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Geometric assumption and verification with RANSAC has become a crucial step for corresponding to local features due to its wide applications in biomedical feature analysis and vision computing. However, conventional RANSAC is very time-consuming due to redundant sampling times, especially dealing with the case of numerous matching pairs. This paper presents a novel preprocessing model to explore a reduced set with reliable correspondences from initial matching dataset. Both geometric model generation and verification are carried out on this reduced set, which leads to considerable speedups. Afterwards, this paper proposes a reliable RANSAC framework using preprocessing model, which was implemented and verified using Harris and SIFT features, respectively. Compared with traditional RANSAC, experimental results show that our method is more efficient.

Feature matching is a basic problem in computer vision. Corresponding to local features has become the dominant paradigm for structure from motion [

Consequently, numerous extensions for RANSAC have been proposed to speed up different RANSAC stages, such as SCRANSAC [

This paper proposes a fast and simple RANSAC framework based on a preprocessing model. It can result in a reduced correspondence set with a higher inlier percentage, on which RANSAC will converge faster to a correct solution. This model can successfully acquire a subset

The rest of this paper is organized as follows. In Section

RANSAC has become the most popular tool to solve the geometric estimation problems in datasets containing outliers, which was first proposed by Fischler and Bolles in 1981 [

RANSAC operates in a hypothesized-and-verified framework. The basic assumption of RANSAC algorithm is that the data consists of “inliers”, that is, the data whose distribution can be explained by some set of model parameters. And “outliers” are the data which do not fit the model. The outliers probably result from errors of measurement, unreasonable assumptions, or incorrect calculations. RANSAC randomly samples a minimal subset

The iteration ensures a bounded runtime as well as a guarantee on the quality of the estimated result. As mentioned above, there are some limits in RANSAC processing. Time-consuming is the most urgent problem, especially when the initial inliers rate is low. Hence, this paper proposes a novel RANSAC framework with a preprocessing model to improve it.

The main effort of this preprocessing model is to explore a reduced set with reliable correspondences from initial matching dataset and estimate the geometric model. This model can be divided into the following two steps.

When verifying hypotheses in RANSAC, the corresponding pairs are categorized into inliers and outliers. Since the number of samples taken by RANSAC depends on the inlier ratio, it is desirable to reduce the fraction of outliers in the matching set. Selecting a reduced set with higher inlier ratio is the first step of this preprocessing model. Our approach is motivated by the observation that extracting and exploring a subset

Zhang et al. [

RANSAC is a stochastic optimization method, whose efficiency can be improved by Monte Carlo sampling method [

Monte Carlo sampling method.

Bucketing technique.

Therefore, the fundamental matrix

An improved RANSAC algorithm with preprocessing model is proposed in this section. This model can be easily integrated into the RANSAC procedure. The main idea is to suppose knowing some matching pairs being inliers with high probability, which are put into subset

matching set

2.1 select the minimal sample

2.2 compute solution(s) for

matrix

In Algorithm

In the following, this paper experimentally evaluates the improved RANSAC and compares it with a classical approach. As we know, Harris and SIFT features are most commonly used in correspondence [

In the experiments based on Harris feature, this paper chooses match-by-correlation algorithm to obtain the initial matching set

Figure

Comparison between our proposed RANSAC and traditional RANSAC.

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Currently, SIFT is a popular and reliable algorithmto detect and describe local features in images. However, the initial matching by SIFT still exists in outliers. In this section, this paper uses the proposed approach to reject the outliers for the initial corresponding based on SIFT. The object is a model of scalp, which is usually used in biomedical modeling. The results are shown in Figure

Results of the proposed method and classical RANSAC for correspondences based on SIFT.

Initial matching by SIFT

Result of our RANSAC approach

Result of classical RANSAC

From the comparison results in Figure

In conclusion, this paper argues that our method can be generally used in outlier rejection, no matter which kind of feature is used. Moreover, the preprocessing model is adaptive for the condition of low-matching rate.

As is shown above, the proposed RANSAC succeeds in reducing the iteration times. Our framework’s success owes to the preprocessing model, which is effective for selecting the reliable corresponding pairs. Figure

The number of iterations for RANSAC in set

In this paper, a novel framework was presented for improving RANSAC’s efficiency in geometric matching applications. The improved RANSAC is based on Preprocessing Model that lets RANSAC operate on a reduced set of more confident correspondences with a higher inlier ratio. Compared with classic screening model, this model is simpler and efficient in implement, especially in the case of low-initial matching rate. The experimental results show that our approach can reduce much more iteration times especially when the initial matching rate is lower than 60%. In addition, the experiments were operated on two current features: Harris and SIFT. Therefore, it can be concluded that the proposed RANSAC framework is applicable.

In conclusion, this paper makes the following contributions: (1) this paper proposed a RANSAC framework which does not only rely on appearance but takes into account the quality of neighboring correspondences in the image space; (2) preprocessing model was introduced for selecting reduced set with higher inlier ratio, which improves runtime.

This work was supported by State Scholarship Fund from China Scholarship Council (no. 2011833105), Research Project of Department of Education of Zhejiang Province (no. Y201018160), Natural Science Foundation of Zhejiang Province (nos. Y1110649 and 61103140), and Commonweal Project of Science and Technology Department of Zhejiang Province (nos. 2012C33073 and 2010C33095), China.