Synthetic image of multiresolution sketch leads to new features

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

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VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

Journal of Imaging ◽

10.3390/jimaging7030050 ◽

2021 ◽

Vol 7 (3) ◽

pp. 50

Author(s):

Anselmo Ferreira ◽

Ehsan Nowroozi ◽

Mauro Barni

Keyword(s):

Large Scale ◽

State Of The Art ◽

Child Pornography ◽

Forensic Analysis ◽

Synthetic Image ◽

Image Detection ◽

Face Images ◽

Large Scale Dataset ◽

Scanned Images ◽

Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

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Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

Small Methods ◽

10.1002/smtd.202100223 ◽

2021 ◽

pp. 2100223

Author(s):

Leonid Mill ◽

David Wolff ◽

Nele Gerrits ◽

Patrick Philipp ◽

Lasse Kling ◽

...

Keyword(s):

Deep Learning ◽

Synthetic Image ◽

Image Rendering

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Cellular automata + LFSR + synthetic image: A trio approach to image encryption

2017 International Conference on Computer Communication and Informatics (ICCCI) ◽

10.1109/iccci.2017.8117702 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sundararaman Rajagopalan ◽

Sivaraman Rethinam ◽

Siva Janakiraman ◽

Har Narayan Upadhyay ◽

Rengarajan Amirtharajan

Keyword(s):

Cellular Automata ◽

Image Encryption ◽

Synthetic Image

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Motion correction for myocardial T1 mapping using image registration with synthetic image estimation

Magnetic Resonance in Medicine ◽

10.1002/mrm.23153 ◽

2011 ◽

Vol 67 (6) ◽

pp. 1644-1655 ◽

Cited By ~ 135

Author(s):

Hui Xue ◽

Saurabh Shah ◽

Andreas Greiser ◽

Christoph Guetter ◽

Arne Littmann ◽

...

Keyword(s):

Image Registration ◽

Motion Correction ◽

T1 Mapping ◽

Synthetic Image ◽

Image Estimation ◽

Myocardial T1

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Identification of Orthotropic Elastic Properties of Wood by a Synthetic Image Approach Based on Digital Image Correlation

Materials ◽

10.3390/ma15020625 ◽

2022 ◽

Vol 15 (2) ◽

pp. 625

Author(s):

João Henriques ◽

José Xavier ◽

António Andrade-Campos

Keyword(s):

Digital Image Correlation ◽

Digital Image ◽

Pinus Pinaster ◽

Model Updating ◽

Image Correlation ◽

Synthetic Image ◽

Tangential Plane ◽

Full Field ◽

The Impact ◽

Constitutive Parameters

This work aims to determine the orthotropic linear elastic constitutive parameters of Pinus pinaster Ait. wood from a single uniaxial compressive experimental test, under quasi-static loading conditions, based on two different specimen configurations: (a) on-axis rectangular specimens oriented on the radial-tangential plane, (b) off-axis specimens with a grain angle of about 60(radial-tangential plane). Using digital image correlation (DIC), full-field displacement and strain maps are obtained and used to identify the four orthotropic elastic parameters using the finite element model updating (FEMU) technique. Based on the FE data, a synthetic image reconstruction approach is proposed by coupling the inverse identification method with synthetically deformed images, which are then processed by DIC and compared with the experimental results. The proposed methodology is first validated by employing a DIC-levelled FEA reference in the identification procedure. The impact of the DIC setting parameters on the identification results is systematically investigated. This influence appears to be stronger when the parameter is less sensitive to the experimental setup used. When using on-axis specimen configuration, three orthotropic parameters of Pinus pinaster (ER, ET and νRT) are correctly identified, while the shear modulus (GRT) is robustly identified when using off-axis specimen configuration.

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A Closed-Form Solution for Estimating the Accuracy of Circular Feature’ Pose for Object 2D-3D Pose Estimation System

Mathematical Problems in Engineering ◽

10.1155/2021/6675607 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Cui Li ◽

Derong Chen ◽

Jiulu Gong ◽

Yangyu Wu

Keyword(s):

Closed Form ◽

Covariance Matrix ◽

Pose Estimation ◽

Closed Form Solution ◽

Optimization Method ◽

Form Solution ◽

Synthetic Image ◽

Point Selection ◽

3D Pose Estimation ◽

Estimation System

Many objects in the real world have circular feature. In general, circular feature’s pose is represented by 5-DoF (degree of freedom) vector ξ = X , Y , Z , α , β T . It is a difficult task to measure the accuracy of circular feature’s pose in each direction and the correlation between each direction. This paper proposes a closed-form solution for estimating the accuracy of pose transformation of circular feature. The covariance matrix of ξ is used to measure the accuracy of the pose. The relationship between the pose of the circular feature of 3D object and the 2D points is analyzed to yield an implicit function, and then Gauss–Newton theorem is employed to compute the partial derivatives of the function with respect to such point, and after that the covariance matrix is computed from both the 2D points and the extraction error. In addition, the method utilizes the covariance matrix of 5-DoF circular feature’s pose variables to optimize the pose estimator. Based on pose covariance, minimize the mean square error (Min-MSE) metric is introduced to guide good 2D imaging point selection, and the total amount of noise introduced into the pose estimator can be reduced. This work provides an accuracy method for object 2D-3D pose estimation using circular feature. At last, the effectiveness of the method for estimating the accuracy is validated based on both random data sets and synthetic images. Various synthetic image sequences are illustrated to show the performance and advantages of the proposed pose optimization method for estimating circular feature’s pose.

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