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

共 16408 条记录，以下是431-440 订阅

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Two-dimensional Kapur Entropy Multilevel Thresholding Algorithm Based on Dynamic Programming and Spatial Contextual Information 19

Two-dimensional Kapur Entropy Multilevel Thresholding Algori...

引用

19th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Li, Jinming Lei, Bo Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an China

ISBN: (纸本)9798350304398

The Kapur entropy multilevel thresholding image segmentation algorithm based on the two-dimensional histogram does not consider the spatial contextual information of image pixels, and the algorithm's time complexity increases exponentially with the increase of the number of thresholds. To address the above problems, this paper proposes a 2D-Kapur entropy multilevel thresholding image segmentation algorithm based on an energy curve by using both the neighborhood information and spatial contextual information of pixels. To improve the computational efficiency of the multilevel thresholding segmentation algorithm, a dynamic programming computational framework of the 2D-Kapur entropy multilevel thresholding algorithm based on the energy curve is constructed. The performance of the proposed algorithm is compared with several different image thresholding algorithms on the noise and complex images using several evaluation indexes. The experimental results verify the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Dynamic programming

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An adaptive fusion of RGB channel features U-Net for retinal vessel segmentation

An adaptive fusion of RGB channel features U-Net for retinal...

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2024 International conference on image processing and Artificial Intelligence, ICIPAl 2024

作者： xi, Mingyuan Gong, Zikang Shanghai Maritime University 1550 Haigang Ave. Pudong New Area Shanghai200135 China

ISBN: (纸本)9781510681514

Accurate segmentation of retinal vessels in fundus color photos holds significant importance in diagnosing ophthalmic conditions. This process plays a crucial role in identifying and analyzing various eye diseases. However, the retinal vascular structure is highly complex, with imbalances in multiscale and foreground-background proportions, making automatic segmentation challenging. Studies have indicated that valuable information is contained in the three RGB channels of color retinal vessel images. Nevertheless, there has been no research to date on integrating the information from the RGB channels with U-shaped segmentation networks for training. Therefore, in this paper, we propose an Attention U-Net with adaptive fusion of RGB three-channel features. The selective channel attention fusion module can adaptively integrate RGB three-channel features, and the fused features are input into the U-shaped network for training. In addition, an Attention Gate module is added before each upsampling concatenation operation in the decoder stage of the U-Net network. This effectively highlights specific target areas while suppressing irrelevant regions of the input image. Experimental results demonstrate that our method outperforms the segmentation results of other algorithms in three datasets in recent years. © 2024 SPIE.

关键词： Diagnosis

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive Learning

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A Comprehensive Survey of image Steganography 2

A Comprehensive Survey of Image Steganography

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2nd International conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Kalaiarasi, G. Sudharani, B. Jonnalagadda, Sharon Christiana Battula, Harsha Vardhan Sanagala, Bhavana Vignans Fdn Sci Technol & Res Dept ACSE Guntur Andhra Pradesh India

ISBN: (纸本)9798350391558;9798350379990

This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical overview, this study investigates the evolution of steganography from its ancient roots to its present iterations in the digital world. Next, the study progresses towards analyzing the fundamental principles and theoretical frameworks that underpin steganographic systems, such as cryptography and digital signal processing. Finally, this study presents a thorough evaluation of contemporary steganographic technologies, which range from simple LSB (Least Significant Bit) substitution techniques to advanced adaptive algorithms and machine learning methods by including deep-learning based steganography and coverless steganography. Notably, this study identifies key challenges, including detection resistance, payload capacity, and robustness against attacks. Overall, this study presents a thorough understanding of steganography, emphasizing its significance as a versatile tool for communication in the digital era, while also highlighting the challenges that pave way for future innovations.

关键词： image steganography Information hiding Convolutional Neural Network Steganography (CNN) Generative Adversarial Network Steganography (GAN) Stochastic computing

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Research on RGB-D Visual SLAM Algorithm Based on Adaptive Target Detection

Research on RGB-D Visual SLAM Algorithm Based on Adaptive Ta...

引用

2023 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2023

作者： Wei, Baoguo Zhao, Lina Li, Lixin Li, Xu School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350316728

Most of the traditional SLAM systems are based on static environment assumptions, but they are easily affected by dynamic targets in real environments, resulting in a serious degradation of the robustness and accuracy of the algorithms. In this paper, we focus on visual SLAM systems in dynamic scenes, introducing an object detection network in SLAM to obtain the low-level semantic information of dynamic targets, and adopting a new dynamic point selection strategy to classify the detected targets into three motion types, and then fusing the semantic information to eliminate the dynamic feature points. Experiments show that the proposed method outperforms traditional methods in dynamic scenarios, and the real-time performance of the proposed method is improved compared with the semantic segmentation-based SLAM system. © 2023 IEEE.

关键词： Object detection

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Research on UAV Aerial image Dehazing Algorithm Based on CycleGAN

Research on UAV Aerial Image Dehazing Algorithm Based on Cyc...

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2024 International conference on Intelligent Computing and Robotics, ICICR 2024

作者： Wang, Henan He, Yin Department of Information Systems Naval Dalian Ship Academy Liaoning Dalian China

ISBN: (纸本)9798350372274

Effective perception of the environment is a prerequisite for autonomous detection by unmanned aerial vehicles (UAVs), and accurate acquisition of visual images is the most crucial aspect. Considering that the visibility of UAV visual images is greatly affected by natural factors such as fog, and current dehazing algorithms are not very effective, this paper proposes a dehazing method for UAV images based on a cyclic generative adversarial network with an atrous spatial pyramid pooling and attention module. The unpaired image unsupervised dehazing task is performed using the constrained transfer learning ability of the CycleGAN. Considering the severe loss of edge details in actual foggy images and the complex distribution of fog, it is necessary to combine real visual features for dehazing. This paper replaces the ordinary convolution with the ASPP module and incorporates the Convolutional Block Attention Module into the network to non-uniformly process different features and areas in foggy images. Experimental results show that this method achieves better results on the foggy image dataset. ©2024 IEEE.

关键词： Generative adversarial networks

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VeXKD: The Versatile Integration of Cross-Modal Fusion and Knowledge Distillation for 3D Perception 38

VeXKD: The Versatile Integration of Cross-Modal Fusion and K...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Ji, Yuzhe Chen, Yijie Yang, Liuqing Ding, Rui Yang, Meng Zheng, xinhu Guangzhou China Hong Kong University of Science and Technology Hong Kong Xi'an Jiaotong University Xi'an China

Recent advancements in 3D perception have led to a proliferation of network architectures, particularly those involving multi-modal fusion algorithms. While these fusion algorithms improve accuracy, their complexity often impedes real-time performance. This paper introduces VeXKD, an effective and Versatile framework that integrates Cross-Modal Fusion with Knowledge Distillation. VeXKD applies knowledge distillation exclusively to the Bird's Eye View (BEV) feature maps, enabling the transfer of cross-modal insights to single-modal students without additional inference time overhead. It avoids volatile components that can vary across various 3D perception tasks and student modalities, thus improving versatility. The framework adopts a modality-general cross-modal fusion module to bridge the modality gap between the multi-modal teachers and single-modal students. Furthermore, leveraging byproducts generated during fusion, our BEV query guided mask generation network identifies crucial spatial locations across different BEV feature maps from different tasks and semantic levels in a data-driven manner, significantly enhancing the effectiveness of knowledge distillation. Extensive experiments on the nuScenes dataset demonstrate notable improvements, with up to 6.9%/4.2% increase in mAP and NDS for 3D detection tasks and up to 4.3% rise in mIoU for BEV map segmentation tasks, narrowing the performance gap with multi-modal models. © 2024 Neural information processing systems foundation. All rights reserved.

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Accelerated Event-Based Feature Detection and Compression for Surveillance Video systems 24

Accelerated Event-Based Feature Detection and Compression fo...

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15th ACM Multimedia systems conference (ACM MMSys)

作者： Freeman, Andrew C. Mayer-Patel, Ketan Singh, Montek Univ North Carolina Chapel Hill NC 27515 USA

ISBN: (纸本)9798400704123

The strong temporal consistency of surveillance video enables compelling compression performance with traditional methods, but downstream vision applications operate on decoded image frames with a high data rate. Since it is not straightforward for applications to extract information on temporal redundancy from the compressed video representations, we propose a novel system which conveys temporal redundancy within a sparse decompressed representation. We leverage a video representation framework called AD Delta ER to transcode framed videos to sparse, asynchronous intensity samples. We introduce mechanisms for content adaptation, lossy compression, and asynchronous forms of classical vision algorithms. We evaluate our system on the VIRAT surveillance video dataset, and we show a median 43.7% speed improvement in FAST feature detection compared to OpenCV. We run the same algorithm as OpenCV, but only process pixels that receive new asynchronous events, rather than process every pixel in an image frame. Our work paves the way for upcoming neuromorphic sensors and is amenable to future applications with spiking neural networks.

关键词： event representation event video video processing event vision

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Parameter estimation of the GTD model based on performance-enhanced MUSIC algorithms 3

Parameter estimation of the GTD model based on performance-e...

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3rd International conference on Signal image processing and Communication, ICSIPC 2023

作者： Xu, Jia-Hua Zhang, xiao-Kuan Zong, Bin-Feng Li, NingHui Lv, Fan Wang, ZhaoLong The Graduate School of Air Force Engineering University Xi’an710051 China Air and Missile Defence College Air Force Engineering University Xi’an710051 China

ISBN: (纸本)9781510670945

Scattering centers are important features of targets at high frequency regions and the geometric theory of diffraction (GTD) scattering center model is the typical one to describe scattering centers. Therefore, it is quite vital to estimate parameters of GTD model accurately. The classical multiple signal classification (MUSIC) algorithm can effectively estimate these parameters at high signal-to-noise ratios (SNR) but it suffers from poor parameter estimation performance at low SNR scenarios. To solve this problem, we propose a modified MUSIC algorithm which enhances the noise robustness. The modified MUSIC algorithm construct a new total covariance matrix R by averaging the auto-correlation matrix of the original back-scattered data and the auto-correlation matrix of its conjugate data. Then, we take even powers of R , which can broaden the differences between the eigenvalues of noises and signals and avoid overlapping spectral peaks. The theoretical computational complexities of the main modified step are discussed in this paper. Simulation results verify that the proposed algorithms achieve superior accuracy in parameter estimation of the GTD model and obtain better noise robustness. What is more, a target recognition method based on the GTD model and the artificial intelligence algorithm are proposed in this paper. Simulation results validate the effectiveness of this method. © 2023 SPIE.

关键词： Covariance matrix

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SAR image Despeckling based on Gradient Domain Convolutional Sparse Coding 15

SAR Image Despeckling based on Gradient Domain Convolutional...

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15th International conference on Signal processing systems, ICSPS 2023

作者： Chen, Gao Li, Zifeng Zhou, Qingfeng Liu, Chanzi Dongguan University Of Technology 1 University Rd Dongguan China

ISBN: (纸本)9781510675056

Sparse regularization is an effective tool for synthetic aperture radar (SAR) image *** effective sparse regularization terms plays a very important role in this kind of method. Existing sparse regularization despeckling methods use conventional patch-based sparse representation to design regularization term. This patch-based manner will lose some important spatial information along edges between patches, resulting in staircase effect. In this paper, we propose a new Gradient domain Convolutional Sparse Coding-based (GCSC) method for SAR image despeckling, and derive a feasible algorithm to efficiently solve the corresponding nonconvex optimization problem. In contrast to the well-known sparse regularization despeckling methods that divide a SAR image into patches and process patches individually in the spatial domain or the transform domain, GCSC works on the whole SAR image to learn a convolutional sparsifying regularizer in gradient domain. By taking advantage of the gradient domain convolutional sparse coding, GCSC can capture the correlation between local neighborhoods and exploit the gradient image global correlation to produce better edges and sharp features of SAR image. Experiments conducted on real SAR images demonstrate that the proposed GCSC outperforms those state-of-the-art SAR despeckling methods in terms of subjective and objective evaluation. © 2024 SPIE.

关键词： Synthetic aperture radar

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