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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

共 16230 条记录，以下是4871-4880 订阅

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SAR Ship Detection Based on Convnext and New Feature Pyramid Network 8

SAR Ship Detection Based on Convnext and New Feature Pyramid...

引用

8th international conference on Signal and image processing, ICSIP 2023

作者： Wei, Fan-Ming Wang, Xiao Nanjing Tech University College of Computer and Information Engineering Nanjing China

ISBN: (纸本)9798350397932

In the field of SAR ship detection, numerous detecting methods with different backbones and feature enhancement algorithms have been proposed. However, existing backbones cannot generate high-quality multiscale feature maps, and existing feature enhancement algorithms cannot fully enrich semantic feature information. The above problems lead to poor performance on SAR ship detection, especially on small target detection. In this paper, a SAR ship detection method based on Convnext and New Feature Pyramid Network is proposed. First, Convnext is used as the backbone to extract high-quality multiscale feature maps. Second, new Feature Pyramid Network (NFPN) is designed to perform feature enhancement on each layer of feature maps. Experiments on SAR ship detection dataset (SSDD) have shown that our method has the best performance than other methods. © 2023 IEEE.

关键词： Feature extraction

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CAESAR: A CNN Accelerator Exploiting Sparsity and Redundancy Pattern

CAESAR: A CNN Accelerator Exploiting Sparsity and Redundancy...

引用

2023 international Technical conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Kim, Seongwook Kim, Yongjun Byeon, Gwangeun Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Samsung Lsi Suwon Korea Republic of

ISBN: (纸本)9798350326413

Convolutional Neural Networks (CNN) have shown outstanding performance in many computer vision applications. However, CNN Inference on mobile and edge devices is challenging due to high computation demands. Recently, many prior studies have tried to address this challenge by reducing the data precision with quantization techniques, leading to abundant redundancy in the CNN models. This paper proposes CAESAR, a CNN accelerator that eliminates redundant computations to reduce the computation demands of CNN inference. By analyzing the computation pattern of the convolution layer, CAESAR predicts the location where the redundant computations occur and removes them in the executions. After that, CAESAR remaps the remaining effectual computations on the processing elements originally mapped to the redundant computations so that all processing elements are fully utilized. Based on our evaluation with a cycle-level microarchitecture simulator, CAESAR achieves an overall speedup of up to 2.13x and saves energy by 78% over the TPU-like baseline accelerator. © 2023 IEEE.

关键词： Redundancy

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Blockchain-Based Consent Management for Privacy Persevering and Transparency in Intelligent Surveillance Systems 5

Blockchain-Based Consent Management for Privacy Persevering ...

引用

5th international conference on Blockchain Computing and Applications, BCCA 2023

作者： Jaafar, Fehmi Amayed, Darine Salhab, Wissam Bouani, Hajer Cheriet, Mohamed Quebec University at Chicoutimi Department of Computer Science Canada École de Technologie Supérieure Systems Engineering Department Canada ENSI L'École Nationale des Sciences de l'Informatique Tunisia

ISBN: (纸本)9798350339239

Video cameras are becoming ubiquitous. They'd be interconnected within a larger pervasive infrastructure supported by artificial intelligence which helps ensure smart services relaying in real-time video analysis and image processing. However, such services and technologies involve a set of privacy concerns. If privacy concerns can be addressed, enabling real-time analytics on video streams in public spaces can be valuable in supporting many applications for security, public safety, and urban space management. This study aims to highlight privacy concerns in image and video processing in the context of intelligent IoT systems by proposing a blockchain-based approach for data transparency and privacy management. As a result, our proposed approach will provide IoT customers the ability to manage their consent using a distributed ledger securely. © 2023 IEEE.

关键词： Blockchain

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RHViT: A Robust Hierarchical Transformer for 3D Multimodal Brain Tumor Segmentation Using Biased Masked image Modeling Pre-training

RHViT: A Robust Hierarchical Transformer for 3D Multimodal B...

引用

2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhou, Qian Zou, Hua Luo, Fei Qiu, Yishi Wuhan University School of Computer Science Wuhan China Hong Kong Baptist University United International College Beijing Normal University Beijing China

ISBN: (纸本)9798350337488

Accurate brain tumor segmentation in medical image analysis is crucial for diagnosis and treatment planning. While computer-aided methods have shown promise, several challenges persist. Most existing methods struggle with smaller tumors, treating all regions uniformly. Additionally, they lack robustness when dealing with data corruption and handling missing modalities, common in clinical settings. In this paper, we present a robust hierarchical vision transformer (RHViT) for 3D multimodal brain tumor segmentation, employing an encoder-decoder structure. Our approach combines 3D convolutions and self-attention, offering efficient and effective training. 3D convolutions help capture local information and generate hierarchical features, improving tumor segmentation accuracy. To enhance robustness, we pre-train the encoder using masked image modeling (MIM). This pre-training equips the model to handle data corruption, resulting in improved segmentation even in challenging scenarios. Furthermore, we introduce a novel biased masking strategy during MIM to focus the model's attention on tumor regions. This facilitates better tumor representations and effective fusion of multimodal features. Importantly, our biased masking technique strengthens the model's resilience when dealing with incomplete multimodal data during testing, making it a practical choice. Extensive experiments confirm the superiority of our model over existing approaches. © 2023 IEEE.

关键词： biased masking image modeling Brain tumor segmentation robust multimodal learning

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Multi-focus image fusion based on edge feature interaction and global reconstruction 4

Multi-focus image fusion based on edge feature interaction a...

引用

4th international conference on Signal image processing and Communication, ICSIPC 2024

作者： Li, Qing Huang, Man College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Transsion Communication Technology Co. Ltd. Chongqing401120 China

ISBN: (纸本)9781510682467

Multi-focus image fusion aims to synthesize key features of different images to enhance visual performance and produce high-quality images with higher resolution and finer details to enrich overall visual perception. However, existing methods mainly focus on feature extraction without paying much attention to the feature reconstruction process. In this paper, we propose a fusion method based on edge feature interaction and global reconstruction. By combining spatial attention and channel attention mechanisms, image features are effectively extracted at the encoder stage. And the edge information is detected using the canny operator, and subsequently an interactive edge feature fusion block is designed to facilitate the edge information fusion of two images detected by the canny operator. In the decoder stage, a self-attention mechanism is used for image reconstruction to further improve the quality of image fusion. Extensive experiments show that our model achieves state-of-the-art level and performs well in both subjective perception and objective evaluation. © 2024 SPIE.

关键词： image fusion

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Pansharpening Methods Based on a Redundant Contourlet Transform

Pansharpening Methods Based on a Redundant Contourlet Transf...

引用

2023 international conference on Earth Observation and Geo-Spatial Information, ICEOGI 2023

作者： Ourabia, Soumeya Baaziz, Nadia Smara, Youcef University Center of Tipaza -Morsli Abdelah-Institut of Sciences Laboratory of Image Processing and Radiation -USTHB Departement of Electronics Tipaza Algeria University of Quebec in Outaouais - Uqo Research Laboratory in Multimedia Information Department of Computer Science and Engineering GatineauQC Canada University of Sciences and Technology Houari Boumediene -USTHB Laboratory of Image Processing and Radiation Faculty of Electrical Engineering Algiers Algeria

ISBN: (纸本)9798350396720

The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information derived from both types of images. However, this may cause a distortion of spectral information or a lack in details and structures. The multiresolution analysis using wavelet transforms has proved its efficiency in the pansharpening domain, due to the representation of the image content over different resolution levels. In image fusion applications, we always need more detail information to be incorporated in the pansharpening procedure in order to produce enhanced results. However, the limitation of the wavelet transform to three types of oriented detail coefficients prevents this need from being met. To overcome this limitation, we propose to use the redundant contourlet transform (RCT) which extracts a richer multiscale directional information from the image. For this purpose, two RCT-based pansharpening methods are introduced and suitable data fusion procedures are described. We conducted several experiments on two different datasets acquired respectively by ALSAT-2A and IKONOS satellites. The visual results as well as quantitative results from evaluation metrics demonstrate the performance of the proposed methods. © 2023 IEEE.

关键词： image enhancement

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SmartFire Car: An image processing and Artificial Intelligence-Based Fire Detection and Extinguishing System 9

SmartFire Car: An Image Processing and Artificial Intelligen...

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9th IEEE international Women in Engineering (WIE) conference on Electrical and computer Engineering, WIECON-ECE 2023

作者： Sarkar, Indrojit Rabbi, Fazle Azim, Md. Ayenul Chowdhury, Md. Ashiqul Haider Ferdous, Maliha Ali, Mohammad Bangladesh University of Engineering and Technology Department of Electrical and Electronic Engineering Bangladesh

ISBN: (纸本)9798350319651

In this work, we present a novel image processing and artificial intelligence-based autonomous vehicle system for swift fire detection and suppression. The suggested approach offers a unique and economical option, especially for developing nations, to properly tackle fire-related threats in crowded residential spaces and outdoors. The algorithm used by the system, the Haar cascade, is effective in spotting fire in real-time video feeds. After detecting and determining the ideal stopping distance from the fire, the SmartFire car begins to effectively and efficiently extinguish it. To put the proposed theoretical system to the test in actual practical settings, we developed our prototype. The capacity of the SmartFire prototype to measure distances from the fire source and continue operating amid several fire sources has been used to evaluate its effectiveness. To make the system reliable and efficient, its limitations, such as the smallest fire size needed for detection, have also been examined. This work has the potential to significantly contribute to the modernization of the traditional fire-fighting system, particularly in developing countries, leading to more effective fire-handling capabilities. © 2023 IEEE.

关键词： Fires

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Deep Learning Classification and Segmentation of Brain MRI images

Deep Learning Classification and Segmentation of Brain MRI I...

引用

2023 international conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Pooja Ahuja, S. Likhith, S.R. Shiva Balan, A. Navya, K. Bengaluru India Bio-intelligence Lab Presidency University Department of School of Computer Science and Engineering and Information Science Bengaluru India Nagarjuna College of Engineering and Technology Department of Ise Bengaluru India

ISBN: (纸本)9798350317060

Unique Clinical pictures have a basic impact on the specialist's capability to the right conclusion and in the treatment of the patient. Diagnostic imaging can quickly identify lesions thanks to sophisticated algorithms, and it is essential to glean characteristics from photographs. In a number of studies, a number of algorithms have been incorporated into imaging in medicine. Using convolutional neural network, CNN's fundamental architecture is constructed using picture component extraction. To be able to circumvent the limitations imposed by both machine vision and human vision, the study is extended to include CNN with multiple input channels for visual feature extraction. This study uses approximately 3300 MRI samples from Kaggle to investigate four classifications: tumors of the pituitary gland, meningioma, glioma, and none. The implemented BrainNet has a accuracy of 98.31 percent and a validation accuracy of 87.80 percent. In order to determine which strategy performed better, deep architectures like InceptionNet and ResNet were also put through tests with or without learning through transfer. © 2023 IEEE.

关键词： image segmentation

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Multi-Class Price Tag Detection in images of Supermarket Shelves 13

Multi-Class Price Tag Detection in Images of Supermarket She...

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13th international Symposium on image and Signal processing and Analysis, ISPA 2023

作者： Sikic, Franko Filipovic, Branimir Kalafatic, Zoran Subasic, Marko Loncaric, Sven University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9798350315363

This paper presents a novel deep learning-based method for multi-class price tag detection in shelf images that capture the full shelf height. In the proposed method, the YOLOv8 price tag detection model is used to localize and classify the price tags in an image. We develop a new augmentation method where some of the existing price tags in a shelf image are duplicated and shifted horizontally. In addition, price tag crops are used as templates to generate synthetic price tag images. In order to solve the price tag class imbalance problem, we also develop a method for balancing the ratio of different price tag classes in shelf images using the aforementioned synthetic price tags. Finally, we train the price tag detection model using balanced data. Experimental results show that the five-class price tag detection model trained on unbalanced data achieves mean average precision (m-AP) of 73.1%, while training on balanced data helps to boost the result of price tag detection up to 93.9% mAP. © 2023 IEEE.

关键词： computer vision

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A Shallow Convolutional Neural Network Model for Breast Cancer Histopathology image Classification 11th

A Shallow Convolutional Neural Network Model for Breast Canc...

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11th international conference on Recent Trends in Computing, ICRTC 2022

作者： Saxena, Shweta Shukla, Praveen Kumar Ukalkar, Yash School of Computing Science Engineering VIT Bhopal University Bhopal India School of Computing and Information Technology Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811988240

Identification of malignancy using histopathology image processing is a crucial method for cancer diagnosis. A model to classify images based on deep convolutional neural networks (CNNs) attains a promising performance in the computer vision for a large dataset but creates a computational complexity for a comparatively small histopathology dataset. A shallow CNN model is proposed in this paper, which classifies the histopathology images of breast tissues. The images are classified as either malignant or benign by the model. It is tested on two publicly available, benchmark breast cancer histopathology datasets. According to the result, the proposed model achieves (95.19 ± 8.50) accuracy and requires a less no. of parameters (8, 052,062) for training as compared to the existing deep CNN models implemented on the same protocol. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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