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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Generative-CNN for pattern recognition in Finance 24

Generative-CNN for Pattern Recognition in Finance

引用

5th International conference on AI in Finance

作者： Natarajan, Jeevesh Wang, Wayne Jiang, Yaqiao Zhang, Zeqi Ye, Huanhui Kuang, Lingxi Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9798400710810

Convolutional neural networks (CNNs) have achieved considerable success across a spectrum of computer vision tasks, with applications ranging from healthcare to automated driving. Recent literature has also explored its potential utility in trading and risk management within the finance industry. Despite their versatility, CNNs are substantially constrained by their data-hungry nature. The lack of well-labeled image datasets poses a major challenge to the widespread adoption of CNNs in financial machine learning research across academia and industry. To address these concerns, this work presents Generative-CNN, a novel approach that utilizes a generative adversarial network (GAN) to synthetically generate images to enhance the performance of a CNN with applications in trading.

关键词： Generative Adversarial Networks Convolutional Neural Networks Candlestick patterns Financial Time Series Image Classification

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InverseForm: A Loss Function for Structured Boundary-Aware Segmentation

InverseForm: A Loss Function for Structured Boundary-Aware S...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Borse, Shubhankar Wang, Ying Zhang, Yizhe Porikli, Fatih Qualcomm AI Res San Diego CA 92121 USA

ISBN: (纸本)9781665445092

We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network, which efficiently learns the degree of parametric transformations between estimated and target boundaries. This plug-in loss term complements the cross-entropy loss in capturing boundary transformations and allows consistent and significant performance improvement on segmentation backbone models without increasing their size and computational complexity. We analyze the quantitative and qualitative effects of our loss function on three indoor and outdoor segmentation benchmarks, including Cityscapes, NYU-Depth-v2, and PASCAL, integrating it into the training phase of several backbone networks in both single-task and multi-task settings. Our extensive experiments show that the proposed method consistently outperforms baselines, and even sets the new state-of-the-art on two datasets.

关键词： Training computer vision Computational modeling Semantics computer architecture Benchmark testing pattern recognition

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BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

BasicVSR++: Improving Video Super-Resolution with Enhanced P...

引用

2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Chan, Kelvin C.K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Nanyang Technological University S-Lab Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A recurrent structure is a popular framework choice for the task of video super-resolution. The state-of-the-art method BasicVSR adopts bidirectional propagation with feature alignment to effectively exploit information from the entire input video. In this study, we redesign BasicVsr by proposing second-order grid propagation and flow-guided deformable alignment. We show that by empowering the re-current framework with enhanced propagation and align-ment, one can exploit spatiotemporal information across misaligned video frames more effectively. The new components lead to an improved performance under a simi-lar computational constraint. In particular, our model Ba-sicVSR++ surpasses BasicVSR by a significant 0.82 dB in PSNR with similar number of parameters. BasicVSR++ is generalizable to other video restoration tasks, and obtains three champions and one first runner-up in NTIRE 2021 video restoration challenge. © 2022 ieee.

关键词： computer vision Computational modeling Superresolution Spatiotemporal phenomena pattern recognition Task analysis

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Searching lost dog by breeds sorting using computer vision and deep neural networks 3

Searching lost dog by breeds sorting using computer vision a...

引用

3rd ieee International conference on Advanced Learning Technologies on Education and Research, ICALTER 2023

作者： Mendoza-Montoya, Javier Cuarite, Flavio Abel Yancapallo Quispe, Gustavo Walther Paredes Paz, Antonio Arroyo Universidad Tecnológica Del Perú Dept. Systems and Informatic Lima Peru

ISBN: (纸本)9798350395471

The loss of a pet has a significant emotional impact on the owner and the pet. This sad experience generates worrying in the owner, while the pet gets stressed with fear and loneliness. Supporting owners to find their pets in a short time is crucial because of loss or probable death. Regarding social media advertisements as fallible methods, this research proposes a lost dog identification based on breed sorting classification using computer vision recognition. A convolutional neural network of 19 layers deep (VGG-19) was used to achieve this purpose. A dataset of 1706 images of five popular dog breeds in Perú was chosen (chihuahua, Yorkshire terrier, pug, Pastor Aleman, and Beagle), of which 668 were used to test and 971 to validate the model. The trained model achieved high accuracy, averaging 92% after 30 training epochs. In conclusion, using computer vision algorithms, this model-based convolutional neural network looks promising for finding lost dogs quickly based on their breed's recognition. © 2023 ieee.

关键词： computer vision

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An AI-Based Classroom Monitoring System Leveraging computer vision and Machine Learning 20

An AI-Based Classroom Monitoring System Leveraging Computer ...

引用

20th International Bhurban conference on Applied Sciences and Technology (IBCAST)

作者： Siddiqui, Nofil Khalid, Maaz Ahmed, Ayza Aleem, Abdul Irfan, Muhammad Ahmad, Waqar Bin Muslim, Fahad GIK Inst Swabi Fac Elect Engn Swabi Pakistan GIK Inst Fac Comp Sci & Engn Swabi Pakistan

ISBN: (纸本)9798350308266;9798350308259

This paper presents an AI-based classroom monitoring system utilizing computer vision and machine learning. The system employs Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection, FaceNet for facial recognition, and a CNN model trained on the Facial Expression recognition (FER) 2013 dataset for emotional analysis. A novel heterogeneous approach combines Field Programmable Gate Arrays (FPGA) and a central processor to overcome the limitations of BRAM and complex computation constraints. Evaluation in real-world classrooms yielded a promising 70% accuracy in emotion detection, marking a significant stride in the field. This research not only advances AI-based monitoring systems but also indicates potential applications in surveillance and security.

关键词： Heterogeneous Systems FPGA Emotion Detection Facial recognition Artificial Intelligence Neural Networks

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Healthcare Monitoring Revolution through Face Detection Using BP and SpO2 5

Healthcare Monitoring Revolution through Face Detection Usin...

引用

5th ieee International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Nongmeikapam Thoiba Kaur, Harkamal Dhiman, Richa Kumar, Manish Hans, Aarti Sharma, Preety Chandigarh University Department of Computer Science and Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350383522

The integration of facial recognition technologies with the monitoring of vital signs, especially blood pressure (BP) and oxygen saturation (SpO2) measurements, has emerged as a promising approach in healthcare quality management. Facial recognition and SpO2 pressure detection represent a major advance in health and wellness monitoring, providing a seamless and comprehensive approach to monitoring individual well-being. As technology continues to evolve, this system has the potential to improve how we manage and manage healthcare, delivering positive outcomes and improving the lives of individuals around the world. © 2024 ieee.

关键词： computer vision face detection healthcare monitoring machine learning SpO2 detection vital sign monitoring

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Overview of Underwater Object Detection Based on Image Processing Techniques 7

Overview of Underwater Object Detection Based on Image Proce...

引用

7th International conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Aamir, Muhammad Gao, Jinxiong Zhang, Yonghui Tang, Hao Bhatti, Uzair Aslam College of Computer Science Huanggang Normal University Huanggang438000 China School of Information and Communication Engineering Hainan University Haikou570100 China

ISBN: (纸本)9798350350890

underwater object detection and recognition are critical technologies in marine science and engineering, enabling advancements in environmental monitoring, resource exploration, and autonomous underwater vehicles. The rapid progression of computing power, underwater robotics, and artificial intelligence has driven substantial academic and industrial interest in this domain. This paper presents a comprehensive overview of the current methodologies in image-based underwater object recognition, where optical images or videos captured under various lighting conditions are processed to detect and identify underwater objects. The discussion includes an analysis of the prevailing algorithms, a review of the contemporary research landscape, and an exploration of emerging trends. Additionally, this paper offers insights into the anticipated technological advancements that will shape the future of underwater object recognition. © 2024 ieee.

关键词： Underwater imaging

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A HYBRIDIZED MODEL USING INDEPENDENT COMPONENT ANALYSIS WITH AN IMPROVED GENETIC ALGORITHM FOR CLASSIFYING BLURRED TEXT DETECTION IN WILD SCENE

A HYBRIDIZED MODEL USING INDEPENDENT COMPONENT ANALYSIS WITH...

引用

2024 ieee SmartBlock4Africa, SmartBlock4Africa 2024

作者： Nwufoh, Chinonyelum Sakpere, Wilson Olufemi, Tolulope Kolapo, Ridwan Federal College of Animal Health and Production Technology Computer Science Department Oyo State Ibadan Nigeria Lead City University Computer Science Department Oyo State Ibadan Nigeria West Midland University Lagos Computer Science Depart Lagos Nigeria Nile University of Nigeria Information Technology Department Abuja FCT Nigeria

ISBN: (纸本)9798350376838

Removing text from images is a challenging task, particularly when dealing with fuzzy borders, shading, and embossing. This process is essential but can be difficult to achieve using existing solutions, which may not be adequate to address the multifaceted issues that occur in the wild. The history of writing spans several centuries and having accurate and rich semantic information in text is crucial in various vision-based use cases. As a result, researchers in the fields of computer vision and pattern recognition have been actively investigating text recognition in natural settings. Deep learning has gained popularity in recent years, resulting in the development of unique, useful, and successful approaches. In this study, we propose a robust approach for detecting blurred text in wild scenes using a hybridized model. Our model integrates independent component analysis (ICA) with an improved genetic algorithm (IGA) to enhance feature extraction and selection, respectively, resulting in improved classification accuracy. We compare our approach with existing state-of-the-art methods using a publicly available dataset and demonstrate that our hybridized model outperforms them, achieving a significantly higher accuracy of around 98.65%. We employ support vector machine (SVM) and k-nearest neighbor (KNN) classifiers to classify the extracted features. The results show that both classifiers perform well, with the KNN classifier achieving slightly higher accuracy than the SVM classifier. This approach can be extended to other image classification tasks in various domains, providing a powerful tool for real-world applications. The findings of this study have significant implications for the development of more accurate and efficient methods for detecting blurred text in challenging environments, paving the way for the use of semantic information in various vision-based applications. © 2024 ieee.

关键词： Semantics

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Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes

Rethinking Style Transfer: From Pixels to Parameterized Brus...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kotovenko, Dmytro Wright, Matthias Heimbrecht, Arthur Ommer, Bjoern Heidelberg Univ Heidelberg Collaboratory Image Proc IWR Heidelberg Germany

ISBN: (纸本)9781665445092

There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually consist of brushstrokes rather than pixels. We propose a method to stylize images by optimizing parameterized brushstrokes instead of pixels and further introduce a simple differentiable rendering mechanism. Our approach significantly improves visual quality and enables additional control over the stylization process such as controlling the flow of brushstrokes through user input. We provide qualitative and quantitative evaluations that show the efficacy of the proposed parameterized representation. Code is available at https://***/CompVis/brushstroke-parameterizedstyle-transfer.

关键词： Visualization computer vision Codes Process control Rendering (computer graphics) pattern recognition Painting

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Reconsidering CO2 emissions from computer vision

Reconsidering CO2 emissions from Computer Vision

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Fu, Andre Hosseini, Mahdi S. Plataniotis, Konstantinos N. Univ Toronto Toronto ON Canada Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9781665448994

Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the computer vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.

关键词： computer vision Climate change conferences computer architecture Pressing pattern recognition Artificial intelligence

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