scholarly journals Visibility Detection Algorithm of Single Fog Image Based on the Ratio of Wavelength Residual Energy

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhixiang Chen ◽  
Binna Ou
Keyword(s):  
Single Channel ◽  
Color Difference ◽  
Detection Algorithm ◽  
Residual Energy ◽  
Experimental Results ◽  
Image Defogging ◽  
Visibility Range ◽  
Energy Ratios ◽  
Deviation Problem

Different visibilities and different wavelength attenuations can cause color deviation problem in some dehazing algorithms. A visibility detection algorithm based on a single fog image is proposed. First, the visibility range of the image is preliminarily determined according to the transmissivity; then, the normalized differences between the residual energy ratios of different wavelengths of RGB channels are calculated, and the pixels with large gray deviation of a single channel are filtered to improve the calculation accuracy; finally, the image visibility detection value is calculated. The experimental results show that the proposed algorithm not only effectively reflects the fog image visibility but is also well suited for evaluating the effectiveness of the image defogging algorithms and the restoration degree of the defogging color difference.

Pilot study of a single-channel EEG seizure detection algorithm using machine learning

2021 ◽  
Author(s):  
Seungjun Ryu ◽  
Seunghyeok Back ◽  
Seongju Lee ◽  
Hyeon Seo ◽  
Chanki Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Single Channel ◽  
Seizure Detection ◽  
Detection Algorithm

scholarly journals Distributed Beacon Drifting Detection for Localization in Unstable Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ming Xia ◽  
Peiliang Sun ◽  
Xiaoyan Wang ◽  
Yan Jin ◽  
Qingzhang Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Location Information ◽  
Detection Accuracy ◽  
Research Issue ◽  
Fundamental Research

Localization is a fundamental research issue in wireless sensor networks (WSNs). In most existing localization schemes, several beacons are used to determine the locations of sensor nodes. These localization mechanisms are frequently based on an assumption that the locations of beacons are known. Nevertheless, for many WSN systems deployed in unstable environments, beacons may be moved unexpectedly; that is, beacons are drifting, and their location information will no longer be reliable. As a result, the accuracy of localization will be greatly affected. In this paper, we propose a distributed beacon drifting detection algorithm to locate those accidentally moved beacons. In the proposed algorithm, we designed both beacon self-scoring and beacon-to-beacon negotiation mechanisms to improve detection accuracy while keeping the algorithm lightweight. Experimental results show that the algorithm achieves its designed goals.

Moving Target Detection Based on Principle of Connectivity and Texture Gradient

2012 ◽  
Vol 605-607 ◽  
pp. 2117-2120
Author(s):  
Min Huang ◽  
Yang Zhang ◽  
Gang Chen ◽  
Guo Feng Yang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Texture Gradient ◽  
Moving Target ◽  
Moving Target Detection ◽  
Gradient Information ◽  
Target Segmentation ◽  
Target Profile

In target detection, “hole” phenomenon is present in the detection result, and the shadow is difficult to remove. To solve these problems, we propose a target detection algorithm based on principle of connectivity and texture gradient. Firstly, we use the connectivity principle to find the largest target prospects connection area to get a complete target contour, secondly we use target texture gradient information to further remove the shadow of the target. At last, the experimental results show that the algorithm can obtain a clear target profile and improve the accuracy of the moving target segmentation.

Color-matching model of digital rotor spinning viscose mélange yarn based on the Kubelka–Munk theory

2021 ◽  
pp. 004051752110408
Author(s):  
Ruihua Yang ◽  
Chuang He ◽  
Bo Pan ◽  
Zhuo Wang
Keyword(s):  
Least Squares ◽  
Color Difference ◽  
Test Sample ◽  
Experimental Results ◽  
Color Matching ◽  
Matching Model ◽  
K Value ◽  
Rotor Spinning ◽  
Viscose Fibers ◽  
Relative Value

The color-matching model is conducive to expanding the scope of application of colorful fabrics and can speed up the achievement of intelligent production. To solve the problem in which the existing color-matching system of intelligent colored spun yarn cannot be applied to the digital rotor-spinning products of dope dyed viscose fiber, 66 types of mélange yarn were spun with a digital rotor-spinning frame using red, yellow, and blue dope dyed viscose fibers at a ratio gradient of 10%. Furthermore, the knitted fabric samples were produced using a circular machine. Meanwhile, a Datacolor 650 spectrophotometer was used for color testing, and the experimental results were recorded. Based on the color-matching model of the Kubelka–Munk theory, a color-matching model is built based on the experimental results. In addition, the accuracy of the model was analyzed and verified using the least-squares and relative value methods. The results show that, compared with the relative value method, the color-matching model constructed using the absorption coefficient K value and scattering coefficient S value calculated based on the least-squares approach is more accurate. The error between the predicted ratio of the test sample and the actual ratio was only 0.0979, the average color difference was only 0.465, and there were no visible differences between the predicted color of the sample and the actual color.

Nucleation Rate Surfaces for Solidphase Polymethylmethacrylate Laser Ablation

2013 ◽  
Vol 8 (3) ◽  
pp. 121-127
Author(s):  
Mikhail Anisimov ◽  
Olga Petrova-Bogdanova ◽  
Anatoliy Baklanov
Keyword(s):  
Laser Ablation ◽  
Nucleation Rate ◽  
Single Channel ◽  
Laser Pulses ◽  
Experimental Results ◽  
Nucleation Theory ◽  
Surface Topology ◽  
Research Results

Experimental results for laser ablation of polymethylmethacrylate (PMM) by laser pulses are presented in this paper. Schematic construction of nucleation rate surface topology for glass and products under laser ablation is done. It follows from the research results that the using of a single channel version of the nucleation theory is incorrect to describe the nucleation rate in the glass and in the products of ablation, where several channels of nucleation are realized

scholarly journals Cloud screening in AVHRR digital data over Arctic regions

1993 ◽  
Vol 17 ◽  
pp. 386-390 ◽  
Author(s):  
Sonia C. Gallegos ◽  
Jeffrey D. Hawkins ◽  
Chiu Fu Cheng
Keyword(s):  
Single Channel ◽  
Screening Method ◽  
Detection Algorithm ◽  
Digital Data ◽  
Cloud Detection ◽  
Arctic Regions ◽  
Identification Technique ◽  
Multiple Band ◽  
Cloud Screening ◽  
Very High

A cloud screening method initially generated to mask cloud contaminated pixels over the ocean in visible/infrared imagery, has been revised and adapted to detect clouds over Arctic regions with encouraging results. Although the method is quite successful in eliminating very cold clouds, it underestimates low level clouds. However, this does not appear to interfere with monitoring of ice related features such as leads or the ice edge in Advanced Very High Resolution Radiometer (AVHRR) scenes. The method uses: a multiple-band approach to produce signatures not readily available in single channel data, an edge detection/dilation technique to locate features in the clouds and to join isolated edges, and a polygon identification technique to remove noise in the form of isolated pixels and separate clear regions from cloud contaminated areas. The method has been tested over a limited set of data with consistent results. Initial evaluation of the usefulness of this cloud-detection algorithm in data-fusion experiments indicate a potential in locating areas in AVHRR data which are cloud contaminated and which could yield a far superior representation of the ice features if replaced with data from a different sensor such as the Special Sensor Microwave/lmager (SSM/I).

scholarly journals A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zuopeng Zhao ◽  
Zhongxin Zhang ◽  
Xinzheng Xu ◽  
Yi Xu ◽  
Hualin Yan ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Recognition Accuracy ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Identification Accuracy ◽  
Convolution Kernel ◽  
Small Target ◽  
Resource Constrained ◽  
Embedded Devices

It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network—Lightweight Microscopic Detection Network (LMS-DN). The network can be implemented on embedded devices such as NVIDIA Jetson TX2. The experimental results show that LMS-DN only needs fewer parameters and calculation costs to obtain higher identification accuracy and stronger anti-interference than other popular object detection models.

Analysis to the Influence of Rendering Intents on Image Reproduction

2012 ◽  
Vol 236-237 ◽  
pp. 360-365
Author(s):  
Lin Lin Chen ◽  
Guang Xue Chen ◽  
Qi Feng Chen ◽  
Rui Xin Xu
Keyword(s):  
Great Influence ◽  
Color Difference ◽  
Experimental Results ◽  
Color Management ◽  
Color Gamut ◽  
Image Reproduction

Rendering intent is one of the key elements of color management, which has a great influence on image reproduction. Matching principles of four kinds of rendering intents in ICC are elaborated. Based on the color management workflow of ICC, the chromaticity of the color patches are measured with the spectrophotometer. And then the color difference and color gamut volume of the color patches are calculated and analyzed. The experimental results play an instructive role in the choice of rendering intents and the implementation of the color management.

Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network

2018 ◽  
Vol 63 (2) ◽  
pp. 177-190 ◽  
Author(s):  
Junming Zhang ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Experimental Results ◽  
Sleep Stages ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Complex Valued ◽  
Learned Features

AbstractMany systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

scholarly journals Parameter Measurement of Biaxial Braided Composite Preform Based on Phase Congruency

2019 ◽  
Vol 19 (1) ◽  
pp. 8-16
Author(s):  
Zhitao Xiao ◽  
Lei Pei ◽  
Fang Zhang ◽  
Ying Sun ◽  
Lei Geng ◽  
...  
Keyword(s):  
Automatic Measurement ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Block Matching ◽  
Experimental Results ◽  
Corner Detection ◽  
New Method ◽  
Two Dimensional ◽  
Phase Congruency ◽  
Braided Composite

Abstract In this paper, a new method based on phase congruency is proposed to measure pitch lengths and surface braiding angles of two-dimensional biaxial braided composite preforms. Lab space transform and BM3D (block-matching and 3D filter) are used first to preprocess the original acquired images. A corner detection algorithm based on phase congruency is then proposed to detect the corners of the preprocessed images. Pitch lengths and surface braiding angles are finally measured based on the detected corner maps. Experimental results show that our method achieves the automatic measurement of pitch lengths and the surface braiding angles of biaxial braided composite preforms with high accuracy.

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