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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16439 条记录，以下是1751-1760 订阅

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Comparative analysis of three advanced deep learning algorithms for Multiple Sclerosis lesion segmentation in FLAIR MRI

Comparative analysis of three advanced deep learning algorit...

引用

International conference on Signal processing Proceedings (ICSP)

作者： Yi Zhu Thomas Grenier Chantal Revol-Muller CREATIS University of Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

This paper addresses the pressing need for enhanced tools in the diagnosis and management of Multiple Sclerosis (MS), particularly in the accurate detection and segmentation of MS lesions. Leveraging recent advances in deep learning, we evaluate the performance of three state-of-the-art algorithms, focusing on their potential to improve both precision and efficiency in MS lesion segmentation from medical images. Our study provides critical insights into the strengths and limitations of each model, offering valuable guidance for future applications of AI in MS diagnosis and treatment.

关键词： Deep learning image segmentation Accuracy Multiple sclerosis Computational modeling Signal processing algorithms Signal processing Real-time systems Lesions Reliability

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Gaussian Blur through Parallel Computing

Gaussian Blur through Parallel Computing

引用

International conference on image processing and Vision Engineering (IMPROVE)

作者： Ibrahim, Nahla M. Abou ElFarag, Ahmed Kadry, Rania Arab Acad Sci & Technol & Maritime Transport Dept Comp Engn Alexandria Egypt

ISBN: (纸本)9789897585111

Two dimensional 2D convolution is one of the most complex calculations and memory intensive algorithms used in image processing. In our paper, we present the 2D convolution algorithm used in the Gaussian blur which is a filter widely used for noise reduction and has high computational requirements. Since, single threaded solutions cannot keep up with the performance and speed needed for image processing techniques. Therefore, parallelizing the image convolution on parallel systems enhances the performance and reduces the processing time. This paper aims to give an overview on the performance enhancement of the parallel systems on image convolution using Gaussian blur algorithm. We compare the speed up of the algorithm on two parallel systems: multi-core central processing unit CPU and graphics processing unit GPU using Google Colaboratory or "colab".

关键词： CUDA Parallel Computing image Convolution Gaussian Blur Google Colaboratory

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Multi-sequence Satellite image Cloud Removal Based on Dual Residual Network 8th

Multi-sequence Satellite Image Cloud Removal Based on Dual R...

引用

8th International conference on Artificial Intelligence and Security (ICAIS)

作者： xiao, Changcheng Wu, xi Chengdu Univ Informat Technol Chengdu 610000 Peoples R China

ISBN: (纸本)9783031067884;9783031067877

This paper presents a multi-sequence satellite image removal algorithm to remove cloud cover in remote sensing images. The algorithm is based on dual residual network. Above all, the network uses multi-temporal cloud images as the input of the model. Then, it fuses the global and local feature information through the dual residual connection structure, which makes the overall structure of the generated restored image reasonable and the edge details clearer. Finally, it uses pixel up-sampling to enhance the utilization of spatial information and improve the restoration effect. Experiments on the European Sentinel-2 remote sensing satellite image dataset with different cloud removal algorithms demonstrate that our network achieves higher quality than the state-of-the-art methods. The proposed method was verified on the dataset, and the PSNR and SSIM indexes were 27.04 and 0.8479, both exceeding the original processing method STGAN of the dataset, and improving the effectiveness of remote sensing image cloud removal.

关键词： image cloud removal Remote sensing image image restoration Dual Residuals Network Multitemporal images

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TinyCompNet: Minimal Computational Load for Mobile Devices

TinyCompNet: Minimal Computational Load for Mobile Devices

引用

International conference on Vision, image and Signal processing (ICVISP)

作者： Jiale Wang Zhe Bai ximing Zhang Yuehong Qiu Xi'an Institute of Optics and Precision Mechanics of CAS Xi'an China University of Chinese Academy of Sciences Beijing China Space Optics Technology Research Laboratory Xi'an Institute of Optics and Precision Mechanics of CAS Xi'an China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

With the ongoing advancements in imaging technology and convolutional neural networks, the increased resolution of images and more complex network architectures have resulted in a significant rise in computational requirements, posing challenges for the deployment of object detection algorithms on mobile and embedded devices. Existing lightweight networks mainly focus on reducing the number of parameters and model size. However, for low-computation devices, the bottleneck in computational resource consumption is the critical factor for the deployment of object detection algorithms. Therefore, the current research challenge is to significantly reduce the computational load while ensuring detection accuracy, thereby lowering computational resource requirements. In this study, we propose a novel ultralightweight, computationally efficient object detection network called TinyCompNet. Through the ingenious design of the submodule TinyComputationBlock, utilizing deep feature extraction and efficient feature reuse to balance computational efficiency and representation capability, ensuring suitability in resource-limited environments. Moreover, we address the lack of cross-channel communication in deep convolutions by implementing channel shuffle and channel attention mechanisms to enable rich feature interactions, which helps maintain a balance between computational efficiency and the ability to capture diverse features. To validate the effectiveness of TinyCompNet, we conducted extensive experiments on the Exdark dataset and comprehensively compared it with other advanced lightweight object detection networks in terms of computational load and detection accuracy. The experimental results indicate that TinyCompNet significantly reduces computational complexity while maintaining detection accuracy on par with existing methods, further attesting to its superiority and effectiveness.

关键词： Accuracy Attention mechanisms Convolution Object detection Feature extraction Mobile handsets Computational efficiency Convolutional neural networks Computational complexity Guidelines

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3D image Reconstruction and processing for Augmented and Virtual Reality Applications: A Computer Generated Environment 2

3D Image Reconstruction and Processing for Augmented and Vir...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Baskar, Radhika Krishnammal, P. Muthu Aeron, Anurag Ali, Syed Sumera Leonid, T. Thomas Arun, M.R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India School of Computing Dit University Computer Science and Engineering Dehradun India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India Ece Dept Kcg College of Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350369960

New technologies like AR and VR are changing our online and offline experiences. Augmented and virtual reality applications need 3D picture reconstruction and processing. In augmented and virtual reality, this abstract explains 3D image reconstruction and processing concepts, techniques, and effects. Creating engaging AR and VR experiences starts with 3D data. Depth sensors, stereoscopic images, and photogrammetry are essential for accurate 3D reconstructions of real-world objects and settings. These data streams support spatial cognition and interaction in AR/VR. Reconstruction technologies like point cloud and mesh reconstruction are essential for turning 3D data into models. Virtual features that complement or replace the actual environment in AR experiences or build fully fabricated VR settings are based on these models. By applying 2D textures to 3D geometry, 'texture mapping' enhances scene realism. Realtime augmented and virtual reality processing requires effective rendering, tracking, and physics simulation algorithms. Rendering engines provide beautiful, responsive experiences, while SLAM lets users explore and interact with virtual information in real life. Occlusion culling and level-of-detail rendering are two optimisation methods needed for smooth performance on high-end VR systems and mobile AR devices. Virtual reality is defined by 3D object interaction and control. Natural and engaging interfaces are created using gesture detection and real-time physics simulations. AR and VR software developers encounter new challenges with each iteration. AR requires accurate spatial mapping, dependable tracking, and simple user interfaces to blend the virtual and real worlds. In contrast, virtual reality (VR) immerses users in realistic computer settings. Portability across operating systems is crucial for greater adoption. Developers must optimise programmes for several hardware platforms to reach the most people. © 2023 IEEE.

关键词： 3D image Augmented reality Reconstruction Virtual Reality

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Comparison of feature selection algorithms for Data classification problems 8

Comparison of feature selection algorithms for Data classifi...

引用

8th International conference on Information Technology and Nanotechnology, ITNT 2022

作者： Tislenko, M.D. Gaidel, A.V. Kupriyanov, A.V. Samara National Research University Samara Russia Image Processing Systems Institute RAS-Branch of the FSRC 'Crystallography and Photonics' RAS Samara Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

This article discusses various feature selection algorithms, namely SelectKBest with different statistical criteria and Random Forest algorithm, compares classification accuracy with and without feature selection algorithms on different datasets. As a result, it was found that in the general case, Random Forest algorithm shows the best classification, but on some datasets it is better to use SelectKBest with certain statistical criteria. © 2022 IEEE.

关键词： Classification (of information)

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Domain Adaptation of Digital Pathology images using Joint Stain Color and image Quality Constraints

Domain Adaptation of Digital Pathology Images using Joint St...

引用

IEEE International conference on image processing

作者： xi Long Jingxin Liu xianxu Hou School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

Digital pathology diagnosis systems face significant domain shift problems that hinder their performance on new datasets. Existing methods for aligning digital pathology images from different domains mainly focus on stain color and overlook the potential domain shifts caused by variations in image quality. To address this issue, we propose a novel parametric model that incorporates both stain color and image quality constraints for domain adaptation of digital pathology images. We evaluate our approach on the domain adaptive mitosis detection task through extensive experiments and ablation studies, showing that our method outperforms other state-of-the-art methods.

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Enhancing Vision: Harmonizing Frequency for Imaging Quality and Perception Accuracy

Enhancing Vision: Harmonizing Frequency for Imaging Quality ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hongyang Chen Kaisheng Ma Xi’an Jiaotong University Tsinghua University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In low-level vision tasks, achieving harmony between visual quality and recognition accuracy is often challenging, as the two do not always align. Many existing approaches focus on optimizing downstream tasks by linking image quality to machine perception, typically incurring additional burdens such as extensive annotations and joint training. In this work, we demonstrate that independent low-level reconstruction algorithms can simultaneously enhance imaging quality and downstream perception accuracy. By conducting a comprehensive frequency-domain analysis, we identify high-frequency components as critical for both visual and perceptual tasks. To counteract the frequency information loss typically seen in ISP pipelines, we propose a Multi-Frequency Fusion Block (MFFB) for on-the-fly upsampling, alongside a Frequency-Aware Supervision (FAS) mechanism guided by discrete wavelet transform. Our method achieves a notable +0.32 dB improvement in smart ISP performance on the Zurich dataset. Moreover, without relying on assistance from downstream tasks, our approach demonstrates significant improvements in object detection and instance segmentation.

关键词： Instance segmentation Training Visualization Accuracy Frequency-domain analysis Signal processing algorithms Imaging Object detection Transforms Speech processing

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RGB image Guided Depth Hole-Filling Using Bidirectional Attention Mechanism 1

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17th International conference on Intelligent Information Hiding and Multimedia Signal processing (IIH-MSP) / 14th International conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Chen, Anran Zhao, Yao Lin, Chunyu Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (数字)9789811910531

ISBN: (纸本)9789811910531;9789811910524

Depth maps obtained by commercial depth sensors are more likely to have missing values due to the occlusion effect, low-reflection objects, etc. Filling holes of depth maps is an important way to meet the demands of depth related computer vision tasks. In this paper, we propose an efficient end-to-end network that takes RGB image and mask of the hole to jointly guide the depth hole-filling. Previous algorithms indistinguishably treat valid pixels and holes, resulting in inaccurate depth values prediction and blurred boundaries. Nevertheless, the proposed algorithm uses the bidirectional attention mechanism which takes the surrounding valid values as the auxiliary information to focus on the process of depth hole-filling from edge to center. The proposed method achieves competitive performance on existing public datasets.

关键词： Bidirectional attention mechanism Depth map Hole-filling RGB guided

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Vision-based Autonomous Landing of UAV on Dynamic Apron

Vision-based Autonomous Landing of UAV on Dynamic Apron

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2023 IEEE International conference on Unmanned systems, ICUS 2023

作者： Yue, Pengyu xin, Jing Mao, Zhijie Lu, Yongchang Zhang, Youmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China Industrial & Aerospace Engineering Concordia University Department of Mechanical MontrealQCH3G 1M8 Canada

ISBN: (纸本)9798350316308

Aiming at the landing problem of Unmanned Aerial Vehicle (UAV) without landmarks and global coordinates, this paper proposes an efficient vision-based UAV autonomous landing solution. Firstly, image information is obtained from the bottom visual sensor of the UAV, and the YOLOV5 object detection method is employed to determine the initial position of the dynamic apron. Subsequently, an improved dynamic apron tracking method based on Spatio-Temporal Transformer is designed. This method updates an initial template through target detection algorithm and utilizes multi-source perceptual data for confidence evaluation and dynamic template updates. It outputs the distribution estimation of the predicted bounding box corner coordinates, and achieves the high reliability continuous positional smoothing estimation and dynamic apron loss re-detection based on UAV vision. Finally, the UAV calculates the horizontal control quantity based on the horizontal relative position between the UAV and the apron obtained. The vertical control quantity is calculated based on the vertical relative position between the UAV and the apron. The vertical relative position is estimated using laser ranging and visual odometry techniques. Based on the results of horizontal tracking control and vertical regulation, velocity commands for the UAV in all three directions are generated, enabling the UAV to achieve autonomous landing on the dynamic apron. This paper achieves precise landing for dynamic apron in real-world environments. The experimental results demonstrate that this method avoids the limitations of labeled data and global information on autonomous landing of UAV. Even under GPS-denied conditions, the UAV can generate control commands based on visual data and achieve autonomous landing for randomly moving apron. © 2023 IEEE.

关键词： Antennas

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