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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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Expansion and Architecture of Spiking neural networks 11th

Expansion and Architecture of Spiking Neural Networks

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11th International conference on applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Wei, Yanxi Yao, Fenggang Kang, Yuru School of Artificial Intelligence Xi'an Aeronautical Polytechnic Institute Xi'an China

Among various data processing tools, neural networks stand out prominently in the fields of computer vision, intelligent control, and brain-like computing. They emulate the operational processes of the human brain, constituting artificial neural networks. In the ongoing technological evolution, spiking networks have progressively captivated the attention of scholars across diverse disciplines. Integrating principles from neuroscience and artificial intelligence, these networks employ impulse signals for event-driven processing and utilize communication signals within the network, exhibiting a closer resemblance to the operational mode of the biological nervous system when compared to traditional neural networks. Pulses manifest as discrete signals, diverging from the generally continuous nature of input signals. Simultaneously, there exists limited comprehension regarding the brain's coding stimulus and signal processing procedures, rendering the encoding of information into pulse signals a challenging task. Furthermore, while convolutional neural networks excel in image recognition, they suffer from the drawback of poor biological interpretability. To bolster the self-extraction capability of spiking neural networks, a pivotal focus lies in enhancing their sign extraction proficiency. Consequently, this paper proposes a hybrid structure for a spiking neural network, with experimental results demonstrating significant improvements in feature extraction compared to preceding neural network models. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Advancements in artificial Intelligence for Better image Analysis, Recognition, and Interpretation in the Field of image processing 2

Advancements in Artificial Intelligence for Better Image Ana...

引用

2nd IEEE Global conference on Communications and Information Technologies, GCCIT 2024

作者： Saravanan, V. Adaikkammai, A. Sasikala, G. Iyswariya, A. Babisha, A. Latha, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India R.M.D. Engineering College Department of Computer Science and Business Systems India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India R.M.K. Engineering College Department of Ece Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science India Srm Institute of Science and Technology Department of It India

ISBN: (纸本)9798350388916

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional neural networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Convolutional neural networks

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Sparse Auto Encoder Based Deep neural Network for image processing Technology 2

Sparse Auto Encoder Based Deep Neural Network for Image Proc...

引用

2nd IEEE International conference on networks, Multimedia and Information Technology, NMITCON 2024

作者： Yang, Zufang Zeng, Heqiong Wuhan Technology and Business University College of Artificial Intelligence Wuhan China Wuhan Technology and Business University Institute of Information and Intelligent Engineering Applications Wuhan China

ISBN: (纸本)9798350372892

The image processing technology has been widely used in various field including video and image surveillance automatic capture the various application in image way representing. However, image process in different way and maximum amount of linear layer then easily fall into local optimum and occur overfitting in learn the data. This paper proposed Sparse Auto Encoder based Deep neural Network (SAE-DNN) for image classification is efficiently achieve high accuracy especially noisy data and dimensionality reduces. The SAE-DNN technique help to remove noise in data by learning the image pattern efficiently, which improve classification accuracy in noisy environments. Initial data is obtained from CIFAR-100 dataset and Pre-processing involves data augmentation, where transformations like translation and rotation are frequently applied during training phase of neural networks for image classification. The MobileNetV2 involved feature extraction, which efficiently extracts relevant features from training data. The proposed SAE-DNN technique are evaluated achieve better outcomes such as accuracy of 92.02% on CIFAR100 dataset existing technique like Fully Convolutional neural Network (FCNN) and Mobile Net V2 with Wavelet-Attention Convolutional neural Network (WA-CNN) are evaluated for proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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image Matting Based on Deep Equilibrium Models 33rd

Image Matting Based on Deep Equilibrium Models

引用

33rd International conference on artificial neural networks and Machine Learning (ICANN)

作者： Liu, Xinshuang Zhao, Yue Univ Calif San Diego La Jolla CA 92093 USA Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9783031723346;9783031723353

Recent works have shown that additional improvement stages can further enhance the performance of image matting models. Inspired by this, we propose deep equilibrium matting (DEQ-Matt), which improves the feature maps for infinite times to achieve optimal performance by using the deep equilibrium (DEQ) models. We further tailor a loss function to train the DEQ models on the image matting task. Besides, we propose to use saliency maps to guide the image matting models, because they can be automatically and reliably predicted. In experiments, our method outperforms state-of-the-art methods and is superior in both semantic estimation and detail processing. Furthermore, we observe an increasing trend in the model's performance as the number of feature improvement steps approaches infinity, which supports the motivation of this paper. The code is available at https://***/XinshuangL/DEQ-Matt.

关键词： image Matting Deep Equilibrium Models

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Bionic model of information processing in the retina

Bionic model of information processing in the retina

引用

2024 International conference on artificial Intelligence, Computer, Data Sciences, and applications, ACDSA 2024

作者： Bohlmann, Sabine Clausthal University of Technology Institute of Computer Science Clausthal-Zellerfeld Germany

ISBN: (纸本)9798350394528

Today, bionic models for vision applications base on the general information pathways, structure and characteristics of the visual system implemented in intelligent algorithms, mostly based on AI, to improve the resolution, colour and contrast of images. These approaches often use machine learning techniques and neural networks, or simply mathematical pre-processing filters. The major drawback of all these methods is that they require a lot of computing power. But nature can't afford that kind of processing power just to improve contrast/resolution, detect contours or the true colours of an image and it doesn't use learning strategies or high-order mathematical equations to detect contours. Nature works very simply and very efficiently, it can only work with potentials and their differences. There is therefore a huge gap between AI-based approaches and image processing in the human visual system. This opens the way for a new bionic model of the general pathways of the retina - using only the simple principles of nature, potentials and their differences. © 2024 IEEE.

关键词： image enhancement

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Crop Disease and Pest Detection using Convolutional neural networks (CNN) 5

Crop Disease and Pest Detection using Convolutional Neural N...

引用

5th International conference on image processing and Capsule networks, ICIPCN 2024

作者： Kalaimanivel, S. France, K. Hindustan institute of technology and science Department of Computer Applications Chennai22295014 India Hindustan institute of technology and science Department of Computer Applications Chennai India

ISBN: (纸本)9798350367171

Agriculture is often known as the art and science of nurturing soil. It involves preparing plants and animals for use in products. Agriculture is the process of growing crops and rearing animals for human consumption, fiber production, and other reasons. It is one of the oldest and most important human activities, laying the groundwork for food production and billions of people's lives throughout the world. As technology advances, additional capabilities for crop protection and disease prevention become accessible. artificial Intelligence (AI) and Machine Learning (ML) algorithms capture features such as crop and soil monitoring, crop maturity detection, autonomous weeding, intelligent crop spraying, pest and disease detection, and more. This study suggests a novel technique for automated crop disease identification by utilizing Convolutional neural networks (CNNs) from the field of computer vision. By performing a thorough testing and validation on separate test sets, the proposed methodology outperforms other existing methods in terms of accuracy. The Plant Village collection, maintained by the Centers for Disease Control and Prevention (CDC), includes damaged plant leaf photos and labels. The proposed method has achieved an accuracy of about 99.6%. © 2024 IEEE.

关键词： Convolutional neural networks

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Computer System of Evaluation of the Mass Exam Results Based on Recognition of Handprinted Azerbaijani Characters 2nd

Computer System of Evaluation of the Mass Exam Results Based...

引用

2nd International conference on Information Technologies and Their applications

作者： Mustafayev, Elshan Azimov, Rustam Inst Control Syst Lab Recognit Identificat & Methods Optimal Solut Baku Azerbaijan

ISBN: (纸本)9783031734199;9783031734205

The utilization of pattern recognition is on the rise extensively in information systems. The convergence of progress in image processing and the accessibility of open-source libraries enables the implementation of innovative solutions for diverse practical problems. One notable challenge pertains to automatically processing responses in mass large-scale exams. This paper introduces a developed system tailored for recognizing such exam results, showcasing its capacity to deliver dependable, effective, and impartial assessments. This system can be configured on almost any type of form. Its use also allows you to abandon the expensive and difficult to use OMR scanners. To increase productivity of system we propose to use the multicore/multithreading property of modern processors to parallelize processes within a single workstation. As a result of experiments, itwas found that the transition to multi-threaded recognition can increase productivity up to 3.5 times in comparison with single-threaded. To reduce the physical size of exam cards, it is proposed to fill in the answers with handwritten symbols instead of filling in the circles. Multilayer and convolutional neural networks were used as a recognition module. A comparative evaluation of the dependence of recognition results on the architecture of neural networks and the feature extraction algorithm was carried out.

关键词： Evaluation of Exam Results Intelligent System Information processing Multithreading Recognition image processing OCR

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Enhanced Multimodal Conversational AI Using Speech and image Integration 5th

Enhanced Multimodal Conversational AI Using Speech and Image...

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5th International conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Dixit, Deepali Tyagi, Mihul Kandoi, Raunak Singh, Raghuraj School of Engineering and Technology Sharda University Greater Noida201310 India

ISBN: (纸本)9789819773701

The development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. The emphasis is on cutting-edge developments in multimodal conversational AI, particularly the smooth fusion of sophisticated image and speech processing methods to improve user experiences. The study addresses the applications, difficulties and potential applications of multimodal conversational AI by conducting a thorough review of the existing literature and case studies. By emphasizing the significance of enhancing voice recognition algorithms and utilizing advanced image processing techniques, the study promotes contextual understanding and customization. We have developed an innovative multimodal conversational AI system that integrates speech, text and image processing capabilities for seamless human–computer interactions. This study presents an improved multimodal conversational AI system that integrates several techniques, such as Google Text-to-Speech (gtts), speech recognition, sound playback and Convolutional neural networks (CNN). Our model architecture combines RNNs for sequential data analysis, CNNs for image processing and attention mechanisms for context extraction. Trained on diverse datasets, our model is optimized for accuracy and robustness. Evaluation against existing models demonstrates superior performance in terms of accuracy, speed and resource efficiency. Our study highlights how improved multimodal conversational AI can advance the field of human–computer interaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Research on Driving Behavior Recognition Algorithm and Implementation Based on Convolutional neural networks 24

Research on Driving Behavior Recognition Algorithm and Imple...

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4th International conference on artificial Intelligence, Automation and High Performance Computing, AIAHPC 2024

作者： Xu, Qiangqiang Guo, Junhua Chen, Xi School of Transportation Engineering East China Jiaotong University China

ISBN: (纸本)9798400710049

The technology of driver behavior recognition holds significant importance in modern traffic management and autonomous driving systems. This paper revolves around Convolutional neural networks (CNNs) as the cornerstone, delving into the design and implementation of driver behavior recognition algorithms. Initially, image preprocessing using OpenCV lays the foundation for subsequent neural network processing. Subsequently, critical components within the neural network, such as activation functions and loss functions, are elaborated upon, alongside discussions on gradient-based optimization methods and backpropagation algorithms. Furthermore, this study provides an in-depth analysis of the roles of convolutional layers and pooling layers within CNNs, emphasizing their applications in driver behavior recognition. Lastly, by detailing specific algorithmic workflows and network architectures, the development of a driver behavior detection system is achieved. This research supports advancements in autonomous driving and traffic safety, demonstrating the vast potential of convolutional neural networks in practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Deep Residual Learning for Fruits of Ceremonial Plants Recognition 2

Deep Residual Learning for Fruits of Ceremonial Plants Recog...

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2nd International conference on Data, Information and Computing Science, CDICS 2024

作者： Negara, Ida Bagus Kade Dwi Suta Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Udayana University Denpasar Indonesia

ISBN: (纸本)9781643685762

Fruit detection refers to a method within image processing and computer vision that focuses on automatically recognizing and distinguishing different types of fruits using advanced algorithms and techniques. Fruits are essential and widely used in ceremonial offerings (Banten). They are pivotal in ensuring the completeness of ceremonial offerings. The main goal of fruit detection is to recognize fruits in image form, making it applicable in various applications, including inventory management, automatic classification in the agricultural industry, and medical applications for monitoring dietary patterns. Commonly used methods include artificial neural networks (deep learning), digital image processing, and feature extraction techniques to distinguish between shapes, colours, textures, and other visual features of different types of fruits. In this research, the deep learning algorithm CNN (Convolutional neural Network) is employed. This technique, a deep learning algorithm, excels in accurate image recognition and classification. A total of 500 fruit images are used for five types of ceremonial fruits commonly used in religious activities, namely coconut, banana, candlenut, nutmeg, and areca nut. The residual learning method RESNET152 is used as the CNN algorithm architecture. According to the test findings, the highest accuracy rate achieved was 93% in correctly identifying ceremonial fruit images. © 2025 The Authors.

关键词： Convolutional neural networks

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