scholarly journals Analyses of body measurement with depth image data using motioncapture sensor

2020 ◽  
Vol 71 (06) ◽  
pp. 530-537
Author(s):  
HAKAN YÜKSEL ◽  
MELIHA OKTAV BULUT
Keyword(s):  
Low Cost ◽  
Image Data ◽  
Real Data ◽  
Image Sensor ◽  
Industrial Applications ◽  
Sensor Data ◽  
Depth Image ◽  
Depth Data ◽  
Military Security ◽  
The Difference

Sensors can capture and scan many objects in real time for military, security, health and industrial applications. Sensorscan be made smaller, cheaper and more energy efficient due to rapid changes in technology. Low-cost sensors areattractive alternatives to high cost laser scanners in recent years. The Kinect sensor can measure depth data with lowcost and high resolution by scanning the environment. In this study, this sensor collected data on users in front of ascanner, and the depth data results were tested. The process was repeated with four different body positions, and theresults were analysed. The sensor data was reliable versus real measurements. When compared the depth data takenby the sensor with the real measures, the reliability rate is found significance. The difference between the depth imagedata of different users, different positions and different body measures and real data is 0.35 to 1.15 cm. This shows thatthe sensor’s results are close to real data. When the accuracy of the sensor against real measurements is examined,it is seen that these values are between 98.46 % and 99.6 %. Thus, this depth image sensor is reliable and can be usedas an alternative and cheaper way for body measurements.

scholarly journals Analyses of body measurement with depth image data using motioncapture sensor

2020 ◽  
Vol 71 (06) ◽  
pp. 530-537
Author(s):  
HAKAN YÜKSEL ◽  
MELIHA OKTAV BULUT
Keyword(s):  
Low Cost ◽  
Image Data ◽  
Real Data ◽  
Image Sensor ◽  
Industrial Applications ◽  
Sensor Data ◽  
Depth Image ◽  
Depth Data ◽  
Military Security ◽  
The Difference

Sensors can capture and scan many objects in real time for military, security, health and industrial applications. Sensorscan be made smaller, cheaper and more energy efficient due to rapid changes in technology. Low-cost sensors areattractive alternatives to high cost laser scanners in recent years. The Kinect sensor can measure depth data with lowcost and high resolution by scanning the environment. In this study, this sensor collected data on users in front of ascanner, and the depth data results were tested. The process was repeated with four different body positions, and theresults were analysed. The sensor data was reliable versus real measurements. When compared the depth data takenby the sensor with the real measures, the reliability rate is found significance. The difference between the depth imagedata of different users, different positions and different body measures and real data is 0.35 to 1.15 cm. This shows thatthe sensor’s results are close to real data. When the accuracy of the sensor against real measurements is examined,it is seen that these values are between 98.46 % and 99.6 %. Thus, this depth image sensor is reliable and can be usedas an alternative and cheaper way for body measurements.

scholarly journals Overcoming Limitations of LoRa Physical Layer in Image Transmission

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3257 ◽  
Author(s):  
Akram Jebril ◽  
Aduwati Sali ◽  
Alyani Ismail ◽  
Mohd Rasid
Keyword(s):  
Data Transfer ◽  
Low Cost ◽  
Image Transmission ◽  
Image Sensor ◽  
Physical Layer ◽  
Forest Monitoring ◽  
Sensor Data ◽  
Area Network ◽  
Wide Area Network ◽  
The Future

As a possible implementation of a low-power wide-area network (LPWAN), Long Range (LoRa) technology is considered to be the future wireless communication standard for the Internet of Things (IoT) as it offers competitive features, such as a long communication range, low cost, and reduced power consumption, which make it an optimum alternative to the current wireless sensor networks and conventional cellular technologies. However, the limited bandwidth available for physical layer modulation in LoRa makes it unsuitable for high bit rate data transfer from devices like image sensors. In this paper, we propose a new method for mangrove forest monitoring in Malaysia, wherein we transfer image sensor data over the LoRa physical layer (PHY) in a node-to-node network model. In implementing this method, we produce a novel scheme for overcoming the bandwidth limitation of LoRa. With this scheme the images, which requires high data rate to transfer, collected by the sensor are encrypted as hexadecimal data and then split into packets for transfer via the LoRa physical layer (PHY). To assess the quality of images transferred using this scheme, we measured the packet loss rate, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index of each image. These measurements verify the proposed scheme for image transmission, and support the industrial and academic trend which promotes LoRa as the future solution for IoT infrastructure.

The Research of the Real-Time High-Resolution Image Acquisition Based on Embedded System

2014 ◽  
Vol 631-632 ◽  
pp. 508-511
Author(s):  
Xi Ye Feng ◽  
Xiu Qing Huang
Keyword(s):  
High Resolution ◽  
Embedded System ◽  
Real Time ◽  
Low Cost ◽  
Image Acquisition ◽  
Image Data ◽  
Image Sensor ◽  
High Definition ◽  
The Real ◽  
Embedded Linux System

This paper presents the design of a real-time high-definition image acquisition. The hardware platform combines Intel Xscale PXA270 processor, high-resolution camera and SAA7114H. The system is based on the embedded Linux system. Beetween the image sensor and the system memory,there is a quick capture interface.The interface receives the data from the image sensor,and converts the raw image data to a suitable format, and sends H.264 stream to the memory via the DMA channel. The result shows that the design can realize the real-time and high-definition image acquisition in a complicated environment. The advantage of this system is small volume, low power consumption and low cost. It can be widely used in agricultural and hydrological monitoring, intelligent transportation, security monitoring and intelligent home.

A DSP-Based System Design for Image Capturing and Transmitting

2014 ◽  
Vol 1049-1050 ◽  
pp. 1730-1735
Author(s):  
Yong Cheng Wang ◽  
He Ming Zhao ◽  
Lei Shao ◽  
Li He
Keyword(s):  
Low Power ◽  
System Design ◽  
Low Cost ◽  
Image Data ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Driving Mechanism ◽  
Timing Control ◽  
Image Capturing

In present paper, a DSP-based image capturing and transmitting system is described. As a shortage of peripherals on DSP, external image capturing and data transmitting components are needed in this case. A CMOS image sensor was used to capture images, and an Ethernet MAC controller with PHY was used to transmit image data. In addition, a CPLD was used as the co-controller for timing control. ARP, IP and UDP were functioning during data transmitting through Ethernet. The Driving Mechanism of two main chips and implementing of the protocols were described in detail. Images displayed on the PC show that the system provids good performance. The system is low-cost, simple and low-power.

Fusion of Multiple Ultrasonic Sensor Data and Image Data for Measuring an Object’s Motion

2005 ◽  
Vol 17 (1) ◽  
pp. 36-43
Author(s):  
Kazunori Umeda ◽  
◽  
Jun Ota ◽  
Hisayuki Kimura ◽  
◽  
...  
Keyword(s):  
Image Data ◽  
Image Sensor ◽  
Measurement Range ◽  
Ultrasonic Sensor ◽  
Sensor Data ◽  
Wide Angle ◽  
Robot System ◽  
Object Movement ◽  
Intensive Observation ◽  
Angle Observation

Robot sensing requires two types of observation – intensive and wide-angle. We selected multiple ultrasonic sensors for intensive observation and an image sensor for wide-angle observation in measuring a moving object’s motion with sensors in two kinds of fusion – one fusing multiple ultrasonic sensor data and the other fusing the two types of sensor data. The fusion of multiple ultrasonic sensor data takes advantage of object movement from a measurement range of an ultrasonic sensor to another sensor’s range. They are formulated in a Kalman filter framework. Simulation and experiments demonstrate the effectiveness and applicability to an actual robot system.

scholarly journals Foot 3D Reconstruction and Measurement using Depth Data

2020 ◽  
Vol 6 (1) ◽  
pp. 37
Author(s):  
Doni Setio Pambudi ◽  
Lailatul Hidayah
Keyword(s):  
Measurement Error ◽  
3D Model ◽  
Image Sensor ◽  
Depth Image ◽  
System Configuration ◽  
Transformation Techniques ◽  
Automatic Instrument ◽  
Depth Data ◽  
Foot Length ◽  
Foot Model

Background: The need for shoes with non-standard sizes is increasing, but this is not followed by the competence to measure the foot effectively. The high cost of such an instrument in the market has led to the development of a precise yet affordable measurement system.Objective: This research attempts to solve the measuring problem by employing an automatic instrument utilizing a depth image sensor that is available on the market at an affordable price.Methods: Data from several Realsense sensors that have been preprocessed are combined using transformation techniques and noise cleaning is performed afterward. Finally the 3D model of the foot is ready and hence the length and width can be obtained.Results: The experimental results show that the proposed method produces a measurement error of 0.351 cm in foot length, and 0.355 cm in foot width.Conclusion: The result shows that multiple angles of a static Realsense sensor can produce a good 3D foot model automatically. This proposed system configuration can reduce complexity as well as being an affordable solution.  

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

Study of pumping effect in flow-through electrolysers

1983 ◽  
Vol 48 (8) ◽  
pp. 2232-2248 ◽  
Author(s):  
Ivo Roušar ◽  
Michal Provazník ◽  
Pavel Stuhl
Keyword(s):  
Natural Convection ◽  
Balance Equation ◽  
Volume Fraction ◽  
Industrial Applications ◽  
Pumping Effect ◽  
Flow Through ◽  
The Mean ◽  
The Difference

In electrolysers with recirculation, where a gas is evolved, the pumping of electrolyte from a lower to a higher level can be effected by natural convection due to the difference between the densities of the inlet electrolyte and the gaseous emulsion at the outlet. An accurate balance equation for calculation of the rate of flow of the pumped liquid is derived. An equation for the calculation of the mean volume fraction of bubbles in the space between the electrodes is proposed and verified experimentally on a pilot electrolyser. Two examples of industrial applications are presented.

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

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