Random sample consensus

Fischler, Martin A.; Bolles, Robert C.

doi:10.1145/358669.358692

Cited by 19,679 publications

(1,120 citation statements)

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“…Nishida and Kurita (2008) proposed a hybrid algorithm (RANSAC-SVM) which couples random sampling, consensus approach (Fischler and Bolles 1981) and a simple evolutionary technique to retrieve T 's. In their approach, several refined training sets of a small size are randomly drawn at first.…”

Section: Evolutionary Methodsmentioning

confidence: 99%

Selecting training sets for support vector machines: a review

2018

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Support vector machines (SVMs) are a supervised classifier successfully applied in a plethora of real-life applications. However, they suffer from the important shortcomings of their high time and memory training complexities, which depend on the training set size. This issue is especially challenging nowadays, since the amount of data generated every second becomes tremendously large in many domains. This review provides an extensive survey on existing methods for selecting SVM training data from large datasets. We divide the state-of-the-art techniques into several categories. They help understand the underlying ideas behind these algorithms, which may be useful in designing new methods to deal with this important problem. The review is complemented with the discussion on the future research pathways which can make SVMs easier to exploit in practice.

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Section: Evolutionary Methodsmentioning

confidence: 99%

Selecting training sets for support vector machines: a review

2018

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“…For comparative analysis, precision and putative match ratio [17] were used. Precision of matching has a practical significance, since all returned pairs often undergo further processing, e.g., using RANSAC [30]. Putative match ratio (PMR) counts how many detected keypoints were returned as matched.…”

Section: Discussionmentioning

confidence: 99%

“…It can be seen, the obtained mean values are promising, and the results are characterised by small standard deviations. The best result (F C (x best ) = 2119.1) was obtained for the set of P = 12 patches with the following multipliers S p = [7,10,16,19,23,28,30,34,44,56,69,89]. This gives the descriptor with 1296 bits.…”

Section: Optimisationmentioning

confidence: 99%

An optimisation approach to the design of a fast, compact and distinctive binary descriptor

Oszust

2016

SIViP

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The desired local feature descriptor should be distinctive, compact and fast to compute and match. Therefore, many computer vision applications use binary keypoint descriptors instead of floating-point, rich techniques. In this paper, an optimisation approach to the design of a binary descriptor is proposed, in which the detected keypoint is described using several, scale-dependent patches. Each such patch is divided into disjoint blocks of pixels, and then, binary tests between blocks' intensities, as well as their gradients, are used to obtain the binary string. Since the number of image patches and their relative sizes influence the descriptor creation pipeline, a simulated annealing algorithm is used to determine them, optimising recall and precision of keypoint matching. The simulated annealing is also used for dimensionality reduction in long binary strings. The proposed approach is extensively evaluated and compared with SIFT, SURF and BRIEF on public benchmarks. Obtained results show that the binary descriptor created using the resulted pipeline is faster to compute and yields comparable or better performance than the state-of-the-art descriptors under different image transformations.

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“…Among the algorithms that can be used for tracking objects with simple geometries, two are widely known, namely the Hough transform [46] and the RANSAC paradigm [47]. In the prototype application presented here, the RANSAC approach is used for shape detection.…”

Section: Methodology Of Kinect-based Trackingmentioning

confidence: 99%

Recent Developments in Game-Based Virtual Reality Educational Laboratories Using the Microsoft Kinect

Zhang

Chang

et al. 2018

Int. J. Emerg. Technol. Learn.

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Abstract-Virtual Reality (VR) is a well-known concept and has been proven to be beneficial in various areas. However, several disadvantages inherent in VR prevent its broad deployment in the educational arena. These limitations include non-realistic representation, lack of customizability and flexibility, financial feasibility, physical and psychological discomforts of the users, simulator sickness, etc. In this paper, an innovative method that uses the Microsoft Kinect as an essential component for developing game-based VR educational laboratories is presented. This technique addresses three different aspects. First, it represents an efficient method for creating the VE using the Kinect as a measuring tool. Second, the Kinect is employed as a substitute DAQ system for acquiring range data and tracking the motion of objects of interest. At last, the Kinect serves as a novel human-computer interface for tracking the users' entire body motion and recognizing their voices. Using the method described here, three major aspects of educational VR development can be accomplished with an inexpensive and commercially available Kinect.

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Random sample consensus

Abstract: Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information.

Cited by 19,679 publications

References 1 publication

Selecting training sets for support vector machines: a review

Selecting training sets for support vector machines: a review

An optimisation approach to the design of a fast, compact and distinctive binary descriptor

Recent Developments in Game-Based Virtual Reality Educational Laboratories Using the Microsoft Kinect

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