Signal Processing - November 2017 - 21
READER'S CHOICE
Top Downloads in IEEE Xplore
E
ach "Reader's Choice" column focuses on a different publication of
the IEEE Signal Processing Society (SPS). This month we are highlighting articles featured at the IEEE
International Conference on Image
Processing (ICIP).
The ICIP, sponsored by the IEEE
SPS, is the premier forum for the presentation of technological advances
and research results in the fields of
theoretical, experimental, and applied
image and video processing.
This issue's "Reader's Choice" column lists the top 15 articles from ICIP
2014, ICIP 2015, and ICIP 2016, indicated by the year listed after the
abstract, that were the most downloaded from Ja nuary 2015 to June
2017. Please send your suggestions and
comments on this column to Associate Editor Changshui Zhang (zcs@
mail.tsinghua.edu.cn).
Hand Gesture Recognition with
Leap Motion and Kinect Devices
Marin, G.; Dominio, F.; Zanuttigh, P.
This paper proposes a novel hand gesture recognition scheme explicitly targeted to leap motion data. An adhoc
feature set based on the positions and
orientation of the fingertips is fed into
a multiclass support vector machine
classifier to recognize the performed
gestures. A set of features is also ex-
tracted from the depth computed from
the Kinect and combined with the leap
motion ones to improve the recognition performance.
2014
hierarchy. The LBPNet retains the same
topology of the convolutional neural
network, whereas the trainable kernels
are replaced by the off-the-shelf computer vision descriptor (i.e., LBP).
2016
Local Binary Pattern Network:
A Deep Learning Approach
for Face Recognition
Road Crack Detection Using Deep
Convolutional Neural Network
Digital Object Identifier 10.1109/MSP.2017.2743111
Date of publication: 13 November 2017
Xi, M.; Chen, L.; Polajnar, D.;
Tong, W.
I n this paper, a novel unsupervised
deep-learning-based methodology,
named local binary pattern network
(LBPNet), is proposed to efficiently
extract and compare high-level
overcomplete features in a multilayer
Zhang, L.; Yang, F.; Zhang, Y.D.;
Zhu, Y.J.
A deep-learning-based method for
crack detection is proposed in this paper.
A supervised deep convolutional neural
network is trained to classify each image
patch in the collected images.
2016
1053-5888/17©2017IEEE
IEEE SIGNAL PROCESSING MAGAZINE
|
November 2017
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21
Table of Contents for the Digital Edition of Signal Processing - November 2017
Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
Signal Processing - November 2017 - 2
Signal Processing - November 2017 - 3
Signal Processing - November 2017 - 4
Signal Processing - November 2017 - 5
Signal Processing - November 2017 - 6
Signal Processing - November 2017 - 7
Signal Processing - November 2017 - 8
Signal Processing - November 2017 - 9
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Signal Processing - November 2017 - 11
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Signal Processing - November 2017 - 17
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Signal Processing - November 2017 - 19
Signal Processing - November 2017 - 20
Signal Processing - November 2017 - 21
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Signal Processing - November 2017 - 24
Signal Processing - November 2017 - 25
Signal Processing - November 2017 - 26
Signal Processing - November 2017 - 27
Signal Processing - November 2017 - 28
Signal Processing - November 2017 - 29
Signal Processing - November 2017 - 30
Signal Processing - November 2017 - 31
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Signal Processing - November 2017 - 33
Signal Processing - November 2017 - 34
Signal Processing - November 2017 - 35
Signal Processing - November 2017 - 36
Signal Processing - November 2017 - 37
Signal Processing - November 2017 - 38
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Signal Processing - November 2017 - 40
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Signal Processing - November 2017 - 70
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Signal Processing - November 2017 - 81
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Signal Processing - November 2017 - 84
Signal Processing - November 2017 - 85
Signal Processing - November 2017 - 86
Signal Processing - November 2017 - 87
Signal Processing - November 2017 - 88
Signal Processing - November 2017 - 89
Signal Processing - November 2017 - 90
Signal Processing - November 2017 - 91
Signal Processing - November 2017 - 92
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Signal Processing - November 2017 - 95
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Signal Processing - November 2017 - 97
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Signal Processing - November 2017 - 100
Signal Processing - November 2017 - 101
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Signal Processing - November 2017 - 125
Signal Processing - November 2017 - 126
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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