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检索条件"任意字段=Proceedings of the 5th International Conference on Image Processing and its Applications"

共 4852 条记录，以下是1-10 订阅

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proceedings of the 5th international conference on image processing and its applications

Proceedings of the 5th International Conference on Image Pro...

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proceedings of the 5th international conference on image processing and its applications

the proceedings contains 170 papers. Topics discussed include image processing, image coding, labelling and classification, medical applications, motion, stereo and three dimensional, image analysis, image interpretation, image coding and communications, shape description and recognition, image processing applications, computer architectures, image segmentation, neural networks, industrial inspection, filtering and morphology, texture and color, transport, security and remote sensing.

关键词： image processing

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proceedings of the 1997 6th international conference on image processing and its applications. Part 2 (of 2)

Proceedings of the 1997 6th International Conference on Imag...

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proceedings of the 1997 6th international conference on image processing and its applications. Part 2 (of 2)

the papers submitted to the Sixth international conference on image processing and its applications are presented. the issues considered include shape description and recognition;image processing applications;texture;image segmentation;neural networks;colour;inspection and document processing;filtering and morphology;medical applications;transport, security and remote sensing.

关键词： image processing

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proceedings of the 1997 6th international conference on image processing and its applications. Part 1 (of 2)

Proceedings of the 1997 6th International Conference on Imag...

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proceedings of the 1997 6th international conference on image processing and its applications. Part 1 (of 2)

the proceedings contains 93 papers from the 6th IEE international conference on image processing and its applications. Topics discussed include: image coding and coding standards;interactive multimedia services;medical radiograph image processing;shape contours progressive polygon encoding;image fractal encoding by classified domain trees;parallel computing;LBG image vector quantization;entropy-constrained design;quadtree video coding schemes;low refresh-rate video sequences compression technique;local motion tracking;semantic-based coding;videophone sequences;and binary-tree recursive motion estimation.

关键词： image processing

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A Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image processing 5th

A Comprehensive Analysis for Advancements and Challenges in ...

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5th international conference on Data Science, Machine Learning and applications

作者： Ch, Ravikumar Chary, Kalvog Prakasha Srinivas, S. Bhavani, Tedla Veeranna Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Cyber Secur Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Sri Indu Coll Engn & Technol Dept Informat Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. the rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of image processing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional Neural Networks (CNNs), clarifying their theoretical foundations within the image processing context, opening the door for further research. CNNs are notably essential for image processing due to their ability to handle complex images effectively.

关键词： Deep learning image processing Backpropagation algorithm Convolutional Neural Networks (CNNs) Model structures

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Attention-Based image Caption Generation 5th

Attention-Based Image Caption Generation

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5th international conference on Data Science, Machine Learning and applications

作者： Manasa, M. Sowmya, D. Reddy, Y. Supriya Sreedevi, Pogula G Pulla Reddy Engn Coll Dept Comp Sci & Engn Data Sci Kurnool 5185007 India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool 5185007 India Rajeev Gandhi Mem Coll Engn & Technol Dept Comp Sci & Engn Nandyal 518501 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and natural language processing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.

关键词： image Caption Computer Vision Natural Language processing Attention Mechanism Performance

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Leaf Disease Detection and Correction 5th

Leaf Disease Detection and Correction

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5th international conference on Data Science, Machine Learning and applications

作者： Indrani, P. Regulwar, Ganesh B. Ahmed, Md Sohel Reddy, K. Sneha Vardhaman Coll Engn Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Pests and plant conditions provide an essential part when assessing the yield as well as the quality of plants. Nowadays plant disease identification is carried out by the deep learning and image processing. Agriculture is the backbone of Indian government. We need to increase the yield in the crop as the population is increasing so the urbanization increases which cause in decrease of cultivation land. Because of the diseases that we observe frequently in plants, the crop production is being decreased. To reduce the disease in plant we need to use some pesticides which also helps to increase in production. In the recent times, deep learning is very useful in the field of image processing. How to exercise deep learning technology to study factory conditions and pests identification has come a exploration conclusion of great company to experimenters. Now we use image processing and find out the disease in plant and also provide the required pesticide suggestions which makes detection of solution for a disease easier and helps in good yield of crop for farmers.

关键词： Leaf disease detection Plant disease detection deep learning image processing convolutional neural networks TensorFlow Keras agricultural technology crop yield pesticide recommendations preprocessing model construction training testing performance evaluation

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Deep Learning Model Development for an Automatic Healthcare Edge Computing Application 5th

Deep Learning Model Development for an Automatic Healthcare ...

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5th international conference on Data Science, Machine Learning and applications

作者： Talal, Hadi Khamis, Ruaa Ali Al-Frady, Laith Middle Tech Univ Baghdad Iraq

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning (DL) is assisting academicians and medical professionals in uncovering latent opportunities in data and enhancing the healthcare industry. the edge computing applications like smart healthcare systems where accurate decision-making is required for fast medical treatment. DL in healthcare allows clinicians to correctly analyze any ailment and treat it, resulting in improved medical decisions. We present a unique DL model for the autonomous healthcare edge computing application in this paper. Computer Aided Diagnosis (CAD) is an essential requirement of healthcare edge computing where the patient's medical data is used for fast and accurate disease prediction. Propose the DL-based CAD model for automatic disease classification from the input medical images. the model consists of pre-processing, DL-based feature engineering, and classification. Input medical image is first pre-processed for quality improvement and then automatic features are extracted using the pre-trained DL models (ResNet50 and Densenet201). the pre-trained models are improved by performing the feature scaling followed by a separate classification phase. the proposed CAD model is experimentally evaluated using the medical images dataset. the results reveal the efficiency of the proposed model compared to underlying solutions.

关键词： Automatic classification deep learning edge computing features engineering healthcare image processing

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A Comparative Analysis of Digital image Forgery Detection Methods 5th

A Comparative Analysis of Digital Image Forgery Detection Me...

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5th international conference on Data Science, Machine Learning and applications

作者： Prashanth, Mallellu Sai Karnati, Ramesh Velpuru, Muni Sekhar Reddy, H. Venkateshwara Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad India Vardhaman Coll Engn Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Digital image Forgery Detection refers to the process of identifying whether a digital image has been manipulated or altered in any way, with the intention of deceiving or misleading the viewer. Digital image forgery detection has become an increasingly important research area due to the widespread availability of powerful image editing tools and the potential misuse of manipulated images. In this survey paper, we present a comprehensive comparative analysis of various digital image forgery detection methods. Our objective is to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the existing approaches and their effectiveness in detecting different types of image forgeries. the survey begins by outlining the different types of digital image forgeries, including copy-move, splicing, retouching, and more. We then proceed to review and compare a range of forgery detectionmethods proposed in the literature. these methods include traditional techniques based on handcrafted features, as well as more recent approaches that leverage advanced machine learning and deep learning algorithms. For each method, we discuss its underlying principles, key components, and specific algorithms utilized. We also analyse the strengths and limitations of each method, considering factors such as detection accuracy, robustness to image transformations, computational efficiency, and the ability to handle complex forgery scenarios. Additionally, we examine the evaluation metrics employed to assess the performance of these methods, including detection accuracy, localization accuracy, processing time, and other relevant metrics. through our comparative analysis, we identify common trends, challenges, and advancements in the field of digital image forgery detection. We highlight the trade-offs between different methods and provide insights into their suitability for specific forgery types and real-world applications. Furthermore, we discuss the limitations

关键词： image Digital Forgery Detection

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Research on the Application of Deep Learning-Based image Recognition Algorithm in Signal processing 5

Research on the Application of Deep Learning-Based Image Rec...

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5th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Lv, Jing Hunan Chemical Vocational and Technical College Zhuzhou China

ISBN: (纸本)9798331533694

A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. the generator can produce real images or remove noise by learning the signal image. then, the image is compared with the real image. In the pre-processing phase, the standard, denotation and enhancement of the signal image are carried out to guarantee the data quality and the validity of the characteristic. Experiments show that the precision of GAN-based image recognition is up to 96.3%, which is more than 15% than traditional methods (e.g., SVM). In the signal denoising task, the model significantly improves the signal-to-noise ratio (SNR) by 20% and effectively removes image noise. In addition, GAN also performs extremely well in pattern recognition tasks, with a correct recognition rate of 94.7%. Compared with traditional deep learning algorithms, the GAN model shows stronger robustness when processing signals with more noise or incomplete data. © 2025 IEEE.

关键词： image denoising

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Hand Gesture Recognition for Smart Television Using GRU 5th

Hand Gesture Recognition for Smart Television Using GRU

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5th international conference on Data Science, Machine Learning and applications

作者： Suresh, Garugu Yaswanth Lakshmi Sravani, Ravinuthala Gayatri Venkata VIT AP Univ Sch Comp Sci & Engn Inavolu AP Secretariat Amaravati Andhra Pradesh India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Even with the rapid advancement of technology, using a television still requires a physical remote control. Apart from occasionally losing sight of the television remote control, we also sometimes run out of batteries. the goal is to discover an effective way of controlling television and develop 3D hand gesture-based smart television control using Gated Recurrent Unit (GRU). the results of the research show that hand gesture recognition-based interface technology is capable of performing the majority of smart TV operations. It is a comfortable and delightful experience for consumers. the existing research features static image gesture recognition with predefined models for training in addition to the existing research the proposed model features sample video dataset, Custom design with Gated Recurrent Unit. the suggested model is trained using five hand gestures. the camera positioned on the TV continually records the motions. Each gesture is associated with a certain command. the proposed model has achieved an accuracy of about 94% in recognizing the gestures.

关键词： Smart Television image processing Deep Learning Convolutional Neural Networks Gated Recurrent Unit (GRU)

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