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Conference on Electro-Optical and Infrared Systems - Technology and applications XIX

作者： Wegner, Daniel Kessler, Stefan Fraunhofer IOSB Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (纸本)9781510655461

A current approach for performance assessment of imagers is triangle orientation discrimination (TOD). This approach requires observers or human visual system (HVS) models to recognize equilateral triangles pointing in four different directions. imagers may apply embedded advanced digital signal processing (ADSP) for contrast enhancement, noise reduction, edge sharpening, etc. Unfortunately, applied methods are in general not documented and hence unknown. Within the last decades a vast amount of techniques for constrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks such as object recognition In this work different algorithms for contrast enhancement are compared in terms of TOD assessments by convolutional neural networks (CNN) as models. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed by natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms are applied, such as the constrast-limited adaptive histogram equalization or local range modification. Then accuracies of the trained models on these images are compared for different ADSP algorithms. Accuracy gains for low signal-to-background ratio and sufficiently large triangles are found, while impairments are found for high signal-to-background ratio and small triangles. Finally, implications of replacing triangles by real target signatures when using such ADSP algorithms are discussed. The results can be a step towards the assessment of those algorithms for generic target recognition.

关键词： CNN TOD image contrast enhancement advanced digital signal processing imager assessment method comparison

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An End-to-End Traditional Chinese Medicine Constitution Assessment System Based on Multimodal Clinical Feature Representation and Fusion 36

An End-to-End Traditional Chinese Medicine Constitution Asse...

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36th AAAI Conference on artificial Intelligence / 34th Conference on Innovative applications of artificial Intelligence / 12th Symposium on Educational Advances in artificial Intelligence

作者： Mao, Huisheng Zhang, Baozheng Xu, Hua Gao, Kai Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang 050018 Hebei Peoples R China

ISBN: (纸本)9781577358763

Traditional Chinese Medicine (TCM) constitution is a fundamental concept in TCM theory. It is determined by multimodal TCM clinical features which, in turn, are obtained from TCM clinical information of image (face, tongue, etc.), audio (pulse and voice), and text (inquiry) modality. The auto assessment of TCM constitution is faced with two major challenges: (1) learning discriminative TCM clinical feature representations;(2) jointly processing the features using multimodal fusion techniques. The TCM Constitution Assessment System (TCM-CAS) is proposed to provide an end-to-end solution to this task, along with auxiliary functions to aid TCM researchers. To improve the results of TCM constitution prediction, the system combines multiple machine learning algorithms such as facial landmark detection, image segmentation, graph neural networks and multimodal fusion. Extensive experiments are conducted on a four-category multimodal TCM constitution dataset, and the proposed method achieves state-of-the-art accuracy. Provided with datasets containing annotations of diseases, the system can also perform automatic disease diagnosis from a TCM perspective.

关键词： image segmentation

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Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

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artificial INTELLIGENCE REVIEW 2024年第6期57卷 149-149页

作者： Lei, Lian Yang, Qiliang Yang, Ling Shen, Tao Wang, Ruoxi Fu, Chengbiao Kunming Univ Sci & Technol Fac Informat Engn & Automat Jingmingnanlu 727 Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Modern Agr Engn Jingmingnanlu 727 Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Comp Technol Applicat Kunming 650500 Peoples R China

image segmentation is a crucial task in computer vision, which divides a digital image into multiple segments and objects. In agriculture, image segmentation is extensively used for crop and soil monitoring, predicting the best times to sow, fertilize, and harvest, estimating crop yield, and detecting plant diseases. However, image segmentation faces difficulties in agriculture, such as the challenges of disease staging recognition, labeling inconsistency, and changes in plant morphology with the environment. Consequently, we have conducted a comprehensive review of image segmentation techniques based on deep learning, exploring the development and prospects of image segmentation in agriculture. Deep learning-based image segmentation solutions widely used in agriculture are categorized into eight main groups: encoder-decoder structures, multi-scale and pyramid-based methods, dilated convolutional networks, visual attention models, generative adversarial networks, graph neural networks, instance segmentation networks, and transformer-based models. In addition, the applications of image segmentation methods in agriculture are presented, such as plant disease detection, weed identification, crop growth monitoring, crop yield estimation, and counting. Furthermore, a collection of publicly available plant image segmentation datasets has been reviewed, and the evaluation and comparison of performance for image segmentation algorithms have been conducted on benchmark datasets. Finally, there is a discussion of the challenges and future prospects of image segmentation in agriculture.

关键词： image segmentation Deep learning Agricultural image processing Plant diseases identification Crop growth monitoring Plant image datasets Crop yield estimation and counting Weed identification

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Optimization of tilt-shift lens camera calibration parameters based on genetic algorithm and artificial neural network 4

Optimization of tilt-shift lens camera calibration parameter...

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4th International Conference on image processing and Intelligent Control, IPIC 2024

作者： Zhang, Kunlun Li, Haobo Ju, Dongjie Wang, Xiaofei Guan, Chen Fan, Hua Jinan250000 China Intelligent Technology Co. Ltd Weifang261000 China Shandong JINGWEI Intelligent Equipment Co. Ltd Weifang261000 China

ISBN: (纸本)9781510682313

Camera calibration is a critical process in computer vision applications, ensuring accurate mapping between image coordinates and real-world coordinates. We propose a method to fine-tune the calibration parameters of cameras by using machine learning technology, aiming to improve the calibration accuracy and stability. The proposed method involves data collection, data preprocessing, dataset division, model selection, model training, model evaluation, parameter refinement, and iterative optimization. Real-world data is collected and pre-processed to ensure data quality and consistency. The dataset is then divided into training and test sets for model training and performance evaluation. A machine learning model using linear regression to model the relationship between camera parameters and calibration errors. The model is trained and evaluated, and the grid search is used for cross-validation to adjust the parameters of the model to make the model better. Based on the model completed by parameter tuning, new camera parameters are input, fine-tuned by genetic algorithm, and the average error is calculated until the set error is satisfied, and the adjusted parameters are output. Finally, the HALCON measurement assistant is used to verify the output results. The results show that although the calibration data obtained by HALCON is not excellent, the calibration accuracy and stability can be improved by using the method in this paper. The research results can be used to optimize the calibration parameters of tilt-shift lenses, which is very helpful for 2D data measurement and 3D reconstruction. © 2024 SPIE.

关键词： neural networks

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Vessel Site Selection for Autonomous Cannulation Under NIR image Guidance 15th

Vessel Site Selection for Autonomous Cannulation Under NIR I...

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15th International Conference on Intelligent Robotics and applications (ICIRA ) - Smart Robotics for Society

作者： Zhao, Yibo Ji, Jiarui Xie, Tenghui Du, Fuxin Qi, Peng Tongji Univ 1239 Siping Rd Shanghai 200092 Peoples R China Shandong Univ Sch Mech Engn Jinan Peoples R China

ISBN: (纸本)9783031138416;9783031138409

Venipuncture is a nearly ubiquitous part of modern clinical practice. However, currently venipuncture procedures are mainly applied by manual operation, whose the success rate might decrease below 50% in some situations, including pediatric, and geriatric patients. Thus, robotic technologies to guide autonomous vascular access attracts research attention. For venipuncture robots, near-infrared (NIR) images are widely used for real-time servoing and further to segment subcutaneous vessels for puncture with a series of deep convolutional neural networks. It has been realized that the success rate of puncture largely relies on the performance of segmentation models. However, the small size and low quality of NIR image dataset severely limit the accuracy and efficiency of segmentation models. This paper aims to address this issue by proposing a novel data processing method to improve the performance of segmentation models. With those novel image processing strategies, the segmentation results are improved. The Dice-mean value has increased by an average of 1.12%. Additionally, an algorithm of vessel site selection for puncture is proposed in this paper. Such data processing methods and the puncture site selection algorithm are expected to finally improve the performance of venipuncture robots.

关键词： Venipuncture robot Data augmentation image processing The Bezier curve Gaussian blur

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Optical coherent dot-product chip for sophisticated deep learning regression

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Light(Science & applications) 2021年第12期10卷 2330-2341页

作者： Shaofu Xu Jing Wang Haowen Shu Zhike Zhang Sicheng Yi Bowen Bai Xingjun Wang Jianguo Liu Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong University800 Dongchuan RoadShanghai 200240China State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Institution of Semiconductors Chinese Academy of SciencesBeijing 100083China

Optical implementations of neural networks(ONNs)herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of ***,due to the problems of the incomplete numerical domain,limited hardware scale,or inadequate numerical accuracy,the majority of existing ONNs were studied for basic classification *** that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications,it is necessary to master deep learning regression for further development and deployment of ***,we demonstrate a silicon-based optical coherent dot-product chip(OCDC)capable of completing deep learning regression *** OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive *** reusing,a single chip conducts matrix multiplications and convolutions in neural networks of any ***,hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip ***,the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network,the AUTOMAP(a cutting-edge neural network model for image reconstruction).The quality of reconstructed images by the OCDC and a 32-bit digital computer is *** the best of our knowledge,there is no precedent of performing such state-of-the-art regression tasks on ONN *** is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving,natural language processing,and scientific study.

关键词： neural hardware coherent

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Reproducibility of artificial intelligence models in computed tomography of the head: a quantitative analysis

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INSIGHTS INTO IMAGING 2022年第1期13卷 1-8页

作者： Gunzer, Felix Jantscher, Michael Hassler, Eva M. Kau, Thomas Reishofer, Gernot Med Univ Graz Dept Radiol Div Neuroradiol Vasc & Intervent Radiol Auenbruggerpl 2 A-8036 Graz Austria Know Ctr GmbH Res Ctr Data Driven Business Big Data Analyt Inffeldgasse 13-6 A-8010 Graz Austria Landeskrankenhaus Villach Dept Radiol Nikolaigasse 43 A-9500 Villach Austria Med Univ Graz Dept Radiol Auenbruggerpl 2 A-8036 Graz Austria BioTechMed Graz Mozartgasse 12 A-8010 Graz Austria

When developing artificial intelligence (AI) software for applications in radiology, the underlying research must be transferable to other real-world problems. To verify to what degree this is true, we reviewed research on AI algorithms for computed tomography of the head. A systematic review was conducted according to the preferred reporting items for systematic reviews and meta-analyses. We identified 83 articles and analyzed them in terms of transparency of data and code, pre-processing, type of algorithm, architecture, hyperparameter, performance measure, and balancing of dataset in relation to epidemiology. We also classified all articles by their main functionality (classification, detection, segmentation, prediction, triage, image reconstruction, image registration, fusion of imaging modalities). We found that only a minority of authors provided open source code (10.15%, n 0 7), making the replication of results difficult. Convolutional neural networks were predominantly used (32.61%, n = 15), whereas hyperparameters were less frequently reported (32.61%, n = 15). Data sets were mostly from single center sources (84.05%, n = 58), increasing the susceptibility of the models to bias, which increases the error rate of the models. The prevalence of brain lesions in the training (0.49 +/- 0.30) and testing (0.45 +/- 0.29) datasets differed from real-world epidemiology (0.21 +/- 0.28), which may overestimate performances. This review highlights the need for open source code, external validation, and consideration of disease prevalence.

关键词： artificial intelligence Head CT Reproducibility Epidemiology Machine learning

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Application of Super-Resolution-Based networks to Enhance images in Electrical Impedance Tomography 12

Application of Super-Resolution-Based Networks to Enhance Im...

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12th IFAC Symposium on Biological and Medical Systems (BMS)

作者： Bastos, Alexandre S. Duran, Guilherme C. Ueda, Edson K. Martins, Thiago C. Tsuzuki, Marcos S. G. Escola Politecn Univ Sao Paulo Sao Paulo Brazil

Electrical Impedance Tomography (EIT) is a method of imaging that relies on determining the distribution of electrical conductivity within a body. EIT reconstructions usually results in low-resolution (LR) images. In this paper, a novel approach is presented to improve the resolution of EIT images. The proposed approach focuses on reconstructing conductivity distributions within objects, examining circular and non-circular geometry. The study demonstrates notable improvements in spatial resolution and noise suppression, especially in handling complex shapes like squares and triangles. This work contributes to advancing EIT imaging capabilities, offering a promising solution for faster and more accurate applications in various fields, with future work aimed at refining the network architecture and validating its performance in practical EIT scenarios. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Electrical Impedance Tomography Super-Resolution neural Network Residual Network image Reconstruction.

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Rotation Invariant Deep Learning Approach for image Inpainting 22

Rotation Invariant Deep Learning Approach for Image Inpainti...

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23rd Symposium on image, Signal processing and artificial Vision (STSIVA)

作者： Rivera, Sebastian Ortiz, Ivan Gelvez, Tatiana Rojas, Fernando Arguello, Henry Univ Ind Santander Dept Comp Sci Bucaramanga Colombia Univ Ind Santander Dept Elect Engn Bucaramanga Colombia

ISBN: (纸本)9781665416696

Matrix completion technique aims to fill the missing information of a bidimensional structure, supporting applications such as recommender systems, image restoration, and image inpainting. In particular, most of literature methods for image inpainting take advantage of intrinsic high spatial correlations by incorporating low-rank regularizers into an optimization problem. However, the presence of geometric distortions such as the rotation of the objects in the image or of the patterns of the missing information can affect the quality in the completion task. To overcome such limitation, this work presents a non-local geometric similarity regularizer, invariant to the rotations in the spatial domain, the similarities presented in some sub-regions of the image with the same structure but rotated at some angle. The rotation invariant regularizer is integrated into a convolutional neural network and a generative adversarial neural network, improving the completion quality, specially for images with high rotation distortions.

关键词： Matrix Completion Deep Learning Rotation Invariance Generative Adversarial neural networks Convolutional neural networks image Inpainting

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Optimized Approach for image Design processing in Optical networks: A Comparative Study

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Hu, Yunjie Yang, Fan School of Electrical and Information Engineering Wuhan Institute of Technology Hubei Wuhan430000 China

Deep space detection and remote sensing both require optical imaging devices. The optical imaging system often needs a bigger aperture mirror to attain high spatial resolution. As a result, several novel optical imaging systems, such as big segmented mirror telescopes, large aperture membrane diffractive optical telescopes, and others, have been researched in recent years. Real-time wavefront measurement is not required for the wavefront sensorless (WFSless) applied optics (AO) approach. The wavefront corrector is directly regulated via feedback following an image quality measure of the far-field image to correct for wavefront aberration. Integrating artificial neural networks (ANN) and deep learning plays a vital role in developing WFSless AO systems. This paper evaluated various important aspects to provide an in-depth review of the state-of-the-art machine learning-based algorithms deployed in WFSless AO systems. Finally, the applications and prospects were outlined. © 2023 Yunjie Hu et al., published by Sciendo.

关键词： neural networks

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