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256 × 256 grayscale image  (Stamm GmbH)

 
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    Structured Review

    Stamm GmbH 256 × 256 grayscale image
    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 <t>256</t> × 256 sized <t>grayscale</t> image.
    256 × 256 Grayscale Image, supplied by Stamm GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/256+%C3%97+256+grayscale+image/256+%C3%97+256+grayscale+image/pmc07770461-51-12-2
    Average 90 stars, based on 1 article reviews
    256 × 256 grayscale image - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "Interpol review of imaging and video 2016–2019"

    Article Title: Interpol review of imaging and video 2016–2019

    Journal: Forensic Science International: Synergy

    doi: 10.1016/j.fsisyn.2020.01.017

    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.
    Figure Legend 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.

    Techniques Used:

    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.
    Figure Legend 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.

    Techniques Used:

    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.
    Figure Legend 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.

    Techniques Used:

    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 .
    Figure Legend 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 .

    Techniques Used:

    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 .
    Figure Legend 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 .

    Techniques Used:

    Related Articles

    other:

    Article Title: Interpol review of imaging and video 2016–2019
    Article Snippet: Bayar and Stamm (2017) [ ] as cited in [ ] , 256 × 256 grayscale image , , 98,91% , , , , , , 98.93 , 94.25% , 97,83% , 83,72% , 92.7% , 92,81%.



    Similar Products

    256 × 256 grayscale image  (Stamm GmbH)
    90
    Stamm GmbH 256 × 256 grayscale image
    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 <t>256</t> × 256 sized <t>grayscale</t> image.
    256 × 256 Grayscale Image, supplied by Stamm GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/256+%C3%97+256+grayscale+image/256+%C3%97+256+grayscale+image/pmc07770461-51-12-2
    Average 90 stars, based on 1 article reviews
    256 × 256 grayscale image - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    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.

    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 Stamm (2017) [ ] as cited in [ ] , 256 × 256 grayscale image , , 98,91% , , , , , , 98.93 , 94.25% , 97,83% , 83,72% , 92.7% , 92,81%.

    Techniques:

    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.

    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 Stamm (2017) [ ] as cited in [ ] , 256 × 256 grayscale image , , 98,91% , , , , , , 98.93 , 94.25% , 97,83% , 83,72% , 92.7% , 92,81%.

    Techniques:

    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.

    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 Stamm (2017) [ ] as cited in [ ] , 256 × 256 grayscale image , , 98,91% , , , , , , 98.93 , 94.25% , 97,83% , 83,72% , 92.7% , 92,81%.

    Techniques:

    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 .

    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 Stamm (2017) [ ] as cited in [ ] , 256 × 256 grayscale image , , 98,91% , , , , , , 98.93 , 94.25% , 97,83% , 83,72% , 92.7% , 92,81%.

    Techniques:

    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 .

    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 Stamm (2017) [ ] as cited in [ ] , 256 × 256 grayscale image , , 98,91% , , , , , , 98.93 , 94.25% , 97,83% , 83,72% , 92.7% , 92,81%.

    Techniques: