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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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Freshy Pal: A Proposal of an Interactive Web based Tool to Automatically Detect Microbial Growth on White Bread

Freshy Pal: A Proposal of an Interactive Web based Tool to A...

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international conference for Convergence of Technology (I2CT)

作者： Harshee Pitroda Abhay Kolhe Department of Computer Engineering NMIMS University Mukesh Patel School of Technology Management & Engineering Mumbai India

Bread has long been a staple food in kitchens across the country and is devoured by billions of people worldwide. Despite being so widely consumed, the bread usually only remains fresh for a short while. Bread can develop molds and microbial growth, making it unhealthy to ingest and potentially harmful to one's health. As a result, it is necessary to determine whether the bread is safe to consume. Hence, Freshy Pal, an interactive web-based application tool was created, that makes significant use of new technologies such as image processing, machine learning, and deep learning to identify bread spoilage given an image of the slice of bread. The primary goal of designing this application is to assist users in determining whether bread is safe to ingest because bread spoilage can cause allergic responses and respiratory difficulties, further removing the clearly spoilt portion of the bread and consuming the remainder is equally harmful. So, after Freshy Pal classifies bread as unsafe to consume, it instructs the users on how to properly dispose of and handle the moldy bread. Freshy Pal also educates its users on how to keep their bread safe from mold growth. Two different methodologies were used to develop the system. The first approach employed extensive image processing and Machine Learning methods. The following machine learning algorithms were used: Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes, and Ada Boost. Random Forest and Ada boost achieved the greatest accuracy of 96.67%. The second approach employed Deep Learning-based image Classification utilizing Convolutional Neural Networks, here an accuracy of 98% was achieved using the pre-trained model VGG16. The outcomes of both techniques are contrasted on a variety of grounds. The deep learning-based method achieved higher accuracy, but it required longer training time and larger data size.

关键词： Deep learning Training Support vector machines Microorganisms image processing Neural networks Minerals

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An Effective Sentiment Analysis Classification for Opinion Prediction using Machine Learning Schemes

An Effective Sentiment Analysis Classification for Opinion P...

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international conference on computer Communication and Informatics (ICCCI)

作者： T.M. Saravanan M. Jagadeesan P.A. Selvaraj B.M. Rithika G. Karan R.C. Tamil Selvan Department of Computer Applications Kongu Engineering College Perundurai Tamil Nadu India

In the blooming of web technology, the rise of electronic commerce websites is rapidly increasing. Users started expressing their views similar to text reviews or comments. This research deals with the identification and extraction of the exact opinion of the customers across reviews, feedbacks, or comments. Pre-processing techniques are used to clean the dataset and the machine learning algorithm like k-nearest neighbor (KNN) is used as a solution to this problem, to have a good evaluation of sentiments (Positive, Negative, Neutral). In accumulation, the precision of the algorithm highly depends on the value of k and distance metric for classification. In our work we used k value of 6 and euclidean distance equation for classification of the KNN algorithm. The proposed KNN algorithm has been applied on publicly available standard review dataset repository, to show the exact nature and features based on customer’s opinions, and the accuracy of determining the feasibility of the k-nearest neighbor algorithm of 98% is predicted.

关键词： Measurement Sentiment analysis Machine learning algorithms image recognition Machine learning Prediction algorithms Classification algorithms

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image Enhancement Using HSV Color Space, DWT, and BiHE Techniques

Image Enhancement Using HSV Color Space, DWT, and BiHE Techn...

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international conference on Engineering Technology and their applications (IICETA)

作者： Tameem Hameed Obaida Salman Abd Kadum Fallah H. Najjar Hassan M. Al-Jawahry Technical Institute of Najaf Al-Furat Al-Awsat Technical University Najaf Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Faculty of Engineering The Islamic University Najaf Iraq

image enhancement is vital in computer vision and image processing applications. This study introduces an innovative approach combining HSV color space, Discrete Wavelet Transforms (DWT), and Bi Histogram Equalization (BiHE) techniques. By converting the image from RGB to HSV, we separate color and intensity information, allowing us to enhance the intensity component for improved visual quality. The DWT is then applied to decompose the image into frequency sub bands, enabling targeted manipulation of specific frequency components to enhance image details and overall appearance. Additionally, the BiHE technique enhances contrast while preserving local image details. The proposed method was evaluated on various images, including tissue, football, Lena, and yellow lily. The results show that the proposed method achieved high Peak Signal-to-Noise Ratio (PSNR) values, with tissue achieving 32.1853 dB, football achieving 30.5452 dB, Lena achieving 27.1462 dB, and yellow lily achieving 25.2015 dB. The Mean Squared Error (MSE) values were also low, with tissue having an MSE of 0.0006, football having an MSE of 0.0009, Lena having an MSE of 0.0022, and yellow lily having an MSE of 0.0031. These results demonstrate the effectiveness of the proposed method in enhancing the visual quality, details, and contrast of the images while maintaining naturalness and avoiding artifacts. Experimental results demonstrate that our method outperforms traditional techniques, significantly enhancing visual quality, details, and contrast while maintaining naturalness and avoiding artifacts.

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Green IoT Edge Computing Towards Sustainable and Distributed Data processing

Green IoT Edge Computing Towards Sustainable and Distributed...

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ieee UP Section conference on Electrical computer and Electronics (UPCON)

作者： K.R. Ananth Hari Shankar Punna Krishnamoorthy Selvaraj Rajagopal K Vaishali Mahajan Sakthivel S. Department of Computer Science (Artificial Intelligence and Data Science) Nandha Arts and Science College Erode Tamilnadu Department Of CSE CVR College Of Engineering (An Autonomous Institution) Telangana Department of ERP Keller Marketing Symbiosis Centre for Management & Human Resource Development (SCMHRD) Pune Maharashtra Marketing Symbiosis Centre for Management & Human Resource Development (SCMHRD) Pune Maharashtra Department of Computer Applications K.S.Rangasamy College of Arts and Science Tiruchengode Tamilnadu

Data creation in the network's periphery has increased exponentially as IoT devices have become more commonplace. To address the challenges this large data deluge poses, sustainable and efficient data processing technologies are required. The term “Green IoT Edge Computing” refers to a new strategy that uses distributed data processing and renewable energy to improve the efficiency and longevity of Internet of Things (IoT) applications. In the Internet of Things (IoT), edge computing is used to process data locally, at the network's periphery, rather than forwarding it to a centralized data center. This method improves real-time decision-making, minimizes network latency, and helps preserve bandwidth. However, in locations with limited access to power or other resources, the high power consumption of edge computing devices continues to be a major issue. Green IoT edge computing addresses this challenge by combining renewable energy sources like solar panels and wind turbines with traditional edge computing facilities. This guarantees low- impact and long-term power for edge devices, cutting down on their environmental impact and operational expenses. This strategy harmonizes IoT operations with environmental sustainability targets by utilizing renewable energy sources. The ability to handle data in a distributed manner is also crucial to green IoT edge computing. Rather than a centralized server, a distributed group of edge devices processes data. This improves energy efficiency as well as fault tolerance and dependability. Edge devices working together to properly spread processing activities can reduce the demand for powerful, energy-hungry servers. Green IoT Edge Computing's primary invention is the “Renewable Edge Optimizer.” This clever piece of code automatically adjusts data processing tasks in accordance with the availability of renewable power. When clean energy is plentiful, the optimizer may shift more data processing to distributed nodes, relieving press

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Online Visual Localization Combining Space and Feature Distance Constraints

Online Visual Localization Combining Space and Feature Dista...

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computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), ieee international conference on

作者： Zequan Chen Dong Xu Ming Zhang Yang Liu Guangzhou Urban Planning & Design Survey Research Institute Guangzhou China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Visual Localization is widely used in autonomous driving, virtual reality, and autonomous robot applications. However, the existing visual localization solutions require a cumbersome sparse scene model and suffer from coarse place recognition, making inefficient and inaccurate localization. To resolve these challenges, we present a visual localization solution adopting an online local reconstruction method and a space and feature distance constraints (SFDC) place recognition strategy. Comprehensive experiments confirm that the SFDC improves place recognition precision by about 50% over the popular visual localization method. Besides, the proposed method can achieve faster localization results by performing local online reconstruction of the retrieved images than traditional methods.

关键词： Location awareness Visualization Solid modeling image recognition Pipelines image retrieval Virtual reality

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Cost-Effective image based Skin Disease Detection in Resource-Constrained Settings using Machine Learning

Cost-Effective Image based Skin Disease Detection in Resourc...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： G. S. Annie Grace Vimala E. Jones Merlin M. Amanullah E. Manigandan Bura Vijay Kumar S. Padmakala Department of ECE Saveetha School of Engineering SIMATS Chennai India Department of CSE Sri Ramakrishna college of Engineering Perambalur India Department of CSE Saveetha School of Engineering SIMATS Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India

In regions like India, where skin diseases are prevalent due to hot weather and desert conditions, this research study presents a cost-effective solution for early detection and classification of skin diseases using image processing techniques. Leveraging digital images of affected skin areas, the proposed approach utilizes image analysis, including feature extraction through a pretrained Regional Convolutional Neural Network (RCNN), followed by Support Vector Machine (SVM) classification. The system achieves a commendable 82% accuracy rate in identifying Melanoma, a particularly dangerous form of skin disease. With the scarcity of medical knowledge among the general public and the potential delay in symptom recognition, this simple and efficient method, requiring only a camera and a computer, can bridge the diagnostic gap, aiding in timely disease identification and severity assessment. The dataset compilation adheres to ethical considerations, ensuring patient privacy. By addressing issues related to database creation and image dimensions, the research contributes to enhancing the performance of skin disease detection systems, ultimately offering an accessible and impactful tool for healthcare professionals and individuals alike.

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image flare removal using Deep Convolutional Generative Adversarial Networks

Image flare removal using Deep Convolutional Generative Adve...

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international conference on Signal processing and Integrated Networks (SPIN)

作者： Ishan Prakash Aryan S. Indu Mini Sreejeth Department of Electrical Engineering Delhi Technological University Delhi India Department of Electrnoics and Communication Engineering Delhi Technological University Delhi India

In this paper, a novel approach is proposed for removing flare artefacts from images using a two-stage method. Flare is a phenomenon that occurs when a strong source of light hits the lens of the camera. The converging light rays are scattered due to imperfections in the lens, creating a glare or halo effect in the image. This degrades the image quality and can make it challenging to extract useful information from the image, making it problematic for industries such as autonomous driving and medical robotics that rely heavily on image processing algorithms. To solve this problem, we utilize a flare removal model based on a conditional generative network. This model is trained on a dataset created using computer vision techniques. The dataset creation process involves scraping internet images of different scenes under various lighting conditions without the presence of flare. These images are then pre-processed to extract the flare-corrupted images, which serve as input for the flare removal model, which produces flare-free output. However, in order to make the model more generalizable and able to handle real-world data, we incorporate the flare removal module in a self-supervised setting along with a flare addition module. This allows us to fine-tune the flare removal model using real images and improve its performance. The flare removal module achieves an SSIM score of 0.916, which was previously 0.887. Our approach can be used in a variety of applications, such as autonomous driving and camera photo optimization, and has the potential to be further improved by incorporating a flare mask detector module.

关键词： Training Computational modeling Neural networks Lighting Detectors Signal processing Cameras

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The Influence of Text-guidance on Visual Attention

The Influence of Text-guidance on Visual Attention

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ieee international Symposium on Circuits and Systems (ISCAS)

作者： Yinan Sun Xiongkuo Min Huiyu Duan Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

Visual attention analysis and prediction have long been important tasks in computer vision and image processing. However, images often come along with various text descriptions in real applications, while the influence of these text-guidances on the visual saliency of corresponding images have rarely been studied. Therefore, in this paper, we mainly focus on the problem of whether and how the text-guidance influences the visual attention during image viewing, and perform subjective experiments, qualitative and quantitative comparisons as well as model evaluations on this new task. Specifically, we first conduct eye tracking experiments on 300 images under text-visual (TV) and visual (V) test conditions, respectively. Based on the subjective experiments, we perform qualitative and quantitative comparisons between the visual attention data collected under TV and V conditions, and conclude that the text-guidance can significantly influence the visual attention, especially when the text-described target is a non-salient object. Finally, we evaluate the existing saliency models on our database, and find that existing models cannot well handle this text-induced saliency prediction task. Our constructed database will be publicly available to facilitate future research.

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A Cross-direction Task Decoupling Network for Small Logo Detection

A Cross-direction Task Decoupling Network for Small Logo Det...

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ieee international conference on Multimedia and Expo (ICME)

作者： Sujuan Hou Xingzhuo Li Weiqing Min Jiacheng Li Jing Wang Yuanjie Zheng Shuqiang Jiang School of Information Science and Engineering Shandong Normal University Jinan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Logo detection plays an integral role in many applications. However, handling small logos is still difficult since they occupy too few pixels in the image, which burdens the extraction of discriminative features. The aggregation of small logos also brings a great challenge to the classification and localization of logos. To solve these problems, we creatively propose Cross-direction Task Decoupling Network (CTDNet) for small logo detection. We first introduce Cross-direction Feature Pyramid (CFP) to realize cross-direction feature fusion by adopting horizontal transmission and vertical transmission. In addition, Multi-frequency Task Decoupling Head (MTDH) decouples the classification and localization tasks into two branches. A multi-frequency attention convolution branch is designed to achieve more accurate regression by combining discrete cosine transform and convolution creatively. Comprehensive experiments on four logo datasets demonstrate the effectiveness and efficiency of the proposed method.

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Millimeter-Wave Wide-Scanning Phased Array Integrated in Package for applications in Satellite Communication

Millimeter-Wave Wide-Scanning Phased Array Integrated in Pac...

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ieee international conference on Information Communication and Signal processing (ICICSP)

作者： Guanhan Lai Jinghao Li Wanchen Yang Ap International Department of Guangdong Experimental School Guangzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China College of Computer and Information Hohai University Nanjing China

A novel low-profile wideband dual-circularly polarized metasurface antenna is proposed. It consists of dual-layer rectangle metasurface cells and is excited by a simple two-port feeding network based on traveling-wave sequential feeding mechanism, which includes four crossed slots etched on the ground and a ring strip line. By adjusting the length and width of the four crossed slots and feedline, the equal amplitude of two orthogonal electric fields can be achieved and good right- and left-handed circular polarization (RHCP, LHCP) can be realized. Moreover, a dual-layer metasurface is used as a radiator for miniaturization and improving the radiation performance. For demonstration, one prototype is fabricated and measured. The array system adopts an integrated stacked design of antenna and chip. The measured results show that the proposed array integrated in package can scan up to 60°, which indicates a good application prospect in satellite communication.

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