AN INTEGRATED RANSAC AND GRAPH BASED MISMATCH ELIMINATION APPROACH FOR WIDE-BASELINE IMAGE MATCHING

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W5, 2015 International Conference on Sensors & Models in Remote Sensing & Photogrammetry, 23–25 Nov 2015, Kish Island, Iran AN INTEGRATED RANSAC AND GRAPH BASED MISMATCH ELIMINATION APPROACH FOR WIDE-BASELINE IMAGE MATCHING M. Hasheminasaba, *, H. Ebadia, A. Sedaghata a, Faculty of Geodesy and Geomatics Eng, K.N.Toosi University of Technology, Tehran, Iran. - , , Commission VI, WG VI/4 KEY WORDS: Image Matching, Outlier Detection, Epipolar Line, Graph Matching, Wide-baseline Matching, ABSTRACT: In this paper we propose an integrated approach in order to increase the precision of feature point matching. Many different algorithms have been developed as to optimizing the short-baseline image matching while because of illumination differences and viewpoints changes, wide-baseline image matching is so difficult to handle. Fortunately, the recent developments in the automatic extraction of local invariant features make wide-baseline image matching possible. The matching algorithms which are based on local feature similarity principle, using feature descriptor as to establish correspondence between feature point sets. To date, the most remarkable descriptor is the scale-invariant feature transform (SIFT) descriptor , which is invariant to image rotation and scale, and it remains robust across a substantial range of affine distortion, presence of noise, and changes in illumination. The epipolar constraint based on RANSAC (random sample consensus) method is a conventional model for mismatch elimination, particularly in computer vision. Because only the distance from the epipolar line is considered, there are a few false matches in the selected matching results based on epipolar geometry and RANSAC. Aguilariu et al. proposed Graph Transformation Matching (GTM) algorithm to remove outliers which has some difficulties when the mismatched points surrounded by the same local neighbor structure. In this study to overcome these limitations, which mentioned above, a new three step matching scheme is presented where the SIFT algorithm is used to obtain initial corresponding point sets. In the second step, in order to reduce the outliers, RANSAC algorithm is applied. Finally, to remove the remained mismatches, based on the adjacent K-NN graph, the GTM is implemented. Four different close range image datasets with changes in viewpoint are utilized to evaluate the performance of the proposed method and the experimental results indicate its robustness and capability. 1. INTRODUCTION Image matching, which is a process to detect corresponding points in the overlapping area of a stereo pair, has still remained as an important and challenging issue in digital photogrammetry, remote sensing and computer vision. Therefore it is not a surprising fact that many image matching algorithms have been developed during last years. These algorithms can be categorized into two area-based and featurebased methods. Area-based approaches use pixel intensities and matching is done by measuring the correlation between windows of predefined size. The main disadvantage of these methods is that they are sensitive to the intensity changes introduced by noise, different viewpoints or by different sensor types (Jogelkar and Gedam, 2012). Instead of pixel values, feature-based methods attempt to extract local shapes and structures, such as edges and corners, and match them using feature descriptors. The most well-known feature-based approach is the Scale Invariant Feature Transform (SIFT) method (Lowe, 1999 and 2004), which it is invariant to image rotation and scale, and it remains robust across a substantial range of affine distortion, presence of noise and changes in illumination. Even the best algorithms for image matching make some mistakes and output some mismatches (Adam et al., 2001). Due to this, different methods for outlier removal have been described recently (Fishier and Boles, 1981; Aguilar et al., 2009; Liu et al., 2012; Zhao et al., 2013; Wu et al., 2015). Among various mismatch elimination methods, the epipolar constraint based on RANSAC (RANdom SAmpling Consensus) is the most widely used robust estimator, particularly in computer vision. But the algorithm does not output good results when there are more than 50% outliers. Another simple and popular approach for outlier detection is the Graph Transformation Matching (GTM). The algorithm depends on finding a consensus nearest-neighbor graph from initial matches. In the other word, GTM uses local structures to find the inliers, so not enough global information is considered in this method. According to the methods mentioned above, generally, there are a few false matches in the selected matching results based on these approaches. Furthermore, image matching is much more difficult for widebase line images due to the large geometric transformation and illumination changes. However, because of constructing a strong geometry, wide-baseline image matching yields more accurate results than its short-baseline counterpart. Motivated by these facts, it seems that there is a need to search for finding solutions to achieve more reliable corresponding points in the wide-baseline image matching. In addition some applications, such as image registration, are very sensitive to outliers and sometimes existence of one mismatch in the corresponding set yields bad results. Therefore, to tackle the difficult task of widebaseline image matching and obtain an accurate pair of matched points, an improvement is proposed in this study which it incorporates previous approaches. In this paper, we use advantages of two well-known RANSAC and GTM approaches to eliminate outliers and obtain a high accuracy set of matched points. To do so, a three step matching scheme is presented. First, the SIFT algorithm is used to obtain initial corresponding point sets. In the second step, in order to reduce the outliers, RANSAC algorithm is applied. At the end, to remove the remained mismatches, based on local adjacent relation, the GTM algorithm is implemented. Indeed, both spatial relations and local structure are considered. This contribution has been peer-reviewed. doi:10.5194/isprsarchives-XL-1-W5-297-2015 297 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W5, 2015 International Conference on Sensors & Models in Remote Sensing & Photogrammetry, 23–25 Nov 2015, Kish Island, Iran 2.3 Graph Transformation Matching 2. INTEGRATED APPROACH In order to obtain reliable corresponding point sets, three steps are considered here. Each step would be mentioned in the following. 2.1 Scale Invariant Feature Transform A scale invariant interest point detector and a descriptor based on the gradient distribution in the detected local region are incorporated in the SIFT algorithm. The interest points are detected based on loc (...truncated)