Application of feature matching trajectory detection algorithm for particle streak velocimetry

2020 ◽  
Vol 23 (6) ◽  
pp. 971-979
Author(s):  
Yusaku Tsukamoto ◽  
Shumpei Funatani
Keyword(s):  
Feature Matching ◽  
Detection Algorithm ◽  
Particle Streak Velocimetry

scholarly journals Natural Feature Detection on Mobile Phones with 3D FAST

2010 ◽  
Vol 9 (4) ◽  
pp. 29-34 ◽  
Author(s):  
Achim Weimert ◽  
Xueting Tan ◽  
Xubo Yang
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Hessian Matrix ◽  
Detection Algorithm ◽  
Scale Invariant ◽  
Natural Feature ◽  
Detection Approach ◽  
Scale Levels ◽  
Speed Up ◽  
Low Performance

In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calculated for different scale levels on which a modified9 FAST algorithm operates to obtain the values of the corner response function. We combine the detection with an adapted version of SURF (Speed Up Robust Features) descriptors, providing a system with all means to implement feature matching and object detection. Experimental evaluation on a Symbian OS device using a standard image set and comparison with SURF using Hessian matrix-based detector is included in this paper, showing improvements in speed (compared to SURF) and robustness (compared to FAST)

Normalized Moment of Inertia-Based Detection Algorithm for Copy-Paste Image Tampering

2019 ◽  
Vol 33 (06) ◽  
pp. 1954023
Author(s):  
Chunbo Ma ◽  
Xuewei Lv ◽  
Jun Ao
Keyword(s):  
Gaussian Noise ◽  
Feature Matching ◽  
Detection Algorithm ◽  
Moment Of Inertia ◽  
Distance Threshold ◽  
Feature Extracting ◽  
Hash Algorithm ◽  
The Difference ◽  
Feature Dimension ◽  
Speed And Accuracy

Copy-paste tampering is one of the most common image content attacking methods. Considering the low accuracy and high feature dimension of the existing algorithms, a normalized moment of inertia method is proposed in this paper to overcome these defects. In the phase of feature extracting, the algorithm first transforms the tested image using wavelet, and then selects the similar subbands to build overlapped blocks, finally uses Perceived Hash Algorithm (PHA) to make binarization processing for the subblocks and carries out the normalize moment of inertia of the subblocks which satisfy the coarse matching conditions between adjacent two lines after performing dictionary sorting. In feature matching phase, the algorithm first counts the similar subblocks whose shift is above the distance threshold, and then obtains the main shift vectors with specific frequencies, finally performs feature matching according to the difference of the normalized moment of inertia in the neighborhood. Experiment results illustrate that the proposed algorithm with lower feature dimension can efficiently improve the matching speed and accuracy. Furthermore, it has better robustness for some post-processing operations, such as compression, Gaussian Blur, adding Gaussian noise, etc.

A Novel Human Pose Detection from Videos Algorithm Based on Motion Capture Data

2010 ◽  
Vol 20-23 ◽  
pp. 833-837
Author(s):  
Ou Yang Yi
Keyword(s):  
Human Body ◽  
Feature Matching ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Pose Recognition ◽  
Pose Detection ◽  
Spatio Temporal ◽  
Human Pose ◽  
Static Background ◽  
Change Monitoring

This video image of static background frame and deduction, the pixel, pixels for sports change monitoring and static pixels. By combining the feature of deformation of human body positioning movement of template, the human body pose detection algorithm put in spatio-temporal detection to human pose recognition using feature matching, accelerate matching speed probability. This method in the testing result is superior to other pose recognition algorithm, and also has the ability to quickly identify.

scholarly journals A NOVEL VISION-BASED FINGER-WRITING CHARACTER RECOGNITION SYSTEM

2007 ◽  
Vol 16 (03) ◽  
pp. 421-436 ◽  
Author(s):  
LIANWEN JIN ◽  
DUANDUAN YANG ◽  
LI-XIN ZHEN ◽  
JIAN-CHENG HUANG
Keyword(s):  
Character Recognition ◽  
Feature Matching ◽  
Recognition System ◽  
Detection Algorithm ◽  
Virtual Character ◽  
English Alphabet ◽  
Cluttered Background ◽  
Digital Pen ◽  
Human Finger ◽  
Additional Device

A novel video-based finger writing virtual character recognition system (FVCRS) is described in this paper. With this FVCR system, one can enter characters into a computer by just using the movement of fingertip, without any additional device such as a keyboard or a digital pen. This provides a new wireless character-inputting method. A simple but effective background model is built for segmenting human-finger movements from cluttered background. A robust fingertip detection algorithm based on feature matching is given, and recognition of the finger-writing character is by a DTW-based classifier. Experiments show that the FVCRS can successfully recognize finger-writing uppercase and lowercase English alphabet with the accuracy of 95.3% and 98.7%, respectively.

Multi-Vehicle Tracking Based Traffic Illegal Parking Detection

2012 ◽  
Vol 532-533 ◽  
pp. 540-545
Author(s):  
Jian Wang ◽  
Feng Chen
Keyword(s):  
Object Tracking ◽  
Feature Matching ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Vehicle Tracking ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Moving Vehicles ◽  
Long Time ◽  
Vehicle Trajectory

Vehicle detection and tracking are the foundations of traffic violation punishment. The current methods have more parameters to be adjusted and can not adapt to the complex traffic scenes. For the purpose of practice application, we propose an illegal parking detection algorithm based on vehicle tracking theory. In order to filter the dynamic interferences such as vehicles parking for a long time, moving vehicles overlapping, and the vehicle losing momentarily from the video, a Gaussian mixture model is combined with the correlations among adjacent pixels to update background and eliminate noises. In allusion to complex traffic scenes, a fast multi-object tracking algorithm is presented to track occluded vehicles effectively. This method predicts vehicle trajectory by Kalman filter and achieves real-time object tracking state judgment by using the feature matching matrix. The experimental results show that this method can track multiple moving vehicles effectively and has high detection precision for illegal parking incidents.

scholarly journals A feature matching and fusion-based positive obstacle detection algorithm for field autonomous land vehicles

2017 ◽  
Vol 14 (2) ◽  
pp. 172988141769251 ◽  
Author(s):  
Tao Wu ◽  
Huihai Cui ◽  
Yan Li ◽  
Wei Wang ◽  
Daxue Lui ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Feature Matching ◽  
Computing Time ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Land Vehicle ◽  
Land Vehicles ◽  
Field Robotics ◽  
Data Density ◽  
Scan Line

Positive obstacles will cause damage to field robotics during traveling in field. Field autonomous land vehicle is a typical field robotic. This article presents a feature matching and fusion-based algorithm to detect obstacles using LiDARs for field autonomous land vehicles. There are three main contributions: (1) A novel setup method of compact LiDAR is introduced. This method improved the LiDAR data density and reduced the blind region of the LiDAR sensor. (2) A mathematical model is deduced under this new setup method. The ideal scan line is generated by using the deduced mathematical model. (3) Based on the proposed mathematical model, a feature matching and fusion (FMAF)-based algorithm is presented in this article, which is employed to detect obstacles. Experimental results show that the performance of the proposed algorithm is robust and stable, and the computing time is reduced by an order of two magnitudes by comparing with other exited algorithms. This algorithm has been perfectly applied to our autonomous land vehicle, which has won the champion in the challenge of Chinese “Overcome Danger 2014” ground unmanned vehicle.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

Word-Finding Support From Mobile Technology Benefits a Woman With Aphasia

2015 ◽  
Vol 24 (1) ◽  
pp. 26-39 ◽  
Author(s):  
Yvonne Gillette
Keyword(s):  
Mobile Technology ◽  
Clinical Education ◽  
Augmentative And Alternative Communication ◽  
Feature Matching ◽  
Mobile Technologies ◽  
Alternative Communication ◽  
Alternative Intervention ◽  
Communication Assessment ◽  
Word Finding ◽  
Intervention Costs

Mobile technology provides a solution for individuals who require augmentative and alternative intervention. Principles of augmentative and alternative communication assessment and intervention, such as feature matching and the participation model, developed with dedicated speech-generating devices can be applied to these generic mobile technologies with success. This article presents a clinical review of an adult with aphasia who reached her goals for greater communicative participation through mobile technology. Details presented include device selection, sequence of intervention, and funding issues related to device purchase and intervention costs. Issues related to graduate student clinical education are addressed. The purpose of the article is to encourage clinicians to consider mobile technology when intervening with an individual diagnosed with mild receptive and moderate expressive aphasia featuring word-finding difficulties.

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