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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...

引用

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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Multimodal Deep Kernel Learning: Application to Disaster Response in Social Media Network 4

Multimodal Deep Kernel Learning: Application to Disaster Res...

引用

4th international conference on Information Communication and signal processing, ICICSP 2021

作者： Yang, Xuanqi Jiu, Mingyuan Sun, Yi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665407571

Recently multimodal learning has become a popular research topic because variety of types of sensors can be used to describe the events, for example, images and texts in the social network for disaster detection and response. It is useful because of different sources of information that data will cause heterogeneous problems. The kernel function can measure the similarity of data, and the fusion of different kernels can handle this problem well. This paper proposes a method based on multimodal deep kernel learning to detect specific categories of content posted on Twitter during a disaster for humanitarian response. Specifically, the convolutional neural network is used to extract image and text features, and obtain multiple kernel functions in each mode. Then a deep neural network is used to fuse multiple basic kernel functions corresponding to the two modalities. Finally, we use Support Vector Machine (SVM) for classification. Applying this method to a real disaster data set, and compared with the baseline, the results prove the effectiveness of our model. © 2021 IEEE.

关键词： image classification

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Densely connected convolutional networks for breast cancer histopathological image classification

Densely connected convolutional networks for breast cancer h...

引用

2021 international conference on signal image processing and Communication, ICSIPC 2021

作者： Li, Jie Chen, Jinling Zhao, Chengming Sichuan Chengdu610599 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

The computer-assisted classification of breast cancer histopathological image in the future is an essential method for the improvement of the diagnostic performance, thus reducing breast cancer deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs, many image classification tasks still remain challenging due to the insufficiency of training data and the lack of the ability to focus on improving classification efficiency. To address these issues, we share a densely connected convolutional network (DenseNet) model that is composed of 3 layers DenseNet, pooling layer, and a classification layer for breast cancer classification in microscopic images. Each layer of DenseNet contains 4 dense blocks. Each dense block jointly uses the dense connection and a novel attention learning mechanisms to increase its ability for discriminative representation. Meanwhile, the transfer learning algorithm is applied to determine the model parameters to extract the features of the patient image that is performed. In order to ensure sufficient data volume, a data enhancement method based on the quad-tree principle is proposed for high-resolution images. On the other hand, the classification probability of each part after dicing is fused by three algorithms of addition, product, and maximum. We evaluated our DenseNet model on the BreastKHis dataset. Our results indicate that the DenseNet model and data enhancement method we adopted can adaptively focus on the study of breast cancer histopathological image classification, thus achieving the state-of-the-art performance in breast cancer classification. The results of the experiments are in terms of patient-level and image-level accuracy. The best recognition accuracy increased to 90.9%-92.5% and 89.3%-91.8%, respectively, compared with previous studies. © 2021 SPIE.

关键词： image classification

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Research on wavelet transform technology and image processing performance based on matlab 2

Research on wavelet transform technology and image processin...

引用

2021 2nd international conference on signal processing and Computer Science, SPCS 2021

作者： Xie, Wendi Zhao, Yurong Wang, Yang Communication AnHui XinHua University AnHui HeFei230088 China

This paper first introduces the basic concept, principle and application of wavelet transform. Then, the performance characteristics of Haar wavelet, dbN wavelet and symN wavelet, two different threshold shrinkage algorithms Visushrink and Bayesshrink, and two common threshold functions are described in detail. Finally, Matlab software programming is used to realize the simulation of the original image based on different wavelet bases in the influence of Gaussian noise, Poisson noise and salt and pepper noise, different threshold acquisition algorithm, different threshold function, and compare their peak signal to noise ratio (PSNR) value to complete the image processing performance analysis. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Wavelet transforms

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Design of an Access Control System for Unmanned Bathroom Based on image processing Technology 2

Design of an Access Control System for Unmanned Bathroom Bas...

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2nd IEEE international conference on Electrical Engineering, Big Data and algorithms, EEBDA 2023

作者： Sun, Jichao Wang, Jun Liu, Tao Cheng, Bin Li, Yu Anhui Polytechnic University Wuhu China Chery Commercial Vehicle Company Limited Wuhu China

ISBN: (纸本)9781665462532

In order to improve the efficiency and intelligence of the access control management system, this paper uses image processing technology to identify the ID card number, and uses the gender results after identification as the basis for identifying the switch of the male and female bathroom access control system, and compares the membership information in the database with the chip information in the second-generation ID card and the image recognition information to verify the authenticity of the ID card. Therefore, an intelligent bathroom access control system based on image processing technology is developed. The system is mainly composed of image acquisition and reading module, image processing module, magnetic card induction module, signal processing module and signal execution module. The image processing module is based on image processing technology, and uses the powerful mathematical processing ability of MATLAB to complete a series of image processing processes such as eigenvalue data extraction. Identify whether it is a registered member by comparing with the data in the database. The signal processing module is based on server processing technology to realize unmanned automatic door opening. © 2023 IEEE.

关键词： Eigenvalues and eigenfunctions

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Action Recognition with Domain Invariant Features of Skeleton image 17

Action Recognition with Domain Invariant Features of Skeleto...

引用

17th IEEE international conference on advanced Video and signal Based Surveillance (AVSS)

作者： Chen, Han Jiang, Yifan Ko, Hanseok Korea Univ Sch Elect Engn 145 Anam Ro Seoul South Korea

ISBN: (纸本)9781665433969

Due to the fast processing-speed and robustness it can achieve, skeleton-based action recognition has recently received the attention of the computer vision community. The recent Convolutional Neural Network (CNN)-based methods have shown commendable performance in learning spatio-temporal representations for skeleton sequence, which use skeleton image as input to a CNN. Since the CNN-based methods mainly encoding the temporal and skeleton joints simply as rows and columns, respectively, the latent correlation related to all joints may be lost caused by the 2D convolution. To solve this problem, we propose a novel CNN-based method with adversarial training for action recognition. We introduce a two-level domain adversarial learning to align the features of skeleton images from different view angles or subjects, respectively, thus further improve the generalization. We evaluated our proposed method on NTU RGB+D. It achieves competitive results compared with state-of-the-art methods and 2.4%, 1.9% accuracy gain than the baseline for cross-subject and cross-view.

关键词： Convolutional neural networks

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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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Multifocus image Fusion using TE-CSR Technique 3

Multifocus Image Fusion using TE-CSR Technique

引用

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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Intellectual Property for DWT based image Fusion

Intellectual Property for DWT based Image Fusion

引用

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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image-TEXT ALIGNMENT AND RETRIEVAL USING LIGHT-WEIGHT TRANSFORMER 47

IMAGE-TEXT ALIGNMENT AND RETRIEVAL USING LIGHT-WEIGHT TRANSF...

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47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Li, Wenrui Fan, Xiaopeng Harbin Inst Technol Sch Comp Sci & X0026 Technol Harbin Peoples R China

ISBN: (纸本)9781665405409

With the increasing demand for multi-media data retrieval in different modalities, cross-modal retrieval algorithms based on deep learning are constantly updated. However, most of them have trouble with large model parameters and insufficient intrinsic nature between different modalities. We proposed a Light-weight Transformer Alignment Network (LTAN), which adopts the current mainstream visual and textual feature extraction methods. With convolutional neural network combined with light-weight transformer architecture and fully connected neural network, LTAN improves the generalization ability of the model while maintaining high performance. In order to extract visual features that lay emphasis on global details, enhancement paths are constructed to fuse precise location signals stored in low-level features with semantic information extracted from high-level to improve the model retrieval accuracy. It obtains the state-of-the-art results on image and sentence retrieval on MS-COCO and Flickr30k datasets. On the MS-COCO 1K test set, our model obtains an improvement of 3:9% and 2:5% respectively for the image and sentence retrieval tasks on the Recall@1 metric. The size of our model is 15% smaller than models using standard transformer as backbone.

关键词： Cross-modal retrieval deep learning multi-modal matching computer vision natural language

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