DOCSLIB.ORG

Robust Regression Sample Consensus Estimator Model: Performed on Inliers

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Robust Regression Sample Consensus Estimator Model: Performed on Inliers International Journal of Engineering Applied Sciences and Technology, 2018 Vol. 3, Issue 6, ISSN No. 2455-2143, Pages 22-28 Published Online October 2018 in IJEAST (http://www.ijeast.com) ROBUST REGRESSION SAMPLE CONSENSUS ESTIMATOR MODEL: PERFORMED ON INLIERS R.Akila Dr.J.Ravi S.Dickson Assistant Professor Associate Professor Assistant Professor Dept. of Maths & Stat. Dept. of Maths & Stat. Dept. of Maths & Stat. Vivekanandha College for Women Vivekanandha College for Women Vivekanandha College for Woemn Tiruchengode, Namakkal Tiruchengode, Namakkal Tiruchengode, Namakkal Tamilnadu, India Tamilnadu, India Tamilnadu, India Abstract-Robust regression play vital role in many fields Lindquist (1953); and Glass, Peckham, and Sanders (1972). specially image processing. Corner detection is the one of the Countless studies have shown that even when classic important role in image process. Much corner detection is to parametric tests are robust to Type I errors, they are usually develop day to day; such corner detection is based on robust considerably less powerful than their modern robust regression. SAmple Consensus (SAC) has been popular in counterparts [2]. The RANSAC families formulate regression regression problems with samples contaminated with with outliers as a minimization problem. This formulation is outliers. It has been a milestone of many researches on similar with least square method, which minimize sum of robust estimators, but there are a few survey and squared error values. Robust fitting algorithms are mostly performance analysis on them. According to SAC family to based on sampling strategies, hypotheses are generated and analyze the performance evaluation based on being accurate their support is measured in the point cloud. Examples for inliers (Maximum), being fast, and being robust. this strategy are the least-median-of squares (LMS) [3]. Performance evaluation performed online fitting with various The problem of estimation in the presence of high data distributions. These papers analyze the performance of a percentages of outliers. This type of problem occurs in all more SAC family and also find a unique robust regression on stages of automatic calibration, orientation and surface the maximum number of inliers. Total Least Square (TLS) reconstruction, as automatic image matching procedures are model fitting estimation was utilized the present performance error prone. Regression and probability theory are in real data. indispensable for handling uncertainty and estimating parameters under these conditions. There exits powerful Keywords: RANSAC, fitting, robust regression, robust estimation techniques. However, no unique techniques Regression. exists which is applicable in all situations [4]. The paper gives the demonstration on the ability I. INTRODUCTION (performance) of RANSAC family to correctly estimate Robust regression is to develop day to day in computer vision parameters, mean and number of inliers to be taken. Even and other related regression field. The theory of robust most of the robust regression method the percentage of regression deals with deviations from the assumptions of the outliers is far beyond 50% and the outliers hide the true model and is concerned with the construction of regression solution. As an example we take the classical problem of procedures which are still reliable and reasonably efficient in straight line fitting. Experiments have been made with a neighborhood of the model; see the books by Huber (1981), MATLAN a development package. Section 2 details the Hampel, Ronchetti, Rousseeuw, and Stahel (1986), Maronna, introduction of RANSAC families. Section 3 presents the Martin, and Yohai (2006), and Dell’Aquila and Ronchetti performance of RANSAC families with comparisons and the (2006) for an overview. Robust regression are now some 40 paper is concluded in section 4. years old. Indeed, one can consider Tukey (1960), Huber (1964), and Hampel (1968) the fundamental papers which II. ROBUST REGRESSION laid the foundations of modern robust regression. Outliers are sample values that cause surprise in relation to Robust regression control Type I error and also maintain the majority of the sample. adequate regression power. In contrast, claims that classic outliers may be correct, but they should always be checked parametric tests are robust usually only consider a Type I for transcription errors. They can play havoc with standard error, not power. An overview of the robustness argument regression methods, and many robust and resistant methods can be found in Sawilowsky (1990). The origin of the have been developed since 1960 to be less sensitive to robustness argument can be traced back to several key outliers. In computer vision widely using robust estimator articles and books, including Boneau (1960); Box (1953); specially RANSAC families. The following chart is to be 22 International Journal of Engineering Applied Sciences and Technology, 2018 Vol. 3, Issue 6, ISSN No. 2455-2143, Pages 22-28 Published Online October 2018 in IJEAST (http://www.ijeast.com) indicating that the unique model robust regression performed MASAC has two goals. The first is to develop a variety of of RANSAC family. robust methods for the computation of the Fundamental A. RAndom SAmple Consensus (RANSAC) Matrix, the calibration-free representation of camera motion. One of the most Robust attractive techniques is The methods are drawn from the principal categories of Random Sample Consensus (RANSAC) [5]. It randomly robust estimators, viz. case deletion diagnostics, M- chooses a minimal se of observations and evaluates their estimators and random sampling, and the paper develops the likelihood until a good solution is found or a preset number theory required to apply them to non-linear orthogonal of trials is reached. RANSAC is a technique which is best regression problems. Although a considerable amount of suited for estimated problems with a small number of interest has focused on the application of robust estimation in parameters and a large percentage of outliers. It has regularly computer vision, the relative merits of the many individual been applied matching, detection and registration. methods are unknown, leaving the potential practitioner to The idea of the RANSAC algorithm is to repeatedly select a guess at their value. The second goal is therefore to compare random subset S of the data, to determine a solution p=F(S) and judge the methods. Comparative tests are carried out and evaluate it with other data. RANSAC uses three using correspondences generated both synthetically in a parameters: regression controlled fashion and from feature matching in 1. The number n of trials, real imagery. In contrast with previously reported methods 2. The threshold k for determining where a data point agrees the goodness of fit to the synthetic observations is judged not with model in [6], and possibly. in terms of the fit to the observations per se but in terms of fit 3. The threshold t for the required number of inliers. to the ground truth. A variety of error measures are The number of iterations, n, is chosen high e3nough to ensure examined. The experiments allow a regression satisfying and that the probability p (usually set to 0.99) that at least one of quasi-optimal method to be synthesized, which is shown to the sets of random samples does not include an outlier. Let u be stable with up to 50 percent outlier contamination, and represent the probability that any selected data point is an may still be used if there are more than 50 percent outliers. inlier and v= 1-u the probability of observing an outlier. N Performance bounds are established for the method, and a iterations of the minimum number of points denoted m are variety of robust methods to estimate the standard deviation required, where of the error and covariance matrix of the parameters are m n examined. The results of the comparison have broad p= 1-(1-(1-u) ) and nmin(Pmin, u ,n)= . applicability to vision algorithms where the input data are B. Recursive RANSAC (RRANSAC) corrupted not only by noise but also by gross outliers [9]. The ransom sample consensus (RANSAC) algorithm is E. Locally Optimized RANSAC (LORANSAC) frequently used in computer vision to estimate the parameters A new enhancement of RANSAC, the locally optimized of a signal in the presence of noisy and even spurious RANSAC (LO-RANSAC). It has been observed that, to find observations call gross errors. Instead of just one signal, we an optimal solution (with a given probability), the number of desire to estimate the parameters of multiple signals, where at samples drawn in RANSAC is significantly higher than each time step a set of observations of generated from the predicted from the mathematical model. This is due to the underlying signals and gross errors are received. RRANSAC incorrect assumption, that a model with parameters computed develop the recursive RANSAC (RRANSAC) algorithm to from an outlier free sample is consistent with all inliers. The solve the inherent data association problem and recursively assumption rarely holds in practice. The locally optimized estimate the parameters of multiple signals without prior RANSAC makes no new assumptions about the data, on the knowledge of the number of true signals. The performance of contrary it makes the above-mentioned assumption valid by RRANSAC with several existing algorithms, and also applying local optimization to the solution estimated from the demonstrate the capabilities of RRANSAC in an aerial random sample. The performance of the improved RANSAC geolocation problem [7]. The general form of RRANSAC is is evaluated in a number of epipolar geometry and denoted by ri [t]= homographic estimation experiments. Compared with C. N Adjacent Points SAmple Consensus (NAPSAC) standard RANSAC, the speed-up achieved is two to three The most effective algorithms for model parameterization in fold and the quality of the solution (measured by the number the presence of high noise, such as RANSAC, MINPRAN of inliers) is increased by 10-20%. The number of samples and Least Median Squares, use random sampling of data drawn is in good agreement with theoretical predictions [10].
Load more

Recommended publications
  • Pnp-Net: a Hybrid Perspective-N-Point Network
    PnP-Net: A hybrid Perspective-n-Point Network Roy Sheffer1, Ami Wiesel1;2 (1) The Hebrew University of Jerusalem (2) Google Research Abstract. We consider the robust Perspective-n-Point (PnP) problem using a hybrid approach that combines deep learning with model based algorithms. PnP is the problem of estimating the pose of a calibrated camera given a set of 3D points in the world and their corresponding 2D projections in the image. In its more challenging robust version, some of the correspondences may be mismatched and must be efficiently dis- carded. Classical solutions address PnP via iterative robust non-linear least squares method that exploit the problem's geometry but are ei- ther inaccurate or computationally intensive. In contrast, we propose to combine a deep learning initial phase followed by a model-based fine tuning phase. This hybrid approach, denoted by PnP-Net, succeeds in estimating the unknown pose parameters under correspondence errors and noise, with low and fixed computational complexity requirements. We demonstrate its advantages on both synthetic data and real world data. 1 Introduction Camera pose estimation is a fundamental problem that is used in a wide vari- ety of applications such as autonomous driving and augmented reality. The goal of perspective-n-point (PnP) is to estimate the pose parameters of a calibrated arXiv:2003.04626v1 [cs.CV] 10 Mar 2020 camera given the 3D coordinates of a set of objects in the world and their corre- sponding 2D coordinates in an image that was taken (see Fig. 1). Currently, PnP can be efficiently solved under both accurate and low noise conditions [15,17].
    [Show full text]
  • A Multi-Temporal Image Registration Method Based on Edge Matching and Maximum Likelihood Estimation Sample Consensus
    A MULTI-TEMPORAL IMAGE REGISTRATION METHOD BASED ON EDGE MATCHING AND MAXIMUM LIKELIHOOD ESTIMATION SAMPLE CONSENSUS Yan Songa, XiuXiao Yuana, Honggen Xua aSchool of Remote Sensing and Information Engineering School,Wuhan University, 129 Luoyu Road,Wuhan,China 430079– [email protected] Commission III, WG III/1 KEY WORDS: Change Detection, Remote Sensing, Registration, Edge Matching, Maximum Likelihood Sample Consensus ABSTRACT: In the paper, we propose a newly registration method for multi-temporal image registration. Multi-temporal image registration has two difficulties: one is how to design matching strategy to gain enough initial correspondence points. Because the wrong matching correspondence points are unavoidable, we are unable to know how many outliers, so the second difficult of registration is how to calculate true registration parameter from the initial point set correctly. In this paper, we present edge matching to resolve the first difficulty, and creatively introduce maximum likelihood estimation sample consensus to resolve the robustness of registration parameter calculation. The experiment shows, the feature matching we utilized has better performance than traditional normalization correlation coefficient. And the Maximum Likelihood Estimation Sample Conesus is able to solve the true registration parameter robustly. And it can relieve us from defining threshold. In experiment, we select a pair of IKONOS imagery. The feature matching combined with the Maximum Likelihood Estimation Sample Consensus has robust and satisfying registration result. 1. INTRODUCTION majority. So in the second stage, we pay attention on how to calculate registration parameter robustly. These will make us Remote sensing imagery change detection has gain more and robustly calculate registration parameter successfully, even more attention in academic and research society in near decades.
    [Show full text]
  • Density Adaptive Point Set Registration
    Density Adaptive Point Set Registration Felix Jaremo¨ Lawin, Martin Danelljan, Fahad Shahbaz Khan, Per-Erik Forssen,´ Michael Felsberg Computer Vision Laboratory, Department of Electrical Engineering, Linkoping¨ University, Sweden {felix.jaremo-lawin, martin.danelljan, fahad.khan, per-erik.forssen, michael.felsberg}@liu.se Abstract Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the den- sity of 3D points. This fundamental problem is primar- ily caused by changes in the sensor location across point sets. We revisit the foundations of the probabilistic regis- tration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probabil- ity distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimiz- ing the Kullback-Leibler divergence in an Expectation Max- imization based framework. Our density-adaptive regis- tration successfully handles severe density variations com- monly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real- world Lidar datasets. The results demonstrate that our ap- proach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling. Figure 1. Two example Lidar scans (top row), with signifi- cantly varying density of 3D-points. State-of-the-art probabilistic method [6] (middle left) only aligns the regions with high density. This is caused by the emphasis on dense regions, as visualized by 1.
    [Show full text]
  • Accurate Motion Estimation Through Random Sample Aggregated Consensus
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. X, AUGUST 2016 1 Accurate Motion Estimation through Random Sample Aggregated Consensus Martin Rais, Gabriele Facciolo, Enric Meinhardt-Llopis, Jean-Michel Morel, Antoni Buades, and Bartomeu Coll Abstract—We reconsider the classic problem of estimating accurately a 2D transformation from point matches between images containing outliers. RANSAC discriminates outliers by randomly generating minimalistic sampled hypotheses and verifying their consensus over the input data. Its response is based on the single hypothesis that obtained the largest inlier support. In this article we show that the resulting accuracy can be improved by aggregating all generated hypotheses. This yields RANSAAC, a framework that improves systematically over RANSAC and its state-of-the-art variants by statistically aggregating hypotheses. To this end, we introduce a simple strategy that allows to rapidly average 2D transformations, leading to an almost negligible extra computational cost. We give practical applications on projective transforms and homography+distortion models and demonstrate a significant performance gain in both cases. Index Terms—RANSAC, correspondence problem, robust estimation, weighted aggregation. F 1 INTRODUCTION EVERAL applications in computer vision such as im- the parameters of the underlying transformation by regres- S age alignment, panoramic mosaics, 3D reconstruction, sion. RANSAC achieves good accuracy when observations motion tracking, object recognition, among others, rely on are noiseless, even with a significant number of outliers in feature-based approaches. These approaches can be divided the input data. in three distinct steps. First, characteristic image features are Instead of using every sample in the dataset to per- detected on the input images.
    [Show full text]
  • Fast Rigid Motion Segmentation Via Incrementally-Complex Local Models
    Fast Rigid Motion Segmentation via Incrementally-Complex Local Models Fernando Flores-Mangas Allan D. Jepson Department of Computer Science, University of Toronto fmangas,[email protected] Abstract The problem of rigid motion segmentation of trajectory data under orthography has been long solved for non- degenerate motions in the absence of noise. But because real trajectory data often incorporates noise, outliers, mo- tion degeneracies and motion dependencies, recently pro- posed motion segmentation methods resort to non-trivial (a) Original video frames (b) Class-labeled trajectories representations to achieve state of the art segmentation ac- curacies, at the expense of a large computational cost. This paper proposes a method that dramatically reduces this cost (by two or three orders of magnitude) with minimal accu- racy loss (from 98.8% achieved by the state of the art, to 96.2% achieved by our method on the standard Hopkins 155 dataset). Computational efficiency comes from the use of a (c) Model support region (d) Model inliers and control points simple but powerful representation of motion that explicitly incorporates mechanisms to deal with noise, outliers and motion degeneracies. Subsets of motion models with the best balance between prediction accuracy and model com- plexity are chosen from a pool of candidates, which are then used for segmentation. (e) Model residuals (f) Segmentation result Figure 1: Model instantiation and segmentation. a) f th orig- 1. Rigid Motion Segmentation inal frame, Italian Grand Prix c 2012 Formula 1. b) Class- Rigid motion segmentation (MS) consists on separating labeled, trajectory data W (red, green, blue and black cor- regions, features, or trajectories from a video sequence into respond to chassis, helmet, background and outlier classes f spatio-temporally coherent subsets that correspond to in- respectively).
    [Show full text]
  • Automatic Panoramic Image Stitching Using Invariant Features
    Automatic Panoramic Image Stitching using Invariant Features Matthew Brown and David G. Lowe {mbrown|lowe}@cs.ubc.ca Department of Computer Science, University of British Columbia, Vancouver, Canada. Abstract can provide very accurate registration, but they require a close initialisation. Feature based registration does not re- This paper concerns the problem of fully automated quire initialisation, but traditional feature matching meth- panoramic image stitching. Though the 1D problem (single ods (e.g., correlation of image patches around Harris cor- axis of rotation) is well studied, 2D or multi-row stitching is ners [Har92, ST94]) lack the invariance properties needed more difficult. Previous approaches have used human input to enable reliable matching of arbitrary panoramic image or restrictions on the image sequence in order to establish sequences. matching images. In this work, we formulate stitching as a In this paper we describe an invariant feature based ap- multi-image matching problem, and use invariant local fea- proach to fully automatic panoramic image stitching. This tures to find matches between all of the images. Because of has several advantages over previous approaches. Firstly, this our method is insensitive to the ordering, orientation, our use of invariant features enables reliable matching of scale and illumination of the input images. It is also insen- panoramic image sequences despite rotation, zoom and illu- sitive to noise images that are not part of a panorama, and mination change in the input images. Secondly, by viewing can recognise multiple panoramas in an unordered image image stitching as a multi-image matching problem, we can dataset. In addition to providing more detail, this paper ex- automatically discover the matching relationships between tends our previous work in the area [BL03] by introducing the images, and recognise panoramas in unordered datasets.
    [Show full text]
  • Tracking Algorithms for Tpcs Using Consensus-Based Robust Estimators
    Tracking Algorithms for TPCs using Consensus-Based Robust Estimators J. C. Zamora∗, G. F. Fortino Instituto de Fisica, Universidade de Sao Paulo, Sao Paulo 05508-090, Brazil Abstract A tracking algorithm1 based on consensus-robust estimators was implemented for the analysis of experiments with time-projection chambers. In this work, few algorithms beyond RANSAC were successfully tested using experimental data taken with the AT-TPC, ACTAR and TexAT detectors. The present track- ing algorithm has a better inlier-outlier detection than the simple sequential RANSAC routine. Modifications in the random sampling and clustering were included to improve the tracking efficiency. Very good results were obtained in all the test cases, in particular for fitting short tracks in the detection limit. Keywords: Time Projection Chambers, Active Target, Tracking Algorithm, RANSAC, clustering 1. Introduction Time Projection Chambers (TPC) are among the most efficient devices exist- ing for charged-particle tracking. They enable measurements of energy loss and position hits in the 3-dimensional space which are employed for particle identifi- cation and momentum reconstruction. TPCs have been successfully used during arXiv:2011.07138v1 [physics.ins-det] 13 Nov 2020 the last three decades as tracking systems in many particle-physics experiments, e.g., TOPAZ [1], STAR [2] or ALICE [3]. ∗Corresponding author Email address: [email protected] (J. C. Zamora ) 1Code available at https://github.com/jczamorac/Tracking_RANSAC.git Preprint submitted to NIM A November 17, 2020 In the past few years, the operation of TPCs in Active Target (AT) mode have gained great attention for investigation in nuclear physics. The AT prin- ciple brings the advantage to use a gas as detector material and target medium simultaneously.
    [Show full text]
  • Two-View Geometry Estimation by Random Sample and Consensus
    Two-View Geometry Estimation by Random Sample and Consensus A Dissertation Presented to the Faculty of the Electrical Engineering of the Czech Technical University in Prague in Partial Fulfillment of the Re- quirements for the Ph.D. Degree in Study Programme No. P2612 - Electrotech- nics and Informatics, branch No. 3902V035 - Artificial Intelligence and Bio- cybernetics, by Ondrejˇ Chum July 1, 2005 Thesis Advisor Dr. Ing. Jirˇ´ı Matas Center for Machine Perception Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Karlovo nam´ estˇ ´ı 13, 121 35 Prague 2, Czech Republic fax: +420 224 357 385, phone: +420 224 357 465 http://cmp.felk.cvut.cz Abstract The problem of model parameters estimation from data with a presence of outlier measurements often arises in computer vision and methods of robust estimation have to be used. The RANSAC algorithm introduced by Fishler and Bolles in 1981 is the a widely used robust estimator in the field of computer vision. The algorithm is capable of providing good estimates from data contaminated by large (even significantly more than 50%) fraction of outliers. RANSAC is an optimization method that uses a data-driven random sampling of the parameter space to find the extremum of the cost function. Samples of data define points of the parameter space in which the cost function is evaluated and model parameters with the best score are output. This thesis provides a detailed analysis of RANSAC, which is recast as time-constrained op- timization – a solution that is optimal with certain confidence is sought in the shortest possible time.
    [Show full text]
  • Comparative the Performance of Robust Linear Regression Methods 817
    Applied Mathematical Sciences, Vol. 13, 2019, no. 17, 815 - 822 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ams.2019.97106 Comparative the Performance of Robust Linear Regression Methods and its Application Unchalee Tonggumneada and Nikorn Saengngamb a Department of Mathematics and Computer Science, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi, Phatum Thani 12110, Thailand b Department of Technical Education , Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Phatum Thani 12110, Thailand This article is distributed under the Creative Commons by-nc-nd Attribution License. Copyright © 2019 Hikari Ltd. Abstract This research aims to apply the robust estimation method with actual data. M- estimation, quantile regression and Random Sample Consensus (RANSAC) are considered. The result show that, when no outlier, the parameter estimate using M- estimation, quantile regression and random sample consensus (RANSAC) produce a very similary. When the rate of outliers increasing is also conducted the parameter estimate using RANSAC method more different from M- estimation and quantile regression. The RANSAC method is more robust to outlier than the M- estimation and quantile regression method. Which can be clearly seen that, The RANSAC methods are more robust against the outlier than M- estimation and quantile regression ,because in parameter estimation do not include the outlier into the model, only inliers are used to run the model. Keywords: robust regression, M- estimation, quantile regression, Random Sample Consensus. 1 Introduction Regression analysis is the common and useful statistical tool which can be used to quantify the relationship between a response variable (y) and explanatory variables (x), Ordinary Least Square (OLS) is A common used for parameter estimate.
    [Show full text]
  • Automated Fast Initial Guess in Digital Image Correlation
    An International Journal for Experimental Mechanics Automated Fast Initial Guess in Digital Image Correlation † ‡ Z. Wang*,M.Vo, H. Kieu* and T. Pan *Department of Mechanical Engineering, The Catholic University of America, Washington, DC 20064, USA † Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA ‡ Department of Civil Engineering, The Catholic University of America, Washington, DC 20064, USA ABSTRACT: A challenging task that has hampered the fully automatic processing of the digital image correlation (DIC) technique is the initial guess when large deformation and rotation are present. In this paper, a robust scheme combining the concepts of a scale-invariant feature transform (SIFT) algorithm and an improved random sample consensus (iRANSAC) algorithm is employed to conduct an automated fast initial guess for the DIC technique. The scale-invariant feature transform algorithm can detect a certain number of matching points from two images even though the corresponding deformation and rotation are large or the images have periodic and identical patterns. After removing the wrong matches with the improved random sample consensus algorithm, the three pairs of closest and non-collinear matching points serve for the purpose of initial guess calculation. The validity of the technique is demonstrated by both computer simulation and real experiment. KEY WORDS: digital image correlation, improved random sample consensus, initial guess, scale-invariant feature transform Introduction analysis is to minimise the coefficient C in Equation (1) to find the best matching. For a representative Digital image correlation (DIC) technique is one of the pixel (x ,y ) to be analysed in the reference image, a most widely used methods for shape, motion and 0 0 square pattern of N =(2M +1)×(2M +1)pixelswithits deformation measurements [1].
    [Show full text]
  • Recursive RANSAC: Multiple Signal Estimation with Outliers
    Recursive RANSAC: Multiple Signal Estimation with Outliers Peter C. Niedfeldt, Randal W. Beard Brigham Young University, Provo, UT USA ([email protected], [email protected]). Abstract: The random sample consensus (RANSAC) algorithm is frequently used in computer vision to estimate the parameters of a signal in the presence of noisy and even spurious observations called gross errors. Instead of just one signal, we desire to estimate the parameters of multiple signals, where at each time step a set of observations of generated from the underlying signals and gross errors are received. In this paper, we develop the recursive RANSAC (RRANSAC) algorithm to solve the inherent data association problem and recursively estimate the parameters of multiple signals without prior knowledge of the number of true signals. We compare the performance of RRANSAC with several existing algorithms, and also demonstrate the capabilities of RRANSAC in an aerial geolocation problem. 1. INTRODUCTION they require solving a nonlinear minimization problem. See Rousseeuw and Leroy [1987], Huber [1996]. The random sample consensus (RANSAC) algorithm de- Regression analysis describes a collection of techniques veloped by Fischler and Bolles [1981] is a novel approach used to determine the relationships between inputs to a to regression analysis. Many previous algorithms formed system and the outputs. A common scenario is where a model using all or most of the available data set, and the system model is assumed to be known while the then removed observations inconsistent with the model specific parameters of the underlying signals are to be before producing a final estimate. Alternatively, RANSAC estimated. One well known algorithm is least-squares (LS), approaches the problem by forming numerous model es- which minimizes the mean-squared error between a set timates using the minimum number of required points of observations and the estimated model.
    [Show full text]
  • Feature Matching with Improved SIRB Using RANSAC by Meet Palod, Manas Joshi, Amber Jain, Viroang Rawat & Prof
    Global Journal of Computer Science and Technology: F Graphics & Vision Volume 21 Issue 1 Version 1.0 Year 2021 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Online ISSN: 0975-4172 & Print ISSN: 0975-4350 Feature Matching with Improved SIRB using RANSAC By Meet Palod, Manas Joshi, Amber Jain, Viroang Rawat & Prof. K.K. Sharma Abstract- In this paper, we suggest to improve the SIRB (SIFT (Scale-Invariant Feature Transform) and ORB (Oriented FAST and Rotated BRIEF)) algorithm by incorporating RANSAC to enhance the matching performance. We use multi-scale space to extract the features which are impervious to scale, rotation, and affine variations. Then the SIFT algorithm generates feature points and passes the interest points to the ORB algorithm. The ORB algorithm generates an ORB descriptor where Hamming distance matches the feature points. We propose to use RANSAC (Random Sample Consensus) to cut down on both the inliers in the form of noise and outliers drastically, to cut down on the computational time taken by the algorithm. This post- processing step removes redundant key points and noises. This computationally effective and accurate algorithm can also be used in handheld devices where their limited GPU acceleration is not able to compensate for the computationally expensive algorithms like SIFT and SURF. Experimental results advocate that the proposed algorithm achieves good matching, improves efficiency, and makes the feature point matching more accurate with scale in-variance taken into consideration. GJCST-F Classification: G.4 FeatureMatchingwithImprovedSIRBusingRANSAC Strictly as per the compliance and regulations of: © 2021. Meet Palod, Manas Joshi, Amber Jain, Viroang Rawat & Prof.
    [Show full text]