Random sample consensus (RANSAC) is a successful algorithm in model fitting applications. It is vital to have strong exploration phase when there are an enormous amount of outliers within the dataset. Achieving a proper model is guaranteed by pure exploration strategy of RANSAC. However, finding the optimum result requires exploitation. GASAC is an evolutionary paradigm to add exploitation capability to the algorithm. Although GASAC improves the results of RANSAC, it has a fixed strategy for balancing between exploration and exploitation. In this paper, a new paradigm is proposed based on genetic algorithm with an adaptive strategy. We utilize an adaptive genetic operator to select high fitness individuals as parents and mutate low fitness ones. In the mutation phase, a training method is used to gradually learn which gene is the best replacement for the mutated gene. The proposed method adaptively balance between exploration and exploitation by learning about genes. During the final Iterations, the algorithm draws on this information to improve the final results. The proposed method is extensively evaluated on two set of experiments. In all tests, our method outperformed the other methods in terms of both the number of inliers found and the speed of the algorithm.
Bail-out test @cite_1 is another paradigm to terminate the verification early which is similar to R-RANSAC @cite_9 . More specifically, it considers the number of inliers @math within @math which is a subset of all points @math follows a hyper-geometric distribution @math ,where @math is number of inliers for @math , @math total number of points in @math , and @math size of @math . It considers @math inlier count for the best current model and if the probability of @math is below a given threshold, the verification can continue.
@cite_9 @cite_1
2129363926 2151843422
Abstract Many computer vision algorithms include a robust estimation step where model parameters are computed from a data set containing a significant proportion of outliers. The ransac algorithm is possibly the most widely used robust estimator in the field of computer vision. In the paper we show that under a broad range of conditions, ransac efficiency is significantly improved if its hypothesis evaluation step is randomized . A new randomized (hypothesis evaluation) version of the ransac algorithm, r-ransac , is introduced. Computational savings are achieved by typically evaluating only a fraction of data points for models contaminated with outliers. The idea is implemented in a two-step evaluation procedure. A mathematically tractable class of statistical preverification test of samples is introduced. For this class of preverification test we derive an approximate relation for the optimal setting of its single parameter. The proposed pre-test is evaluated on both synthetic data and real-world problems and a significant increase in speed is shown. The random sample consensus (RANSAC) algorithm, along with its many cousins such as MSAC and MLESAC, has become a standard choice for robust estimation in many computer vision problems. Recently, a raft of modifications to the basic RANSAC algorithm have been proposed aimed at improving its efficiency. Many of these optimizations work by reducing the number of hypotheses that need to be evaluated. This paper proposes a complementary strategy that aims to reduce the average amount of time spent computing the consensus score for each hypothesis. A simple statistical test is proposed that permits the scoring process be terminated early, potentially yielding large computational savings. The proposed test is simple to implement, imposes negligible computational overhead, and is effective for any given size of data set. The approach is evaluated by estimation of the fundamental matrix for a large number of image pairs and is shown to offer a significant reduction in computational cost compared to recently proposed RANSAC modifications.