Outliers Detection by Ransac Algorithm in the Transformation of 2d Coordinate Frames

Janicka, Joanna; Rapiński, Jacek

doi:10.1590/s1982-21702014000300035

Cited by 8 publications

(9 citation statements)

References 11 publications

(7 reference statements)

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“…The method is based on the assumption that an observation set is a mixture of 'good' observations (inliers) and 'bad' observations (outliers probably stem from gross errors). The general idea behind the RANSAC paradigm is to find the model that is defined by the inliers, and it can be summarized in the following points [51][52][53]:…”

Section: Outlier Detection Data Cleaning Methods and Similar Approachesmentioning

confidence: 99%

Robust procedures in processing measurements in geodesy and surveying: a review

Duchnowski,

Wyszkowska

2024

Meas. Sci. Technol.

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Outlying observations are undesirable but possible elements of geodetic measurements. In such a context, the primary and trivial solution is to repeat “suspected” observations. The question arises: what if the measurements cannot be performed again, or if one cannot flag outliers easily and efficiently? In such a case, one should process data by applying methods that consider the possible occurrence of outlying observations. Historically, except for some previous attempts, the statistical approach to robust estimation originates in the 60s of the 20th century and refers to the pioneer papers of Huber, Tukey, Hampel, Hodges, and Lehmann. Also, the statistical procedures known as data snooping (data dredging) were developed at a similar time. It took not a long time before robust procedures were implemented for processing geodetic observations or adjustment of observation systems. The first works of Baarda and Pope encouraged other scientists or surveyors to elaborate robust procedures adapted for geodetic or surveying problems, which resulted in their rapid development in the last two decades of the 20th century. The question for the 21st century is whether robustness is still an important issue relating to modern measurement technologies and numerical data processing. One should realize that modern geodetic techniques do not decrease the probability of outlier occurrence. Considering measurement systems that yield big data, it is almost certain that outliers occur somewhere. The paper reviews different approaches to robust processing of geodetic observations, from the data snooping methods, random sampling, M-estimation, R-estimation, and Msplit estimation to robust estimation of the variance coefficient. Such a variety reflects different natures, origins, or properties of outliers and the apparent fact that there is no best and most efficient and universal robust approach. The methods presented are indeed the basis for future solutions based on, e.g., machine learning.

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Section: Outlier Detection Data Cleaning Methods and Similar Approachesmentioning

confidence: 99%

Robust procedures in processing measurements in geodesy and surveying: a review

Duchnowski,

Wyszkowska

2024

Meas. Sci. Technol.

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“…Since these generated patches may be partially or completely overlapped, a fine matching is used to overcome this issue. RANSAC approach [20] is used to coherently remove the outliers. e remaining inliers set of matched points are then utilized to generate the required transformation matrix to align the sensed features to their correspondence in the reference set.…”

Section: Feature Matchingmentioning

confidence: 99%

“…Two matching methods are used for this purpose to detect the most powerful descriptors. e first matching method is a prematching step using Euclidean distance [19], and the second one is a fine matching step using Random Sample Consensus (RANSAC) algorithm [20]. en, a transformation matrix based on the most powerful key points is generated for mapping one image to the other image.…”

Section: Introductionmentioning

confidence: 99%

SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network

Elwan¹,

Amein

Mousa

et al. 2022

Security and Communication Networks

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Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.

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“…If it is above 40-50%, these methods do not provide the desired results. The advantage of RANSAC method is that correct results can be obtained even in case when the contents of outliers is above 50% of the entire data set [9], [14]. The number of iterations is determined by the following formula:…”

Section: Ransac Algorithmmentioning

confidence: 99%

Applying RANSAC Algorithm for Fitting Scanning Strips from Airborne Laser Scanning

Błaszczak-Bąk¹,

Janicka²,

Sobieraj-Żłobińska³

2016

Civil and Environmental Engineering Reports

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During the development of the data acquired by airborne laser scanning the important issue is the fitting and georeferencing of ALS point clouds by means of the tie surfaces and the reference planes. The process of scanning strips adjustment is based on mutual integration of point clouds (scanning strips) and their adaptation to the reference planes. In simultaneous adjustment all strips are combined into one geometrically coherent block, to which the coordinates are given. In the process of determining discrepancies between scanning strips it is important to determine the correct size of the shifts (offsets). Authors propose to do this by using RANSAC algorithm.

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Outliers Detection by Ransac Algorithm in the Transformation of 2d Coordinate Frames

Cited by 8 publications

References 11 publications

Robust procedures in processing measurements in geodesy and surveying: a review

Robust procedures in processing measurements in geodesy and surveying: a review

SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network

Applying RANSAC Algorithm for Fitting Scanning Strips from Airborne Laser Scanning

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