Adaptation of Convolution and Batch Normalization Layer for CNN Implementation on FPGA

Sledevič, Tomyslav

doi:10.1109/estream.2019.8732160

Cited by 30 publications

(11 citation statements)

References 14 publications

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“…It is worth mentioning that each convolutional layer has a batch normalization layer. It acts as a regularizer to accelerate network training 14 times [28]. We apply the stochastic gradient descent method [29] for training the network.…”

Section: Network Architecture and Training Parametersmentioning

confidence: 99%

An intelligent strabismus detection method based on convolution neural network

et al. 2022

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Strabismus is one of the widespread vision disorders in which the eyes are misaligned and asymmetric. Convolutional neural networks (CNNs) are properly designed for analyzing images and detecting texture patterns. In this paper, we proposed a system that uses deep learning CNN applications for automatically detecting and classifying strabismus disorder. The proposed system includes two main stages: first, the detection of facial eye segmentation using the viola-jones algorithm. The second stage is to map the segmented eye area according to the iris position of each eye. This method is applied to three strabismus datasets, gathered as digital images. The second section covers the segmentation of the eye region. Besides, the evaluation equations for measuring system performance. The system has undergone numerous experiments in various stages to simulate and analyze the detection performance of CNN layers through different classifiers and variant thresholds ratio. The researchers investigated the experimental outcomes during the training and testing phases and obtained promising results that exhibit the effectiveness of the proposed system. According to the results, the accuracy of this technique reached 95.62%.

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Section: Network Architecture and Training Parametersmentioning

confidence: 99%

An intelligent strabismus detection method based on convolution neural network

et al. 2022

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“…3. CLAHE is derived from Adaptive Histogram Equalization (AHE) [28]. AHE is employed on small areas of the contextual region in the image known as tiles 2×2 on both CLAHE.…”

Section: 2mentioning

confidence: 99%

“…The output of the convolutional layer, the bias factor can change the sizes of channels. Therefore, batch normalization is employed for channels normalization for ReLU activation [28].…”

Section: D-cnn Architecturementioning

confidence: 99%

Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

Noroz

Ahmed

Kumar

et al. 2022

SJCMS

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The suggested process enhances the low contrast of the finger-vein image using dual contrast adaptive histogram equalization (DCLAHE) for visual attributes. The finger-vein histogram intensity is split out all over the image when dual CLAHE is used. For preprocessing, the finger-vein image dataset is obtained from the SDUMLA-HMT finger-vein database. Following the deployment of DCLAHE, the updated dataset is used to recognize objects using an improved 2D-CNN model. The 2D CNN model learns features by optimizing values of a preprocessed dataset. The accuracy of this model stands at 91.114%.

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“…We used dual CLAHE to enhance the vein region twice after cropping the finger-vein image, as shown in Figure 3. CLAHE is a modified form of Adaptive Histogram Equalization (AHE) that works on small parts of the contextual region [28] in the finger-vein picture known as tiles 22 on both CLAHE and AHE. CLAHE with the same exponential distribution [7], [27], with clipping limits of 0.03 on the first and 0.04 on the second.…”

Section: Sobel Edge Detector and Poly Roimentioning

confidence: 99%

Finger-vein Image Enhancement and 2D CNN Recognition

Baloch

Saleem

Kumar

2021

IJIST

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Finger vein recognition technology is a novel biometric technology with multiple features such as live capture, stability, difficulty in stealing and imitating, and more in the field of information security that has been utilized in a wide range of applications. In this proposed method, the finger region is separated from the background using a Sobel Edge detector and a Poly ROI which helps shape the finger. The background separation enhancement of low contrast using dual contrast limited adaptive histogram equalization which works on the visual characteristics of the finger-vein image dataset. When dual CLAHE is applied, the finger-vein histogram intensity is separated all across the image. Following the implementation of DCLAHE, an enhanced 2D-CNN model is utilized to recognize objects with the updated dataset. By maximizing the values of a preprocessed dataset, the 2D CNN model learns features. This model has a 94.88% accuracy rate.

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Adaptation of Convolution and Batch Normalization Layer for CNN Implementation on FPGA

Cited by 30 publications

References 14 publications

An intelligent strabismus detection method based on convolution neural network

An intelligent strabismus detection method based on convolution neural network

Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

Finger-vein Image Enhancement and 2D CNN Recognition

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