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Novel Convolution Kernels for Computer Vision and Shape Analysis based on Electromagnetism
[article]
2018
arXiv
pre-print
The objective of this paper is to present a novel convolution kernels, based on principles of electromagnetic potentials and fields, for a general use in computer vision and to demonstrate its usage for ...
Therefore, this paper focuses on the development of the electromagnetic kernels and on their application on images for shape and stroke analysis. ...
Acknowledgment We would like to thank NSERC, through the discovery grant program, and FRQNT/INTER for their financial support as well as MEDITIS (Biomedical technologies training program) through NSERC ...
arXiv:1806.07996v1
fatcat:tgmo3yeu5va2nlmxsbizszztsi
Vision-Based Fall Detection Using Dense Block With Multi-Channel Convolutional Fusion Strategy
2021
IEEE Access
Different from most traditional vision-based methods relying on hand-crafted features, fall detection methods based on deep learning technology can automatically mine features to detect fall events due ...
To solve the abovementioned problem, we propose a fall detection method based on dense block with a multi-channel convolutional fusion (MCCF) strategy. ...
PROPOSED METHOD In this work, a novel method based on MCCF-DenseBlock is proposed for fall detection. ...
doi:10.1109/access.2021.3054469
fatcat:wizqwlkbjvdvtaknpuuqyy36fa
Front Matter: Volume 10696
2018
Tenth International Conference on Machine Vision (ICMV 2017)
The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee. ...
. The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03, ...
0N
Graphic matching based on shape contexts and reweighted random walks [10696-59]
10696 0O
A comparative analysis of image features between weave embroidered Thangka and
piles embroidered Thangka ...
doi:10.1117/12.2319685
dblp:conf/icmv/X17
fatcat:jb7w6d2ewbe57gaqkvxcumxpum
Front Matter: Volume 10033
2016
Eighth International Conference on Digital Image Processing (ICDIP 2016)
. The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03, 04, ...
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. ...
]
SESSION 17
COMPUTER VISION AND VISUALIZATION
10033 5M
Generating dynamic street view images [10033-3]
10033 5N
A real time vision system for traffic surveillance at intersections [10033-160] ...
doi:10.1117/12.2257252
fatcat:v2ipfp2mp5gedjypzpecahpo7e
Deep Convolution Neural Network for Big Data Medical Image Classification
2020
IEEE Access
Due to recent developments in imaging technology, classifying medical images in an automatic way is an open research problem for researchers of computer vision. ...
Deep learning is one of the most unexpected machine learning techniques which is being used in many applications like image classification, image analysis, clinical archives and object recognition. ...
We have proposed a novel deep convolution network-based approach that is assist of doctors and physicians in making reasonable decisions. ...
doi:10.1109/access.2020.2998808
fatcat:e3fo3fh3encujji643ikghklyy
Bibliography
[chapter]
2014
Computer Vision Metrics
Vedaldi, A., and S. Soatto. "Quick Shift and Kernel Methods for Mode Seeking." European Conference on Computer Vision, 2008. 268. Vincent, L., and P. Soille. ...
International Journal on Computer Vision (2013).
225. Fowers, Spencer G., D. J. Lee, Dan Ventura, and Doran K. Wilde. "A Novel, Efficient,
Tree-Based Descriptor and Matching Algorithm (BASIS)." ...
doi:10.1007/978-1-4302-5930-5_13
fatcat:bnsbslueindjrdsxjvt5ulcozq
Vision-based Fall Detection with Multi-task Hourglass Convolutional Auto-encoder
2020
IEEE Access
Different from most conventional vision-based fall detection methods typically relying on hand-crafted features, fall detection methods based on deep learning techniques can automatically learn features ...
To solve the above problem, we propose a vision-based fall detection method using multi-task hourglass convolutional auto-encoder (HCAE). ...
PROPOSED METHOD In this work, a novel method based on the HCAE with HRUs and a multi-task mechanism is proposed for fall detection. ...
doi:10.1109/access.2020.2978249
fatcat:hhtygpuusvaadlrjzxrjz6jt5u
A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread
2022
IEEE Access
We implemented an image acquisition system and created an efficient machine learning algorithm, based on support vector machines, for the segmentation and estimation of image measurements for Carasau bread ...
The study focuses on one of the most critical activities for obtaining an efficient degree of automation: the estimation of the size and shape of the bread sheets during the production phase, to study ...
S.N.C for participating in this study and for the information they freely provided.(Matteo Baire and Katiuscia Mannaro contributed equally to this work.) ...
doi:10.1109/access.2022.3147206
fatcat:pitz2vqp6rcfvjhoxuf256cpra
Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications
[article]
2019
arXiv
pre-print
In Computer Vision, edge detection is one of the favored approaches for feature and object detection in images since it provides information about their objects boundaries. ...
To answer this objective, we developed a new approach that uses electromagnetic convolutions and repulsion optimization to compute the required probabilities. ...
Acknowledgment We would like to thank NSERC, through the discovery grant program RGPIN-2014-06289, and FRQNT/INTER for their financial support as well as MEDITIS (Biomedical technologies training program ...
arXiv:1806.01339v2
fatcat:56si5gxy4ne6bo7pbpdrmpvowi
Attendee List
2020
2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
Sørensen
41
Design and test an intelligent irrigation system for small surfaces
Romania
Mircea Nitulescu
42
Vision-Based Drowsiness Detection System Using Convolutional Neural Networks
Turkey ...
14 IoT Novel ACO-OFDM System Based on Fast Walsh Hadamard Transform Turkey Busra Avcı Design and Implementation of a Novel Power Conditioner for Linear Induction Launchers Turkey Abdulkadir Balikci 422 ...
doi:10.1109/icecce49384.2020.9179198
fatcat:ryry4suqzrfh3ch2v3veh4z6ei
Deep learning and machine vision for food processing: A survey
2021
Current Research in Food Science
In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. ...
Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. ...
convolution kernel W. ...
doi:10.1016/j.crfs.2021.03.009
pmid:33937871
pmcid:PMC8079277
fatcat:cqzvzbwwjrdulnve6shf7o2agu
Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis
2020
IEEE Open Journal of Antennas and Propagation
INDEX TERMS Computer vision, electromagnetics, finite difference methods, machine learning, recurrent neural networks, unsupervised learning. ...
In this paper, we propose a deep neural network based model to predict the time evolution of field values in transient electrodynamics. ...
ACKNOWLEDGMENT The authors would like to thank the University of New Mexico Center for Advanced Research Computing, supported in part by the National Science Foundation, for providing the high-performance ...
doi:10.1109/ojap.2020.3013830
fatcat:civbopkuyjaptogk6ddil2fs2m
Automatic Defect Identification Method for Magnetic Particle Inspection of Bearing Rings Based on Visual Characteristics and High-Level Features
2022
Applied Sciences
Due to the complexity of bearing ring surfaces in inspection, automatic detection for bearing rings based on image processing is difficult to apply. ...
Therefore, we proposed a bearing ring defect identification method based on visual characteristics and high-level features. ...
(a) Original image; (b) The method based on shape features and SVM; (c) The method based on Hu moment invariant feature and BP; (d) The method based on SIFT features and SVM; (e) Our method. ...
doi:10.3390/app12031293
fatcat:7psw2yjfkbh4bca2sa7w2fdo54
Automated Grapevine Cultivar Identification via Leaf Imaging and Deep Convolutional Neural Networks: A Proof-of-Concept Study Employing Primary Iranian Varieties
2021
Plants
For on-time evaluations, molecular genetics have been successfully performed, though in many instances, they are limited by the lack of referable data or the cost element. ...
This paper presents a convolutional neural network (CNN) framework for automatic identification of grapevine cultivar by using leaf images in the visible spectrum (400–700 nm). ...
We thank the laboratory staff for their contributions, continued diligence, and dedication to their craft.
Conflicts of Interest: No potential conflict of interest was reported by the authors. ...
doi:10.3390/plants10081628
pmid:34451673
pmcid:PMC8399703
fatcat:lp6ehzd5unaofcbs7u77edjpgi
2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57
2019
IEEE Transactions on Geoscience and Remote Sensing
., and Drake, V.A., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674 ...
and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging ...
., +, TGRS June 2019 4121-4145
Computer graphics
A Novel Octree-Based 3-D Fully Convolutional Neural Network for Point
Cloud Classification in Road Environment. ...
doi:10.1109/tgrs.2020.2967201
fatcat:kpfxoidv5bgcfo36zfsnxe4aj4
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