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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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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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Covid-19 Chest CT Scan image Classification Using LCKSVD and Frozen Sparse Coding

Covid-19 Chest CT Scan Image Classification Using LCKSVD and...

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international conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021

作者： Liyanage, Kaveen Ramezani, Fereshteh Whitaker, Bradley M. Montana State University BozemanMT59717 United States

ISBN: (纸本)9789811638794

The coronavirus disease 2019 (COVID-19) is a fast transmitting virus spreading throughout the world and causing a pandemic. Early detection of the disease is crucial in preventing the rapid propagation of the virus. Although Computed Tomography (CT) technology is not considered to be a reliable first-line diagnostic tool, it does have the potential to detect the disease. While several high performing deep learning networks have been proposed for the automated detection of the virus using CT images, deep networks lack the explainability, clearness, and simplicity of other machine learning methods. Sparse representation is an effective tool in image processing tasks with an efficient algorithm for implementation. In addition, the output sparse domain can be easily mapped to the original input signal domain, thus the features provide information about the signal in the original domain. This work utilizes two sparse coding algorithms, frozen dictionary learning, and label-consistent k-means singular value decomposition (LC-KSVD), to help classify Covid-19 CT lung images. A framework for image sparse coding, dictionary learning, and classifier learning is proposed and an accuracy of 89 % is achieved on the cleaned CC-CCII CT lung image dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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

Towards Automatic Detection and Quantification of Mildew on ...

引用

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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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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PolSAR image Classification Based on Resblock Combined with Attention Model 6

PolSAR Image Classification Based on Resblock Combined with ...

引用

6th international conference on signal and image processing, ICSIP 2021

作者： Yang, Zhongao Zhang, Qingyuan Chen, Weidong Chen, Chang University of Science and Technology of China School of Microelectronics Anhui Hefei China University of Science and Technology of China Department of EEIS Anhui Hefei China

ISBN: (纸本)9780738133737

Deep learning technology can effectively extract the rich polarimetric information contained in polarimetric Synthetic aperture radar (PolSAR) data. However, the lack of labeled samples limits its performance. The majority of methods use iterative algorithms or need preprocessing on dataset to expand the labeled sample are time consuming. In this paper, an end-to-end method which can better utilize mid-level features and the input polarimetric features of CNN network is proposed. First, high-dimensional polarimetric features are extracted from raw data and sent into the CNN network to produce sufficient polarimetric information, then the Convolutional Block Attention Module (CBAM) is not only inserted into the network layer but also after the input data to achieve better classification performance and accelerate network convergence under tiny amounts of labeled samples. Meanwhile, a feature selection method is proposed based on the channel attention module to reduce the memory consumption and computational complexity while maintaining performance. The experiments tested on Flevoland dataset prove the effectiveness of proposed method compared with traditional method. © 2021 IEEE.

关键词： image classification

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LOCALIZING FEATURES WITH MASKING FOR SATELLITE AND DEBRIS CLASSIFICATION

LOCALIZING FEATURES WITH MASKING FOR SATELLITE AND DEBRIS CL...

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28th IEEE international conference on image processing Challenges (IEEE ICIP)

作者： Chaudhary, Shubham Jain, Parima Jakhetiya, Vinit Guntuku, Sharath C. Subudhi, Badri N. Indian Inst Technol Jammu Jagti India Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9781665426015

In this work, we propose a localization and masking-based satellite and debris classification technique. SPAce-craft Recognition leveraging Knowledge of space environment (SPARK) dataset consists of 120K images where both RGB and corresponding Depth images are available. However, the depth images are noisy and inaccurate and significantly affect the classification task performance. To address this issue, we first create mask images of the RGB images which are used as input to the Convolutional Neural Network (CNN) for efficient classification of different satellites and debris. The depth images are first de-noised and hole filled using a simple morphological opening operation. Then masked images are calculated using both RGB and processed depth images. This masking operation provides two advantages: 1. it removes noise and fills the holes in the depth images and 2. it highlights satellites and debris while suppressing other information which does not contribute towards the classification task. We use the pre-trained EfficientNet B4 architecture and fine-tuned it with an edition of Global average pooling (GAP) and three dense layers. Our results show that the inclusion of the masking operation significantly improves the overall classification performance, achieving 97.76% accuracy on the validation data.

关键词： Location awareness Satellites image recognition conferences Classification algorithms Convolutional neural networks Sparks

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