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Deformable Multi-Scale Network for Snow Removal in Video 27th

Deformable Multi-Scale Network for Snow Removal in Video

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27th international conference on pattern recognition, ICPR 2024

作者： He, Runlin Zhou, Gang Xue, Tianhao Liu, Zhaoxi Jia, Zhenhong Key Laboratory of Signal Detection and Processing Department of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9783031781247

Snowfall severely degrades outdoor video visibility while reducing the performance of subsequent vision tasks. Although video recovery methods based on deep learning have achieved amazing accomplishments, video snow removal still faces problems such as varying scales and intricate trajectories of snowflakes, which makes it difficult to remove snowflakes and easy to create artifacts on moving objects. To address these issues, we propose a deformable multi-scale video desnowing network. Specifically, we design a multi-scale pseudo-3D residual block(MSRB-P3D) that can effectively remove snowflakes of different scales. Furthermore, a deformable large kernel attention 3Dblock(D-LKA 3Dblock) is introduced to capture the inter-frame dynamic information and reduce the artifacts. Due to the scarcity of dataset, we proposed a new dataset named Synthetic and Real Snowy Video Dataset(SRSVD). Extensive experiments have proven that our proposed method not only outperforms other state-of-the-art methods on both synthetic and real snowy videos, but also effectively improves performance on subsequent vision task. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Snow

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Design and Implement for Intelligent Four-legged Robot Dog SPRESENSE -Base 4

Design and Implement for Intelligent Four-legged Robot Dog S...

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4th international conference on Frontiers Technology of Information and computer, ICFTIC 2022

作者： Kang, Xiaona Shen, Sai Chengdu Neusoft University Dept. School of Computer and Software Chengdu China

ISBN: (纸本)9798350321951

this paper mainly describe the design and implementation of the intelligent four-legged robot dog SPRESENSE-base. It tries to simulate the real dog's behavior. It can walk, run, talk with you, and active track. this article take it as example to study how to design and implement the intelligent four-legged robot dog. It use the self-developed Sobot bionic quadruped robot control system;SpotMicroAI open source model structure was used to construct the hardware model;Sony SPRESENSE development board is used for the main board;Using gyroscope for detecting gait;Using ultrasonic wave for detecting obstacles ahead;using laser radar for detecting the environment around;using voice control module for voice recognition;Using image recognition processing technology to realize the active track. After more than one year of research and development, we have realized the mechanical model of the four-legged robot dog;it can follow the command to walk, trot and other gaits;it can automatically avoid obstacles ahead. the system also has the characteristics of high execution efficiency, portability, compatibility, reliability, practicability and easy expansion because of using C/C++ development language. © 2022 IEEE.

关键词： image recognition

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MR image Denoising Based on Improved Multipath Matching Pursuit Algorithm 5th

MR Image Denoising Based on Improved Multipath Matching Purs...

引用

5th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Li, Chenxi Luo, Yitong Yang, Jing Fan, Hong Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9783031189098;9783031189104

Magnetic resonance images are accompanied by random Rician noise due to the influence of uncertain factors in the process of imaging, storage, which brings a lot of inconvenience to the subsequent processing of the image and clinical diagnosis. this paper proposes an improved multipath matching pursuit algorithm based on learning Gabor pattern dictionary atom for image reconstruction and denoising. Firstly, Gabor wavelet transform based on neurophysiological constraints is used to generate dictionary atoms that match the local features of the image;then this paper introduces adaptive differential evolution algorithm optimization to the process of solving multiple candidate atoms matching the local image features in each iteration of the multipath matching pursuit. It combines the advantages of adaptive differential evolution and multipath matching pursuit algorithm, not only avoids the genetic falling into the local optimal defect, but also obtains the best matching parameters with higher accuracy, and effectively reduces the computational complexity of the multipath matching pursuit. In the reconstruction experiment of the simulated MR images, compared with state-of-the-art denoising algorithms, our algorithm not only shows better denoising performance, but also retains more detailed information, and the running time is reduced nearly 50% than multipath matching pursuit;which fully shows the clinical application value.

关键词： Gabor pattern Multipath matching pursuit algorithm Adaptive differential evolution algorithm Dictionary atom MR image denoising

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Real Time Quality Detection and Automatic recognition of Concrete Structures: image processing Algorithm Based on Deep Learning 4

Real Time Quality Detection and Automatic Recognition of Con...

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4th international conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Liu, Bing College of Intelligent Manufacturing and Automobil Chongqing Vocational College of Transportation Chongqing402247 China

the safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. this algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set;then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. the maximum detection time is 366ms and the shortest is 213 ms. the successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value i

关键词： Decision making

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A Study of Neural Collapse for Text Classification 1

引用

4th international conference on Deep Learning theory and Applications (DeLTA)

作者： Feng, Jia Hui Lai, Edmund M-K Li, Weihua Auckland Univ Technol Sch Engn Comp & Math Sci Auckland New Zealand

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

In this study, we explore the phenomenon of neural collapse (NC) in text classification using convolutional neural networks (CNNs) applied to the AG News dataset [23]. Initially, our findings indicate the occurrence of NC, which initially underperforms compared to a non-collapsed CNN. However, upon closer examination, we uncover an intriguing insight: certain data points converge towards an unknown cluster during NC. Further analysis reveals that this additional cluster represents an additional topic within the dataset, challenging the initial assumption of four distinct classes in AG News. this significant discovery suggests a promising research direction, where NC can serve as a tool for cluster discovery in semi-supervised learning scenarios.

关键词： Neural networks Natural language processing Deep learning Semi-supervised learning Neural collapse Text classification

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ColDBin: Cold Diffusion for Document image Binarization 17th

ColDBin: Cold Diffusion for Document Image Binarization

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17th international conference on Document Analysis and recognition (ICDAR)

作者： Saifullah, Saifullah Agne, Stefan Dengel, Andreas Ahmed, Sheraz German Res Ctr Artificial Intelligence GmbH DFKI Smart Data & Knowledge Serv SDS Trippstadter Str 122 D-67663 Kaiserslautern Germany RPTU Kaiserslautern Landau Dept Comp Sci Erwin Schrodinger Str 52 D-67663 Kaiserslautern Germany DeepReader GmbH D-67663 Kaiserslautern Germany

ISBN: (纸本)9783031417337;9783031417344

Document images, when captured in real-world settings, either modern or historical, frequently exhibit various forms of degradation such as ink stains, smudges, faded text, and uneven illumination, which can significantly impede the performance of deep learning-based approaches for document processing. In this paper, we propose a novel end-to-end framework for binarization of degraded document images based on cold diffusion. In particular, our approach involves training a diffusion model with the objective of generating a binarized document image directly from a degraded input image. To the best of the authors' knowledge, this is the first work that investigates diffusion models for the task of document binarization. In order to assess the effectiveness of our approach, we evaluate it on 9 different benchmark datasets for document binarization. the results of our experiments show that our proposed approach outperforms several existing state-of-the-art approaches, including complex approaches utilizing generative adversarial networks (GANs) and variational auto-encoders (VAEs), on 7 of the datasets, while achieving comparable performance on the remaining 2 datasets. Our findings suggest that diffusion models can be an effective tool for document binarization tasks and pave the way for future research on diffusion models for document image enhancement tasks. the implementation code of our framework is publicly available at: https://***/saifullah3396/coldbin.

关键词： Document Binarization Document image Enhancement Diffusion Models Cold Diffusion Document image Analysis

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An improved image segmentation model of FCN based on residual network

An improved image segmentation model of FCN based on residua...

引用

computer vision, image and Deep Learning (CVIDL), international conference on

作者： Wenchao Du Haotian Yang Teoh Teik Toe School of Computer science JiangSu University Zhenjiang China NTU Business AI Lab NanyangTechnological University Singapore

In this paper, the fcn model with Resnet as backbone is used for training and testing on voc2007 dataset. Twenty categories were trained, tested, and trained for around of about 10,000 pictures. there cognition and segmentation ofimages are realized. the overall recognition rate reaches 92.6%. the overall IoU reaches 68.3%, which is higher than the 5.6% of FCN-8s in theoriginal paper. We have adjusted the network structure of Resnet and improved the algorithm to achieve the accuracy of identification and segmentation with less time cost. the weight of thisalgorithm after training can be applied to specificoccasions of image recognition or object detection.

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SMART AND MULTI-WAY ATTENDANCE TRACKING SYSTEM USING AN image-processing TECHNIQUE 4

SMART AND MULTI-WAY ATTENDANCE TRACKING SYSTEM USING AN IMAG...

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Muthumari, M. Akash, V. Prudhvi Charan, K. Akhil, P. Deepak, V. Phani Praveen, S. Electronics and Communication Engineering Department Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of CSE PVPSIT Andhra Pradesh India

ISBN: (纸本)9781665401180

the primary goal of this system is to develop voice and face recognition for student attendance systems, which is beneficial in a variety of ways, most importantly it aids the lecturers in efficiently managing their students' attendance. Human biometrics is used as a medium for students to mark their attendance in this system. Cheating by fake attendance by students will be prevented because each face is unique, just like fingerprints. Smart attendance by voice and face recognition is a system that tracks attendance for students can be used in various institutes and can also be used in businesses. this system will also be useful in determining a student's eligibility for attendance. the goal of creating such a system is to automate the traditional method of taking attendance. the system's scope is that the device on which the PyCharm software is installed allows to run the python code and it works for any teacher or faculty. © 2022 IEEE

关键词： Face recognition

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Wide Line Segments Detection in Grey-Level images via Guided Scale Space Radon Transform

Wide Line Segments Detection in Grey-Level Images via Guided...

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international image processing, Applications and Systems conference (IPAS)

作者： Aicha Baya Goumeidane Djemel Ziou Nafaa Nacereddine Nawal Yala Research Center in Industrial Technologies CRTI Algiers Algeria Département D’Informatique Université de Sherbrooke Québec Canada

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Line segment detection is a fundamental procedure in computer vision, pattern recognition, and image analysis applications. the paper proposes a novel method for wide line segment detection especially endpoints determination based on the Guided Scale Space Radon Transform and Hessian orientations. the method begins by determining the centerlines of wide lines and then exploit the image Hessian orientations around these lines to define binary region support of the line segments and then detect endpoints. the method shows to be robust against blur and noise on synthetic images where, the evaluation of the outcomes reveals the correctness of the detection by achieving low errors. In addition, results on real images are very promising.

关键词： Radiography image segmentation image analysis Error analysis Welding Noise Transforms Robustness Iron pattern recognition

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Detecting plant pathogens: By using deep learning with VGG-16 CNN 4

Detecting plant pathogens: By using deep learning with VGG-1...

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4th IEEE international conference on Data Engineering and Communication Systems, ICDECS 2024

作者： Ashok, K. Rajalakshmi, B. Kumarswamy, Y.M. Likith, M. Reddy, Kiran Raghav S Computer Science & Engineering New Horizon College of Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350393354

Usually crops will get unused because of some plant diseases or with respect to transport issues and some storage facility. Greater than 15% of crops are left unused because of these diseases so it became a major issue to solve it. As many crops are getting wasted farmers need an automation system so that it could be helpful for the farmers to detect the plant diseases and help famers to overcome from it in prior to advance. Earlier Farmers used to detect the plant diseases by their eyes but it is not possible for all farmers to have a same way of recognizing the disease. With respect to the advance Artificial Intelligence concepts, we could able to adopt computer vision technique in the field of agriculture. We have a advance libraries available in the field of deep learning along with the user with developer creates a friendly environment to work together, with all these facilities make this concept to adopt in the field of agriculture to resolve the problem. As image Classification is the base to this project, we have used a Deep learning concepts as it offers a easy working with the images. the concepts used in this project is taking the defect leaves and to title them with a particular disease pattern. With this defect images we will apply a pixel manipulation in order to get a useful information from the defected leaves. After this next step we have a feature extraction which can be done by image segmentation and finally we will do the classification of defected leaves depending upon the patterns that is taken from diseased leaves. For the purpose of classification of diseases, we will use CNN (Convolutional Neural Network) and for the demo purpose we have chosen public dataset which contains 87k images (RGB type images) which includes both healthy as well as diseased leaves. © 2024 IEEE.

关键词： Crops

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