A novel framework for making dominant point detection methods non-parametric

2012 ◽  
Vol 30 (11) ◽  
pp. 843-859 ◽  
Author(s):  
Dilip K. Prasad ◽  
Maylor K.H. Leung ◽  
Chai Quek ◽  
Siu-Yeung Cho
Keyword(s):  
Detection Methods ◽  
Dominant Point ◽  
Point Detection ◽  
Non Parametric

scholarly journals Planar Contour Primitive Recognition of Thin Sheet Part Dimensional Inspection System

2011 ◽  
Vol 2-3 ◽  
pp. 463-468
Author(s):  
Ji Gang Wu ◽  
Kuan Fang He ◽  
Bin Qin
Keyword(s):  
Hough Transform ◽  
Thin Sheet ◽  
Detection Methods ◽  
Inspection System ◽  
Threshold Method ◽  
Dimensional Inspection ◽  
Straight Line ◽  
Dominant Point ◽  
Contour Point ◽  
Point Detection

According to the two indices of inspection accuracy and inspection speed, a planar contour primitive recognition method of thin sheet part dimensional inspection system based on curvature and HOUGH transform is proposed. A contour point classification algorithm based on neighborhood values is developed, and a curvature threshold method is selected to filter the contour points, and a projection height method is selected to distinguish the property of the primitive and classify the contour points, and the straight line primitive and arc primitive segmentation and merging algorithms are constructed respectively by HOUGH transform. The inspection accuracy and inspection speed of the proposed method are compared and analyzed by contrast experiments between the proposed method and four dominant point detection methods such as Chung & Tsai method and so on. The dominant point detection ability of the proposed method is tested by a simulation planar contour which includes all kinds of dominant points. The experimental results indicate that the proposed method can recognize primitives exactly, the inspection speed is fast and the universality is good.

Non-parametric dominant point detection

1991 ◽  
Vol 24 (9) ◽  
pp. 849-862 ◽  
Author(s):  
Nirwan Ansari ◽  
Kuo-wei Huang
Keyword(s):  
Dominant Point ◽  
Point Detection ◽  
Non Parametric

scholarly journals Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

2016 ◽  
Vol 49 (3) ◽  
pp. 988-1005 ◽  
Author(s):  
Jedelyn Cabrieto ◽  
Francis Tuerlinckx ◽  
Peter Kuppens ◽  
Mariel Grassmann ◽  
Eva Ceulemans
Keyword(s):  
Time Series ◽  
Change Point ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Detection Methods ◽  
Parametric Change ◽  
Point Detection ◽  
Non Parametric

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

scholarly journals Taxonomy of Object Detection methods: A Survey

2022 ◽  
Vol 10 (1) ◽  
pp. 20-25
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Real Life ◽  
Object Motion ◽  
Detection Methods ◽  
Illumination Variation ◽  
Human Machine Interaction ◽  
Research Areas ◽  
To Come ◽  
Point Detection

Object detection (OD) within a video is one of the relevant and critical research areas in the computer vision field. Due to the widespread of Artificial Intelligence, the basic principle in real life nowadays and its exponential growth predicted in the epochs to come, it will transmute the public. Object Detection has been extensively implemented in several areas, including human-machine Interaction, autonomous vehicles, security with video surveillance, and various fields that will be mentioned further. However, this augmentation of OD tackles different challenges such as occlusion, illumination variation, object motion, without ignoring the real-time aspect that can be quite problematic. This paper also includes some methods of application to take into account these issues. These techniques are divided into five subcategories: Point Detection, segmentation, supervised classifier, optical flow, a background modeling. This survey decorticates various methods and techniques used in object detection, as well as application domains and the problems faced. Our study discusses the cruciality of deep learning algorithms and their efficiency on future improvement in object detection topics within video sequences.

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