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Image Search Results
Journal: Forensic Science International: Synergy
Article Title: Interpol review of imaging and video 2016–2019
doi: 10.1016/j.fsisyn.2020.01.017
Figure Lengend Snippet: CNN architecture as proposed by Kim and Lee consisting of 1 HPF, 2 convolutional layers, 2 max pooling layers, and 2 fully connected layers with softmax function for classification. The networks input dimension is a 256 × 256 sized grayscale image.
Article Snippet: Bayar and
Techniques:
Journal: Forensic Science International: Synergy
Article Title: Interpol review of imaging and video 2016–2019
doi: 10.1016/j.fsisyn.2020.01.017
Figure Lengend Snippet: CNN architecture as proposed by Bayar and Stamm consisting of 1 constrained convolutional layer, 2 convolutional layers, 2 max pooling layers, and 3 fully connected layers with softmax function for classification. The networks input dimension is a 227 × 227 sized grayscale image.
Article Snippet: Bayar and
Techniques:
Journal: Forensic Science International: Synergy
Article Title: Interpol review of imaging and video 2016–2019
doi: 10.1016/j.fsisyn.2020.01.017
Figure Lengend Snippet: Overview of the performance of the proposed CNN architectures for the detection of JPEG compression, resampling and image processing operations. AWGN = Gaussian noise; UMS = unsharp masking sharpening.
Article Snippet: Bayar and
Techniques:
Journal: Forensic Science International: Synergy
Article Title: Interpol review of imaging and video 2016–2019
doi: 10.1016/j.fsisyn.2020.01.017
Figure Lengend Snippet: CNN architecture as proposed by Bayar and Stamm consisting of 1 constrained convolutional layer, 4 convolutional layers, 3 max pooling layers, 1 average pooling layer and 3 fully connected layers with softmax function/extremely randomised tree for classification. The networks input dimension is a 256 × 256 sized grayscale image .
Article Snippet: Bayar and
Techniques:
Journal: Forensic Science International: Synergy
Article Title: Interpol review of imaging and video 2016–2019
doi: 10.1016/j.fsisyn.2020.01.017
Figure Lengend Snippet: CNN architecture as proposed by Yu et al. The networks input dimension is a 128 × 128 grayscale image (16.384 neurons). The architecture 5 convolutional layers, two pooling layers and one fully connected layer connected to the output layer through a soft max function .
Article Snippet: Bayar and
Techniques: