NDLI: Outlier percentage estimation for shape- and parameter-independent outlier detection

Content Provider	IET Digital Library
Author	Goh, Michael Joon Seng Chiew, Yeong Shiong Foo, Ji Jinn
Abstract	Accurate and robust three-dimensional reconstruction of objects allows for applications in many aspects of modern life. Yet, it typically suffers from outliers and noise which often need to be post-processed. Although many algorithms are able to effectively remove the outliers, most require a certain amount of manual tuning of the parameter(s) or to have a parameter(s) set based on the rule of thumb. New machine learning and artificial intelligence-based methods have also been introduced but may require vast parallel computing resources as well as training data. In the present study, a novel combinatory-distance-based method capable of high accuracy outlier detection named as the sorted distance divergence point (SDDP) is introduced. Results show that SDDP is able to achieve an average accuracy of 98% in outlier detection. Moreover, the introduced distance function and outlier percentage allow clear labelling of inliers and outliers cloud points. Therefore, SDDP presents an attractive enhancement to existing methods; namely, the manual parameter(s) tuning may not be necessary. The adaptability and utility of SDDP is further demonstrated by incorporating SDDP with current methods, to produce a high accuracy outlier detector. When tested with 17 objects with 20–50% outliers, attain F1 and F2 scores averaging 0.960 and 0.968, respectively.
Starting Page	3414
Ending Page	3421
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 14, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0334
Journal	IET Image Processing
Publisher Date	2020-08-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Artificial Intelligence-based Method Computer Vision And Image Processing Technique Data Handling Technique Data Mining Feature Extraction High Accuracy Outlier Detection Image Reconstruction Introduced Distance Function Knowledge Engineering Technique Learning in AI Manual Parameter Novel Combinatory-distance-based Method Optical, Image And Video Signal Processing Outlier Percentage Estimation Parameter-independent Outlier Detection Robust Three-dimensional Object Reconstruction SDDP Sorted Distance Divergence Point Vast Parallel Computing ReSource
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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