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Removing rain from images via combining detection and removal

Removing rain from images via combining detection and remova...

引用

2022 International conference on image, Signal processing. and pattern recognition, ISPP 2022

作者： Wen, Li Kangying, Wang Chao, Xiong Xiaochuan, Guo the college of Computer Science Sichuan University Chengdu610065 China

ISBN: (纸本)9781510654846

The performance of rain removal methods which are based on deep learning is largely affected by the designed models and training datasets for the image rain removal tasks. Most of current state-of-the-art focus on how to construct powerful deep models. But in this paper, we start from two perspectives of training dataset and model. We propose a novel rain model that includes a rain layer, a background layer and and a way how rainy image is generated. Based on this model, we develop a multi-task deep learning architecture that learns features of both the rain layer and the clean background layer. The additional information of rain layer is important because its loss function can provide additional powerful information to the network. Then we collected a large number of images of real rain streaks and outdoor scenes, and produced datasets for training. The effectiveness of our model and architecture was shown in tests on synthetic datasets. © 2022 SPIE.

关键词： Rain

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Dictionary Learning on Graph Data with Weisfieler-Lehman Sub-Tree Kernel and Ksvd 48

Dictionary Learning on Graph Data with Weisfieler-Lehman Sub...

引用

48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Liyanage, Kaveen Pearsall, Reese Izurieta, Clemente Whitaker, Bradley M. Electrical and Computer Engineering United States Montana State University Gianforte School of Computing BozemanMT United States

ISBN: (纸本)9781728163277

Graph representation has gained wide popularity as a data representation method in many applications. Graph embedding methods convert graphs to a vector representation and are an important part of a data processing.pipeline. In this paper, we utilize sparse dictionary learning techniques as a graph embedding solution. Sparse representation has notable applications in signal image processing. Inspired by the Graph2Vec algorithm, we aim to modify the Doc2Vec model training portion of the Graph2Vec by incorporating unsupervised dictionary learning. We investigate the viability of using the sparse dictionary learning technique KSVD for graph data. We train the dictionary on Weisfeiler-Lehman graph sub-tree kernel features. Furthermore, we use graph-based labeled data sets to compare classification results with several existing graph embedding methods. Findings show that using the learned sparse coefficients as features for a supervised machine learning algorithm provides on-par classification results when compared to other graph embedding methods. © 2023 ieee.

关键词： Dictionary Learning Graph embedding KSVD Sparse representation

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Dyadic Human Relation recognition

Dyadic Human Relation Recognition

引用

2022 ieee International conference on Multimedia and Expo, ICME 2022

作者： Kogashi, Kaen Nobuhara, Shohei Nishino, Ko Kyoto University Japan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

We introduce a new dataset and novel method for dyadic human relation (DHR) recognition. DHR recognition is a new image understanding task that concerns the recognition of the type (i.e., verb) and roles of a two-person interaction. Unlike past human action and dyadic interaction detection, our goal is to extract richer information regarding the roles of actors, i.e., which person is acting on whom. For this, we introduce the DHR-Webimages dataset which consists of a total of 22,046 images of 51 verb classes of DHR with per-image annotation of the verb and roles. We derive a novel model for DHR recognition by consolidating information from three streams corresponding to the visual, pose, and spatial relation features of the two people. Experimental results show that our model achieves accurate DHR recognition robust to severe occlusions and lacking visibility of actors. We believe these results will lead to richer visual information extraction. © 2022 ieee.

关键词： computer vision

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GPU-accelerated Faster Mean Shift with euclidean distance metrics 46

GPU-accelerated Faster Mean Shift with euclidean distance me...

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46th Annual ieee-computer-society International computers, Software, and Applications conference (COMPSAC) - computers, Software, and Applications in an Uncertain World

作者： You, Le Jiang, Han Chang, C. Hwa Chen, Lingxi Cui, Xintong Zhao, Mengyang Hu, Jinyong Tufts Univ Dept Biomed Engn Medford MA 02155 USA Tufts Univ Dept Comp Sci Medford MA 02155 USA Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA Middlebury Coll Sch Art & Sci Middlebury VT 05753 USA Hosei Univ Dept Comp & Informat Sci Tokyo 1028160 Japan Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA

ISBN: (纸本)9781665488105

Handling clustering problems are important in data statistics, pattern recognition and image processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used to solve clustering problems. However, the meanshift algorithm is restricted by its huge computational resource cost. In previous research [1], we proposed a novel GPUaccelerated Faster Mean-shift algorithm, which greatly speed up the cosine-embedding clustering problem. In this study, we extend and improve the previous algorithm to handle Euclidean distance metrics. Different from conventional GPU-based meanshift algorithms, our algorithm adopts novel Seed Selection & Early Stopping approaches, which greatly increase computing speed and reduce GPU memory consumption. In the simulation testing, when processing.a 200K points clustering problem, our algorithm achieved around 3 times speedup compared to the state-of-the-art GPU-based mean-shift algorithms with optimized GPU memory consumption. Moreover, in this study, we implemented a plug-and-play model for faster mean-shift algorithm, which can be easily deployed. (Plug-and-play model is available: https://***/masqm/Faster-Mean-Shift-Euc)

关键词： Mean Shift Clustering algorithms GPU image segmentation Euclidean distance

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Artificial Intelligence Analysis for Small Object Detection in Urine Sediment images 49

Artificial Intelligence Analysis for Small Object Detection ...

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49th Annual conference of the ieee Industrial Electronics society, IECON 2023

作者： Cui, Jiaao Yu, Minming Chan, Sixian Wang, Hongqiang College of Computer Science and Technology Zhejiang University of Technology Hangzhou China Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Science Hefei China

ISBN: (纸本)9798350331820

Over recent years, artificial intelligence (AI) methods have reformed the zone of medical imaging. Many AI-based models have been created and improved to related medical image analysis and interpretation. Object detection in medical images is a fundamental task in computer vision. With the continuous development of deep learning technology in recent years, we have been able to achieve good detection performance for conventional objects, but it has still been challenging for small objects due to their small size. In this paper, we propose an artificial intelligence analysis method for object detection in urine sediment images for auxiliary diagnosis. Firstly, we introduce a New Path Aggregation Network (NPANet), which improves the multi-scale fusion section. It can be more beneficial to help detect relatively small cellular objects, thus reducing the rate of missed detection. Secondly, we design an Adaptive-IoU (AIoU) loss that can be more easily adopted to achieve better detection accuracy for the model. Finally, the experiments are conducted on our Dataset (named UriSed2K) and the public Urine Microscopic image Dataset, and achieve the state-of-the-art results. Especially, our detector reaches 61.8% on AP-{small} on the UriSed2K and our work also contributes to the development of medical image processing. © 2023 ieee.

关键词： Object detection

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Enhancing Human-computer Interaction: Hand Detection for Air Writing Utilizing NumPy and OpenCV 3

Enhancing Human-Computer Interaction: Hand Detection for Air...

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3rd International conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Shukla, Pranjal Das, Prasenjit Chandigarh University Department of Cse Punjab Mohali India

ISBN: (纸本)9798350342338

In recent years, there has been a remarkable increase in interest and challenges in image processing.and pattern recognition, specifically in the context of air writing. This exciting research area has significant potential to advance automation processes and improve human-machine interfaces in various applications. The emergence of faster computers, affordable high-performance video cameras, and the need for automated analysis of videos has led to an increase in the popularity of object tracking, a critical task in computer vision. The process of video analysis typically encompasses object detection, tracking, and behavior analysis. Object tracking involves four main aspects choosing the suitable object representation, selecting features for tracking, detecting the object, and tracking the object. Object tracking algorithms find applications in different domains, including vehicle navigation, video indexing, and surveillance that are automated. The objective of the paper is to create a software application for smart wearable devices that utilizes computer vision to track finger gestures in the air, functioning as a motion-to-text converter for air-writing. The technology will facilitate communication for people by enabling them to generate text for multiple purposes, like sending emails and messages, through intermittent gestures. This is a productive means of communication that curbs the usage of laptops and mobiles, making it particularly beneficial for individuals who are deaf. © 2023 ieee.

关键词： Air Writing computer Vision Digital sketching Gesture recognition Hand Detection Hand Tracking Interactive whiteboard

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Dense Pedestrian Detection Based on Multiple Link Feature Pyramid Networks 3

Dense Pedestrian Detection Based on Multiple Link Feature Py...

引用

3rd International conference on computer Vision and pattern Analysis, ICCPA 2023

作者： Huang, Tao Yang, Siyu Yu, Tingjian Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Software Nanchang Hangkong University Nanchang China

ISBN: (纸本)9781510667563

Pedestrian detection is widely employed in a variety of industries, including security, autonomous driving, and pedestrian flow statistics. However, because most pedestrians in real-world scenes are densely distributed with occlusion issues, scale variation and pose variability, majority pedestrian detection algorithms have low detection accuracy and a high miss detection rate. In this paper, we propose a new FPN neck module, Multiple Link Feature Pyramid Networks (MLFPN) for dense pedestrian detection, which innovative information transfer pathways and incorporation of Poly-Scale Convolution (PSconv) capable of long-range interaction into feature extraction tasks to reduce feature information loss and enhanced representation of effective pedestrian features, achieving 0.6MR-2, 0.35AP, and 0.21JI improvements on CrowdHuman dataset, improving detection accuracy and reducing missed detection rates. © 2023 SPIE.

关键词： Feature extraction

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Size and Defect Detection of Valve Based on computer Vision

Size and Defect Detection of Valve Based on Computer Vision

引用

2022 International conference on computer Graphics, Artificial Intelligence, and Data processing. ICCAID 2022

作者： Yan, Xiaobo Feng, Le Tan, Mian Yang, Yuan Zhang, Yu Wang, Lin Guizhou Key Laboratory of Pattern Recognition and Intelligent System China Guizhou Minzu University Guiyang550025 China Guiyang Fusheng Intelligent Technology Co. Ltd China

ISBN: (纸本)9781510663350

Engine valve is the core component of the engine, and its quality determines the performance of the engine. In industrial production quality inspection, it is necessary to detect the size of the valve and whether there are defects on the surface. Usually, the quality of the valve is determined by comparing the image of the valve surface with the standard image. However, the existing surface defect detection technology cannot detect the curved surface device. In order to solve this problem, this paper designs a valve size and defect detection method based on computer vision. The experimental results show that the method can quickly and accurately detect the rod diameter, groove radius and surface defects of the valve. The method is practical, robust and real-time. © 2023 SPIE.

关键词： computer vision

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8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819603121

The proceedings contain 158 papers. The special focus in this conference is on Life System Modeling and Simulation and Intelligent Computing for Sustainable Energy and Environment. The topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater image recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature processing.and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel Distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-Thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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MedDietAgent: An AI-based Mobile App for Harmonizing Individuals' Dietary Choices with the Mediterranean Diet pattern 46

MedDietAgent: An AI-based Mobile App for Harmonizing Individ...

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46th Annual International conference of the ieee Engineering in Medicine and Biology society, EMBC 2024

作者： Konstantakopoulos, Fotios S. Sfakianos, Michail Georga, Eleni I. Mavrokotas, Konstantinos I. Katsarou, Daphne N. Chalatsis, Konstantinos Zapadiotis, Charalambos Panousi, Anastasia Plimakis, Sifis Eleftheriou, Sofia Kanellou, Anastasia Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece Biomedical Research Institute Forth IoanninaGR 45110 Greece AppArt a Software Solutions Company Based in Athens GR 15343 Greece Dbc Diadikasia a Leading Business Consulting Firm in Athens 296 Kifissias Avenue & 40 Navarinou str HalandriGR 15232 Greece University of West Attica Department of Food Science and Technology Egaleo AthensGR 12243 Greece

ISBN: (纸本)9798350371499

Recently, there has been an increasing interest in applying technological advances to offer specific dietary recommendations in the field of nutrition and health. Dietary recommendation systems are advanced tools designed to assist individuals in making well-informed and health-conscious decisions on their food choices, taking into account their personal needs, preferences, and health targets or habits. In this study, we present an AI-based mobile app for harmonizing individuals' dietary choices with the pattern of the Mediterranean diet. A combination of computer vision, natural language processing. machine learning, and reinforcement techniques are used to record the nutritional information via images or speech and to generate dynamic recommendations tailored to the user's performance across key nutritional areas, encompassing calories, combined fats, proteins, carbohydrates, sugars, dietary fibers, sodium intake, fruits, vegetables, and dairy products. The image-based dietary assessment subsystem achieves a mean absolute percentage error of 3.73%, while the reinforcement learning subsystem achieves a 96% average reward. Then, a well-designed approach was taken to develop the MedDietAgent mobile app, using cutting-edge technologies and applying a simplistic approach. One of the key aspects of MedDietAgent is its ability to offer dynamic recommendations by monitoring the user's environment. © 2024 ieee.

关键词： Electronic health record

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