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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1643 条记录，以下是1611-1620 订阅

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Optimization of Aquaponics System Efficiency Based on Artificial Intelligence Approach

Optimization of Aquaponics System Efficiency Based on Artifi...

引用

Computing and Machine Intelligence (ICMI), international conference on

作者： Jayden Liu William Jiang The Harker School San Jose USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This paper aims to design and implement an ML-based approach to learn from NeuroAqua - the AI and IoT-based aquaponics system set up in our previous research at both a lab setting and larger-scale Ouroboros Aquaponics Farm (Half Moon Bay, CA) to enhance system stability and efficiency. Utilizing the data gathered from the wireless sensors, a structured database was formed to store the aquaponics environmental conditions, water quality, nutrient components, and plant images. We used the ML model to find the important factors having the largest impact on plant growth and their optimal amount levels. First, computer vision with image processing was applied to develop auto plant growth monitoring and to measure plant growth rate as the target variable more accurately and automatically for ML. Then feature engineering on the input variables was performed to enhance model performance and accuracy for a smaller dataset. ML algorithms including Linear Regression, Bagging Regressor, Decision Tree, Random Forest, XGBoost and Artificial Neural Network were applied and evaluated based on key performance metrics. The findings show that XGBoost outperformed the other models with 91.6% accuracy and also had the lowest MAE. Random Forest came in second with 90.9% accuracy and then Bagging Regressor in third with 88.5% accuracy. Lastly, according to the feature importance analysis conducted on the best model of XGBoost, Nitrogen had the largest impact on plant growth, followed by Nitrate, Nitrite, Light, and Phosphorus. Hence the initial results would recommend to closely monitor these top important factors together with plant growth in NeuroAqua's monitoring applications.

关键词： Wireless communication Wireless sensor networks Accuracy Databases image processing Computational modeling Sensors

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An Efficient and Dynamical Way for Local Feature Extraction on Point Cloud 20

An Efficient and Dynamical Way for Local Feature Extraction ...

引用

Proceedings of the 3rd international conference on Control and computer vision

作者： Xu Rui Chaochen Gu Zhenghua He Kaijie Wu Shanghai Jiao Tong University China Shanghai Jiao Tong University china

ISBN: (纸本)9781450388023

This paper presents a flexible module that utilizes the 3D position attention mechanism to extract contextual features from local regions of point cloud. The key point is to create an effective representation of local features. Due to the irregularity of point cloud, previous algorithms for point cloud processing have not fully explored how to enhance the extraction of local features. Inspired by the position attention mechanism in the 2D image segmentation algorithm, we propose a Point Attention Graph (PAG) module that can be used to improve the fusion of local features and make it better and faster. The PAG module uses the point attention mechanism to adaptively calculate the interaction between all nodes of the local graph. It can efficiently definite the relations of local points to enhance the performance of feature extraction both in accuracy and time efficiency, especially compared with some related models like PointWEB. Experiments show that our method can be effectively applied to semantic segmentation datasets.

关键词： scene segmentation 3D attention mechanism point cloud neural network

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Autonomous FOREX Trading Agents 19

Autonomous FOREX Trading Agents

引用

Proceedings of the 2019 3rd international conference on Advances in image processing

作者： Wang Ye Wang Duo Continuous Education College Guiyang University Guiyang China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781450376754

In this paper we describe an infrastructure for implementing autonomous Forex trading agents without human supervision; the agents are based on traditional trading strategies including ARIMA+GARCH, Kalman Filter, expert system, empirical experiences, etc. the infrastructure of combined above strategies is rule based, which are capable of implementing traditional trading algorithms, rules of expert systems and empirical experiences from third parties; We used this infrastructure for four major foreign currency pairs trading, i.e. USD/JPY, USD/CHF, EUR/USD, GBP/USD, with daily historical data dated from 1st January 2003 until 31st December 2018; the simulated trading results of the agents show that the suggested infrastructure is profitable and worth further research.

关键词： Autonomous Infrastructure Forex trading Agents

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A No-Reference image Quality Assessment Method Based on Random Forest for Power Scenario

A No-Reference Image Quality Assessment Method Based on Rand...

引用

international conference on Artificial Intelligence and computer Information Technology (AICIT)

作者： Junwei Li Jijun Zeng Tianwen Kong Huaquan Su Yang Wu Yejing Huo Shiting Wu Hanyu Shi Information Center Guangdong Power Grid Co. Ltd. Guangzhou China Dept. School of Computer Science Guangdong University of Technology Guangzhou China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China Dept. School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

No-reference image quality assessment (NR-IQA) is a crucial task in computer vision, aiming to evaluate the visual quality of images solely through distorted images without the need for original (reference) images. This evaluation method is particularly useful in practical applications where obtaining the original image is not feasible, such as in the task scenario discussed in this article: assessing whether a camera is blurred, overexposed, etc., in power grid scenarios. Traditional no-reference image quality assessment methods usually face issues such as over-reliance on manually designed features, which may not comprehensively capture all aspects of image quality, and good performance on specific datasets but significant performance degradation across different datasets or types of image distortion, resulting in poor consistency with actual subjective scores. Although with the continuous development of deep learning, many models are able to extract the features of an image with relatively high efficiency for effective evaluation, many of the models require high computational resources, which is not conducive to the needs of real-world scenarios where low-computing power is deployed to achieve timely image quality assessment. In order to solve these problems, we propose a random forest-based approach for reference-free image quality assessment in power scenarios. First, we collect original power scene images and perform image expansion, including abnormal and normal images, to increase the diversity of the dataset. The expanded images were merged with the original images to form a balanced dataset. Next, we take the merged dataset as input and perform data processing, including random cropping and simulated image damage, to enhance the model's adaptability to different observation viewpoints and damage scenarios. The processed images are used for feature extraction, and these features are subsequently used to train the random forest model. The experimental result

关键词： image quality Deep learning Adaptation models Computational modeling Feature extraction Power grids Power industry Computational efficiency Random forests Monitoring

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Partial Relay Selection Based Energy Harvesting Cooperative System with TAS and Outdated Channel State Information 2019

Partial Relay Selection Based Energy Harvesting Cooperative ...

引用

Proceedings of the 3rd international conference on vision, image and Signal processing

作者： Kehinde O. Odeyemi Pius A. Owolawi Department of Computer Systems Engineering Tshwane University of Technology Pretoria South Africa

ISBN: (纸本)9781450376259

In this paper, the performance of an energy harvesting based partial relay selection (PRS) cooperative system with transmit antenna selection (TAS) and outdated channel state information (CSI) is presented. The system dual-hops links are assumed to follow Rayleigh distribution and the relay selection is based on outdated CSI of the first link. The amplified-and-forward (AF) relay nodes are equipped with a single receive antenna and multiple transmit antenna while the destination utilized multiple receive antenna. Consequently, the cooperative system then employs the TAS technique at the relay nodes and maximum ratio combining (MRC) scheme is used at the destination. Based on this, the analytical closed-form expressions for the outage probability and throughput in delay-limited transmission mode are derived. The results demonstrated that the energy harvesting time, relay distance, channel correlation coefficient, the number of relay transmit antennas and destination received antenna have significant impact on the system performance. The accuracy of the derived expressions is validated by Monte-carol simulation.

关键词： Energy Harvesting Partial relay selection Maximum ratio combining Transmit antenna selection Rayleigh distribution

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Epidemic Prevention System Based on Voice Recognition Combined with Intelligent Recognition of Mask and Helmet 2021

Epidemic Prevention System Based on Voice Recognition Combin...

引用

2021 3rd international conference on Video, Signal and image processing

作者： Huaming Zhou Aili Wang Meixin Li Yinghui Zhao Yuji Iwahori Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology China Computer Science Chubu University Japan

ISBN: (纸本)9781450385886

At present, COVID-19 cross-infection is easy to occur in dense places such as elevators. There are no epidemic prevention measures for construction site elevators on the market, and most of them require manual temperature measurement and reminders to wear masks and helmets to avoid the spread of the epidemic. This paper designs an intelligent epidemic prevention system for the elevator ride process in a modern construction site environment, which can achieve non-contact human temperature measurement, mask and helmet recognition and voice call elevator function. The system uses Arduino UNO as the control core, Kendryte K210 as machine vision processing module, non-contact infrared temperature sensor MLX90614, and voice recognition sensor LD3320. The system has the functions of non-contact temperature detection, mask/helmet recognition(YOLOv3) and voice call elevator. Experimental results showed that the recognition accuracy rate of helmet, mask, voice call elevator is 91.5%, 92.0% and 93.0% respectively. The temperature measurement accuracy rate is 0.2℃, which can effectively prevent the spread of the epidemic caused by contact and breathing, and has the advantages of stable, intelligent, and safe work.

关键词： mask recognition infrared temperature measurement YOLOv3 helmet recognition voice call elevator

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Generative Transferable Adversarial Attack 19

Generative Transferable Adversarial Attack

引用

Proceedings of the 3rd international conference on Video and image processing

作者： Yifeng Li Ya Zhang Rui Zhang Yanfeng Wang Cooperative Medianet Innovation Center Shanghai Jiao Tong University China Shanghai Jiao Tong University China

ISBN: (纸本)9781450376822

Despite their superior performance in computer vision tasks, deep neural networks are found to be vulnerable to adversarial examples, slightly perturbed examples that can mislead trained models. Moreover, adversarial examples are often transferable, i.e., adversaries crafted for one model can attack another model. Most existing adversarial attack methods are iterative or optimization-based, consuming relatively long time in crafting adversarial examples. Besides, the crafted examples usually underfit or overfit the source model, which reduces their transferability to other target models. In this paper, we introduce the Generative Transferable Adversarial Attack (GTAA), which generate highly transferable adversarial examples efficiently. GTAA leverages a generator network to produce adversarial examples in a single forward pass. To further enhance the transferability, we train the generator with an objective of making the intermediate features of the generated examples diverge from those of their original version. Extensive experiments on challenging ILSVRC2012 dataset show that our method achieves impressive performance in both white-box and black-box attacks. In addition, we verify that our method is even faster than one-step gradient-based method, and the generator converges extremely rapidly in training phase.

关键词： Generative model Adversarial attack image-to-image translation Transferability Feature representations

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“FOLO”: A vision-Based Human-Following Robot

“FOLO”: A Vision-Based Human-Following Robot

引用

2018 3rd international conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2018)

作者： Evan Chen Department of Information Art ＆ Design Tsinghua University

The main challenge in following robot is solving the real-time user localization problem. In this paper we introduce our solution for it together with our whole robot system called "FOLO". Using extra-tool（such as Bluetooth） may cause inconvenience in interaction;3 D detector plus tracker ID approaches are at risks of computing consume and ROI flash;Approaches based on 2 D appearance have challenges on appearance change, re-detecting and complex background. We present a 2 D-appearance approach on "FOLO" which can work overcome above issues. Our approach utilizes consensus of corresponding method to track, and then, updates features by supervised learning into classifier cascade to against challenges of re-detecting and complex background. Moreover, we give pre-processing procedure on each frame to sharpen edges on image to enhance quality of tracking and use adaptive background to overcome challenge of complex background. This paper illustrates that our tracking approach can work against common challenges of tracking in our own designed experiments, has a rapid speed over 25 fps and can achieve state-of-the-art results. We use two-layer-PID method to control "FOLO" for a long-term task which allows "FOLO" succeeding to follow user in office environment.

关键词： Human-following Learning Detection Autonomous navigation

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CropViT: A light-weight Transformer Model for Crop Disease Detection

CropViT: A light-weight Transformer Model for Crop Disease D...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： G Chemmalar Selvi H J Charan Dinesh Kumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Agricultural industry has grown significantly bring sustainable farming practices in improving the food quality, enhancing agricultural productivity and global food security. However, the crop yield and its quality are impacted when the timely identification and management of the crop diseases are not promptly solved. Thus, the crop disease detection becomes the critical aspect in the smart agriculture. Several researchers addressed this crop disease detection by developing optimized solutions through machine learning, deep learning and image processing techniques. However, these techniques face serious challenges when deployed in real-time crop disease identification problems like annotated training images, capturing long-range dependencies, adaption to real-time changes evolving over time and so on. This leads to performance degradation seeking for more powerful applications like some advanced deep learning techniques, in particular, the transformer models. In this paper, a novel crop disease detection model known as CropViT was proposed based on vision Transformer. This CropViT was developed by fine-tuning the architecture of existing vision Transformer and PlantVillage dataset was used in the experimental study. Nine crop species were selected and trained using CropViT whose performance was then compared with traditional Convolutional Neural Network model. The experimental study clearly showed the highest mean accuracy of 98.64% for CropViT when compared to 95.52% mean accuracy for traditional CNN. The experimental results also outperformed other existing state-of-the-art models. Finally, the work was concluded with the discussion of experimental results and its future scope of work.

关键词： Deep learning Adaptation models Accuracy Crops Food security Transformers Real-time systems

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Temporal Consistency Based Deep Face Forgery Detection Network 1

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3rd international conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Peng, Chunlei Zhang, Wenbo Liu, Decheng Wang, Nannan Gao, Xinbo State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University Xi’an710071 China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi’an710071 China State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Xi’an710071 China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (数字)9783030624637

ISBN: (纸本)9783030624620

With the rapid development of deep learning techniques as well as increasingly more visual information being made publicly available on the Internet, image translation methods have achieved great progress and encouraging performance. The manipulation and fabrication of visual information has become accessible and difficult to distinguish by the naked eye, which will have adverse effects on cloud and communication security. Thus, face forgery detection techniques have recently attracted increasing attention. Most recent works regard the face forgery detection problem as the typical image classification task, ignoring the exploration of inherent properties of forgery visual information itself. In this paper, we first explore the inherent limitation of fake videos, and find that the temporal consistency could help distinguish fake faces from real faces. A temporal consistency based deep face forgery detection network is proposed to directly detect fake videos when given multiple consistent video frames. The proposed method effectively considers the frame consistency property and achieves promising detection performance. Experimental results on the face forgery detection dataset demonstrate the superior performance of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

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