Reconstructing 3D human models with a Kinect

Chen, Guang; Li, Jituo; Wang, Bei; Zeng, Jiping; Lu, Guodong; Zhang, Dongliang

doi:10.1002/cav.1632

Cited by 19 publications

(7 citation statements)

References 30 publications

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“…Some 3D reconstruction technologies are already very mature, such as BodyTalk reconstruction system [15] and Kinect reconstruction system [16], which makes the 3D model building process easier. The mesh parameterization algorithm such as RiccFlow [17] and Authalic [18] make the second step easier to implement.…”

Section: Methodsmentioning

confidence: 99%

Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient

Jiao

Xie

2019

Burns &Amp; Trauma

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Background Burns are life-threatening with high morbidity and mortality. Reliable diagnosis supported by accurate burn area and depth assessment is critical to the success of the treatment decision and, in some cases, can save the patient’s life. Current techniques such as straight-ruler method, aseptic film trimming method, and digital camera photography method are not repeatable and comparable, which lead to a great difference in the judgment of burn wounds and impede the establishment of the same evaluation criteria. Hence, in order to semi-automate the burn diagnosis process, reduce the impact of human error, and improve the accuracy of burn diagnosis, we include the deep learning technology into the diagnosis of burns. Method This article proposes a novel method employing a state-of-the-art deep learning technique to segment the burn wounds in the images. We designed this deep learning segmentation framework based on the Mask Regions with Convolutional Neural Network (Mask R-CNN). For training our framework, we labeled 1150 pictures with the format of the Common Objects in Context (COCO) data set and trained our model on 1000 pictures. In the evaluation, we compared the different backbone networks in our framework. These backbone networks are Residual Network-101 with Atrous Convolution in Feature Pyramid Network (R101FA), Residual Network-101 with Atrous Convolution (R101A), and InceptionV2-Residual Network with Atrous Convolution (IV2RA). Finally, we used the Dice coefficient (DC) value to assess the model accuracy. Result The R101FA backbone network gains the highest accuracy 84.51% in 150 pictures. Moreover, we chose different burn depth pictures to evaluate these three backbone networks. The R101FA backbone network gains the best segmentation effect in superficial, superficial thickness, and deep partial thickness. The R101A backbone network gains the best segmentation effect in full-thickness burn. Conclusion This deep learning framework shows excellent segmentation in burn wound and extremely robust in different burn wound depths. Moreover, this framework just needs a suitable burn wound image when analyzing the burn wound. It is more convenient and more suitable when using in clinics compared with the traditional methods. And it also contributes more to the calculation of total body surface area (TBSA) burned.

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Section: Methodsmentioning

confidence: 99%

Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient

Jiao

Xie

2019

Burns &Amp; Trauma

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“…From the Kinect depth data obtained at the frame, the depth datas are stored in the depth buffer. Then, according to the mounting Kinect position we find the roughly interactive surface [14]. Then the next subsequent frame is obtained, the depth of each frame of data will be compared with each other to interact with the surface in accordance with a pre-obtained in order to confirm whether there is ''touch'' event.…”

Section: B Implementation Methods Of Large-screen Interactive System mentioning

confidence: 99%

Large-Screen Interactive Technology With 3D Sensor Based on Clustering Filtering Method and Unscented Kalman Filter

Fei

2020

IEEE Access

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In this paper, a large screen interactive 3D sensor technology with clustering filtering method and unscented Kalman filter has been addressed. The surface and interactive area with the screen is mapped together using image processing and machine vision processing of 3D sensor. Then, the predictable and accurate multi-point interaction can be obtained. Also some operations can be achieved such as zooming, panning, selecting and moving image components, etc. Vision method, infrared induction, dynamic capture, image processing and other mixed technologies have been simultaneously utilized to recognize human gestures and simulate mouse event which can give participants a fully interactive experience. Experimental results show that this large-screen interactive technology with 3D sensor can achieve accurate multi-point interaction and gesture control, and have good human-machine interaction effect. INDEX TERMS 3D sensor, multi-point interactive, gesture control, human-computer interaction, unscented Kalman filter.

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“…Using point clouds and depth information obtained from multiple cameras and performing object detection on colour images can improve the detection of a person using a combination of multiple Kinects [57]. Different variants of deployment of Kinect devices can be used for obtaining the 3D model of skeleton, for example, by using different Kinect devices to capture different parts of a human body [7], to capture depth data and RGB data from different viewpoints [16], to aggregate tracked data by weighting [4], to solve occlusion problems by data fusion [31]. A human pose recognition system utilizing a combination of body pose estimation and tracking using ridge body parts features from the joints points of the skeleton model, capable of achieving the mean recognition rate of 91.19%, is described in [23].…”

Section: Related Workmentioning

confidence: 99%

Multiple Kinect based system to monitor and analyze key performance indicators of physical training

Ryselis

Petkus²,

Blažauskas

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test–retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent.

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Reconstructing 3D human models with a Kinect

Cited by 19 publications

References 30 publications

Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient

Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient

Large-Screen Interactive Technology With 3D Sensor Based on Clustering Filtering Method and Unscented Kalman Filter

Multiple Kinect based system to monitor and analyze key performance indicators of physical training

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