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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

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Spatial-Angular Multi-Scale Mechanism for Light Field Spatial Super-Resolution

Spatial-Angular Multi-Scale Mechanism for Light Field Spatia...

引用

2023 IEEE/CVF conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Gao, Chen Lin, Youfang Chang, Song Zhang, Shuo School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining China

ISBN: (纸本)9798350302493

Light Field (LF) cameras are promising due to their ability to capture both spatial and angular information of scenes. However, the trade-off between spatial and angular resolution significantly limits the real-world applications. In this paper, we propose a spatial-angular multi-scale decoupling network to reconstruct high-resolution LF images. Considering the epipolar geometry, we propose a spatial-angular multi-scale processing approach to explore the correspondence of sub-pixel information with different disparity ranges between sub-aperture images in LFs. We extract sub-pixel information from various dimensions and fuse it to generate global high-frequency details. Finally, we combine upsampled low-frequency and high-frequency details to generate high resolution results. To further filter the correct interpolation information, we use the shear operation to change the disparity range of the LF images and fine-tune the results. Experimental results on synthetic and real-world datasets demonstrate that our method outperforms other state-of-the-art methods in visual and numerical evaluations, especially on datasets with small disparity ranges. Furthermore, our approach fully considers the epipolar geometry of the LF image, enabling us to recover information that better maintains the imaging consistency of the LF. © 2023 IEEE.

关键词： Numerical methods

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A Comprehensive Analysis of Intrusion Detection Datasets: Evaluation, Challenges, and Insights 7

A Comprehensive Analysis of Intrusion Detection Datasets: Ev...

引用

7th international conference on image Information processing, ICIIP 2023

作者： Rani, Seema Kumar, Sanjeev Guru Jambheshwar University of Science And Technology Department of Computer Science And Engineering Hisar India

ISBN: (纸本)9798350371406

Intrusion detection is an important part of cybersecurity, to protect against unauthorized access and malicious activities within computer networks. This paper offers all-inclusive analysis of intrusion detection datasets, that aims to focusing on evaluation, challenges, and the valuable insights they offer to the ground of cybersecurity. Reviewing notable intrusion detection datasets, the paper evaluates each dataset's dimensions, variation, and data distribution. Additionally, issues with intrusion detection dataset adequacy are examined at, including data imbalance, a lack of diversity, and a lack of real-world representation. We learn important things about the positive aspects and negative aspects of employing the datasets that already exist for to this investigation. These observations help us comprehend how dataset quality impacts intrusion detection model performance. This paper accomplishes by offering recommendations and potential directions for the dataset generation, improvement, and sharing of intrusion detection datasets to address these challenges The insights presented in this study aim to direct researchers and practitioners towards better dataset exercise and improved intrusion detection capabilities as the cybersecurity landscape advances. © 2023 IEEE.

关键词： Intrusion detection

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Deep Bayesian image Set Classification Approach for Defense against Adversarial Attacks

Deep Bayesian Image Set Classification Approach for Defense ...

引用

2023 international conference on Digital image Computing: Techniques and Applications, DICTA 2023

作者： Mirnateghi, Nima Shah, Syed Afaq Ali Bennamoun, Mohammed Edith Cowan University Center for Ai and Machine Learning Australia The University of Western Australia School of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350382204

Deep learning has become an integral part of various pattern recognition and computer vision systems in recent years due to its outstanding achievements in object recognition, facial recognition, and scene understanding. However, deep neural networks (DNNs) are susceptible to being fooled with nearly high confidence by an adversary. In practice, the vulnerability of deep learning systems against carefully perturbed images, known as adversarial examples, poses a dire security threat in physical-world applications. To address this phenomenon, we present, what to our knowledge, is the first ever image-set-based adversarial defense approach. image set classification has shown exceptional performance for object and face recognition, owing to its intrinsic property of handling appearance variability. We propose a robust deep Bayesian image set classification as a defense framework against a broad range of adversarial attacks. We extensively experiment the performance of the proposed technique with several voting strategies. We further analyse the effects of image size, perturbation magnitude, along with the ratio of perturbed images in each image set. We also evaluate our technique with the recent state-of-the-art defense methods and single-shot recognition task. The empirical results demonstrate superior performance on the CIFAR-10, MNIST, ETH-80, and Tiny imageNet datasets. Our code is available at https://***/ai-voyage/***. © 2023 IEEE.

关键词： Object recognition

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An Augmented Perturbed Linear Mixing Model with Scaling Factors for Unmixing 8

An Augmented Perturbed Linear Mixing Model with Scaling Fact...

引用

8th international conference on Signal and image processing, ICSIP 2023

作者： Wang, Ning Bao, Wenxing Qu, Kewen Feng, Wei North Minzu University School of Computer Science and Engineering Yinchuan China XIDIAN University School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350397932

Due to different illumination conditions, complex atmospheric conditions and other reasons, the endmember spectral profile of the same feature shows visible variation at different locations in the hyperspectral image, a phenomenon known as the endmember spectral variability (ESV). The identified endmembers can be considered as changeable instances of reference endmembers, and it can be better constructed by scaling factors and perturbation. The scaling factors are suitable for constructing equal proportional changes and the perturbation can construct non-proportional changes. To address the problem that spectral variability is difficult to be constructed by perturbation or scaling factors alone, the article proposes an augmented perturbed linear mixing model (APLMM) by adding scaling factors on the perturbed linear mixing model (PLMM). The APLMM aims at incorporating spectral variability in the hyperspectral unmixing under the form of equal proportional and non-proportional changes of endmembers. In addition, to control the strength of the equal proportional changes, the article adds penalty terms for the scaling factors to the model. We use the alternating direction method of multipliers (ADMM) to optimise the variables in the APLMM algorithm. Experimental results on synthetic and real datasets demonstrate the effectiveness of the proposed model. © 2023 IEEE.

关键词： Mixing

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A mobile financial reimbursement information collection system based on OCR 2

A mobile financial reimbursement information collection syst...

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2nd international conference on Electrical, Electronics, and Information Engineering, EEIE 2023

作者： Xu, Huinan Lu, Ming Li, Lelin Chen, Yunuo Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University Wenzhou325035 China School of Computer and Artificial Intelligence Wenzhou University Wenzhou325035 China

ISBN: (纸本)9781510672840

In view of the problem that the financial bill of the mobile terminal is not fixed, resulting in serious distortion of the photos taken, and can not have a good recognition effect, this paper mainly studies the financial bill recognition accuracy of the mobile terminal based on OCR technology, and designs the relevant mobile terminal system. The whole research is composed of three parts: OCR recognition, image comprehensive processing and character classification recognition. In the part of text classification and recognition, decision tree and hypothesis tree search are selected to match the layout. The experiment selects many financial invoices as data sources, and tests them on mobile terminals in different environments. The experimental results show that the system has good results in financial bill recognition on mobile terminals. © 2024 SPIE.

关键词： Decision trees

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Predicting the Air Pollution Level and Creating an Awareness Platform 5

Predicting the Air Pollution Level and Creating an Awareness...

引用

5th international conference on Advancements in Computing, ICAC 2023

作者： Nuha, M.N. Wishvajith, G.K.N.P. Caldera, Amitha Weerasinghe, N.T. Weeratunga, G.S. Lokuliyana, Shashika Sri Lanka Institute of Infromation Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Infromation Technology Department of Computer Systems & Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study proposes a comprehensive method to reduce the problems posed by global air pollution. To measure pollution levels and raise public awareness, the research uses a comprehensive strategy that incorporates image processing, time series analysis, chatbot, and facemask recognition. Time series analysis is used to build prediction models by recognizing historical patterns, and image processing techniques are used to examine cloud pictures for precise pollution predictions. Chatbots provide real time communication and may educate the public on environmental facts, health risks, and safety procedures. Additionally, it has a facemask identification system that monitors and promotes the use of masks. Importantly this not only encourages users to take action to reduce pollution but also utilizes predictive models to help mitigate pollution's consequences. These components result in improved public health, environmental conservation, and sustainability. The outcomes reflect significant advantages, with evidence of accuracy and its positive impact on both awareness and proactive measures to combat air pollution. © 2023 IEEE.

关键词： Time series analysis

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Automated Script Evaluation using Machine Learning and Natural Language processing 2

Automated Script Evaluation using Machine Learning and Natur...

引用

2nd international conference for Innovation in Technology, INOCON 2023

作者： Chavan, Sagarika M Prerana, M.S. Bathula, Ramit Saikumar, Sreenath Dayalan, Geetha PES University Department of Computer Science Karnatka India

ISBN: (纸本)9798350320923

Correcting handwritten answer booklets manually can be a challenging task for professors, involving significant time and effort. To address this issue, the paper proposes an automated evaluation system that uses DL and NLP techniques. The suggested approach begins by extracting raw text from image files using a proven GCP OCR text extract model, which is well-known for its better accuracy and efficiency. Furthermore, Natural Language processing methods like BERT and GPT-3 are used to extract keywords and summarize extensive answers. The suggested technique gives marks that are usually comparable to those issued by manual evaluation. Furthermore, the article suggests a web tool that simplifies the evaluation procedure. The application outputs the raw text of student answers and the answer key, a synopsis of the student's response, and the marks gained based on the extracted keywords. © 2023 IEEE.

关键词： Long short-term memory

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Evaluation of Pseudorandom Pixel Placement in Enhancing Face Recognition using VGG-Face 5

Evaluation of Pseudorandom Pixel Placement in Enhancing Face...

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5th international conference on image, Video processing, and Artificial Intelligence, IVPAI 2023

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Dept. of Electrical Engineering and Computer Science Kanazawa University Kakuma-machi Ishikawa Kanazawa920-1192 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University Kumamoto860-8555 Japan

ISBN: (纸本)9781510674660

In efforts to enhance face recognition performance, techniques ranging from super-resolution methods to the use of Local Binary Pattern (LBP) and deep learning have been explored. Among these, the pseudorandom pixel placement (PSE) technique has demonstrated potential in face recognition. Nevertheless, its testing was previously limited to just 8 subjects. This study undertakes a comprehensive evaluation of the PSE technique with a larger sample, utilizing 2000 subjects from the DigiFACE1M dataset and leveraging the state-of-the-art VGG-Face deep learning model. Through experiments involving 10 different PSE patterns on 144.000 face images, our findings indicate that, compared to Regular Pixel Placement (REG), PSE achieved an improvement in average accuracy by 1.05%, reduced the standard deviation by 1.47%, and resulted in 31 additional subjects achieving 100% accuracy. We conclude that PSE consistently outperforms REG in face recognition tasks using the VGG-Face model across the majority of tested scenarios. © 2024 SPIE. All rights reserved.

关键词： Pixels

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High performance nonlinear least square optimization via Domain Specific Language 15

High performance nonlinear least square optimization via Dom...

引用

15th international conference on Graphics and image processing, ICGIP 2023

作者： Yu, Yansong Du, Zhenlong Li, Xiaoli College of Computer and Information Engineering Nanjing Tech University Nanjing211816 China

ISBN: (纸本)9781510675018

The least square method is common and classical in the regression analysis. It is often used to solve the convex optimization problem, but the traditional solving routine for least squares which is done by hand-written codes shows the disadvantages when dealing with common least square problems. One significant drawback for traditional solving routine is it is hard to work along with and produce high performance solver by non-professional users who do not have the knowledge of CPU/GPU architecture, and it is also a tough job to review or improve the solvers which already have been written, since many fine details that relate to the processor structure may be hard-coded in to the source code. In this paper, we propose a new domain specific language (DSL) for the producing of non-linear least square solver for research purpose with a back end of Gauss-Newton and Levenberg-Marquardt methods implemented in cuSPARSE and cuBLAS. The DSL paired with a C/C++ interface has a user-friendly syntax which can be easily used to write energy functions and generate GPU solvers which have the performance close to hand-written CUDA solvers. © 2024 SPIE. All rights reserved.

关键词： C++ (programming language)

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CNN-BASED UNSUPERVISED REGISTRATION of TIME-LAPSE MICROSCOPY image SEQUENCES

CNN-BASED UNSUPERVISED REGISTRATION of TIME-LAPSE MICROSCOPY...

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2023 international Workshop on Photogrammetric and computer vision Techniques for Environmental and infraStructure Monitoring, Biometrics and Biomedicine, PSBB 2023

作者： Anoshina, N.A. Sorokin, D.V. Laboratory of Mathematical Methods of Image Processing Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russia

image registration is widely used in live cell microscopy image analysis to compensate for the cell motion. It is a challenging task as the cell is not only moving (which causes rotation and translation), but also changes its form in time making the motion non-rigid. To address this, we propose a CNN-based unsupervised method for non-rigid registration of live cell image sequences. Our network predicts both the deformation field between a pair of images of the sequence and an affine transformation matrix for the cell motion compensation. The method can be used alone or in combination with other approaches. The proposed approach was successfully applied to real live cell microscopy image *** conducted an experimental comparison with existing methods including contour-based, intensity-based and a deep learning based joint denoising and registration method. In addition, we analyzed different deformation regularizers and their impact on the alignment results. In combination with contour-based method we outperformed the existing approaches in average registration accuracy for two metrics on the standard evaluation dataset. © Author(s) 2023. CC BY 4.0 License.

关键词： Cytology

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