A High Spatial Resolution Depth Sensing Method Based on Binocular Structured Light

Yao, Huimin; Ge, Chenyang; Xue, Jianru; Zheng, Nanning

doi:10.3390/s17040805

Cited by 18 publications

(11 citation statements)

References 28 publications

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“…The performed experiments yielded higher performance in Kinect v2 ToF sensor with less noisy and denser 3D information. Another comparison in [ 210 ] was performed based on two SL sensors (Kinect v1, Asus Xtion Pro), one ToF (Kinect v2), and one ASV sensor (Intel R200). The comparison yielded to the same results and conclusions as [ 209 ], and showed that the Kinect v2 ToF sensor surpasses the rest of the three devices and presents higher spatial resolution…”

Section: Resultsmentioning

confidence: 99%

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Rehouma

Noumeir

Essouri

et al. 2020

Sensors

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Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.

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Section: Resultsmentioning

confidence: 99%

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Rehouma

Noumeir

Essouri

et al. 2020

Sensors

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“…In another study by Ross et al [ 45 ], the mean discrepancy of scan data obtained using smartphones ranged from 0.9 mm to 1.0 mm, depending on the number of photographs taken during scanning. In recent years, infrared structured-light depth-sensing cameras have been incorporated in mobile devices to facilitate 3D optical scans [ 51 ]. 3D depth-sensing cameras work by the time-of-flight principle, measuring the time taken for light emitted by an illumination unit to travel to an object and back to the sensor array [ 52 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Mobile Device–Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis

Mai¹,

Lee²

2020

J Med Internet Res

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Background The accurate assessment and acquisition of facial anatomical information significantly contributes to enhancing the reliability of treatments in dental and medical fields, and has applications in fields such as craniomaxillofacial surgery, orthodontics, prosthodontics, orthopedics, and forensic medicine. Mobile device–compatible 3D facial scanners have been reported to be an effective tool for clinical use, but the accuracy of digital facial impressions obtained with the scanners has not been explored. Objective We aimed to review comparisons of the accuracy of mobile device–compatible face scanners for facial digitization with that of systems for professional 3D facial scanning. Methods Individual search strategies were employed in PubMed (MEDLINE), Scopus, Science Direct, and Cochrane Library databases to search for articles published up to May 27, 2020. Peer-reviewed journal articles evaluating the accuracy of 3D facial models generated by mobile device–compatible face scanners were included. Cohen d effect size estimates and confidence intervals of standardized mean difference (SMD) data sets were used for meta-analysis. Results By automatic database searching, 3942 articles were identified, of which 11 articles were considered eligible for narrative review, with 6 studies included in the meta-analysis. Overall, the accuracy of face models obtained using mobile device–compatible face scanners was significantly lower than that of face models obtained using professional 3D facial scanners (SMD 3.96 mm, 95% CI 2.81-5.10 mm; z=6.78; P<.001). The difference between face scanning when performed on inanimate facial models was significantly higher (SMD 10.53 mm, 95% CI 6.29-14.77 mm) than that when performed on living participants (SMD 2.58 mm, 95% CI 1.70-3.47 mm, P<.001, df=12.94). Conclusions Overall, mobile device–compatible face scanners did not perform as well as professional scanning systems in 3D facial acquisition, but the deviations were within the clinically acceptable range of <1.5 mm. Significant differences between results when 3D facial scans were performed on inanimate facial objects and when performed on the faces of living participants were found; thus, caution should be exercised when interpreting results from studies conducted on inanimate objects.

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“…These technologies use an artificial source of light such as laser or LED source of different wavelengths notably infrared or near infrared, to illuminate the scene and analyze the reflected light. Numerous studies have compared these two imaging technologies, 59,60 demonstrating that SL‐based systems have lower resolution and accuracy, and their performance is inferior when dealing with moving objects or dynamic scenes 61,62 . In Ref.…”

Section: Methodsmentioning

confidence: 99%

“…Numerous studies have compared these two imaging technologies, 59,60 demonstrating that SL-based systems have lower resolution and accuracy, and their performance is inferior when dealing with moving objects or dynamic scenes. 61,62 In Ref. [63], the author reported that ToF cameras do not suffer as much from occlusion in contrast to SL cameras.…”

Section: A Range Imaging Sensorsmentioning

confidence: 99%

Surface imaging for real‐time patient respiratory function assessment in intensive care

et al. 2020

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Purpose Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real‐time respiratory monitoring and function assessment of ICU patients. Methods The proposed system uses a Time‐of‐Flight depth sensor to analyze the patient’s chest wall morphological changes in order to estimate multiple respiratory function parameters. The automatic detection of the patient's torso is also proposed using a deep neural network model trained on the COCO dataset. The evaluation of the proposed system was performed on a mannequin and on 16 mechanically ventilated patients (a total of 216 recordings) admitted in the ICU of the Brest University Hospital. Results The estimation of respiratory parameters (respiratory rate and tidal volume) showed high correlation with the reference method (r = 0.99; P < 0.001 and r = 0.99; P < 0.001) in the mannequin recordings and (r = 0.95, P < 0.001 and r = 0.90, P < 0.001) for patients. Conclusion This study describes and evaluates a novel noncontact monitoring system suitable for continuous monitoring of key respiratory parameters for disease assessment of critically ill patients.

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A High Spatial Resolution Depth Sensing Method Based on Binocular Structured Light

Cited by 18 publications

References 28 publications

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Accuracy of Mobile Device–Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis

Surface imaging for real‐time patient respiratory function assessment in intensive care

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