Fast recognition using convolutional neural network for the coal particle density range based on images captured under multiple light sources

Positioning and focusing of light sources in light concentrating systems using convolutional neural network modelling

Solar Energy ◽

10.1016/j.solener.2021.03.002 ◽

2021 ◽

Vol 218 ◽

pp. 445-454

Author(s):

Daniel Haseler ◽

Arshad M. Ali ◽

Konstantinos E. Kakosimos

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Light Sources ◽

Network Modelling ◽

Neural Network Modelling

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Segmentation of illuminated areas of scene using fully-convolutional neural networks and computer vision algorithms for augmented reality systems

10.30987/graphicon-2019-1-42-46 ◽

2019 ◽

Author(s):

Максим Сорокин ◽

Maksim Sorokin ◽

Дмитрий Жданов ◽

Dmitriy Zhdanov ◽

Игорь Потемин ◽

...

Keyword(s):

Neural Network ◽

Augmented Reality ◽

Convolutional Neural Network ◽

Rapid Development ◽

Ground Truth ◽

Real Space ◽

Input Image ◽

Optical Parameters ◽

Light Sources ◽

Network Training

The relevance of this topic is due to the rapid development of virtual and augmented reality systems. The problem lies in the formation of natural conditions for lighting objects of the virtual world in real space. To solve a light sources determination problem and recovering its optical parameters were proposed the fully-convolutional neural network, which allows catching the 'behavior of light' features. The output of FCNN is a segmented image with light levels and its strength. Naturally, the fully-convolutional neural network is well suited for image segmentation, so as an encoder was taken the architecture of VGG-16 with layers that pools and convolves an input image to 1x1 pixel and wisely classifies it to one of a class which characterizes its strength. Neural network training was conducted on 221 train images and 39 validation images with learning rate 1E-2 and 200 epochs, after training the loss was 0,2. As a test was used an ‘intersection over union’ method, that compares the ground truth area of an input image and output image, comparing its pixels and giving the result of accuracy. The mean IoU is 0.7, almost rightly classifying the first class with a value of 90 percents of accordance and the last class with a probability of 30 percents.

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Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network

Sensors ◽

10.3390/s19214687 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4687 ◽

Cited By ~ 4

Author(s):

Lin ◽

Li ◽

Lu ◽

Sun ◽

Chen ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Nearest Neighbor ◽

Light Emitting Diode ◽

Ultra Violet ◽

Principal Component ◽

Light Sources ◽

K Nearest Neighbor ◽

Tea Leaves ◽

Excitation Light

A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.

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CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

10.1055/s-0040-1705059 ◽

2020 ◽

Author(s):

S Kashin ◽

D Zavyalov ◽

A Rusakov ◽

V Khryashchev ◽

A Lebedev

Keyword(s):

Neural Network ◽

Convolutional Neural Network

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No-Reference Utility Estimation with a Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2018.09.iriacv-202 ◽

2018 ◽

Vol 2018 (9) ◽

pp. 202-1-202-6 ◽

Cited By ~ 2

Author(s):

Edward T. Scott ◽

Sheila S. Hemami

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Utility Estimation

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Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-180 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 181-1-181-7

Author(s):

Takahiro Kudo ◽

Takanori Fujisawa ◽

Takuro Yamaguchi ◽

Masaaki Ikehara

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Large Scale ◽

Blind Deconvolution ◽

Training Dataset ◽

Image Deconvolution ◽

Classic Problem ◽

Key Points ◽

Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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