Rainfall measurement at high quality and spatiotemporal resolution is essential for urban hydrological modeling and effective stormwater management. However, traditional rainfall measurement methods face limitations r...
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Mechanical tests are used to determine a material's behaviour under certain conditions of stress. computervision Systems have been used since the 1980s in mechanical tests, with techniques evolving in order to ac...
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ISBN:
(纸本)9789811398063;9789811398056
Mechanical tests are used to determine a material's behaviour under certain conditions of stress. computervision Systems have been used since the 1980s in mechanical tests, with techniques evolving in order to accommodate modern needs. This paper has the purpose of reviewing research that combines computervision Systems and Mechanical Tests, analysing the main steps for better results from such application.
Respiration movement and respiration rate have been used to monitor breathing status for diagnosis and fitness purposes. From a given video sequence of a person facing a camera, this system here automatically detects ...
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ISBN:
(纸本)9783030306489;9783030306472
Respiration movement and respiration rate have been used to monitor breathing status for diagnosis and fitness purposes. From a given video sequence of a person facing a camera, this system here automatically detects and tracks a region of interest (ROI) on the chest, using the Kanade-Lucas-Tomasi method, after applying the Viola-Jones algorithm and identifying the Harris-Stephens features for tracking the ROI across frames. The displacement in the vertical direction of the ROI (frame by frame) was used to estimate the respiration movement, after low-pass filtering at a proper cutoff frequency. Finally, the respiration rate is estimated from the respiration movement signal by a root MUSIC-based estimator. For the 12 video files provided, we obtained respiration movement signals with correlation indexes respect to the corresponding 'references' of above 90%, in most cases, and respiration rate signals with normalized root mean-squared errors with respect to the inst_freq around 10%. A global ranking index of around 0.8 was consistently obtained. computer vision algorithms are well-suited for estimating respiration movement and respiration rate signals from frontal video sequences.
Magnetic Resonance Imaging (MRI) was introduced as an alternative to destructive methods for determining the quality parameters of meat products, since MRI is a non-destructive, non-ionizing, and innocuous technique. ...
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Magnetic Resonance Imaging (MRI) was introduced as an alternative to destructive methods for determining the quality parameters of meat products, since MRI is a non-destructive, non-ionizing, and innocuous technique. The use of MRI is linked to the use of computer vision algorithms and data mining techniques. Thus, the success of MRI could not be understood without referring to the implementation of powerful algorithms to handle all data generated. In this study, a new texture algorithm was developed in order to obtain new features based on the radial distribution of the textures from MRI. This new algorithm is called Radial Texture Algorithm (RTA) and it was compared with four classical texture features approaches. The results obtained allow describing the semantic context of the texture features for this new algorithm and the relationship among the features from this algorithm and the classical approaches. Besides, the results obtained by means of these computer vision algorithms were correlated with the results obtained by means of physico-chemical and sensory parameters of the Iberian loins. All of computer vision algorithms achieved correlation coefficients higher than 0.500, noting that RTA reached the highest correlation coefficients in almost all cases. These high correlation coefficients confirm the proposed algorithm as an alternative to the other computational texture features approaches in order to compute the physico-chemical and sensory parameters of meat products in an accurate, fast, non-destructive and efficient way.
Outdoor monitoring systems are known to exhibit better performance under normal weather conditions, while it lacks effectiveness under inclement conditions. Often video footage captured by the camera under rainy condi...
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Outdoor monitoring systems are known to exhibit better performance under normal weather conditions, while it lacks effectiveness under inclement conditions. Often video footage captured by the camera under rainy conditions comprises several visual distortions. It eventually leads to flaws when handled with succeeding computer vision algorithms, namely the object identification and tracking. Additionally, eliminating such unpleasant rainy effects is essential prior to the processing of video footage by suitable algorithms. The present work attempts to formulate a new low-rank tensor recovery based deraining algorithm that enables to remove the rain streaks from video footage. The proposed method detects the rain streaks by adopting optical flow estimation along with the brightness features inherent with the rain streaks. A unified framework comprised of tensor singular value decomposition (t-SVD) based weighted nuclear norm minimisation and tensor total variation (TTV) regularisation effectively removes rain streaks and recovers the original rain-free data from the available rainy data. The use of t-SVD enforces the concept of low rankness and also exploits the temporal redundancy among the video frames. Furthermore, TTV regularisation facilitates to promote the temporal continuity for discriminating most of the natural image contents from sparse rain streaks by preserving piece-wise smoothness of video frames. Comprehensive experimental findings based on real and synthetic data with dynamic background show that the rain streaks are more efficaciously eliminated by adopting the proposed method without much loss in the information.
Well-functioning autonomous robot solutions heavily rely on the availability of fast and correct navigation solutions. The presence of dynamic/moving objects in the environment poses a challenge because the risk of co...
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ISBN:
(纸本)9783030177980;9783030177973
Well-functioning autonomous robot solutions heavily rely on the availability of fast and correct navigation solutions. The presence of dynamic/moving objects in the environment poses a challenge because the risk of collision increases. In order to derive the best and most foreseeing re-routing solutions for cases where the planned route suddenly involves the risk of colliding with a moving object, the robot's navigation system must be provided with information about such objects' positions and velocities. Based on sensor readings providing either 2-dimensional polar range scan or 3-dimensional point cloud data streams, we present an efficient and effective method which detects objects in the environment and derives their positions and velocities. The method has been implemented, based on the Robot Operating System (ROS), and we also present an evaluation of it. It was found that the method results in good accuracy in the position and velocity calculations, a small memory footprint and low CPU usage requirements.
This paper considers the problem of controlling a nonholonomic mobile ground robot equipped with an onboard camera characterized by a bounded field-of-view, tasked with detecting and following a potentially moving hum...
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computervision is a field of computer science that includes methods for acquiring, processing, and analyzing images. Image registration is one of the methods used in the computervision field to transform different s...
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computervision is a field of computer science that includes methods for acquiring, processing, and analyzing images. Image registration is one of the methods used in the computervision field to transform different sets of data into one coordinate system to align images. Registration is important in order to be able to compare or integrate the data obtained from multiple measurements. Rigid image alignment is a type of image registration technique used to align two two-dimensional images into a common coordinate system based on two transformation parameters, translation and rotation. Before any comparative studies can be performed on two images acquired at different times, it is crucial to align the two images for correct processing later on. In our research study, we are analyzing the accuracy of registering images using two rigid image alignment algorithms, namely the Principal Axes algorithm and the Fast Fourier Transform (FFT) based phase correlation algorithm. The software for registering images using these two methods is written in MATLAB R2011a. We also compared our results with alignments achieved for the same images using an existing Statistical Parametric Mapping (SPM8) package for registration. Image registration algorithms have been used in many applications and accordingly, algorithms are adopted to suit a particular application. Images used for registration can be derived from different capturing devices like camera, scanner, satellite sensors, etc. Our registration software is based on work with images acquired from a Magnetic Resonance Imaging (MRI) scanner and especially for images taken of a quality assurance (QA) phantom. A QA phantom is used to test the quality of images acquired by measuring different QA parameters on images acquired over a period of time. Images acquired from the MRI scanner at different times are geometrically transformed by rotation and translation. In practice, the maximum angle by which the phantom will get rotated at diff
This study explores the use of several non-parametric statistical tests for evaluating the performances of computer vision algorithms, specifically corner detectors, as a more reliable alternative to the graphical app...
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This study explores the use of several non-parametric statistical tests for evaluating the performances of computer vision algorithms, specifically corner detectors, as a more reliable alternative to the graphical approaches that have been commonly employed to date. Using synthetic images carrying corners of different internal angles and orientations and a carefully designed testing framework, a ranking of the performances of corner detectors was established. It was found that Harris & Stephens and SUSAN out-performed more modern detectors. These are one of the few examples where evaluation of vision operators independent of the application has predicted performance in a real-world problem. A similar exercise on real images of the same patterns produced similar results and the findings of a real-world application that uses corners to identify signage were also consistent. Together, all of the tests considered essentially perform pairwise comparisons of performance, so when many algorithms are involved it is important to take account of the potential for type I statistical errors. Several approaches were evaluated and none were found to affect the conclusions.
A new cyclic redundancy check (CRC) concatenation with polar codes based on partial protection is proposed, where only the crucial bits chosen from the total information bits are protected with the CRC. The index set ...
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A new cyclic redundancy check (CRC) concatenation with polar codes based on partial protection is proposed, where only the crucial bits chosen from the total information bits are protected with the CRC. The index set of crucial bits is determined by the row weight of generator matrix of polar codes and the indexes with minimum row weight make up this crucial set. Using this new concatenation, the number of polar codewords with minimum Hamming weight can be efficiently decreased. Simulation results show that the performance of the proposed partial protection scheme owns a lower error floor than that of the classical blind protection.
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