scholarly journals Model-based Region of Interest Segmentation for Remote Photoplethysmography

2019 ◽  
Author(s):  
Peixi Li ◽  
Yannick Benezeth ◽  
Keisuke Nakamura ◽  
Randy Gomez ◽  
Fan Yang
Keyword(s):  
Region Of Interest ◽  
Model Based ◽  
Remote Photoplethysmography

Algorithm of Moving Vehicle Detection by Improved Gaussian Mixture Model Based on ROI

2015 ◽  
Vol 734 ◽  
pp. 463-467 ◽  
Author(s):  
Pan Pan Zhang ◽  
Chun Yang Mu ◽  
Xing Ma ◽  
Fu Lu Xu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Background Model ◽  
Morphological Filter ◽  
Improved Method ◽  
Moving Vehicle ◽  
Model Based ◽  
Moving Vehicle Detection

Detection of moving object is a hot topic in computer vision. Traditionally, it is detected for every pixel in whole image by Gaussian mixture background model, which may waste more time and space. In order to improving the computational efficiency, an advanced Gaussian mixture model based on Region of Interest was proposed. Firstly, the solution finds out the most probably region where the target may turn up. And then Gaussian mixture background model is built in this area. Finally, morphological filter algorithm is used for improving integrity of the detected targets. Results show that the improved method could have a more perfect detection but no more time increasing than typical method.

scholarly journals Model-Based Region-of-Interest Selection in Dynamic Breast MRI

2004 ◽  
Author(s):  
Florence Forbes ◽  
Nathalie Peyrard ◽  
Chris Fraley ◽  
Dianne Georgian-Smith ◽  
David M. Goldhaber ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Breast Mri ◽  
Model Based

Comfort Evaluation Model Based on Region of Interest and Contrast of Stereo Images

2018 ◽  
Vol 47 (12) ◽  
pp. 1210002
Author(s):  
权巍 QUAN Wei ◽  
赵云秀 ZHAO Yun-xiu ◽  
韩成 HAN Cheng ◽  
丁莹 DING Ying ◽  
姜珊 JIANG Shan ◽  
...  
Keyword(s):  
Evaluation Model ◽  
Region Of Interest ◽  
Stereo Images ◽  
Model Based ◽  
Comfort Evaluation

scholarly journals Improvement of Fast Model-Based Acceleration of Parameter Look-Locker T1 Mapping

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5371
Author(s):  
Michał Staniszewski ◽  
Uwe Klose
Keyword(s):  
T1 Mapping ◽  
Region Of Interest ◽  
Computational Time ◽  
Single Shot ◽  
Mean Values ◽  
Model Based ◽  
Fitting Procedure ◽  
Novel Method ◽  
Space Data ◽  
Brain Measurements

Quantitative mapping is desirable in many scientific and clinical magneric resonance imaging (MRI) applications. Recent inverse recovery-look locker sequence enables single-shot T1 mapping with a time of a few seconds but the main computational load is directed into offline reconstruction, which can take from several minutes up to few hours. In this study we proposed improvement of model-based approach for T1-mapping by introduction of two steps fitting procedure. We provided analysis of further reduction of k-space data, which lead us to decrease of computational time and perform simulation of multi-slice development. The region of interest (ROI) analysis of human brain measurements with two different initial models shows that the differences between mean values with respect to a reference approach are in white matter—0.3% and 1.1%, grey matter—0.4% and 1.78% and cerebrospinal fluid—2.8% and 11.1% respectively. With further improvements we were able to decrease the time of computational of single slice to 6.5 min and 23.5 min for different initial models, which has been already not achieved by any other algorithm. In result we obtained an accelerated novel method of model-based image reconstruction in which single iteration can be performed within few seconds on home computer.

scholarly journals Sparse Ultrasound Imaging via Manifold Low-Rank Approximation and Non-Convex Greedy Pursuit

2018 ◽  
Author(s):  
Thiago Alberto Rigo Passarin ◽  
Daniel Rodrigues Pipa ◽  
Marcelo Victor Wüst Zibetti
Keyword(s):  
Ultrasound Imaging ◽  
Matching Pursuit ◽  
Ultrasound Image ◽  
Simulated Data ◽  
Region Of Interest ◽  
Discrete Models ◽  
Low Rank ◽  
Emission Computed Tomography ◽  
Low Rank Approximation ◽  
Model Based

Model-based image reconstruction has brought improvements in terms of contrast and spatial resolution to imaging applications such as magnetic resonance imaging and emission computed tomography. However, their use for pulse-echo techniques like ultrasound imaging is limited by the fact that model-based algorithms assume a finite grid of possible locations of scatterers in a medium -- which does not reflect the continuous nature of real world objects and creates a problem known as off-grid deviation. To cope with this problem, we present a method of dictionary expansion and constrained reconstruction that approximates the continuous manifold of all possible scatterer locations within a region of interest. The expanded dictionary is created using a highly coherent sampling of the region of interest, followed by a rank reduction procedure based on a truncated singular value decomposition. We develop a greedy algorithm, based on the Orthogonal Matching Pursuit (OMP), that uses a correlation-based non-convex constraint set that allows for the division of the region of interest into cells of any size. To evaluate the performance of the method, we present results of 2-dimensional ultrasound image reconstructions with simulated data in a nondestructive testing application. Our method succeeds in the reconstructions of sparse images from noisy measurements, providing higher accuracy than previous approaches based on regular discrete models.

scholarly journals Model-based Region-of-interest Selection in Dynamic Breast MRI

2006 ◽  
Vol 30 (4) ◽  
pp. 675-687 ◽  
Author(s):  
Florence Forbes ◽  
Nathalie Peyrard ◽  
Chris Fraley ◽  
Dianne Georgian-Smith ◽  
David M. Goldhaber ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Breast Mri ◽  
Model Based

scholarly journals A Model-Based Approach of Foreground Region of Interest Detection for Video Codecs

2019 ◽  
Vol 9 (13) ◽  
pp. 2670
Author(s):  
Zhewei Zhang ◽  
Tao Jing ◽  
Bowen Ding ◽  
Meilin Gao ◽  
Xuejing Li
Keyword(s):  
Video Compression ◽  
Computing Time ◽  
Region Of Interest ◽  
Video Codecs ◽  
Model Based ◽  
Foreground Region ◽  
Wide Range ◽  
Interest Detection ◽  
Different Characteristics ◽  
Fine Tune

Detecting the Region of Interest (ROI) for video clips is a significant and useful technique both in video codecs and surveillance/monitor systems. In this paper, a new model-based detection method is designed which suits video compression codecs by proposing two models: an “inter” and “intra” model. The “inter” model exploits the motion information represented as blocks by global motion compensation approaches while the “intra” model extracts the objects details through objects filtering and image segmentation procedures. Finally, the detection results are formed through a new clustering with fine-tune approach from the “intra” model assisted with the “inter” model. Experimental results show that the proposed method fits well for real-time video codecs and it achieves a good performance both on detection precision and on computing time. In addition, the proposed method is versatile for a wide range of surveillance videos with different characteristics.

scholarly journals Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step

2021 ◽  
Vol 11 (8) ◽  
pp. 3467
Author(s):  
Žan Pirnar ◽  
Miha Finžgar ◽  
Primož Podržaj
Keyword(s):  
Skin Color ◽  
Region Of Interest ◽  
Signal Extraction ◽  
Color Segmentation ◽  
Data Sets ◽  
Success Rates ◽  
Model Based ◽  
Video Recordings ◽  
Lighting Conditions ◽  
Maximization Of Mutual Information

Traditionally, the first step in physiological measurements based on remote photoplethysmography (rPPG) is localizing the region of interest (ROI) that contains a desired pulsatile information. Recently, approaches that do not require this step have been proposed. The purpose of this study was to evaluate the performance of selected approaches with and without ROI localization step in rPPG signal extraction. The Viola-Jones face detector and Kanade–Lucas–Tomasi tracker (VK) in combination with (a) a region-of-interest (ROI) cropping, (b) facial landmarks, (c) skin-color segmentation, and (d) skin detection based on maximization of mutual information and an approach without ROI localization step (Full Video Pulse (FVP)) were studied. Final rPPG signals were extracted using selected model-based and data-driven rPPG algorithms. The performance of the approaches was tested on three publicly available data sets offering compressed and uncompressed video recordings covering various scenarios. The success rates of pulse waveform signal extraction range from 88.37% (VK with skin-color segmentation) to 100% (FVP). In challenging scenarios (skin tone, lighting conditions, exercise), there were no statistically significant differences between the studied approaches in terms of SNR. The best overall performance in terms of RMSE was achieved by a combination of VK with ROI cropping and the model-based rPPG algorithm. Results indicate that the selection of the ROI localization approach does not significantly affect rPPG measurements if combined with a robust algorithm for rPPG signal extraction.

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