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Aashish, Kamath, and A. Vijayalakshmi. "Comparison of Viola-Jones And Kanade-Lucas-Tomasi Face Detection Algorithms." Oriental journal of computer science and technology 10, no. 1 (2017): 151–59. http://dx.doi.org/10.13005/ojcst/10.01.20.
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Face detection technologies are used in a large variety of applications like advertising, entertainment, video coding, digital cameras, CCTV surveillance and even in military use. It is especially crucial in face recognition systems. You can’t recognise faces that you can’t detect, right? But a single face detection algorithm won’t work in the same way in every situation. It all comes down to how the algorithm works. For example, the Kanade-Lucas-Tomasi algorithm makes use of spatial common intensity transformation to direct the deep search for the position that shows the best match. It is much faster than other traditional techniques for checking far fewer potential matches between pictures. Similarly, another common face detection algorithm is the Viola-Jones algorithm that is the most widely used face detection algorithm. It is used in most digital cameras and mobile phones to detect faces. It uses cascades to detect edges like the nose, the ears etc. However, if there is a group of people and their faces are close to each other, the algorithm might not work that well as edges tend to overlap in a crowd. It might not detect individual faces. Therefore, in this work, we test both the Viola-Jones and the Kanade-Lucas-Tomasi algorithm for each image to find out which algorithm works best in which scenario.
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Liu Xinyu, 刘昕宇, 闫铮 Yan Zheng, 段放 Duan Fang, and 戴中颖 Dai Zhongying. "Tracking of Human Respiratory Motion Based on Kanade-Lucas-Tomasi Algorithm." Laser & Optoelectronics Progress 57, no. 22 (2020): 221001. http://dx.doi.org/10.3788/lop57.221001.
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Wang, Lu, Yafeng Zhang, Xiaohong Lin, and Zheng Yan. "Study of lumbar spine activity regularity based on Kanade-Lucas-Tomasi algorithm." Biomedical Signal Processing and Control 49 (March 2019): 465–72. http://dx.doi.org/10.1016/j.bspc.2018.12.023.
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Mstafa, Ramadhan J., and Khaled M. Elleithy. "A video steganography algorithm based on Kanade-Lucas-Tomasi tracking algorithm and error correcting codes." Multimedia Tools and Applications 75, no. 17 (2015): 10311–33. http://dx.doi.org/10.1007/s11042-015-3060-0.
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Belmont, Barry, Ross Kessler, Nikhil Theyyunni, et al. "Continuous Inferior Vena Cava Diameter Tracking through an Iterative Kanade–Lucas–Tomasi-Based Algorithm." Ultrasound in Medicine & Biology 44, no. 12 (2018): 2793–801. http://dx.doi.org/10.1016/j.ultrasmedbio.2018.07.022.
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Liu, Yuan Min, and Lian Fang Tian. "An Improved Algorithm on Adaptive KLT Vision Tracking." Advanced Materials Research 631-632 (January 2013): 1270–75. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1270.
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In view of the shortage of the KLT (Kanade-Lucas-Tomasi) tracking algorithm, an improved adaptive tracking method based on KLT is proposed in this paper, in which a kind of filtering mechanism is applied to decrease the effects of noise and illumination on tracking system. In order to eliminate the error of tracking, the strategies based on forward-backward error and measurement validity are utilized properly. However, because the approach to forward-backward error makes the feature points reduce, which leads to tracking failure especially when the shapes of object change, a method for appending the feature points is introduced. Experimental results indicate that the method presented in this paper is state of the art robustness in our comparison with related work and demonstrate improved generalization over the conventional methods.
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Lailia Rahmawati. "Deteksi Abnormality Dengan Menggunakan Algortima KLT Pada Citra Video CCTV." Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan 8, no. 2 (2017): 67–72. http://dx.doi.org/10.48056/jintake.v8i2.27.
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Abnormality Detection on CCTV video images that are shown on a particular individual or a particular object where the movement is captured by a minority. The use of KLT Algorithm (Kanade-Lucas-Tomasi) as Algortima Tracing results of a video that has been done Residual Spectral process that aims to produce a global movement that allocates an interesting area. From the comparison results it is known that the number of pixels in the detection move smaller than the detection is not moving. Then the result is that, the abnormal minority is that there is an object moving on its own compared to the other objects
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Kim, Hyeon, Jaechan Cho, Yongchul Jung, Seongjoo Lee, and Yunho Jung. "Area-Efficient Vision-Based Feature Tracker for Autonomous Hovering of Unmanned Aerial Vehicle." Electronics 9, no. 10 (2020): 1591. http://dx.doi.org/10.3390/electronics9101591.
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In this paper, we propose a vision-based feature tracker for the autonomous hovering of an unmanned aerial vehicle (UAV) and present an area-efficient hardware architecture for its integration into a flight control system-on-chip, which is essential for small UAVs. The proposed feature tracker is based on the Shi–Tomasi algorithm for feature detection and the pyramidal Lucas–Kanade (PLK) algorithm for feature tracking. By applying an efficient hardware structure that leverages the common computations between the Shi–Tomasi and PLK algorithms, the proposed feature tracker offers good tracking performance with fewer hardware resources than existing feature tracker implementations. To evaluate the tracking performance of the proposed feature tracker, we compared it with the GPS-based trajectories of a drone in various flight environments, such as lawn, asphalt, and sidewalk blocks. The proposed tracker exhibited an average accuracy of 0.039 in terms of normalized root-mean-square error (NRMSE). The proposed feature tracker was designed using the Verilog hardware description language and implemented on a field-programmable gate array (FPGA). The proposed feature tracker has 2744 slices, 25 DSPs, and 93 Kbit memory and can support the real-time processing at 417 FPS and an operating frequency of 130 MHz for 640 × 480 VGA images.
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Hsieh, Chung-Hung, and Jiann-Der Lee. "Markerless Augmented Reality via Stereo Video See-Through Head-Mounted Display Device." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/329415.
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Conventionally, the camera localization for augmented reality (AR) relies on detecting a known pattern within the captured images. In this study, a markerless AR scheme has been designed based on a Stereo Video See-Through Head-Mounted Display (HMD) device. The proposed markerless AR scheme can be utilized for medical applications such as training, telementoring, or preoperative explanation. Firstly, a virtual model for AR visualization is aligned to the target in physical space by an improved Iterative Closest Point (ICP) based surface registration algorithm, with the target surface structure reconstructed by a stereo camera pair; then, a markerless AR camera localization method is designed based on the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm and the Random Sample Consensus (RANSAC) correction algorithm. Our AR camera localization method is shown to be better than the traditional marker-based and sensor-based AR environment. The demonstration system was evaluated with a plastic dummy head and the display result is satisfactory for a multiple-view observation.
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Chermak, L., N. Aouf, and M. A. Richardson. "Scale robust IMU-assisted KLT for stereo visual odometry solution." Robotica 35, no. 9 (2016): 1864–87. http://dx.doi.org/10.1017/s0263574716000552.
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SUMMARYWe propose a novel stereo visual IMU-assisted (Inertial Measurement Unit) technique that extends to large inter-frame motion the use of KLT tracker (Kanade–Lucas–Tomasi). The constrained and coherent inter-frame motion acquired from the IMU is applied to detected features through homogenous transform using 3D geometry and stereoscopy properties. This predicts efficiently the projection of the optical flow in subsequent images. Accurate adaptive tracking windows limit tracking areas resulting in a minimum of lost features and also prevent tracking of dynamic objects. This new feature tracking approach is adopted as part of a fast and robust visual odometry algorithm based on double dogleg trust region method. Comparisons with gyro-aided KLT and variants approaches show that our technique is able to maintain minimum loss of features and low computational cost even on image sequences presenting important scale change. Visual odometry solution based on this IMU-assisted KLT gives more accurate result than INS/GPS solution for trajectory generation in certain context.
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Bedoya-Echeverry, Sebastián, Hernán Belalcázar-Ramírez, Humberto Loaiza-Correa, Sandra Esperanza Nope-Rodríguez, Carlos Rafael Pinedo-Jaramillo, and Andrés David Restrepo-Girón. "Detection of lies by facial thermal imagery analysis." Revista Facultad de Ingeniería 26, no. 44 (2017): 45. http://dx.doi.org/10.19053/01211129.v26.n44.2017.5771.
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An artificial vision system is presented for lie detection by analyzing face thermal image sequences. This system represents an alternative technique to the polygraph. Some of its features are: 1) it has no physical contact with the examinee, 2) it is non-intrusive, 3) it has a potential for private use, and 4) it can simultaneously analyze several persons. The proposed system is based on the detection of physiological changes in temperature in the lacrimal puncta area caused by the subtle increase in blood flow through the nearby vascular network. These changes take place when anxiety appears as a consequence of deception. Thus, the system segments the periorbital area, and tracks consecutive frames using the Kanade-Lucas-Tomasi algorithm. The results show a success rate of 79.2 % in detecting lies using a simple classification based on the comparison between the estimated temperatures in control questions, and the rest of the interrogation procedure. The performance of this system is comparable with previous works, where cameras with better specifications were used.
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Zhang, Hongmou, Jürgen Wohlfeil, and Denis Grießbach. "EXTENSION AND EVALUATION OF THE AGAST FEATURE DETECTOR." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 133–37. http://dx.doi.org/10.5194/isprsannals-iii-4-133-2016.
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Vision-aided inertial navigation is a navigation method which combines inertial navigation with computer vision techniques. It can provide a six degrees of freedom navigation solution from passive measurements without external referencing (e.g. GPS). Thus, it can operate in unknown environments without any prior knowledge. Such a system, called IPS (Integrated Positioning System) is developed by the German Aerospace Center (DLR). <br><br> For optical navigation applications, a reliable and efficient feature detector is a crucial component. With the publication of AGAST, a new feature detector has been presented, which is faster than other feature detectors. To apply AGAST to optical navigation applications, we propose several methods to improve its performance. Based on a new non-maximum suppression algorithm, automatic threshold adaption algorithm in combination with an image split method, the optimized AGAST provides higher reliability and efficiency than the original implementation using the Kanade Lucas Tomasi (KLT) feature detector. Finally, we compare the performance of the optimized AGAST with the KLT feature detector in the context of IPS. The presented approach is tested using real data from typical indoor scenes, evaluated on the accuracy of the navigation solution. The comparison demonstrates a significant performance improvement achieved by the optimized AGAST.
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Zhang, Hongmou, Jürgen Wohlfeil, and Denis Grießbach. "EXTENSION AND EVALUATION OF THE AGAST FEATURE DETECTOR." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 133–37. http://dx.doi.org/10.5194/isprs-annals-iii-4-133-2016.
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Vision-aided inertial navigation is a navigation method which combines inertial navigation with computer vision techniques. It can provide a six degrees of freedom navigation solution from passive measurements without external referencing (e.g. GPS). Thus, it can operate in unknown environments without any prior knowledge. Such a system, called IPS (Integrated Positioning System) is developed by the German Aerospace Center (DLR). <br><br> For optical navigation applications, a reliable and efficient feature detector is a crucial component. With the publication of AGAST, a new feature detector has been presented, which is faster than other feature detectors. To apply AGAST to optical navigation applications, we propose several methods to improve its performance. Based on a new non-maximum suppression algorithm, automatic threshold adaption algorithm in combination with an image split method, the optimized AGAST provides higher reliability and efficiency than the original implementation using the Kanade Lucas Tomasi (KLT) feature detector. Finally, we compare the performance of the optimized AGAST with the KLT feature detector in the context of IPS. The presented approach is tested using real data from typical indoor scenes, evaluated on the accuracy of the navigation solution. The comparison demonstrates a significant performance improvement achieved by the optimized AGAST.
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Pirnar, Žan, Miha Finžgar, and Primož Podržaj. "Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step." Applied Sciences 11, no. 8 (2021): 3467. http://dx.doi.org/10.3390/app11083467.
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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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Zhou, Bin, and Min Chen. "MRI Images under the Optimized Registration Algorithm for Primary Open Angle Glaucoma Visual Path Damage." Scientific Programming 2021 (July 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/4921276.
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To explore the impact of different image registration algorithms on the diagnosis of visual path damage in patients with primary open angle glaucoma (POAG), 60 cases of suspected POAG patients were selected as the research objects. Shape-preserving scale invariant feature transform (SP-SIFT) algorithm, scale invariant feature transform (SIFT) algorithm, and Kanade-Lucas-Tomasi (KLT) algorithm were compared and applied to MRI images of 60 POAG patients. It was found that the SP-SIFT algorithm converged after 33 iterations, which had a higher registration speed than the SIFT algorithm and the KLT algorithm. The mean errors of the SP-SIFT algorithm in the rotation angle, X-direction translation, and Y-direction translation were 2.11%, 4.56%, and 4.31%, respectively. Those of the SIFT algorithm were 5.55%, 9.98%, and 7.01%, respectively. Those of the KLT algorithm were 7.45%, 11.31%, and 8.56%, respectively, and the difference among algorithms was significant ( P < 0.05 ). The diagnostic sensitivity and accuracy of the SP-SIFT algorithm for POAG were 96.15% and 94.34%, respectively. Those of the SIFT algorithm were 94.68% and 90.74%, respectively. Those of the KLT algorithm were 94.21% and 90.57%, respectively, and the three algorithms had significant differences ( P < 0.05 ). The results of MRI images based on the SP-SIFT algorithm showed that the average thickness of the cortex of the patient’s left talar sulcus, right talar sulcus, left middle temporal gyrus, and left fusiform gyrus were 2.49 ± 0.15 mm, 2.62 ± 0.13 mm, 3.00 ± 0.10 mm, and 2.99 ± 0.17 mm, respectively. Those of the SIFT algorithm were 2.51 ± 0.17 mm, 2.69 ± 0.12 mm, 3.11 ± 0.13 mm, and 3.09 ± 0.14 mm, respectively. Those of the KLT algorithm were 2.35 ± 0.12 mm, 2.52 ± 0.16 mm, 2.77 ± 0.11 mm, and 2.87 ± 0.17 mm, respectively, and the three algorithms had significant differences ( P < 0.05 ). In summary, the SP-SIFT algorithm was ideal for POAG visual pathway diagnosis and was of great adoption potential in clinical diagnosis.
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Chermak, Lounis, Nabil Aouf, and Mark Richardson. "HDR imaging for feature tracking in challenging visibility scenes." Kybernetes 43, no. 8 (2014): 1129–49. http://dx.doi.org/10.1108/k-07-2014-0137.
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Purpose – In visual-based applications, lighting conditions have a considerable impact on quality of the acquired images. Extremely low or high illuminated environments are a real issue for a majority of cameras due to limitations in their dynamic range. Indeed, over or under exposure might result in loss of essential information because of pixel saturation or noise. This can be critical in computer vision applications. High dynamic range (HDR) imaging technology is known to improve image rendering in such conditions. The purpose of this paper is to investigate the level of performance that can be achieved for feature detection and tracking operations in images acquired with a HDR image sensor. Design/methodology/approach – In this study, four different feature detection techniques are selected and tracking algorithm is based on the pyramidal implementation of Kanade-Lucas-Tomasi (KLT) feature tracker. Tracking algorithm is run over image sequences acquired with a HDR image sensor and with a high resolution 5 Megapixel image sensor to comparatively assess them. Findings – The authors demonstrate that tracking performance is greatly improved on image sequences acquired with HDR sensor. Number and percentage of finally tracked features are several times higher than what can be achieved with a 5 Megapixel image sensor. Originality/value – The specific interest of this work focuses on the evaluation of tracking persistence of a set of initial detected features over image sequences taken in different scenes. This includes extreme illumination indoor and outdoor environments subject to direct sunlight exposure, backlighting, as well as dim light and dark scenarios.
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Pearce, Sophie, Robert Ljubičić, Salvador Peña-Haro, et al. "An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems." Remote Sensing 12, no. 2 (2020): 232. http://dx.doi.org/10.3390/rs12020232.
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Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.
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Lee, Cho, Lee, and Whang. "Vision-Based Measurement of Heart Rate from Ballistocardiographic Head Movements Using Unsupervised Clustering." Sensors 19, no. 15 (2019): 3263. http://dx.doi.org/10.3390/s19153263.
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Heart rate has been measured comfortably using a camera without the skin-contact by the development of vision-based measurement. Despite the potential of the vision-based measurement, it has still presented limited ability due to the noise of illumination variance and motion artifacts. Remote ballistocardiography (BCG) was used to estimate heart rate from the ballistocardiographic head movements generated by the flow of blood through the carotid arteries. It was robust to illumination variance but still limited in the motion artifacts such as facial expressions and voluntary head motions. Recent studies on remote BCG focus on the improvement of signal extraction by minimizing the motion artifacts. They simply estimated the heart rate from the cardiac signal using peak detection and fast fourier transform (FFT). However, the heart rate estimation based on peak detection and FFT depend on the robust signal estimation. Thus, if the cardiac signal is contaminated with some noise, the heart rate cannot be estimated accurately. This study aimed to develop a novel method to improve heart rate estimation from ballistocardiographic head movements using the unsupervised clustering. First, the ballistocardiographic head movements were measured from facial video by detecting facial points using the good-feature-to-track (GFTT) algorithm and by tracking using the Kanade–Lucas–Tomasi (KLT) tracker. Second, the cardiac signal was extracted from the ballistocardiographic head movements by bandpass filter and principal component analysis (PCA). The relative power density (RPD) was extracted from its power spectrum between 0.75 Hz and 2.5 Hz. Third, the unsupervised clustering was performed to construct a model to estimate the heart rate from the RPD using the dataset consisting of the RPD and the heart rate measured from electrocardiogram (ECG). Finally, the heart rate was estimated from the RPD using the model. The proposed method was verified by comparing it with previous methods using the peak detection and the FFT. As a result, the proposed method estimated a more accurate heart rate than previous methods in three experiments by levels of the motion artifacts consisting of facial expressions and voluntary head motions. The four main contributions are as follows: (1) the unsupervised clustering improved the heart rate estimation by overcoming the motion artifacts (i.e., facial expressions and voluntary head motions); (2) the proposed method was verified by comparing with the previous methods using the peak detection and the FFT; (3) the proposed method can be combined with existing vision-based measurement and can improve their performance; (4) the proposed method was tested by three experiments considering the realistic environment including the motion artifacts, thus, it increases the possibility of the non-contact measurement in daily life.
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Mahadi, Lina Farhana, Nabilah Ibrahim, Shahnoor Shanta, and Hideyuki Hasegawa. "Validation test on 3d heart phantom for mitral valve leaflet tracking." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 717. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp717-725.
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<p><span style="font-family: 'Times New Roman',serif; font-size: 9pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US"><span style="font-family: 'Times New Roman',serif; font-size: 9pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Mitral valve movement is essential to be identified in order to monitor the abnormality of blood flow in right side of heart. The estimation and tracking of mitral valve has seldom been investigated since it required high temporal rate to scan the echocardiography images and it depends on the operator to capture the low-speckle and-noise images. This study presents the validation experiment performed on heart phantom made of t</span><span style="font-family: 'Times New Roman',serif; font-size: 9pt; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">hermoplastic polyurethane (TPU) filament which the objective is to validate the previous </span><span style="font-family: 'Times New Roman',serif; font-size: 9pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">features tracking technique implemented in mitral valve locating in video frames using Kanade-Lucas-Tomasi (KLT) algorithm. The outcome was able to automatically detect the edge of mitral valve and thus in future, it manages to predict the flowing of blood pattern. An in-vitro experiment was conducted which involved a valve phantom scanning in water tank that connected to water pump. It was found in this study that the technique capable to detect and visualize the mitral valve up to 59 frames in 2.36 secondsby tracking the features of minimum eigenvalue within the selected region. It was also produced a good agreement of valve distance between the true value and the measured one, which achieved the minimum of 88% similarity. This yielded the validation of the proposed technique to track and visualize the mitral valves. </span></span></p>
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Ramkumar, E., T. Guna, S. M. Dharshan, and V. S. Ashok Ramanan. "Implementing Facial Recognition by Interfacing MATLAB Along with Arduino." International Journal of Innovative Technology and Exploring Engineering 10, no. 4 (2021): 66–71. http://dx.doi.org/10.35940/ijitee.d8478.0210421.
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Facial recognition has become one of the recent trends in attracting abundant attention within the society of social media network. The face is flat and therefore needs plenty of mathematical computations. Facial knowledge has become one in every of the foremost necessary biometric, we tend to witness it from the day-to-day gadgets like mobile phones. Every transportable electronic device currently being discharged includes a camera embedded in it. Network access management via face recognition not solely makes hackers just about not possible to steal one's "password", however conjointly will increase the user-friendliness in human-computer interaction. For the applications of videophone and conference, the help of face recognition conjointly provides an additional economical secret writing theme. Face detection technologies are employed in an oversized kind of applications like advertising, diversion, video secret writing, digital cameras, CCTV police investigation, and even in military use. Totally different algorithms are used for biometric authentication. The Kanade-Lucas-Tomasi rule makes use of abstraction common intensity transformation to direct the deep explore for the position that shows the simplest match. Another common face detection rule is that the Viola-Jones rule that's the foremost wide used face detection rule. It's employed in most digital cameras and mobile phones to notice faces. It uses cascades to notice edges just like the nose, the ears, etc. Hence, during this paper, we've got planned the Viola-Jones rule because the best one supported our application. The rule is employed within the biometric authentication of individuals and also the pictures are kept during processing. The kept information is employed for recognizing the faces and if the information matches, an impression signal is given to the controller. The MATLAB software is employed to relinquish control signals to the motor, which is employed for gap and shutting the door. The input image is fed by a digital camera and also the image is processed within MATLAB. The output is given to the external controller interfaced with MATLAB. The image process field has several sub-fields, biometric authentication is one in each of them because it gains additional quality for security functions these days. The planned system can be employed in residential buildings, malls, and industrial sectors. Thus, this technique is helpful for homemakers to be safer in their homes.
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Morlier, Joseph, and Guilhem Michon. "Virtual Vibration Measurement Using KLT Motion Tracking Algorithm." Journal of Dynamic Systems, Measurement, and Control 132, no. 1 (2009). http://dx.doi.org/10.1115/1.4000070.
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This paper presents a practical framework and its applications of motion tracking algorithms applied to structural dynamics. Tracking points (“features”) across multiple images are a fundamental operation in many computer vision applications. The aim of this work is to show the capability of computer vision (CV) for estimating the dynamic characteristics of two mechanical systems using a noncontact, markerless, and simultaneous single input multiple output analysis. Kanade–Lucas–Tomasi trackers are used as virtual sensors on mechanical systems’ video from a high speed camera. First we introduce the paradigm of virtual sensors in the field of modal analysis using video processing. To validate our method, a simple experiment is proposed: an Oberst beam test with harmonic excitation (mode 1). Then with the example of a helicopter blade, frequency response functions’ (FRFs) reconstruction is carried out by introducing several signal processing enhancements (filtering and smoothing). The CV experimental results (frequencies and mode shapes) are compared with the classical modal approach and the finite element model (FEM) showing high correlation. The main interest of this method is that displacements are simply measured using only video at fps respecting the Nyquist frequency.
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Cerina, Luca, Luca Iozzia, and Luca Mainardi. "Influence of acquisition frame-rate and video compression techniques on pulse-rate variability estimation from vPPG signal." Biomedical Engineering / Biomedizinische Technik, January 14, 2017. http://dx.doi.org/10.1515/bmt-2016-0234.
Full textAbstract:
AbstractIn this paper, common time- and frequency-domain variability indexes obtained by pulse rate variability (PRV) series extracted from video-photoplethysmographic signal (vPPG) were compared with heart rate variability (HRV) parameters calculated from synchronized ECG signals. The dual focus of this study was to analyze the effect of different video acquisition frame-rates starting from 60 frames-per-second (fps) down to 7.5 fps and different video compression techniques using both lossless and lossy codecs on PRV parameters estimation. Video recordings were acquired through an off-the-shelf GigE Sony XCG-C30C camera on 60 young, healthy subjects (age 23±4 years) in the supine position. A fully automated, signal extraction method based on the Kanade-Lucas-Tomasi (KLT) algorithm for regions of interest (ROI) detection and tracking, in combination with a zero-phase principal component analysis (ZCA) signal separation technique was employed to convert the video frames sequence to a pulsatile signal. The frame-rate degradation was simulated on video recordings by directly sub-sampling the ROI tracking and signal extraction modules, to correctly mimic videos recorded at a lower speed. The compression of the videos was configured to avoid any frame rejection caused by codec quality leveling, FFV1 codec was used for lossless compression and H.264 with variable quality parameter as lossy codec. The results showed that a reduced frame-rate leads to inaccurate tracking of ROIs, increased time-jitter in the signals dynamics and local peak displacements, which degrades the performances in all the PRV parameters. The root mean square of successive differences (RMSSD) and the proportion of successive differences greater than 50 ms (PNN50) indexes in time-domain and the low frequency (LF) and high frequency (HF) power in frequency domain were the parameters which highly degraded with frame-rate reduction. Such a degradation can be partially mitigated by up-sampling the measured signal at a higher frequency (namely 60 Hz). Concerning the video compression, the results showed that compression techniques are suitable for the storage of vPPG recordings, although lossless or intra-frame compression are to be preferred over inter-frame compression methods. FFV1 performances are very close to the uncompressed (UNC) version with less than 45% disk size. H.264 showed a degradation of the PRV estimation directly correlated with the increase of the compression ratio.