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AVHYAS: A Free and Open Source QGIS Plugin for advanced Hyperspectral image Analysis

AVHYAS: A Free and Open Source QGIS Plugin for Advanced Hype...

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2021 international conference on Emerging Techniques in Computational Intelligence, ICETCI 2021

作者： Lyngdoh, Rosly Boy Sahadevan, Anand S Ahmad, Touseef Rathore, Pradyuman Singh Mishra, Manoj Gupta, Praveen Kumar Misra, Arundhati Advanced Microwave and Hyperspectral Techniques Development Group Space Applications Centre Isro Hyperspectral Techniques Development Division Gujarat Ahmedabad India

ISBN: (纸本)9781665415590

advanced Hyperspectral Data Analysis Software (AVHYAS) plugin is a Python-3 based Quantum-GIS (QGIS) plugin designed to process and analyse hyperspectral (Hx) images. Starting with version 1.0, AVHYAS serves as a free and open-source platform for sharing and distributing Hx data analysis methods among research scholars, scientists and potential end-users. It is developed to guarantee full usage of present and future Hx airborne or spaceborne sensors and provides access to advanced algorithms for Hx data processing. The software is freely available and offers a range of basic and advanced tools such as atmospheric correction (for airborne AVIRIS-NG image), standard processing tools as well as powerful machine learning and Deep Learning interfaces for Hx data analysis. This paper gives an overview of the AVHYAS plugin, explains typical workflows and use cases for making it a constantly used platform for hyperspectral remote sensing applications. © 2021 IEEE.

关键词： Python

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Infrared image Face Recognition Method Based on signal Interference Technology 6th

Infrared Image Face Recognition Method Based on Signal Inter...

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6th European-Alliance-for-Innovation (EAI) international conference on advanced Hybrid Information processing (ADHIP)

作者： Chen, Zongren Xie, Wenda Guangdong Polytech Sci & Technol Artificial Intelligence Coll Comp Engn Tech Coll Zhuhai 519090 Peoples R China

ISBN: (纸本)9783031287862;9783031287879

Infrared imaging has the advantages of strong anti-interference, independent of visible light source, anti camouflage and anti fraud. Therefore, an infrared image face recognition method based on signal interference technology is proposed. Based on the in-depth analysis of the characteristics of infrared face image, the characteristics of infrared image face recognition are studied, and a new infrared image statistical face recognition method is proposed. The experimental results show that the recognition method studied in this paper is feasible both theoretically and experimentally, and has good recognition ability. The infrared image face recognition method can solve the difficult problem of face recognition under the change of lighting conditions and face camouflage.

关键词： signal interference technology Infrared image Face recognition Anti interference Visible light source

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Liver Tumor Segmentation Based on Three-Channel Cascaded SEU-Nets and C-LSTM 13

Liver Tumor Segmentation Based on Three-Channel Cascaded SEU...

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13th international conference on Wireless Communications and signal processing, WCSP 2021

作者： Li, Yarong Hu, Dong Nanjing University of Posts and Telecommunications Jiangsu Province's Key Lab of Image Processing and Image Communications Nanjing China

ISBN: (纸本)9781665407854

An abdominal CT liver tumor segmentation algorithm based on three-channel cascade SEU-Nets and C-LSTM is proposed in this paper. Concerning the accuracy of most known algorithms is limited by using single-phase CT images, the proposed method takes advantage of the different characteristics of the lesion area in the venous phase, arterial phase and delayed phase, and the correlation between slices in enhanced CT images. It uses three-channel SEU-Nets as the encoder to extract image features of slices in three phases, and uses 3D convolution to extract the relationship between slices for the same location at different phases. And then it uses C-LSTM to extract the inter-slice sequence feature relationship. Finally, the decoder outputs fine segmentation of liver tumor output. 40 sets of high-quality abdominal-enhanced CT images of the liver and liver tumors used in the experiment were collected in the provincial central hospital and annotated by experts. Comparisons are carried out with well-known methods, such as improved CFCNs, H-DenseUNet network and DBAs-based multi-scale DC-CUNets segmentation network. Experimental results show that the average segmentation accuracy of proposed algorithm is 7.04% higher than that of the multi-scale DC-CUNets segmentation network based on DBAs, and the DICE coefficient is improved by 4.89%. © 2021 IEEE.

关键词： Long short-term memory

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Towards Automatic Detection and Quantification of Mildew on Grape Leaf Disks 18

Towards Automatic Detection and Quantification of Mildew on ...

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18th international conference on signal processing and Multimedia Applications (SIGMAP)

作者： Iqbal, Razib Sargent, Kyle Kovacs, Laszlo Missouri State Univ Coll Nat & Appl Sci Springfield MO 65897 USA

ISBN: (纸本)9789897585258

Downy and powdery mildews are the most serious diseases of the grapevine. A sustainable way to control these pathogens is the breeding and deployment of resistant grape cultivars. For breeding efforts to be effective, accurate quantification of the resistance phenotype is essential. In this paper, we present a computerbased image recognition, processing, and analysis technique for enhancing the detection and quantification of Plasmopara viticola and Erysiphe necator the causal agents of downy and powdery mildew, respectively. We propose a multi-step approach that utilizes background removal and Hue-Saturation-Value (HSV) masking as opposed to multi-faceted color channel breakdowns, photo texture evaluations, or classification-based algorithms for the detection of mildew. Our experimental results show that our method provides reliable results and fast performance.

关键词： Background Removal Downy Mildew Grape Leaf HSV Masking image Analysis

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Analysis of Deep Learning algorithms for image Denoising 1

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1st international conference on Advances in signal processing and Communication Engineering (ICASPACE)

作者： Choudhary, Nikita Sharma, Rakesh Natl Inst Technol Hamirpur Elect & Commun Engn Dept Hamirpur 177005 Himachal Prades India

ISBN: (数字)9789811955501

ISBN: (纸本)9789811955525;9789811955501;9789811955495

A deep learning-based image denoising model for removing non-additive non-gaussian noise from images is proposed in this paper. images are considered unstructured data, and these are easily corrupted in the complex environment. Due to the noise in the digital images, poor image quality-related issues arise in the images. image noise reduction is an issue in the realm of image processing. Learning-based algorithms for image denoising have gotten a lot of focus because of their superior denoising performance. Deep learning models are neural networks that have multi-layer structures. These structures are very helpful for extracting very complex features from input images and then remove noise. An extensive database of images is used to train and validate the model. The qualitative evaluation based on visual quality is used to assess the denoised images. The quantitative assessment with mean squared error, peak signal to noise ratio, and structural similarity index measure is used to evaluate the denoising outcomes.

关键词： image denoising Deep learning (DL) Convolutional neural network (CNN) Optimization method Mean squared error (MSE) Peak signal to noise ratio (PSNR) Structural similarity index measure (SSIM)

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VVS: ACTION RECOGNITION WITH VIRTUAL VIEW SYNTHESIS

VVS: ACTION RECOGNITION WITH VIRTUAL VIEW SYNTHESIS

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IEEE international conference on image processing (ICIP)

作者： Peng, Gao Li, Yong-Lu Zhu, Hao Tang, Jiajun Xia, Jin Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst Qing Yuan Res Inst Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China

ISBN: (纸本)9781665441155

Action recognition research is usually in the single-view setting. But human action is not single-view based in many cases. A lot of simple action is composed of both body movements from the third-person view, and vision guidance from the first-person view. Therefore, linking two viewpoints of data is critical for action recognition algorithms. Currently, the scale of aligned multi-view dataset is small, which limits the advancement in this direction of research. To alleviate the data limitation, we present the novel Virtual View Synthesis (VVS) module. Instead of training and testing on small scale multi-view data, VVS is first pre-trained on multi-view data to generalize the multi-view "supervisory attention". Then it is incorporated into single-view action recognition model to transfer the ability of how to better observe the existing view based on experience from another view. Extensive experiments demonstrate that VVS can improve strong baselines on several single-view action recognition benchmarks.

关键词： Training image processing conferences Benchmark testing

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Intellectual Property for DWT based image Fusion

Intellectual Property for DWT based Image Fusion

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2021 international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

作者： Rajeshwari, B. Swarna Kulkarni, Tanvi Varsha Bajarangbali, B. PES University Dept.of ECE Bengaluru India

ISBN: (纸本)9781665425032

In Recent times, the requirement of high quality images and application of advanced image processing techniques in medical and remote sensing fields has radically increased. Therefore, there is an increase in the data storage requirements to accommodate these high quality images. With image fusion techniques, the data stored in two images is fused into a single image while retaining the necessary information from both the source images, thus reducing the data storage. Implementation of image fusion technique requires a huge amount of computation, for which a dedicated hardware can be designed. To make further enhancements to the image fusion algorithms, Field Programmable Gate Array(FPGA) is a suitable target as it supports reconfigurable computing. Simulation is done using Xilinx System Generator 2019.1 software where the implementation utilizes Zedboard Zynq-7000XC 7Z 020-CLG 484-1 device. The fused image outputs are assessed using well-known image quality metrics like Structural Similarity Index(SSIM), Entropy etc. © 2021 IEEE.

关键词： Discrete wavelet transforms

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Multifocus image Fusion using TE-CSR Technique 3

Multifocus Image Fusion using TE-CSR Technique

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3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Gogu, Lakshmi Bharath Aswin Kumer, S V Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering A.P Vaddeswaram India

ISBN: (纸本)9781665438117

image fusion contains data from different heterogeneous sources to obtain more accurate information about an inherent scene. There are various methods are available to increase the quality of an image for better visualization of the input image. The last few years, the familiar technique is used which provides efficient image fusion at the output called Sparse Representation (SR) with number if effective algorithms being proposed. But there are two drawbacks of SR methods, they are high noise sensitivity and the preservation of image details for further processing. To get the better of these drawbacks, the Convolutional Sparse Representation with Gradient Penalties (CSRGP) technique is adopted, which is a signal decomposition model used to perform image fusion of the deviation components of the processing frame. The energy components of an image are also combined using the technique called averaging. To perform the reconstruction of an image after it is being fused, the inverse Taylor expansion is adopted. There is a gap between the image descriptions, if the existing decomposition algorithms are used. It can be suppressed by the proposed method by introducing the new method which can preserve the image details. The sparse patch coding plays an important role to limit the ability to preserve image details along with the SR based approaches. The efficiency and performance measure of the proposed method is demonstrated with the experimental results. © 2021 IEEE.

关键词： image fusion

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Research on image Dimensionality Reduction algorithms 4

Research on Image Dimensionality Reduction Algorithms

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4th international conference on Sensors, signal and image processing, SSIP 2021

作者： He, Zulun Zhang, Jingjun Shanghai Dehong International School China Ningbo Zhenhai Jiaochuan Academy China

ISBN: (纸本)9781450385725

This paper summarizes the principle of the Scale Invariant Feature (SIFT), Principal Component Analysis (PCA), and PCANet. Also, the paper uses IPython to realize the similarity comparison using SIFT and handwriting recognization using PCANet, then calculate the precision and recall of the result. The main thing of the SIFT is to find the key point descriptor through the Scale-space, Gaussian Pyramid, and Difference of Gaussian Pyramid (DoG), while the primary purpose of PCA is to find the eigenvalue and eigenvector through calculating the covariance. The combination of the PCA and neuro network, PCANet is divided into three stages. © 2021 ACM.

关键词： Principal component analysis

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Development of Contactless Employee Management System with Mask Detection and Body Temperature Measurement using TensorFlow 6

Development of Contactless Employee Management System with M...

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6th international conference on Wireless Communications, signal processing and Networking (IEEE WiSPNET)

作者： Ulleri, Pranesh Mukund, S. Sudarshan, K. Zenith, Kiran Shibu, Sai N. B. Amrita Vishwa Vidyapeetham Dept Comp Sci Engn CSE Amritapuri India Amrita Vishwa Vidyapeetham Ctr Wireless Networks & Applicat WNA Amritapuri India

ISBN: (纸本)9781665440868

CoVID-19 pandemic signified the importance of social distancing and non-contact routines. Even after the pandemic, everyone should abide by the same protocols for a hygienic life. Institutions must put in place pre-emptive measures before people return. These include marking the presence of employees and monitoring their health status. This paper focuses on developing a contactless employee management system with sensor fusion and facial recognition technology. image processing capability in the system track employees entering the institution. Body temperature sensor collect the health status of each employee at the entrance. With the onboard cameras and deep learning algorithms, the system also makes sure if the employee is wearing a mask or not. The aim is to authenticate an employee and check whether they abide by the protocols of the institution. Ease of use, maintenance, and low-cost installation is the motivation behind the system design. The best beneficiaries are educational institutions and corporate offices. The system records employee health data and uses it for the contact tracing. This paper discusses the system design and algorithms of the non-invasive employee management system.

关键词： Raspberry Pi Deep Learning image processing Facial Recognition CoVID Protocol

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