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检索条件"任意字段=Advances in Image Processing and Pattern Recognition, Proceedings of the International Conference."

共 8569 条记录，以下是11-20 订阅

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Research on the Implementation and Application of Improved Canny Algorithm for image Edge Detection Based on FPGA 24

Research on the Implementation and Application of Improved C...

引用

1st international conference.on image processing Machine Learning and pattern recognition

作者： Huang, Haojie Luo, Junzhe Beijing Inst Technol Comp Coll Zhuhai Peoples R China

ISBN: (纸本)9798400707032

With the continuous development of digital image processing technology, edge detection technology is playing an increasingly important role in the field of image processing. The Canny algorithm is a classical gradient-based edge detection algorithm with excellent performance and robustness. However, due to its large computational amount and poor real-time performance, the traditional software implementation method has been unable to meet the demands of modern high-speed image processing. Therefore, the Canny algorithm hardware is turned into a popular research direction. As a programmable logic device, FPGA has the advantages of high flexibility, short development period and strong parallel computing power, which is widely used in the field of digital signal processing. At present, there has been a lot of research work on FPGA in implementing the Canny algorithm, but most of the schemes have some problems, such as slow speed and high resource occupancy rate. Therefore, this paper presents an improved scheme for the hardware design of Canny algorithm based on FPGA, aiming to improve the speed and efficiency of image processing while reducing the utilization of hardware resources.

关键词： FPGA Canny algorithm Hardware design Digital image processing technology

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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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Self-supervised Low-light image Enhancement Based on Retinex and Histogram Equalization Prior 24

Self-supervised Low-light Image Enhancement Based on Retinex...

引用

1st international conference.on image processing Machine Learning and pattern recognition

作者： Kong, Taoru Rey, William P. Mapua Univ Sch Informat Technol Manila Philippines

ISBN: (纸本)9798400707032

images taken in low light often exhibit low visibility, high noise, and uneven lighting distribution, severely limit the application of images in fields such as object detection, image recognition, satellite and aerial imaging. Currently, deep learning-based low-light image enhancement(LLIE) methods have proven to be notably effective. However, the need for a vast collection of genuine paired data restricts the universality of these models. This study introduces a novel self-supervised framework named SID-RetinexHEPNet, which consists of three parts: a Retinex-based self-supervised image decomposition network (RetinexHEPNet), a non-linear illumination enhancement function (NIEF), and an image enhancement module (IEM). Operating as a self-supervised model, SID-RetinexHEPNet performs a direct decomposition of the low-light image into reflectance, illumination, and noise components, without the use of pre-training or reference images. Subsequently, the decomposed illumination map is enhanced through NIEF. Finally, IEM performs image enhancement on the low-light image using the enhanced illumination map. Experiments demonstrate that the images enhanced by this method not only exhibit richer textures and brightness but also effectively avoid over-enhancement or insufficient detail enhancement.

关键词： Low-light image enhancement Self-supervised learning Histogram equalization prior Channel attention

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Reaserch on Aircraft image Segmentation Algorithm Based on Improved YOLOv8 24

Reaserch on Aircraft Image Segmentation Algorithm Based on I...

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1st international conference.on image processing Machine Learning and pattern recognition

作者： Wang, Chengxin Chinese Flight Test Res Estab Xian Peoples R China

ISBN: (纸本)9798400707032

With the rapid development of target segmentation techniques, the YOLO family of algorithms has become popular due to its efficiency. In this paper, we propose an improved YOLOv8 model aimed at improving the performance of instance segmentation of aircraft images. We enhance the model's ability to capture the global dependencies of the aircraft in the image by introducing a Non-local attention mechanism, while integrating a bidirectional feature pyramid network (BiFPN) for finer feature fusion. Experimental results conducted on the publicly available COCO dataset aircraft category show that the improved YOLOv8 model outperforms the original model in several performance metrics, especially the significantly improved detection accuracy in complex backgrounds. These improvements provide effective technical support for real-time aircraft detection and segmentation, demonstrating the potential of attentional mechanisms and advanced feature fusion techniques for practical applications.

关键词： yolov8 airplane image segmentation non-local attention mechanism target detection BiFPN

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AURA-Net: image Dehazing Algorithm with Multi-scale Detail Enhancement 24

AURA-Net: Image Dehazing Algorithm with Multi-scale Detail E...

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1st international conference.on image processing Machine Learning and pattern recognition

作者： Kuai, Chaoyu Liang, Li Chongqing Univ Technol Chongqing Peoples R China

ISBN: (纸本)9798400707032

When processing images, the current dehazing algorithm has problems such as color distortion, loss of texture details, and overall dimness of the image. This paper proposes a new end-to-end network model AURA-Net. First, an enhanced feature residual module (EFR Module) is designed to address the common color distortion and texture detail loss problems in dehazing algorithms. By introducing the residual structure, the EFR module can effectively enhance the expression ability of image features, thereby better retaining the detailed information of the image, reducing color distortion, and improving the quality of the dehazed image. Secondly, a multi-scale spatial attention deblurring module (MSAD Module) is designed. This module combines spatial attention and channel attention mechanisms to focus on blurred areas and important channels in the image. Through multi-scale feature extraction, the MSAD module can more accurately restore the details in the image. Finally, the mean square error (MSE) loss function is redesigned to help the network converge faster. Experimental results show that the dehazed image restored by the algorithm proposed in this paper is not only subjectively natural in color and clear in details, but also outperforms the existing mainstream algorithms in objective indicators.

关键词： image Dehazing Feature Enhancement Multi-scale Spatial Attention Loss Function

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Stacked Multi-Head Cross Attention for image recognition 24

Stacked Multi-Head Cross Attention for Image Recognition

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1st international conference.on image processing Machine Learning and pattern recognition

作者： Liu, Shuaichen Du, Xiao Wu, Tieru Jilin Univ Sch Artificial Intelligence Changchun Jilin Peoples R China Jilin Univ Sch Math Changchun Jilin Peoples R China

ISBN: (纸本)9798400707032

Long-range dependency plays a critical role in extracting intricate image features particularly in tasks involving image recognition. In previous study, the significance of long-range positional dependencies has been proved in both image classification and image segmentation. Based on this, we introduce a Multi-Head Cross Attention module, namely MHCA, along with four different operators, which are designed to capture and integrate contextual information at every pixel position within feature maps, spanning both horizontal and vertical directions, with parallel fashion, thus can transfer information and share weights across multiple heads of features. Moreover, by stacking our module twice, forming MHCA(2) layer, the whole context of each pixel in feature can be captured, with more lighter computation burden than general full connection or Non-local networks, and it is designed to be seamlessly plugged into existing network architectures. By replacing specific convolution layer in convolutional networks with a MHCA(2) layer, we construct MHCA network. Through extensive experiments upon various datasets, we demonstrate the validity of our approach. Furthermore, comparative analysis with similar methodologies highlight the superior performance of our method.

关键词： Cross Attention image Classification image Segmentation Long-range Dependency Lightweight Architecture

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images Inpainting via Region Convolution and Feature Fusion 24

Images Inpainting via Region Convolution and Feature Fusion

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1st international conference.on image processing Machine Learning and pattern recognition

作者： Li, Jinrong Gao, Zhisheng Zhou, Bin Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China

ISBN: (纸本)9798400707032

image inpainting aims to fill damaged regions with reasonable structure, fine texture details, and semantically consistent content. However, existing deep learning algorithms mostly employ standard convolution architecture on damaged images, resulting in meaningless content such as color differences, blurring, and other artifacts. Aiming at the problem that the standard convolution methods cannot fully exploit the known regions to predict the damaged region features, this paper proposes an efficient region convolution (ERC) module to distinguish between known and damaged regions, and effectively prevent interference from invalid pixels through feature migration, additionally, the sampling regions are dynamically expanded to reconstruct the damaged regions more accurately. This article proposes a novel inpainting network that utilizes an encoder-decoder framework to restore images that are both semantically reasonable and visually realistic. Extensive experiments on two public datasets, Paris StreetView and CelebA, both qualitatively and quantitatively prove the strengths of the proposed approach.

关键词： image inpainting Deep learning Efficient region convolution Encoder-decoder framework

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Lower limb movement state pattern recognition based on EEG-EMG signals 24

Lower limb movement state pattern recognition based on EEG-E...

引用

1st international conference.on image processing Machine Learning and pattern recognition

作者： Wang, Tao Xie, Nenggang Anhui Univ Technol Coll Mech Engn Huainan Anhui Peoples R China Anhui Univ Technol Coll Management Sci & Engn Huainan Anhui Peoples R China

ISBN: (纸本)9798400707032

Assessing the lower limb motor states of stroke patients based on biosignals is very important in the field of medical rehabilitation, and the importance of finding effective physiological signal indicators and processing methods for patient rehabilitation training and evaluation is self-evident. In this paper, a CNN-SVM model is constructed based on the CNN classifier to classify the three motion states of the lower limbs of the subjects, and the method is verified in the WAY-EEG-GAL multimodal open dataset to have a better classification effect, and the experimental data are used to verify the effectiveness of the model classification. The results show that the CNN-SVM method proposed in this paper outperforms the CNN classification model for all three classifications on both the WAY-EEG-GAL dataset and the experimental data, with average accuracies of 86.60% and 95.43%, respectively. This study provides a theoretical basis for combining EEG and EMG signals to establish a BCI-based method for lower limb exercise rehabilitation.

关键词： EEG-EMG classification CNN-SVM pattern recognition

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Enhancing Unsupervised Few-Shot Medical image Classification with Weight-Enhanced Contrastive Learning 24

Enhancing Unsupervised Few-Shot Medical Image Classification...

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1st international conference.on image processing Machine Learning and pattern recognition

作者： Liu, Huantong Zhong, Jingze Shanghai Univ Shanghai Peoples R China

ISBN: (纸本)9798400707032

In the realm of contemporary deep learning, the pivotal challenges confronting few-shot and unsupervised learning reside in harnessing scarce labeled samples alongside abundant unlabeled ones, particularly in the context of medical image classification. This paper introduces an innovative approach that seamlessly integrates dynamic clustering with weights augmentation, aimed at bolstering the performance of few-shot medical image classification. Dubbed Weight-Enhanced Contrastive Learning (WECL), our method ingeniously fuses contrastive representation learning with a dynamic memory module during unsupervised pre-training. This fusion facilitates efficient clustering and classification of diverse augmented renditions of the same image. Additionally, the weights augmentation tactic meticulously tunes the weights of both ResNet and teacher-student model branches, thereby mitigating sample bias and enhancing the pre-trained model's proficiency. Extensive experiments across multiple few-shot medical image classification datasets underscore the superiority of our WECL approach, outperforming current state-of-the-art baselines, and effectively addressing issues pertaining to data distribution disparities and sample scarcity.

关键词： Medical image Classification Weights Augmentation Contrastive Learning Unsupervised Learning Few-Shot Learning

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AI-based recognition for road distressed from GPR measurement using artificial neural networks

AI-based recognition for road distressed from GPR measuremen...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Liu, Qian Liu, Zhen The college of electrical engineering Zhejiang University of Water Resources and Electric Power Hangzhou China Department of Civil and Environmental Engineering Penn State University ParkPA16802 United States

ISBN: (纸本)9781510680425

Gprmax is utilized for ground-penetrating radar simulation, enabling to generate imagery delineating diverse road *** autonomous identification and classification of these road afflictions are performed leveraging a dataset trained within the YOLO *** for its efficacy in target detection, YOLO harnesses pre-trained model parameters from extensive databases, thereby achieving commendable recognition *** ameliorate the protracted training duration, the adoption of the random retreat mechanism is *** methodology elucidated in this investigation attains an average recognition rate of 77.83%, effectively achieving the identification of road disease images and thereby enhancing operational ***, the proposed approach exhibits promising prospects for application and dissemination within engineering domains. © 2024 SPIE.

关键词： image enhancement

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