reached a significant level of maturity with some practical success. However,
they continue to face challenges in the presence of illumination, pose and
expression changes, as well as facial disguises, all of which can significantly
decrease recognition accuracy. Amongst various approaches which have been
proposed in an attempt to overcome these limitations, the use of infrared (IR)
imaging has emerged as a particularly promising research direction. This paper
presents a comprehensive and timely review of the literature on this subject.
Our key contributions are: (i) a summary of the inherent properties of infrared
imaging which makes this modality promising in the context of face recognition,
(ii) a systematic review of the most influential approaches, with a focus on
emerging common trends as well as key differences between alternative
methodologies, (iii) a description of the main databases of infrared facial
images available to the researcher, and lastly (iv) a discussion of the most
promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap
with arXiv:1306.160
A sharp square function estimate for the moment curve in
We use high-low frequency methods developed in the context of decoupling to
prove sharp (up to ) square function estimates for the
moment curve in . Our inductive scheme
incorporates sharp square function estimates for auxiliary conical sets, which
allows us to fully exploit lower dimensional information.Comment: arXiv admin note: text overlap with arXiv:2210.1743
Fiber orientation assessment in complex shaped parts reinforced with carbon fiber using infrared thermography
The use of composite materials is growing more and more every day in several applications. The arrangement or orientation of the fibers relative to one another have a significant influence on the strength and other properties of fiber reinforced composites. Thus, evaluation techniques are needed for measuring material fiber orientation. In this work infrared thermography is employed to assess the material’s fiber orientation. More specifically a pulsed infrared diode laser heating spot technique combined with a 3D model of the part is used in order to assess fiber orientation on the surface of carbon fiber-reinforced polymer complex shaped parts made of carbon/PEEK (Polyether ether ketone) randomly-oriented strands
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