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94 GHz Three-Dimensional Imaging Radar for Environmental Detection 8

94 GHz Three-Dimensional Imaging Radar for Environmental Det...

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8th Asia-Pacific Conference on Antennas and Propagation, APCAP 2019

作者： Wu, HongMing Qi, Feng Wang, Jinkuan Northeastern University College of Information Science and Engineering Shenyang110819 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

ISBN: (纸本)9781665400541

At present, With the development of technology, the high resolution of millimeter wave (terahertz) can be applied to environmental sensing imaging. In this paper, Using the frequency-modulated continuous-wave (FMCW) radar technique with a 1GHz bandwidth, we show experimentally that a high-resolution real time imaging radar operating at 93.5-94.5GHz is capable of detecting different car models at standoff ranges of 12 m. We also achieved 3D high-resolution imaging of the building. at 140 meters. © 2019 IEEE.

关键词： Millimeter waves

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Small-size lung nodule modeling and detection with clinical evaluation

Small-size lung nodule modeling and detection with clinical ...

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Cairo International Biomedical Engineering Conference, CIBEC

作者： Amal Farag James Graham Hossam Abdelmunim Salwa Elshazly Mohamed Ei-Mogy Sabry Ei-Mogy Robert Falk Aly A. Farag Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY Computer & Systems Engineering Department Ain Shams University Cairo Mogy-Scan Center Mansoura Egypt Jewish Hospital and 3DR Louisville Kentucky

In this paper examination of the template modeling process using the Active Appearance Modeling (AAM) approach for automatic detection of lung nodules is investigated. A template matching approach is formulated to compute a similarity score between the AAM templates and the input lung CT slice, where the goal is to maximize the similarity measure at different image pixels to increase nodule detection. The template matching approach is implemented using nine similarity measures. Performance validation for the robustness of the generated models is tested on three clinical databases.

关键词： Lungs Databases Computed tomography Principal component analysis Computational modeling Solid modeling

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An Automatic Colorectal Polyps Detection Approach for Ct Colonography

An Automatic Colorectal Polyps Detection Approach for Ct Col...

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IEEE International Conference on image processing

作者： Mohamed Yousuf Islam Alkabbany Asem Ali Salwa Elshazley Albert Seow Gerald Dryden Aly Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY Faculty of Engineering Ain Shams University Cairo Egypt Kentucky Imaging Technologies Louisville KY School of Medicine University of Louisville Louisville KY

In this work, we propose an automatic colorectal polyps detection approach that consists of two cascade stages. In the first stage, a CNN model is trained to detect polyps in axial CT slices, The CNN model has been fed by the segmented colon wall CT slices instead of the original CT slices. Using the segmented images as an input to the CNN model has drastically improved the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, the second stage classifier is deployed to exploit the different views of the CT scans instead of the axial view only. So, the classifier is trained using the 2D images of axial views, i.e., the candidate polyps generated by the detector, as well as their corresponding 2D images of sagittal and coronal views. The experimental results of this approach were validated by 3 radiologists and the approach successfully identified polyps after the classification stage with an AUC ∼ 98.6%.

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An Improved SSD for small target detection

An Improved SSD for small target detection

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作者： Xiang Li Haibo LuoX Key Laboratory of Opt-Electronic Information Processing Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences The Key Laboratory of Image Understanding and Computer Vision

SSD is one of heuristic one-stage target detection *** it has got impressive results in general target detection,it still struggles in small-size object detection and precise *** this paper,we proposed an improved SSD which forces on the small-size target *** include a shallow and high resolution feature into the hierarchical detection feature which are used for ***,we fuse the detection features(including the shallow and high resolution one) as a feature pyramid through some 1 × 1 convolution layers and unsample operations to pass information from deep features to the shallow ones,aiming to enrich the semantic information of the shallow *** make the network easier to converge,we add a L2 normalization to the bottom detection feature of the feature pyramid to make a norm balance between each pyramid *** experimental results on the VEDAI dataset show that the proposed method has obtained impressive progress than the original SSD for the small targets detection.

关键词： Convolution neural network Target detection SSD Small targets

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An AAM-based detection approach of lung nodules from LDCT scans

An AAM-based detection approach of lung nodules from LDCT sc...

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IEEE International Symposium on Biomedical Imaging

作者： Amal Farag Hossam Abdelmunim James Graham Aly A. Farag Cambron Carter Salwa Elshazly Mohamed S. El-Mogy Sabry El-Mogy Robert Falk Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY 40292 University of Louisville Louisville KY US Mogy Scan Center Mansoura Egypt Jewish Hospital and 3DR Louisville Kentucky

ISBN: (纸本)9781457718571

In this, paper a new approach for lung nodules detection from LDCT scans is proposed. Intensity models of the nodules are generated using an active appearance model formulation. Template matching is used to compute a similarity score between the AAM template and the input image. The goal is to maximize the similarity measure at different image pixels to increase nodule detection. Conventional template matching does not account for rotation variations. Our proposed template matching approach is formulated as an energy optimization problem that computes a transformation that includes rotation(s) parameters as well as the AAM weighting coefficients. The approach is flexible to different scans and different nodule locations because of the ability to handle the variations in the rotation between the template and the input images. The approach can employ different similarity measures. Experimental results will be shown using three similarity measures from the literature: NCC, ZNCC and ZSSD; which illustrate the efficiency of the proposed approach. ROC curves for various nodule types are constructed on a clinical study with known ground truth, showing significant enhancements over conventional parametric nodule models and traditional template matching criterion.

关键词： Lungs Computational modeling Active appearance model Solid modeling Correlation Computed tomography Shape

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Edge direction dispersion and its application in automatic suitable-matching area selection

Edge direction dispersion and its application in automatic s...

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Chinese Control and Decision Conference

作者： Qing-Hai, Ding Xin-Rong, Yu Hai-Bo, Luo Hong-Mei, Liu Research Institute of General Development and Demonstration of Equipment Equipment of Air Force Bejing 100076 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Graduate School Chinese Academy of Sciences Beijing 100039 China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Shenyang 110016 China Key Laboratory of Image Understanding Computer Vision Liaoning Province 110016 China AVIC HONGDU Aviation Industry Group Ltd. Nanchang 330024 China

ISBN: (纸本)9781424487363

Automatic suitable-matching area selection is an important research in visual guidance, scene matching and local non-texture target tracking. Meanwhile, it is the major research subject in computer vision and accurate guidance. In this paper, a new characteristic quantity, i.e., the edge direction dispersion was defined. Then the computation method, parameters selection and real-time realization of this characteristic quantity were investigated. Furthermore, the application of its characteristic in automatic suitable-matching area selection was studied. With a reasonable design for calculation precision, solving method for arctan(x) function in [-∞,+∞ ] could be simplified to linearization calculations in [0, 0.25], then a fast algorithm for arctan(x) function was presented. Experimental results showed that the sub-image extracted by our fast algorithm had higher matching level and was suitable to be a matching template. All the calculations involved in the proposed matching algorithm could be done in one filed period and the real-time request could be satisfied. Moreover, this characteristic of edge direction could also be used to measure the comparability of template and real-time images, which could be widely used in automatic target recognition and target tracking. © 2011 IEEE.

关键词： Target tracking

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Blind deconvolution for single noisy and blurry image using alternating maximum a posteriori estimation with low rank prior

Blind deconvolution for single noisy and blurry image using ...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Shijie Sun Huaici Zhao Jinfeng Lv Mingguo Hao Bo Li Key Laboratory of Image Understanding and Computer Vision Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Shenyang China College of Information Shenyang Institute of Engineering Shenyang China

ISBN: (纸本)9781509041008

The purpose of single image blind deconvolution is to estimate the unknown blur kernel from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and even more challenging especially in the condition that the noise in the input image cannot be negligible. In this paper, the main problem we focus on is how to effectively apply low rank prior to blind deconvolution. A single noisy and blurry image blind deconvolution algorithm is proposed, using alternating maximum a posteriori (MAP) estimation combined with low rank prior. When estimating the intermediate latent image, low rank prior is used as the constraint that is used for noise suppression of the restored image. The denoised intermediate latent image in turn leads to higher quality blur kernel estimation. These two operations are iterated in this manner to arrive at reliable blur kernel estimation. Extensive experiments show the superiority of the proposed method over state-of-the-art techniques, both qualitatively and quantitatively.

关键词： C# languages

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A 3D RECONSTRUCTION OF THE HUMAN JAW FROM A SINGLE image

A 3D RECONSTRUCTION OF THE HUMAN JAW FROM A SINGLE IMAGE

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IEEE International Conference on image processing

作者： Aly Abdelrahim Ahmed Shalaby Shireen Elhabian James Graham Aly Farag Computer Vision and Image Processing Laboratory ECE Dept. University of Louisville Louisville KY 40292 USA. Faculty of Computers and Information Cairo Univeristy Egypt ECE Dept. University of Louisville Louisville KY 40292 USA.

ISBN: (纸本)9781479923427

Accurate 3D modeling of the human teeth/jaw helps patients avoid the discomfort of the mold process, and improves the data accuracy for oral orthodontists and dental care personnel. Since the surface of the human tooth is almost textureless, Shape from Shading (SFS) has been successfully adopted in solving this problem. In this paper, we attempt to improve the limitations of previous 3D tooth reconstruction algorithms by developing a new approach for shape reconstruction from single image shading with Two-Dimensional Principle Component Analysis (2D-PCA) shape priors. The surface reflectance is modeled using Oren-Nayar-Wolff model which accounts for the retro-reflection case and we experimentally prove that the teeth surface follows the microfacet theory. Our formulation exploits the shape priors as extracted from a set of training CT scans of real human jaws. Our experiments provide promising quantitative metric results for the proposed approach. This work is fundamental for establishing an optical-based approach for reconstructing the human jaw that is inexpensive and does not use ionizing radiation.

关键词： jaw modeling Oren-Nayar-Wolff reflectance model 2D-PCA SFS shape priors Jaw three-dimensional displays surface reflectance Data Accuracy images shape reconfiguration Tooth tooth flank HUMAN TOOTH retro-reflection artificial shade IONIZING RADIATIONS

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Colorectal Polyps Detection in Virtual Colonoscopy Using 3D Geometric Features and Deep Learning

Colorectal Polyps Detection in Virtual Colonoscopy Using 3D ...

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IEEE International Symposium on Biomedical Imaging

作者： Mohamed Yousuf Samir Harb Islam Alkabbany Asem Ali Salwa Elshazley Aly Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY Faculty of Engineering Ain Shams University Cairo Egypt Higher Technological Institute 10th of Ramadan City Egypt Kentucky Imaging Technologies Louisville KY

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys for treatment. In this work, we propose a framework to help radiologists in identifying polyp candidates using virtual colonoscopy. In the proposed approach, first a colon is segmented from a CT scan, then 3D reconstruction, to generate a surface representation of the colon, is performed. From the reconstructed 3D colon, 2D images are generated using the virtual colonoscopy, Fly-In approach. To enhance polyp detection, we fuse these 2D images and the 3D colon representation by generating 3D geometric feature maps, e.g. depth and curvature maps. CNN-based models are trained and validated to detect polyps using the generated feature maps, which are combined in multi-channel images. These images are successfully used to train a CNN-based model that detects polyps with mAP ∼ 97.1%.Colorectal cancer, colon polyp, computerized tomography (CT), Detection, CNN, segmentation.

关键词： image segmentation Surface reconstruction Three-dimensional displays Virtual colonoscopy Computed tomography Computational modeling Feature extraction

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Accurate and fast 3D colon segmentation in CT colonography

Accurate and fast 3D colon segmentation in CT colonography

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IEEE International Symposium on Biomedical Imaging

作者： Dongqing Chen Rachid Fahmi Aly A. Farag Robert L. Falk Gerald W. Dryden Computer Vision & Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Medical Imaging Jewish Hospital and Saint Marys Healthcare Louisville KY USA Division of Gastroenterology/Hepatology Department of Medicine University of Louisville Louisville KY USA

ISBN: (纸本)9781424439317

This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The proposed method has been tested on 22 real CT colonography datasets with various pathologies, and the segmentation accuracy has achieved 98.40%.

关键词： Colonography Colon Virtual colonoscopy Cancer image segmentation Colonic polyps Level set Deformable models Computed tomography Visualization

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