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arXiv 2021年

作者： Yao, Chang Hu, Menghan Zhai, Guangtao Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Institute of Image Communication and Information Processing Shanghai Jiao Tong University Department of Electrical Computer and Biomedical Engineering Ryerson University

In recent years, computer-aided diagnosis has become an increasingly popular topic. Methods based on convolutional neural networks have achieved good performance in medical image segmentation and classification. Due to the limitations of the convolution operation, the long-term spatial features are often not accurately obtained. Hence, we propose a TransClaw U-Net network structure, which combines the convolution operation with the transformer operation in the encoding part. The convolution part is applied for extracting the shallow spatial features to facilitate the recovery of the image resolution after upsampling. The transformer part is used to encode the patches, and the self-attention mechanism is used to obtain global information between sequences. The decoding part retains the bottom upsampling structure for better detail segmentation performance. The experimental results on Synapse Multi-organ Segmentation Datasets show that the performance of TransClaw U-Net is better than other network structures. The ablation experiments also prove the generalization performance of TransClaw U-Net. © 2021, CC BY.

关键词： Convolution

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An image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors 12

An image enhancement algorithm based on multi-scale Retinex ...

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SPIE 12th International Symposium on Multispectral image processing and Pattern Recognition, MIPPR 2023

作者： Li, Yiyao Chen, Zhong Sun, Wenyuan School of Journalism and Communication Central China Normal University Luoyu Road 152 Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China National Key Laboratory of Science and Technology on Multi-spectral Information Processing Luoyu Road 1037 Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Luoyu Road 1037 Wuhan China Central China Normal University Library Luoyu Road 152 Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Nowadays, image recognition plays a pivotal role in acquiring data via sensors. However, the adaptability of traditional algorithms is hindered by the unpredictable nature of open environments, varying sensor quality, and image dimensions. Challenges arise in adverse conditions like inclement weather, low light, and optical distortions. Retinex-based methods have emerged as a viable solution, effectively enhancing images plagued by shadows or poor lighting. Yet, issues surface when images possess saturated colors;the conventional multi-scale Retinex with color restoration risks color inversion. Moreover, during gain compensation, extreme histogram values occupy significant gray level space, obscuring vital image details. This study delves into these challenges and proposes an enhanced multi-scale Retinex algorithm. Our approach substitutes logarithmic functions with tansig functions, eliminating color inversion risks. Additionally, a novel gain compensation method, integrating histogram stretching with Gamma correction, refines image clarity. The algorithm's robustness is evidenced in diverse scenarios, including adverse weather, low light, underwater imaging, and non-uniform lighting. Experimental results validate our method's superiority, surpassing other Retinex-based techniques both qualitatively and quantitatively. This research contributes valuable insights into image enhancement methodologies, fostering advancements in sensor-based data gathering in Smart Spaces. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image enhancement

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A Novel Group-based Swarm Optimizer for Large-Scale Optimization

A Novel Group-based Swarm Optimizer for Large-Scale Optimiza...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Shanwen Guan Rushi Lan Yijie Zhu Ruomei Wang Guangxi Key Laboratory of Image and Graphic intelligent processing Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142494

In this paper, a novel group-based swarm optimizer (GSO) is proposed for large scale optimization. Inspired from the group learning behavior in human society, the hierarchical learning are involved in GSO. In the mass learning stage, GSO randomly picks three particles to form a study group and then adopts the competitive mechanism to update the members of studing group. In the hierarchical learning phase, elite particles, the better fitness value, are directly retained for the next iteration. Worst particles learn two dominant particles to further search for more promising areas in the team at the same time. Another particles utilize differential evolution to explore search space. Then, theoretically and empirically analysis of GSO are presented and the exploration abilities of proposed method are performed compared the most popular particle swarm optimizer. Further, a set of experiments on two widely used large-scale benchmark sets demonstrate that GSO achieves better performance on large scale problems than several state-of-the-art algorithms.

关键词： Optimization Convergence Graphics Space exploration Sociology Statistics Next generation networking

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An Adaptive Fractional Pixel Search Algorithm

An Adaptive Fractional Pixel Search Algorithm

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International Conference on Intelligent Sensing and Information processing (ICISIP)

作者： Liquan Shen Zhaoyang Zhang Zhi Liu Wenjun Zhang Key Laboratory of Advanced Display and System Application Ministry of Education Shanghai University Shanghai China Institute of Image Communication& Information Processing Shanghai Jiaotong University China

In this paper, we propose an adaptive fractional pixel search algorithm for reduction computation its load. Based on that SAD (sum of absolute difference) error surface is unimodal within the range of plusmn1 pixel, a novel fractional pixel search bypass strategy is first proposed; Then, a strategy of fractional pixel search early termination based on all-zero block detection is proposed. Finally, an adaptive fractional pixel search algorithm adopting above strategies and improved CBFPS (center biased fractional pixel search) algorithm is proposed. Compared with the fractional pixel full search algorithm and JVT-F017 that adopts CBFPS, simulation results show that the proposed algorithm can reduce 76.60% and 68.22% fractional pixel search points respectively while it still maintains similar coding efficiency.

关键词： Motion estimation Vectors Video coding Testing Paramagnetic resonance Pixel image communication Information processing Computer displays Redundancy

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and image Reconstruction 46

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhang, Xusheng Lyu, Tianling Shi, Yijie Zhong, Xinyun Wu, Zhan Xi, Yan Wang, Shijie Chen, Yang Zhu, Wentao Southeast University School of Software Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms. © 2024 IEEE.

关键词： Cones

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Hierarchical reinforcement learning for saliency detection of low-resolution airports

Hierarchical reinforcement learning for saliency detection o...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： au Zhao au Ma au Wang au Jiang Beijing Key Laboratory of Digital Media Haidian District Beijing China Image Processing Center Beihang University Beijing China Beijing Jinghang Research Institute of Computing and Communication Beijing China

The traditional airport detection methods usually utilize geometric characteristics, which are limited by large amount of data and low-resolution of the remote sensing images. In this paper, we present a novel hierarchical reinforcement learning (HRL) saliency model for quickly airports detecting in large cover area. In contrast with conventional saliency models which usually are effective for high-resolution nature images, our method learns hierarchically high-level features via multi-scale superpixels segmentation and Least Absolute Shrinkage and Selection Operator (LASSO). More importantly, we introduce back-propagation theory for hierarchical learning to adaptively control and generate saliency map. Therefore our unsupervised saliency model is more simple and effective for low-resolution airport detection. Compared with 18 state-of-the-art saliency models, experimental results demonstrate the excellent performance of our method on the remote sensing image datasets. It is more robust and accurate for long-range airports detection.

关键词： Airports Atmospheric modeling Remote sensing Adaptation models image reconstruction Computational modeling Feature extraction

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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MambaMIM: Pre-training Mamba with state space token interpolation and its application to medical image segmentation

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Medical image analysis 2025年 103卷 103606页

作者： Fenghe Tang Bingkun Nian Yingtai Li Zihang Jiang Jie Yang Wei Liu S Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 230026 PR China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu 215123 PR China Center for Medical Imaging Robotics and Analytic Computing & LEarning (MIRACLE) Suzhou Institute for Advanced Research USTC Suzhou Jiangsu 215123 PR China. Department of Automation Shanghai Jiao Tong University Shanghai PR China Institute of Image Processing and Pattern Recognition Shanghai PR China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai PR China. Institute of Medical Robotics Shanghai Jiao Tong University Shanghai PR China. Electronic address: weiliucv@***. Center for Medical Imaging Robotics and Analytic Computing & LEarning (MIRACLE) Suzhou Institute for Advanced Research USTC Suzhou Jiangsu 215123 PR China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology Suzhou Jiangsu 215123 China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei Anhui 230026 China. Electronic address: skevinzhou@***.

Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://***/FengheTan9/MambaMIM.

关键词： Masked image modeling Medical image pre-training Medical image segmentation State space token interpolation

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On Wireless Local Area Networks Network Traffic

On Wireless Local Area Networks Network Traffic

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Chinese Control Conference (CCC)

作者： Rong Hong Liang Yiming Wu Ronghui Xu Cheng Yu Fe College of Electrical and Information Engineering Hunan University Changsha China College of Computer & Communication Hunan University Changsha China Provincial Key Laboratory of Image Processing & Imagecom Communication Nanjing University of Posts and Telecommunications Nanjing China

Through a series of real tests, this paper analyses the characteristic of IEEE 802.11 traffic, explains the results of the tests. More importantly, the results indicate that the characteristic of IEEE 802.11 traffic exhibits obviously self-similarity. As the first step to set up a 802.11 traffic math-model based on self-similarity theory.

关键词： Wireless LAN Telecommunication traffic Traffic control Educational institutions Testing communication system traffic control image processing Telecommunication computing

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Online Pedestrian Tracking Using Ensemble Color Feature

Online Pedestrian Tracking Using Ensemble Color Feature

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International Conference on Computer and Information Technology (CIT)

作者： Bo Zhang Yi Xu Xiaokang Yang Department of Electronic Engineering Shanghai Jiao Tong University Cooperative Medianet Innovation Center Shanghai Key Laboratory of Digital Media Processing and Transmissions Institute of Image Communication and Network Engineering Shanghai China

In this paper, we propose a novel online visual tracking algorithm using ensemble color feature. Visual tracking has long been one of the most important topics in computer vision. In recent years, significant progress has been achieved by the state-of-the-art tracking algorithms. However, some challenging problems still remain unsolved. One general problem is that it is difficult for a tracking algorithm to achieve both high accuracy and efficiency. More specifically, most tracking algorithms base on the hypothesis that the tracking object is a rigid body. Thus, some visual features are disabled when the hypothesis is invalid. Meanwhile, the existing trackers tend to fail when the target object changes poses, because such changes lead to the dramatic mismatch between the momentaneous appearance model and the learned one. The last problem is especially serious when the tracking targets are pedestrians, which suffer irregular pose and shape changes while they walk or run. The proposed algorithm applies ensemble color feature model. It takes pedestrians' physiological structure into consideration, and takes advantage of color histogram information under several color spaces, including the RGB, normRGB, HSV, and Lab. Since histogram is a statistic-based feature, and the garments of pedestrians provide abundant color information, our appearance model using ensemble color feature is more robust to pose and shape changes. Experiments on Caltech Pedestrian Database with evaluation of the state-of-the-art algorithms show better performance of the proposed algorithm.

关键词： Color image color analysis Target tracking Visualization Algorithm design and analysis Histograms Shape

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