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Front Matter: Volume 10341
2017
Ninth International Conference on Machine Vision (ICMV 2016)
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The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon. Please use the following format to cite material from these proceedings: ...
Some conference presentations may not be available for publication. Additional papers and presentation recordings may be available online in the SPIE Digital Library at SPIEDigitalLibrary.org. ...
RECOGNITION
10341 0A
Speaker gender identification based on majority vote classifiers [10341-85]
10341 0B
Deep neural network features for horses identity recognition using multiview horses' face ...
doi:10.1117/12.2276832
dblp:conf/icmv/X16
fatcat:srr4hyfwpfcipadcvjc5jdll6i
A Novel Parameter Initialization Technique Using RBM-NN for Human Action Recognition
2020
Computational Intelligence and Neuroscience
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Thirdly, the local features are encoded into global features using an integrated forward and backward propagation process via RBM-NN. ...
Firstly, human action is detected and tracked from the video dataset to learn the spatial-temporal features. Secondly, the extracted feature descriptors are trained using the RBM-NN. ...
Deep learning neural networks are trained using the SDG optimization algorithm. ...
doi:10.1155/2020/8852404
pmid:32963513
pmcid:PMC7501562
fatcat:yfobsgznmjcwfg3himlhfoyqaa
Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50
2021
Mathematical Problems in Engineering
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Recent trends for remote sensing and scene classification are focused on the use of Convolutional Neural Network (CNN). ...
Earlier approaches for remote sensing images and scene analysis are based on low-level feature representations such as color- and texture-based features. ...
Deep networks faced many complications including the optimization of networks, degradation, and most importantly vanishing gradients. ...
doi:10.1155/2021/5843816
fatcat:sk6arf2jkfa2xfthw7rxcs2ey4
Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review
2019
Mathematical Problems in Engineering
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We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. ...
To search for a relevant image from an archive is a challenging research problem for computer vision research community. ...
(5) How learning can be enhanced by the use of deep neural networks (DNN)? ...
doi:10.1155/2019/9658350
fatcat:dncplhkm6vcrvfh3q7ifxkrdkq
A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications
2020
IEEE Access
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Deep learning approach has been used extensively in image analysis tasks. ...
However, implementing the methods in 3D data is a bit complex because most of the previously designed deep learning architectures used 1D or 2D as input. ...
informative shape descriptor using adversarial neural networks that train a combination of convolutional neural network, adversarial discriminative and recurrent neural network. 3D shape features that ...
doi:10.1109/access.2020.2982196
fatcat:jnya5rscynf3zm7efuucqxafri
An Evaluation of Deep Learning-Based Object Identification
2022
International Journal on Recent and Innovation Trends in Computing and Communication
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Recent developments in deep learning networks for detection have improved object detector accuracy. ...
the using the most recent algorithms and doing more study. ...
Both networks used the identical VGG [45] backbone. ...
doi:10.17762/ijritcc.v10i1s.5795
fatcat:3ysszc7uxrd3jffazeex32sn7u
Human Activity Recognition: Review, Taxonomy and Open Challenges
2022
Sensors
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Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of ...
Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities ...
[14] introduced a Deep Belief Network (DBN) that trained those attributes for HAR. It was seen that DBN outperformed SVM and Artificial Neural Network (ANN) for HAR. Sukor et al. ...
doi:10.3390/s22176463
pmid:36080922
pmcid:PMC9460866
fatcat:lqzdufjrs5aldfvkztbrdu73c4
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
[article]
2019
arXiv
pre-print
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This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. ...
The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the ...
via MK-MMD in deep neural networks. ...
arXiv:1705.04396v3
fatcat:iknfmppi5zca7ljovdlwvdwluu
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition
2019
ACM Computing Surveys
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This article takes a problem-oriented perspective and presents a comprehensive review of transferlearning methods, both shallow and deep, for cross-dataset visual recognition. ...
This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. ...
Except for the discriminative dictionary learning, the label information can also be used for guiding the deep neural networks to reduce domain shift. For example, Koniusz et al. ...
doi:10.1145/3291124
fatcat:thjzho3xsnfalprmkquldhwpvm
A Review of Deep Learning-Powered Mesh Reconstruction Methods
[article]
2023
arXiv
pre-print
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Importantly, to be used in common 3D applications, the reconstructed shapes need to be represented as polygonal meshes, which is a challenge for neural networks due to the irregularity of mesh tessellations ...
We first describe various representations for 3D shapes in the deep learning context. ...
It consists of a coarse PIFu nerwork and a fine PIFu network. The coarse PIFu is almost identical to PIFu, whose feature maps capture both global and local features. ...
arXiv:2303.02879v1
fatcat:vm3audu2s5dylcqsquu7sg3moq
Frontmatter
[chapter]
2019
The Internet of Everything
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protocol to transmit urgent data Abstract: Sensor network is designed to provide monitoring services especially for natural disaster. ...
Congestion is a very important factor in wireless sensor network (WSN) and also it reduces quality of services. ...
Partial face recognition using image fusion
Partial face recognition using image fusion
Partial face recognition using image fusion
Partial face recognition using image fusion
https ...
doi:10.1515/9783110628517-fm
fatcat:3npfgq3o65f5pdbphf6pi4qn3a
Robust Analytics for Video-Based Gait Biometrics
[article]
2021
arXiv
pre-print
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It then describes improving gait recognition accuracy using Genetic Template Segmentation. Members of a wide population can be authenticated using Multiperson Signature Mapping. ...
Finally, the mapping can be improved in a smaller population using Bayesian Thresholding. ...
As attractive as deep learning sounds, its integration with legacy camera networks is not so simple due to its computational complexity. ...
arXiv:2111.06670v1
fatcat:ij7lviwzbvd3hjasnul3b4nrhq
Smart Cameras
[article]
2020
arXiv
pre-print
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Over the past 5 years, deep learning solutions have become superior to traditional algorithms for each of these functions. ...
Modern cameras use physical components and software to capture, compress and display image data. ...
edges and avoid the "zipper" effect of conventional systems or even use a different approach for hair-like features or faces. ...
arXiv:2002.04705v1
fatcat:277yq2oaujdoxbtqqsq6naodma
Embedding Motion and Structure Features for Action Recognition
2013
IEEE transactions on circuits and systems for video technology (Print)
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features, discriminative locality alignment, human action recognition. ...
The proposed method is evaluated on the KTH, the multiview IXMAS, and the challenging UCF sports datasets and outperforms stateof-the-art techniques on action recognition Index Terms-Biologically inspired ...
[22] developed a 3-D convolutional neural network (CNN), which is directly extended from its 2-D counterpart, for feature extraction. ...
doi:10.1109/tcsvt.2013.2240916
fatcat:sbsgdj3isrfyrf444gqncuaf3i
A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata
2021
Data
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We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. ...
It may also be used for face modeling or 3D face recognition. ...
vector machine OR neural network) AND (X) AND (dataset OR ground-truth OR metadata). ...
doi:10.3390/data6020012
fatcat:jov2btfknnet3bwpyn6txnrhda
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