• Journal of Biosystems Engineering
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• v.42 no.4
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• pp.330-338
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• 2017

Purpose: The smartphone is actively being used in many research fields, primarily in medical and diagnostic applications. However, there are cases in which smartphone-based systems have been developed for agriculture, environment, and food applications. The purpose of this review is to summarize the research cases using smartphone cameras in agriculture, environment, and food. Methods: This review introduces seventeen research cases which used smartphone cameras in agriculture, food, water, and soil applications. These were classified as systems involving "smartphone-camera-alone" and "smartphone camera with optical accessories". Results: Detecting food-borne pathogens, analyzing the quality of foods, monitoring water quality and safety, gathering information regarding plant growth or damage, identifying weeds, and measuring soil loss after rain were presented for the smartphone-camera-alone system. Measuring food and water quality and safety, phenotyping seeds, and soil classifications were presented for the smartphone camera with optical accessories. Conclusions: Smartphone cameras were applied in various areas for several purposes. The use of smartphone cameras has advantages regarding high-resolution imaging, manual or auto exposure and focus control, ease of use, portability, image storage, and most importantly, programmability. The studies discussed were achieved by sensitivity improvements of CCDs (charge-coupled devices) and CMOS (complementary metal-oxide-semiconductor) on smartphone cameras and improved computing power of the smartphone, respectively. A smartphone camera-based system can be used with ease, low cost, in near-real-time, and on-site. This review article presents the applications and potential of the smartphone and the smartphone camera used for various purposes in agriculture, environment, and food.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.49-60
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• 2016

This paper presents a set of methods to evaluate the image quality of smartphone cameras as compared with that of a DSLR camera. In recent years, smartphone cameras have been used broadly for many purposes. As the performance of smartphone cameras has been enhanced considerably, they can be considered to be used for precise mapping instead of metric cameras. To evaluate the possibility, we tested the quality of one DSLR camera and 3 smartphone cameras. In the first step, we compare the amount of lens distortions inherent in each camera using camera calibration sheet images. Then, we acquired target sheet images, extracted reference lines from them and evaluated the geometric quality of smartphone cameras based on the amount of errors occurring in fitting a straight line to observed points. In addition, we present a method to evaluate the radiometric quality of the images taken by each camera based on planar fitting errors. Also, we propose a method to quantify the geometric quality of the selected camera using edge displacements observed in target sheet images. The experimental results show that the geometric and radiometric qualities of smartphone cameras are comparable to those of a DSLR camera except lens distortion parameters.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.115-125
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• 2011

The smartphones which have been recently are embedded with high resolution quality camera, assisted GPS, accelerometer, gyroscope and various sensors including magnetometer sensor that could be directly used for measurement. This study aims to suggest the possible application of smartphone camera providing high resolution images in terms of photogrammetry by calibrating it and assessing its accuracy. First of all, prior to the accuracy assessment of smartphone camera, camera calibration was conducted to correct lens distortion of each camera and the accuracy of image coordinates and object coordinates calculated by bundle adjustment during this procedure was analyzed. Also regarding three-dimensional positioning, result analysis depending on considering lens distortion coefficients was conducted, and finally relative accuracy of smartphone camera on metric camera was assessed. The result showed that in terms of distortion correction of smartphone camera, also higher order symmetric radial lens distortion coefficients should be considered, and three dimensional position determined by smartphone images was a little difference from that by metric camera. Therefore it is expected that smartphone images have huge possibility to be used for photogrammetry.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.216-222
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• 2013

It is known that blood circulation in human body causes the skin tone to change concurrently with heartbeats. A number of apps have been developed to measure the heartbeat using smartphone camera; however, no any further analysis is performed. In this paper we propose an algorithm that detects heartbeats from the phone's camera and further extracts the heart rate variability (HRV). We compare the HRV extracted from the camera with the HRV extracted from the electrocardiogram. We estimated a number of commonly used HRV characteristics and compared them. Our results show that smartphone camera leads to slightly overestimated characteristics although the difference in extracted HRV signals is negligible. As a consequence we suggest that a smartphone camera can be employed in a quick heart diagnosis and diagnosis of autonomic nervous system.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.75-81
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• 2020

Approximately 1.3 million people die from traffic accidents each year, and smartphone usage while driving is one of the main causes of such accidents. Therefore, detection of smartphone usage by drivers has become an important part of distracted driving detection. Previous studies have used single camera-based methods to collect the driver images. However, smartphone usage detection by employing a single camera can be unsuccessful if the driver occludes the phone. In this paper, we present a driver smartphone usage detection system that uses multiple cameras to collect driver images from different perspectives, and then processes these images with ensemble convolutional neural networks. The ensemble method comprises three individual convolutional neural networks with a simple voting system. Each network provides a distinct image perspective and the voting mechanism selects the final classification. Experimental results verified that the proposed method avoided the limitations observed in single camera-based methods, and achieved 98.96% accuracy on our dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.723-743
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• 2016

Indoor positioning is considered an enabler for a variety of applications, the demand for an indoor positioning service has also been accelerated. That is because that people spend most of their time indoor environment. Meanwhile, the smartphone integrated powerful camera is an efficient platform for navigation and positioning. However, for high accuracy indoor positioning by using a smartphone, there are two constraints that includes: (1) limited computational and memory resources of smartphone; (2) users' moving in large buildings. To address those issues, this paper uses the TC-OFDM for calculating the coarse positioning information includes horizontal and altitude information for assisting smartphone camera-based positioning. Moreover, a unified representation model of image features under variety of scenarios whose name is FAST-SURF is established for computing the fine location. Finally, an optimization marginalized particle filter is proposed for fusing the positioning information from TC-OFDM and images. The experimental result shows that the wide location detection accuracy is 0.823 m (1σ) at horizontal and 0.5 m at vertical. Comparing to the WiFi-based and ibeacon-based positioning methods, our method is powerful while being easy to be deployed and optimized.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.234-243
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• 2017

With the development of smartphone technologies enable photoplethysmogram (PPG) acquisition by camera and heart rate (HR) measurement. This papers presents improved algorithm to extract HR from PPG signal recorded by smartphone camera and to develop real-time PPG signal processing Android application. 400 video samples recorded by Samsung smartphone camera are imported as input data for further processing and evaluating algorithm on MATLAB. An optimized algorithm is developed and tested on Android platform with different kind of Samsung smartphones. To assess algorithm's performance, medical device Beurer BC08 is used as reference. According to related works, accuracy parameters includes 90% number of samples that has relative errors less than 5%, Person correlation (r) more than 0.9, and standard estimated error (SEE) less than 5 beats-per-minutes (bpm).

• Journal of the Korean Society of Industry Convergence
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• v.25 no.5
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• pp.773-782
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• 2022

Estimation of cable tension through proper measurements is one of the essential tasks in evaluating the safety of cable structures. In this paper, a study on cable tension estimation using the built-in accelerometer and camera in a smartphone was conducted. For the experimental study, visual displacement measurement using a smartphone camera and acceleration measurement using a built-in accelerometer were performed in the cable-stayed bridge model. The estimated natural frequencies and transformed tensions from these measurements were compared with the theoretical values and results from the normal visual displacement method. Through comparison, it can be seen that the error between the method using the smartphone and the normal visual displacement is sufficiently small to be acceptable. It has also been shown that those errors are much smaller than the difference between the values calculated by the theoretical model. These results show that the deviation according to the type of measurement method is not large and it is rather important to use an appropriate mathematical model. In conclusion, in the case of cable tension estimation, it can be said that the visual displacement measurement and acceleration using a smartphone can be a sufficiently applicable method, just like the normal visual displacement method. It is also noteworthy that the smartphone accelerometer has a larger magnitude error and has more limitations such as high-frequency sampling instability compared to the visual displacement method, but shows almost the same performance as the visual displacement method in this cable tension estimation.

• Korean Journal of Optics and Photonics
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• v.32 no.1
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• pp.22-28
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• 2021

We try to extract three-dimensional information, such as the distance from a camera and the actual width and height of an object, from still photographs by a multilens smartphone camera, by means of parallax error. To obtain this information, we develop several formulas and design a method for experimental instrumentation. If the results from this paper were included in algorithms of multilens smartphone cameras, there would be various kinds of applications, such as in the workplace of architectural and civil engineering to obtain an actual dimension, or on a golf course to measure how far away a pin flag is. We expect many more applications of this study, because the multilens smartphone camera is already an important necessity of life.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.143-151
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• 2016

The aim of this study is to investigate the optimal algorithm to extract medical radiation induced pixel signal from complementary metal-oxide semiconductor (CMOS) sensors of smartphones camera. The pixel intensity and pixel number of smartphone camera were measured as the X-ray dose was increased. The front camera of the smartphone camera has low noise property and excellent dose response as compared to the back camera of the smartphone. The indirect method which uses scintillation crystal in front of the smartphone camera, couldn't improve the X-ray detection efficiency as compared to the direct method which does not use any scintillator in front of the smartphone camera. When we used the algorithm which employing threshold level on the pixel intensity and pixel number, the dose linearity was more higher for the pixel intensity rather for the pixel number. The use of pixel intensity of Y color component which represents the grey scale, would be efficient in terms of the radiation detection efficiency and reducing the complexity of the image processing. We expect that the radiation dose monitoring can be managed effectively and systematically by using the proposed radiation detection algorithm, thus eventually will contribute to the public healthcare.