An Efficient Copy-Move Detection Algorithm Based on Superpixel Segmentation and Harris Key-Points

2017 ◽  
pp. 61-73 ◽  
Author(s):  
Yong Liu ◽  
Hong-Xia Wang ◽  
Han-Zhou Wu ◽  
Yi Chen
Keyword(s):  
Detection Algorithm ◽  
Superpixel Segmentation ◽  
Key Points

Detection Algorithm Research of Face Pose Based on Stereoscopic Vision and Biological Characteristics

2012 ◽  
Vol 460 ◽  
pp. 30-34
Author(s):  
Peng Xu ◽  
Yuan Men Zhou
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Stereoscopic Vision ◽  
Accuracy Rate ◽  
Face Images ◽  
Face Features ◽  
Key Points ◽  
Left And Right ◽  
The Impact

The paper introduces a kind of detection method of face pose based on stereoscopic vision technology, approximately divides head’s deflexion into three plane rotations. By calculating the deflexion angle of three directions, you can determine the face’s pose. This method obtains face images by the left and right video channels, first analyses the similarity of double channels’ images to obtain three-dimensional information of face features’ key points. Then calculates three deflexion angles according to these information, therefore can correspondingly adjust and deform the original image to get standard frontal face image, and provides correction image for the latter face recognition. By this method the impact of pose change to face recognition can be reduced obviously in the earlier stage, so the system’s overall recognition accuracy rate is enhanced effectively.

Extraction building based on adaptive detection algorithm of key points

2011 ◽  
Author(s):  
Wei Shao ◽  
Youjing Zhang ◽  
Lingling Wang ◽  
Liwen Chen ◽  
Zhiqi Qian
Keyword(s):  
Detection Algorithm ◽  
Adaptive Detection ◽  
Key Points

scholarly journals Improved visual inertial odometry based on deep learning

2021 ◽  
Vol 2078 (1) ◽  
pp. 012016
Author(s):  
Jiabin Wang ◽  
Faqin Gao
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Time Performance ◽  
Perspective Change ◽  
Key Points ◽  
Strong Robustness ◽  
Point Detection ◽  
Trained Network

Abstract The traditional visual inertial odometry according to the manually designed rules extracts key points. However, the manually designed extraction rules are easy to be affected and have poor robustness in the scene of illumination and perspective change, resulting in the decline of positioning accuracy. Deep learning methods show strong robustness in key point extraction. In order to improve the positioning accuracy of visual inertial odometer in the scene of illumination and perspective change, deep learning is introduced into the visual inertial odometer system for key point detection. The encoder part of MagicPoint network is improved by depthwise separable convolution, and then the network is trained by self-supervised method; A visual inertial odometer system based on deep learning is compose by using the trained network to replace the traditional key points detection algorithm on the basis of VINS. The key point detection network is tested on HPatches dataset, and the odometer positioning effect is evaluated on EUROC dataset. The results show that the improved visual inertial odometer based on deep learning can reduce the positioning error by more than 5% without affecting the real-time performance.

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

Autism and Neurodiversity: Addressing Concerns and Offering Implications for the School-Based Speech-Language Pathologist

2020 ◽  
pp. 1-7
Author(s):  
Laura S. DeThorne ◽  
Kelly Searsmith
Keyword(s):  
Service Provision ◽  
Individualized Education Program ◽  
Institutional Constraints ◽  
Individualized Education ◽  
Existing Structures ◽  
Eligibility Determination ◽  
Speech Language Pathologist ◽  
School Based ◽  
Key Points ◽  
C Center

Purpose The purpose of this article is to address some common concerns associated with the neurodiversity paradigm and to offer related implications for service provision to school-age autistic students. In particular, we highlight the need to (a) view first-person autistic perspectives as an integral component of evidence-based practice, (b) use the individualized education plan as a means to actively address environmental contributions to communicative competence, and (c) center intervention around respect for autistic sociality and self-expression. We support these points with cross-disciplinary scholarship and writings from autistic individuals. Conclusions We recognize that school-based speech-language pathologists are bound by institutional constraints, such as eligibility determination and Individualized Education Program processes that are not inherently consistent with the neurodiversity paradigm. Consequently, we offer examples for implementing the neurodiversity paradigm while working within these existing structures. In sum, this article addresses key points of tension related to the neurodiversity paradigm in a way that we hope will directly translate into improved service provision for autistic students. Supplemental Material https://doi.org/10.23641/asha.13345727

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