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Interventional guidewire segmentation algorithm based on coded deep neural networks 4

Interventional guidewire segmentation algorithm based on cod...

引用

4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Li, Yongming Xiao, Nu Jiang, Shaochen College of Computer Science and Technology Xinjiang University Xinjiang Urumgi830046 China

ISBN: (纸本)9781510682528

TACE is the main non-surgical method for the treatment of intermediate and advanced liver cancer, and during the surgery, the surgeon determines the guidewire position by DSA image guidance. Due to the thin guidewire and complex image background, in this paper, GWnet network is proposed to enhance the guidewire segmentation accuracy. the network first facilitates the model's extraction of elongated tubular features of the guidewire by replacing the conventional convolution with a strip pooled pyramidal dispersed attention module (SPSA);secondly, hollow space convolutional pooling pyramid module (IASPP), which incorporates the inverse bottleneck layer structure, is embedded into the network to expanding the model sensing area while reducing feature loss.;Ultimately, by introducing a coordinated attention mechanism to reduce the effect of background noise and improve the resolution of the model to recognize guidewire targets and their edge details. Experimental results on 4839 anonymized DSA contrast images show that the guidewire segmentation method achieves good results. © 2024 SPIE.

关键词： image segmentation

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Efficiency study of VGG networks in autonomous driving tasks 4

Efficiency study of VGG networks in autonomous driving tasks

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4th international conference on computer vision and Data Mining, ICCVDM 2023

作者： Qi, Junhua School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781510674448

In this study, we aim to evaluate the applicability of the VGG 19 architecture-based neural network for autonomous driving tasks. the objective is to demonstrate the effectiveness of employing this neural network to solve the problem and to compare the identification quality. Experimental methodology is utilized to compare the efficiency of different neural networks. During the experimentation, we observed a disparity in the efficiency of VGG with and without the inclusion of the attention mechanism. Presently, a variety of machine learning methods are increasingly used to address diverse problems. image recognition holds particular significance among these challenges. the practical problem under investigation in this study pertains to the identification of roads with varying curvatures and their correspondence to the vehicle’s steering angles for the purpose of autonomous vehicle control. this paper explores multiple types of neural networks commonly used for image recognition. Previous research has demonstrated the high efficiency of these neural networks in solving various tasks, while this evaluation focuses on the applicability of neural networks in solving the autonomous driving problems and a performance comparison of these methods. the experimental results indicate that VGG 19 achieves the highest accuracy of all neural networks. © 2024 SPIE.

关键词： image recognition

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Leveraging Advanced Deep Learning Techniques for Automated Dermatological image Classification 4

Leveraging Advanced Deep Learning Techniques for Automated D...

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4th IEEE international conference on Applied Electromagnetics, Signal processing, and Communication, AESPC 2024

作者： Saha, Akashdip Suddapalli, Rishik Sahoo, Abhaya Kumar Singh, Jagannath School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798331528171

this paper examines the automatic classification of skin photos using the state-of-the-art deep learning methods, with a focus on the categorization of skin lesions. One of the main goals is to improve the accuracy and user-friendliness of dermatology diagnostics;thus, the diagnosis can be made much earlier and more accurately than now, leading to the detection of skin conditions. We use convolutional neural network-based (CNN) algorithms that are highly complex and the huge international Skin Imaging Collaboration (ISIC) library for facial recognition. this model will revolutionize skin diseases diagnosis, enabling clinical decisions to be made quickly and accurately, thereby enhancing patient care and outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi Class Spectral Discriminative Convolutional Neural Network Towards Hyperspectral image Classification of Crop Disease on Inclusion of Spectral Spatial Feature Fusion Mechanism 4

Multi Class Spectral Discriminative Convolutional Neural Net...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Boopathi Raja, A.S. Poovarasan, K. Udhayakiran, S. Muhammed Aleem, Shaik tharageswari, K. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Crop diseases occurs due to bacterial, viral, parasitic and fungal infection leads to various challenges to farmer and agricultural based business as crop diseases effects the normal growth of the crop and it spreads to other crops surrounding it. Especially those crop infection leads to great economic damage to the farmers and to the entire country. In order to prevent, classify and mitigate those crop infections, effective measures have to be carried out. Manual observation by biologist and agriculturist leads to misinterpretation and time consuming. In particular, remote sensing technologies using hyperspectral imaging techniques helps to monitor and acquire the agriculture information in various target regions effectively. Due to development of computer vision technique and artificial intelligence approaches in form of machine learning and deep learning architectures, countermeasure against the crop disease can be achieved easily on processing the acquired information in form of hyperspectral images. However traditional architecture faces multiple challenges in form of the spectral signatures unmixing as boundaries of the objects of different diseases types is very complex to be filtered. thus, a new spectral unmixing based object recognition method has to be constructed to categorizes different form of crop disease in the agriculture region. In this paper, Multi Class Spectral Discriminative Convolution Neural Network(MCSD-CNNN) using spectral and spatial feature fusion towards hyperspectral image classification for detect, classify and mitigate the crop diseases on various climate and various regions. Initially image preprocessing is carried out to eliminate the spectral noises. Preprocessed image is projected to convolution layer of the network to extract the endmembers of the spectral band representing the various crop species in the different climate and spatial regions. Extracted end member of the spectral band is represented spectral disease index map. th

关键词： Convolutional neural networks

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Application on the Loop of Multimodal image Fusion: Trends on Deep-Learning Based Approaches 13

Application on the Loop of Multimodal Image Fusion: Trends o...

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IEEE 13th international conference on pattern recognition Systems (ICPRS)

作者： Bastidas-Guacho, Gisel Moreno-Vallejo, Patricio Vintimilla, Boris Sappa, Angel D. ESPOL Polytech Univ Guayaquil Ecuador ESPOCH Polytech Univ Riobamba Ecuador

ISBN: (纸本)9798350333374

Multimodal image fusion allows the combination of information from different modalities, which is useful for tasks such as object detection, edge detection, and tracking, to name a few. Using the fused representation for applications results in better task performance. there are several image fusion approaches, which have been summarized in surveys. However, the existing surveys focus on image fusion approaches where the application on the loop of multimodal image fusion is not considered. On the contrary, this study summarizes deep learning-based multimodal image fusion for computer vision (e.g., object detection) and image processing applications (e.g., semantic segmentation), that is, approaches where the application module leverages the multimodal fusion process to enhance the final result. Firstly, we introduce image fusion and the existing general frameworks for image fusion tasks such as multifocus, multiexposure and multimodal. then, we describe the multimodal image fusion approaches. Next, we review the state-of-the-art deep learning multimodal image fusion approaches for vision applications. Finally, we conclude our survey with the trends of task-driven multimodal image fusion.

关键词： multimodal fusion deep learning task-driven

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Enhancing Road Safety in Diminished vision Conditions: A Deep Learning Approach Using Faster MobileNet for Real-time Object Detection 4

Enhancing Road Safety in Diminished Vision Conditions: A Dee...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Subbaiah, D. Venkata Sricharan, Byreddi Aman, Shaik Zeenath Gopinath, Kasarla Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350349900

Despite significant enhancements in car safety technology, traffic accidents still happen, so finding creative ways to reduce the risk is necessary, particularly in situations with poor vision, like fog. this study presents a unique method using deep learning, namely the Faster MobileNet model, to reduce traffic accidents in situations where eyesight is impaired. the suggested solution makes use of a live video feed from the car's front-mounted camera and Faster MobileNet to identify and highlight things on the driver's display instantly. To improve image quality, the system incorporates image processing methods such as data augmentation, normalization, and scaling. Convolutional and pooling layers are used in feature extraction, together with a Region Proposal Network (RPN) to find potential object areas. Classifiers are used in object detection to recognize different kinds of objects and give the driver pertinent information. Non-Maximum Suppression (NMS) is incorporated into the implementation as a post-processing step to eliminate duplicate detections and improve the result. By acting as an intelligent helper, the created application greatly increases poor vision drivers' visibility and promotes a safer driving environment. © 2024 IEEE.

关键词： Intelligent systems

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Enhancing thermal Fiducial Marker Detection: Focus on image processing Techniques 1

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4th international conference on Optimization Learning Algorithms and Applications

作者： Franca, Andre Berger, Guido S. Mendes, Andre Lima, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade Tecnol Reg Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (数字)9783031774324

ISBN: (纸本)9783031774317;9783031774324

this article proposes methods for maximising the detection rates of thermal fiducial markers using thermography. By exploring the combination of image processing techniques with the use of an affordable thermographic camera, the aim is to mitigate the negative effects of thermography and improve accurate marker identification in a variety of mounting and distance conditions. the research identified a diversity of processing techniques capable of improving thermal marker recognition, offering the potential to surpass previous results. the results highlight the possibility of using low-cost thermographic cameras for this purpose, which could democratise and reduce the costs of recognition processes. this methodology validates the proposed approach, providing a robust basis for future improvements in thermal marker detection and promoting the feasibility of practical, low-cost applications in an assortment of fields.

关键词： thermal Fiducial Markers image processing processing techniques

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AI-Driven Alert Systems for an Intuitive Animal Monitoring System 4

AI-Driven Alert Systems for an Intuitive Animal Monitoring S...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Latha, P. Diana Inba Malar, Y. Hemavarthini, C.S. Manjuparkavi, V. Sneha, S. V.S.B. Engineering College Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350364729

All animals that can sight, including humans, do so in a way that appears instinctive and natural. Creatures use their vision system early in life often even from birth to traverse their surroundings, recognize and communicate with other creatures, and learn about their surroundings. Because of this, an animal's vision system is continuously being educated and modified to enable it to do a variety of activities. In humans, for instance, this system works by way of the optic nerve, which transmits light data from the eye to the brain for interpretation and processing. this sophisticated vision system enables us to focus on objects and see the world in three dimensions, all while being able to autonomously adjust to changing light conditions. It is possible to think of computer vision, digital image processing, and digital image analysis as a combination of phrases that are frequently used to refer to related operations. the main reason for this misconception is that they are related fields that arose with the advancement of digital picture capture. thus, it's important to comprehend how these fields relate to one another, how a digital image is created, and how the various sensors that are available each best suited for a particular application differ. Since the introduction of charge- coupled devices, which signaled the beginning of digital imaging, the field has developed very quickly. In this project, a camera that records the surroundings all day long will be used to periodically monitor the entire farm. We can identify when an animal is entering with the aid of a deep learning model, and then we play the right sounds to eject the animal. © 2024 IEEE.

关键词： CCD cameras

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Improved U-Net with gray channel attention for image segmentation 4

Improved U-Net with gray channel attention for image segment...

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4th international conference on Intelligent Communications and Computing

作者： Pan, Feng Geng, Lujing Zhang, Ning Chen, Zuhao China Mobile Grp Design Inst Co Ltd Network Super Prod Dept Beijing Peoples R China China Mobile Grp Co Ltd Network Business Dept Beijing Peoples R China

ISBN: (纸本)9798331530372;9798331530365

image segmentation base on deep learning methods is an important direction in computer vision field. However, these models over-rely on color features in image segmentation tasks, which leads to poor segmentation effect in scenes with the interference of similar background colors. To solve this problem, this paper successfully improves the U-Net model by introducing the technical means of combining gray channel and attention mechanism. the experimental results show that compared with the original U-Net model, the average accuracy of the improved U-Net with gray channel attention has increased from 81.69% to 82.61%. At the same time, we apply this method mechanism to improved models of U-Net such as Attention U-Net and R2U-net, and similar effect is verified. these results verify that the combination of gray channel and attention mechanism can effectively improve the robustness and accuracy of deep learning model when processing color-similar background in image segmentation tasks. this work has important practical application value and provides a new solution for image segmentation tasks in complex scenes.

关键词： image segment U-Net attention mechanism deep learning

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CLIP-DS: Incorporating DataStd and Similarity Networks into CLIP for Scene recognition Task 4

CLIP-DS: Incorporating DataStd and Similarity Networks into ...

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4th international conference on computer Communication and Artificial Intelligence (CCAI)

作者： Zhang, Jiaming Cao, Zheng Wu, Xuan Wu, Yuesong Zhou, You Xiao, Yubin Yu, Biao Li, Yingxuan Jilin Inst Elect Informat Prod Inspect Changchun Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Coll Software Changchun Peoples R China

ISBN: (纸本)9798350362770;9798350362763

Existing deep learning methods encounter challenges when confronted with the scene recognition task, particularly in cases with significant intra-category differences, primarily due to limited available data. To better address this problem, in this paper, we propose Contrastive Language-image Pre-Training with DataStd and Similarity networks (CLIP-DS), a novel scene recognition method built upon CLIP architecture, which utilizes extensive pre-training knowledge. In addition, to comprehensively analyze the similarity sequence provided by CLIP, we enhance it with the specialized Similarity networks, comprising of a Multi-stream Multilayer Perceptron block. To improve the data space of similarity sequences and ensure stable training, we further propose the DataStd network with learnable parameters. To the best of our knowledge, this work proposes the first dataset for the challenging intra-category difference industrial scene recognition task, encompassing 120k images. the experimental results demonstrate that the proposed CLIP-DS outperforms the other methods, achieving an accuracy of 87.59% with fine-tuning on only 90 images. Ablation Studies also demonstrate the effectiveness of the proposed Similarity and DataStd networks and identify optimized-prompt combination as the most suitable prompt strategy.

关键词： industrial scene recognition CLIP computer vision few-shot

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