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Proceedings ArticleDOI: 10.1109/ICACCI.2013.6637380

MC-RANSAC: A Pre-processing Model for RANSAC using Monte Carlo method implemented on a GPU

21 Oct 2013-pp 1380-1383
Abstract: RANSAC is a repeating hypothesize-and-verify procedure for parameter estimation and filtering of noise or outlier data. In the traditional approach, this algorithm is evaluated without any prior information on the set of data points which leads to an increase in the number of iterations and compute time. In this paper, we present a GPU based RANSAC algorithm with pre-processing of the assumed sample set of hypothetical inliers by Monte Carlo method. Based on our implementation and results using the Point Cloud Library and NVIDIA CUDA framework for data intensive tasks we obtain significant improvement in the performance of plane segmentation algorithm over the randomly sampled subset of hypothetical inliers. The final consensus set is formed with less number of iterations using our pre-processing model. We can conclude that a pre-processed sample set of hypothetical inliers results in a faster determination of the consensus set consisting of maximum inliers.

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Topics: RANSAC (64%), Outlier (52%), Data point (51%) ...read more
Citations
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Journal ArticleDOI: 10.1007/S11042-018-6475-6
Abstract: The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field, there have been several attempts to improve its performance. In this paper, after giving a short review on existing methods, a robust and efficient method that detects the gross outliers to increase the inlier to outlier ratio in a reduced set of corresponding image points is proposed. It has a new hypothesis and verification scheme which utilizes spatial relations between extracted corresponding points in two images. It can also be considered as a preprocessing step for RANSAC to improve the accuracy as well as the runtime of RANSAC in estimating the parameters of a geometric model (such as fundamental and homography matrices). Obviously, like almost all previous works for enhancing RANSAC's runtime, the proposed method does not use heavy and compilicated processes. Performance analysis is performed on a variety of standard challenging datasets for estimating the homography and fundamental matrix (as an applicable case used in the literature, especially in the state-of-the-art methods). The performance is also compared quantitatively to RANSAC, PROSAC, and SCRAMSAC robust estimators to demonstrate its superiority. Experimental results show that the proposed method removes about 50% of outliers in most cases and hence extremely reduces the required runtime of RANSAC, while improving its accuracy.

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Topics: RANSAC (73%), Homography (computer vision) (54%), Fundamental matrix (computer vision) (53%) ...read more

6 Citations


Open accessPosted Content
Abstract: We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera.

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Topics: Pose (62%), Iterative closest point (58%), Point cloud (53%) ...read more

5 Citations


Patent
Ezekiel Kruglick1Institutions (1)
17 Jul 2014-
Abstract: Technologies may be generally described to provide viewer optimized compression of a model. In some examples, a computing device may receive a request to compress a master model for a viewer. The computing device may determine shape primitives of the master model through use of a shape primitive identification technique such as a random sample consensus (RANSAC) technique. The identified or determined shape primitives may be subtracted from the master model to determine residues of the master model. A processed model may be generated from the residues of the master model and the shape primitives. Visible subsets, visible based on a view cone of the viewer, of the residues and the shape primitives may be selected from the processed model, from which a compressed model may be generated. The processed model may then be used to generate a second view without redetermining the shape primitives.

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5 Citations


Open accessBook ChapterDOI: 10.1007/978-3-319-33747-0_4
01 Jan 2016-
Abstract: We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to the synthetic models. The proposed framework uses a “pure” point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components.

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Topics: 3D pose estimation (69%), Iterative closest point (64%), Pose (60%) ...read more

2 Citations


Open accessDissertation
01 Jan 2016-
Abstract: Master of Science in Statistics and Computer Science.University of KwaZulu-Natal, Durban 2016.

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Topics: Sample (statistics) (59%)

1 Citations


References
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Journal ArticleDOI: 10.1145/358669.358692
Martin A. Fischler1, Robert C. Bolles1Institutions (1)
Abstract: A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing

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Topics: RANSAC (69%), Smoothing (55%), Image processing (54%) ...read more

20,503 Citations


Open accessJournal ArticleDOI: 10.1007/S00138-005-0006-Y
David Nister1Institutions (1)
13 Oct 2003-
Abstract: A system capable of performing robust live ego-motion estimation for perspective cameras is presented. The system is powered by random sample consensus with preemptive scoring of the motion hypotheses. A general statement of the problem of efficient preemptive scoring is given. Then a theoretical investigation of preemptive scoring under a simple inlier-outlier model is performed. A practical preemption scheme is proposed and it is shown that the preemption is powerful enough to enable robust live structure and motion estimation.

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Topics: Motion estimation (56%), RANSAC (53%), Structure from motion (53%)

513 Citations


Book ChapterDOI: 10.1007/978-3-540-88688-4_37
12 Oct 2008-
Abstract: The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. There have been a number of recent efforts that aim to increase the efficiency of the standard RANSAC algorithm. Relatively fewer efforts, however, have been directed towards formulating RANSAC in a manner that is suitable for real-time implementation. The contributions of this work are two-fold: First, we provide a comparative analysis of the state-of-the-art RANSAC algorithms and categorize the various approaches. Second, we develop a powerful new framework for real-time robust estimation. The technique we develop is capable of efficiently adapting to the constraints presented by a fixed time budget, while at the same time providing accurate estimation over a wide range of inlier ratios. The method shows significant improvements in accuracy and speed over existing techniques.

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Topics: RANSAC (69%)

473 Citations


Open accessProceedings ArticleDOI: 10.1109/ICCV.2003.1238341
Nister1Institutions (1)
01 Jan 2003-
Abstract: A system capable of performing robust live ego-motion estimation for perspective cameras is presented. The system is powered by random sample consensus with preemptive scoring of the motion hypotheses. A general statement of the problem of efficient preemptive scoring is given. Then a theoretical investigation of preemptive scoring under a simple inlier-outlier model is performed. A practical preemption scheme is proposed and it is shown that the preemption is powerful enough to enable robust live structure and motion estimation.

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Topics: Motion estimation (56%), RANSAC (53%)

431 Citations


Proceedings ArticleDOI: 10.5244/C.23.81
Sunglok Choi1, Taemin Kim, Wonpil Yu1Institutions (1)
01 Jan 2009-
Abstract: RANSAC (Random Sample Consensus) has been popular in regression problem with samples contaminated with outliers. It has been a milestone of many researches on robust estimators, but there are a few survey and performance analysis on them. This paper categorizes them on their objectives: being accurate, being fast, and being robust. Performance evaluation performed on line fitting with various data distribution. Planar homography estimation was utilized to present performance in real data.

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Topics: RANSAC (64%), Line fitting (51%), Outlier (51%)

403 Citations


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