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VIOMA: Video-Based Intelligent Ocular Misalignment Assessment

IEEE Transactions on Automation Science and Engineering 2025年 22卷 12470-12484页

作者： Zheng, Yang Fu, Hong Li, Ruimin Lam, Carly Siu Yin Liang, Jimin Guo, Kaitai Lo, Wai-Lun Xidian University School of Electronic Engineering Xi’an710071 China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Hong Kong Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xi’an710071 China The Hong Kong Polytechnic University School of Optometry Hong Kong Hong Kong Hong Kong Science Park Centre for Eye and Vision Research Hong Kong Hong Kong Chu Hai College of Higher Education Department of Computer Science Hong Kong Hong Kong

The measurement of ocular alignment is critical for the diagnosis of strabismus. Current clinical methods for assessing ocular misalignment are subjective and frequently rely on the expertise of practitioners and the extent of patient cooperation. computer-aided diagnosis methods in recent years have improved automation and precision of measurement, but still, fall short of the requirement of clinical practice. In this study, a video-based intelligent ocular misalignment assessment (VIOMA) system, was proposed to provide an objective, repeatable, user-friendly and highly-automated alternative modality for clinical ocular misalignment measurement, in which the automatic cover tests were performed under a control and motor unit, simultaneously the eye movements were tracked using a motion-capture module and assessed through video analysis techniques, determining the presence, type, and magnitude of eye deviation. For system evaluation, an automatic cover tests video dataset for strabismus (StrabismusACT-76) was established, which consists of data from 76 participants. The Bland-Altman plot, used to compare the results of the VIOMA system and human expert, showed a mean value of 1.26 prism diopter (PD) and a half-width of the 95% limit of agreement of ±7.17 PD. VIOMA system presented a mean absolute error of 3.04 PD in measuring the deviation magnitude, within a 5 PD error tolerance. Additionally, the system’s measurements were strongly correlated with that of video labeling with the mean value of -0.26 PD, a half-width of the 95% limit of agreement of ±3.56, and the average error of 1.31 PD. The experiment results indicated that the proposed method has the capability to offer accurate and efficient assessment of ocular misalignment. © 2004-2012 IEEE.

关键词： Diagnosis

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Edge Detection Using Matched Filter 27

Edge Detection Using Matched Filter

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第27届中国控制与决策会议

作者： LUO Hai-bo JIAO An-bo XU Ling-yun SHAO Chun-yan Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Key Laboratory of Image Understanding and Computer Vision University of Chinese Academy of Sciences Key Laboratory of Image Understanding and Computer Vision Liaoning Province

ISBN: (纸本)9781479970186

For target tracking,automatic target recognition and detection applications,they value edge detection sensibility,precision and location accuracy rather than other *** at these three criterions,this paper presents an edge detection algorithm based on matched ***,a matched filter was designed by analyzing the edge model for natural image,then the edge response was computed using the designed matched filter;secondly,the lower and discontinuous filtered responses were further suppressed using a dedicated one-dimension filter;finally,the edge image was obtained by binarizing the edge response with a local adaptive *** results illustrate that the proposed algorithm has more improvement than the Sobel and Canny operators in detection sensibility,precision and location ***,the algorithm can be implemented with parallel pipeline using FPGA,so it is also rather suitable for real-time applications.

关键词： edge detection,matched filter,local adaptive threshold,real-time application,noise suppression

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Uncertainty Quantification in Medical image Segmentation 6

Uncertainty Quantification in Medical Image Segmentation

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6th IEEE International Conference on computer and Communications, ICCC 2020

作者： Li, Haixing Luo, Haibo The Key Lab of Image Understanding and Computer Vision Shenyang City Liaoning Province China

ISBN: (纸本)9781728186351

In medical images, the observer's manual description of different structures is very different, and it spans a wide range of various structures and pathologies. This variability (which is a characteristic of biological issues, imaging modality and expert annotators) has not been fully considered in the design of computer algorithms for medical image quantification. So far, few people predict the uncertainty of medical image segmentation. In this paper, we designed a V-shaped network to quantify the uncertainty in prostate MRI image segmentation. We have embedded a feature pyramid attention module in the backbone network, which can extract high-level semantic context information at different scales and provide a pixel-level attention to the decoder. At the same time, the module will not bring a large computational burden. In our experiments, we tested the performance of the proposed method on 55 clinical subjects. © 2020 IEEE.

关键词： Magnetic resonance imaging

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Static human behavior classification based on LLC features and GIST features 7

Static human behavior classification based on LLC features a...

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7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017

作者： Ende, Wang Xukui, Hou Xuepeng, Li Shenyang Institute of Automation Chinese Academic of Sciences Key Lab of Image Understanding and Computer Vision Key Laboratory of Optical Electrical Image Processing Shenhe District Shenyang Liaoning China

ISBN: (纸本)9781538604892

This paper presents a method for recognizing human behavior in static images based on LLC and GIST features. The feature points in the image is densely located in sub-region of images and we extract SIFT feature from each sub-region. Then using the LLC method to encode the extracted dense SIFT features of each sub-region and each feature point descriptor is assigned to several nearest words, so that each descriptor can be expressed by the linear correlation coefficient of vocabulary. In order to increase the spatial information, we use the SPM model to divide the image into many blocks under different levels. We can get the pooled feature from each sub-region using the max pooling method. Then these pooled features from sub-region are concatenated and normalized as the final pooled features. In order to get GIST features, we divide the image into blocks and construct the Gabor filter group of 32 different scales and directions. Then each Gabor filter in the Gabor filter group does convolution operation with image blocks and we take the mean value of each blocks under each Gabor filter as the feature description. The features from each block under each Gabor filter are concatenated and normalized as the final GIST feature representation of the image. Then concatenating the final pooled features and the GIST features and take as the final feature representation. Finally, the intersection kernel function of SVM is used for classification. We performed experiments on several databases. By contrast, this algorithm has a good effect in accuracy and efficiency. © 2017 IEEE.

关键词： Gabor filters

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Computational Intelligence in Remote Sensing image Registration:A survey

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International Journal of Automation and computing 2021年第1期18卷 1-17页

作者： Yue Wu Jun-Wei Liu Chen-Zhuo Zhu Zhuang-Fei Bai Qi-Guang Miao Wen-Ping Ma Mao-Guo Gong Key Laboratory of Big Data and Intelligent Vision School of Computer Science and TechnologyXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic EngineeringXidian UniversityXi′an 710071China

In recent years,computational intelligence has been widely used in many fields and achieved remarkable *** computing and deep learning are important branches of computational *** methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image *** paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep *** the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is *** application of deep learning in remote sensing image registration is also *** the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.

关键词： Computational intelligence evolutionary computation neural network deep learning remote sensing image registration

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A Shape Retentive Filtering Algorithm for Post-processing of Instance Contour of Cervical Cell Based on Level Set Method 6

A Shape Retentive Filtering Algorithm for Post-processing of...

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6th IEEE International Conference on computer and Communications, ICCC 2020

作者： Liu, Guangqi Ding, Qinghai Ju, Moran Luo, Haibo Jin, Tianming He, Miao The Key Laboratory of Image Understanding and Computer Vision Shenyang110016 China Space Star Technology Company Ltd. Beijing100086 China

ISBN: (纸本)9781728186351

A novel filtering algorithm is proposed based on level set method (LSM) and linear time Euclidean distance transform (LET) algorithm in this paper, which has the property of shape retention and thus is suitable for post-processing of the initial contours for contacting instances in digital Pap image. As one of our contributions, we propose two new metrics based on the pixel-level average false positive rate and false negative rate that used by baseline method. A significant decrease in pixel-level average false positive rate (FP) by 62% can obtain by our proposed method. The result of quantitative and qualitative evaluation shows that our proposed shape retentive filtering algorithm (SRFA) can effectively filter out the false positive fragments of the initial instance contour of cervical cells from the ISBI-2014 dataset. © 2020 IEEE.

关键词： Numerical methods

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Uncertainty Quantification in Medical image Segmentation

Uncertainty Quantification in Medical Image Segmentation

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International Conference on computer and Communications (ICCC)

作者： Haixing Li Haibo Luo The Key Lab of Image Understanding and Computer Vision Shenyang City Liaoning Province China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

关键词： image segmentation Uncertainty Magnetic resonance imaging Semantics Feature extraction Prediction algorithms Biomedical imaging

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Semantic segmentation using regions in natural scenes

Semantic segmentation using regions in natural scenes

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2011 International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2011

作者： Wu, Shilin Zhu, Feng Hao, Yingming Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Graduate School Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Shenyang 110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province 110016 China

ISBN: (纸本)9781612840857

By introducing an over-segmentation algorithm into the conditional model (CM), we propose a new region-based CM model (R-CM), and investigate its performance on semantic segmentation of images. In order to incorporate structure information of objects, we segment an image into regions by using an over-segmentation algorithm. Based on the results of CM model, we first consider assigning all pixels in one region with the same label, and then other feature potentials are included to counteract the influence of false over-segmentation. We compare our results to related work on the Olive & Torralba database and show that aside from improved accuracy of the whole database, our model obtains a perceptual improvement, with boundary of different objects correctly labeled. © 2011 IEEE.

关键词： Semantic Segmentation

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Multiple class segmentation using a scene-based framework

Multiple class segmentation using a scene-based framework

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2011 International Conference on Electronics and Optoelectronics, ICEOE 2011

作者： Wu, Shilin Zhu, Feng Miao, Xikui Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Graduate School of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Shenyang 110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province 110016 China

ISBN: (纸本)9781612842738

In this paper, we propose a new scene-based conditional model and investigate its performance on multiple class segmentation of images. By including the scene of an image, we also propose a new texture-environment potential to represent texture environment of a pixel. We compare our results to related work on the Olive & Torralba database and show that our model obtains greatly improved accuracy of the whole database. More significantly, a large perceptual improvement is gained, i.e. details of different objects are correctly labeled. © 2011 IEEE.

关键词： image segmentation

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Millimeter-wave radar image analysis for the traffic sensing 2

Millimeter-wave radar image analysis for the traffic sensing

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2nd Spring International Conference on Defence Technology, ICDT 2020

作者： Wu, H.M. Qi, F. Wang, J.K. Wang, Y. School of Communication Science and Engineering Northeastern University Liaoning Province Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

Millimeter-wave(MMW) radar sensing is one of the most promising technologies to provide safe navigation for autonomous vehicles due to its expected high-resolution imaging capability However, driverless cars have higher request for different environment and light conditions. Therefore, millimetre-wave imaging is of paramount importance for complex load scenario. In this paper, we have built models of pavement pits and bulges and analysed their with differences ways of antennas. A comparison of the imaging performance of experimental systems operating at a MMW radar and a Lidar is presented with the analysis of features for initial image interpretation Experimental images of the complex road surface are made by a 94GHz frequency-modulated continuous-wave (FMCW) radar technique with 3mm wavelength. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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