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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

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TRAINING AN ARTIFICIAL INTELLIGENCE MODEL FOR THE DETECTION OF GESTURES RELATED TO TRICHOTILLOMANIA 22

TRAINING AN ARTIFICIAL INTELLIGENCE MODEL FOR THE DETECTION ...

引用

22nd International conference on e-Society 2024, ES 2024 and 20th International conference on Mobile learning 2024, ML 2024

作者： de Gois Paulino, Daniel Victor Costa De Sousa Alves, Robinson Luis Instituto Federal de Educação Ciência e Tecnologia do Rio Grande do Norte Brazil

This article presents an artificial intelligence model capable of identifying actions strongly related to trichotillomania, a psychiatric disorder that causes people to have a desire to pull their hair. The model was trained with images and videos collected and variations generated through artificial intelligence to improve the image database. The work focused on the user's frontal perspective to optimize the construction of the dataset and neural network training. As a result, we obtained 89% precision in the model, which requires further testing and optimization to be used in real applications – still limited – that can provide users with statistics and results related to the disorder for possible treatments or alert the user to decrease their involuntary actions. © 2024 International conferences e-Society 2024 and Mobile learning 2024. All rights reserved.

关键词： Artificial Intelligence Behavioral Pattern Recognition Computational Psychiatry image processing machine learning neural networks Trichotillomania

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Splatting-based Synthesis for Video Frame Interpolation 23

Splatting-based Synthesis for Video Frame Interpolation

引用

23rd IEEE/CVF Winter conference on applications of Computer Vision (WACV)

作者： Niklaus, Simon Hu, Ping Chen, Jiawen Adobe Res San Jose CA 95110 USA Boston Univ Boston MA USA Adobe Inc San Jose CA USA

ISBN: (纸本)9781665493468

Frame interpolation is an essential video processing technique that adjusts the temporal resolution of an image sequence. While deep learning has brought great improvements to the area of video frame interpolation, techniques that make use of neural networks can typically not easily be deployed in practical applications like a video editor since they are either computationally too demanding or fail at high resolutions. In contrast, we propose a deep learning approach that solely relies on splatting to synthesize interpolated frames. This splatting-based synthesis for video frame interpolation is not only much faster than similar approaches, especially for multi-frame interpolation, but can also yield new state-of-the-art results at high resolutions.

关键词： Deep learning Interpolation Computer vision image resolution neural networks image sequences Computational efficiency

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Deep learning Optimizers for Histological image Classification: A Comparative Study 10

Deep Learning Optimizers for Histological Image Classificati...

引用

10th International conference on Optimization and applications, ICOA 2024

作者： Haggouni, Jamal Azzouzi, Salma El Hassan Charaf, Moulay Ibn Tofail University LaRI Laboratory Faculty of Sciences Kenitra Morocco

ISBN: (纸本)9798350387353

Over the last few years, there has been a great deal of progress in the field of image recognition, using classical or modern methods based on machine learning algorithms. In this context, numerous studies on object detection have been carried out in various fields, with promising results. The aim of this paper is the design of an automatic classification system for tumor tissue cells (breast cancer) in medical images (histological images). The proposed approach relies on the use of convolutional neural networks (CNNs), with numerous experiments using different optimization algorithms such as RMSprop, SGD or Adam to evaluate the performance of our approach. The findings show the effectiveness of the RMSprop optimizer, with a fast convergence rate and high accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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An Open-Source and Extensible Framework for Fast Prototyping and Benchmarking of Spiking neural Network Hardware 34

An Open-Source and Extensible Framework for Fast Prototyping...

引用

34th International conference on Field-Programmable Logic and applications (FPL)

作者： Matinizadeh, Shadi Das, Anup Drexel Univ Philadelphia PA 19104 USA

ISBN: (纸本)9798331530082;9798331530075

Spiking neural networks (SNNs) are bioplausible machine learning models that use discrete spikes to encode, compute, and transmit information. Combined with event-driven low-power hardware, SNNs can improve the energy efficiency of learning tasks. Although there have been several efforts to build SNN hardware, there is no uniform framework to verify and benchmark these designs in terms of key hardware performance metrics such as inference accuracy, area, power consumption, and throughput. We propose PRONTO, an open-source and extensible framework to verify SNN hardware for different learning tasks and datasets. Given the ubiquity of PyTorch in the machine learning community and for demonstration purposes, the frontend of PRONTO is integrated with a torch-based SNN simulator for model specification and training. Its backend is integrated with an open-source quantized SNN hardware. PRONTO interfaces with a torch code to generate input stimuli which are then driven to SNN hardware through a configurable SystemVerilog testbench, verifying the design across various SNN-specific configurations. PRONTO utilizes a dataflow-based approach to validate SNN models that are segmented and run on a mix of software and hardware platforms. We describe PRONTO and evaluate it using six datasets spanning image, audio, and text classification. We present benchmark results for various input settings. PRONTO is available under an open-source licensing to provide a platform to evaluate all current and future SNN hardware designs. We believe PRONTO will substantially reduce the design verification effort, thus facilitating fast design prototyping.

关键词： spiking neural networks (SNNs) verification testbench SystemVerilog PyTorch

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A Real-time Demonstrator for image Classification using FPGA-based Logic neural networks

A Real-time Demonstrator for Image Classification using FPGA...

引用

conference on Real-Time processing of image, Depth, and Video Information

作者： Concha, David Garcia-Espinosa, Francisco J. Ramirez, Ivan Alberto Aranda, Luis Univ Rey Juan Carlos CAPO Res Grp Madrid 28933 Spain

ISBN: (纸本)9781510673199;9781510673182

Real-time image processing is a key area of focus, but computationally intensive. neural networks effectively address classification tasks, but they are not always a viable option, particularly in environments where high power consumption or computational requirements are limiting factors. Hardware devices such as Field-Programmable Gate Arrays (FPGAs) offer significant parallelization capabilities that can be fully exploited when the implemented circuit is composed solely of logic gates. In addition, FPGAs are also interesting alternatives to traditional GPU-based implementations in terms of power consumption and reconfiguration capabilities. They can be used as a demonstration platform to validate a hardware design that can be later manufactured, creating the final Application-Specific Integrated Circuit (ASIC). This paper introduces a practical demonstration platform based on an FPGA that highlights the great capabilities of logic neural networks, a type of neural network constructed exclusively with logic gates. By harnessing FPGA parallelization and logic gates, we have achieved a balance between computational power and real-time performance. This approach ensures that image classification occurs at speeds on the order of nanoseconds. This ultra-fast processing is well-suited for real-time image analysis applications across various domains. Industries that rely on quality control, such as manufacturing, will benefit from rapid and precise assessments. In the field of medical image processing, where quick diagnoses are crucial, this technology promises transformative advancements. The demonstration platform developed serves as a proof of concept for logic neural networks, offering a solution to the challenge of real-time image processing and representing the first step towards the implementation of future architectures of logic networks in hardware.

关键词： Deep learning Field-Programmable Gate Array (FPGA) Logic neural Network (LNN) Real-time image Classification

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Research on Infrared Target Detection Algorithm Based on Graph Globality

Research on Infrared Target Detection Algorithm Based on Gra...

引用

9th International conference on Signal and image processing (ICSIP)

作者： Xu, Changhan Li, Xuemei Wu, Qinming Chengdu Univ Technol Chengdu Peoples R China

ISBN: (纸本)9798350350920

The current deep learning-based target detection algorithms have problems such as the network perception domain being limited, poor adaptation to scale changes, feature mismatch in feature fusion, and small datasets. Aiming at the current problems in the field of infrared target detection, a global infrared image detection method based on graph convolutional neural network is proposed. In this paper, global feature interaction module and feature pyramid module are designed. It also proposes a graph-based knowledge distillation model compression method to provide support for hardware deployment. Finally, the algorithm proposed in this paper is experimentally demonstrated, using the classical infrared small target dataset for experiments, comparing the mainstream infrared small target detection algorithms, comparing and verifying that the algorithm of this paper has an effective performance enhancement in infrared small targets in infrared targets. Design ablation experiments to verify the performance of individual modules and fusion modules[2], to prove the effectiveness and enhancement of the module. Finally, the visualization analysis facilitates the subjective evaluation by the human eye, proving the excellence of this paper's algorithm.

关键词： Graph Convolutional neural networks Global Interaction Knowledge Distillation Infrared Small Objects

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Prediction of Breast Cancer Via Deep learning 4th

引用

4th International conference on 3D Imaging Technologies- Multidimensional Signal processing and Deep learning, 3D IT-MSP and DL 2022

作者： Huang, Yihe College of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9789819912292

With the continuous progress of machine learning in image processing, artificial neural networks have more and more applications in medical image processing. Aiming at the method of selecting a BP neural network to realize the diagnosis of breast cancer pathological images, the method of deep learning is selected for prediction, which improves the prediction accuracy and obtains a more effective diagnosis effect. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Research on image and text in visual communication design under artificial intelligence

Research on image and text in visual communication design un...

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2024 International conference on image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Yuan, Yuanyi Wu, Longjun Zou, Yong Chengdu Technological University Chengdu China

ISBN: (纸本)9781510680425

The paper examines the integration of AI in visual communication design, particularly focusing on the enhanced impact of images when combined with text. It explores the evolution of visual communication design in the context of AI advancements, emphasizing the increasing significance of this discipline in today's technology-driven society. Key areas of discussion include the role of artificial intelligence, specifically deep learning, in image recognition and natural language processing, and its application in visual communication design. The paper details the development and training of an image recognition and text matching model, showcasing the effective application of convolutional neural networks (CNNs) and Word2Vec in visual design tasks. The research concludes by highlighting the successful translation of deep learning theoretical achievements into practical visual communication design applications, demonstrating the model's proficiency in image recognition and text matching within a business and engineering framework. © 2024 SPIE.

关键词： Convolutional neural networks

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A Comparative Study of Deep learning Models for image Super-Resolution Across Various Magnification Levels

A Comparative Study of Deep Learning Models for Image Super-...

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2024 IEEE International conference on Future machine learning and Data Science, FMLDS 2024

作者： Soni, Jayesh Gurappa, Subhash Upadhyay, Himanshu Applied Research Center Florida International University Miami United States Florida International University Department of Electrical and Computer Engineering Miami United States

ISBN: (纸本)9798350391213

image Super-Resolution (SR) is a vital image processing technique aimed at improving the resolution of lowresolution images to generate high-resolution images, which is essential in applications such as medical imaging, forensic analysis, satellite imagery, etc. Despite its significance, there are still issues with existing SR techniques, such as handling unknown degradation, generalizing across various domains, and recovering the fine details, especially at high scaling factors. Traditional approaches like interpolation and reconstruction-based methods produce poor results, which leads to the use of deep learning models that have demonstrated notable advancements. However, problems like computational complexity, performance drop on real-world data, and dependency on fixed scaling factors remain. In this research, we conduct a comparative analysis of SR models, namely Super-Resolution Convolutional neural networks (SRCNN), SwinIR, and Real-ESRGAN, across multiple image domains (e.g., Animals, Cars, Faces, Humans, Landscapes, Ships) using different scaling factors (e.g., x 2, x 3, x 4) and metrics like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), Learned Perceptual image Patch Similarity (LPIPS), Objective Difference image (ODI) Score, Tenengrad sharpness and Laplacian Variance. This approach helps us better understand the performance of various deep-learning models, guiding further improvements to ensure they are efficient, robust, and applicable to real-world scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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Model Uncertainty Estimation Based on a Dual-Module neural Network Architecture 23

Model Uncertainty Estimation Based on a Dual-Module Neural N...

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23rd International conference on machine learning and Cybernetics, ICMLC 2024

作者： Guo, Weiqing Zhang, Tianlun Peng, Dunlu Wang, Debby D. School of Science and Technology Hong Kong Metropolitan Universit Hong Kong Hong Kong School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798331528041

In recent years, deep learning has achieved remarkable success in various fields, including biology and medicine. However, the interpretability and robustness still face challenges, as erroneous predictions can lead to serious consequences in critical applications. This paper introduces a new framework for estimating model uncertainty that can enhance the decision-making process. Compared to existing uncertainty estimation methods like Bayesian neural networks and Monte Carlo dropout, this paper provides a more direct way to model uncertainty by directly estimating the output variance through a dual-module neural network architecture. Without the need for multiple forward passes or complex Bayesian inference, it can reduce computational intensity. In specific, this framework consists of a prediction module for input processing and an uncertainty module for estimating prediction uncertainty. We demonstrate the efficiency of our method through applications in medical image segmentation and protein-ligand binding affinity prediction. By incorporating model uncertainty estimation, the proposed framework not only improves the accuracy of deep learning models, but also significantly reduces the computational burden which is especially beneficial for processing large datasets. © 2024 IEEE.

关键词： Prediction models

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