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首页> 外文会议>Intelligent robots and computer vision XXXI: algorithms and techniques >Towards Automatic Identification of Mismatched Image Pairs through Loop Constraints
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Towards Automatic Identification of Mismatched Image Pairs through Loop Constraints

机译：通过循环约束实现对不匹配图像对的自动识别

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摘要

Obtaining image sequences has become easier and easier thanks to the rapid progress on optical sensors and robotic platforms. Processing of image sequences (e.g., mapping, 3D reconstruction, Simultaneous Localisation and Mapping (SLAM)) usually requires 2D image registration. Recently, image registration is accomplished by detecting salient points in two images and next matching their descriptors. To eliminate outliers and to compute a planar transformation (homography) between the coordinate frames of images, robust methods (such as Random Sample Consensus (RANSAC) and Least Median of Squares (LMedS)) are employed. However, image registration pipeline can sometimes provide sufficient number of inliers within the error bounds even when images do not overlap. Such mismatches occur especially when the scene has repetitive texture and shows structural similarity. In this study, we present a method to identify the mismatches using closed-loop (cycle) constraints. The method exploits the fact that images forming a cycle should have identity mapping when all the homographies between images in the cycle multiplied. Cycles appear when the camera revisits an area that was imaged before, which is a common practice especially for mapping purposes. Our proposal extracts several cycles to obtain error statistics for each matched image pair. Then, it searches for image pairs that have extreme error histogram comparing to the other pairs. We present experimental results with artificially added mismatched image pairs on real underwater image sequences.
机译：由于光学传感器和机器人平台的快速发展，获取图像序列变得越来越容易。图像序列的处理（例如，映射，3D重构，同时定位和映射（SLAM））通常需要2D图像配准。最近，通过检测两个图像中的显着点，然后匹配它们的描述符来完成图像配准。为了消除离群值并计算图像坐标帧之间的平面变换（单应性），采用了鲁棒的方法（例如随机样本共识（RANSAC）和最小二乘平方（LMedS））。但是，即使图像不重叠，图像配准管线有时也可以在误差范围内提供足够数量的内线。当场景具有重复的纹理并显示出结构相似性时，尤其会发生这种不匹配。在这项研究中，我们提出了一种使用闭环（循环）约束条件识别失配的方法。该方法利用以下事实：当循环中图像之间的所有单应性相乘时，形成一个循环的图像应具有身份映射。当照相机重新访问之前成像的区域时，会出现循环，这是一种常见做法，尤其是对于制图而言。我们的建议提取了几个周期，以获取每个匹配图像对的错误统计信息。然后，它搜索与其他图像对相比具有极端误差直方图的图像对。我们提出了在真实的水下图像序列上人工添加不匹配图像对的实验结果。

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