基于非负矩阵欠逼近和剪枝技术的多结构几何模型拟合  

Robust Geometric Model Fitting Based on Nonnegative Matrix Underapproximation with Pruning Techniques for Multi-Structure Data

作　　者：林舒源 赖桃桃 严严[1] 张立明[3] 王菡子[1] LIN Shu-Yuan;LAI Tao-Tao;YAN Yan;ZHANG Li-Ming;WANG Han-Zi(Fujian Key Laboratory of Sensing and Computing for Smart City,School of Informatics,Xiamen University,Xiamen,Fujian 361005;Fujian Key Laboratory of Information Processing and Intelligent Control,School of Computer and Control Engineering,Minjiang University,Fuzhou 350108;Faculty of Science and Technology,University of Macao,Taipa,Macao 999078)

机构地区：[1]厦门大学信息学院福建省智慧城市感知与计算重点实验室,福建厦门361005 [2]闽江学院计算机与控制工程学院福建省信息处理与智能控制实验室,福州350108 [3]澳门大学科技学院,氹仔澳门999078

出　　处：《计算机学报》2021年第7期1414-1429,共16页Chinese Journal of Computers

基　　金：国家自然科学基金联合基金(U1605252);国家自然科学基金(61872307,61702101,62071404);福建省自然科学基金面上项目(2020J01001);澳门大学研究基金(MYRG2018-00111-FST)资助.

摘　　要：鲁棒几何模型拟合是计算机视觉中一项非常重要且具有挑战性的研究问题.它已被广泛应用于人工智能领域的多个相关任务,如车道线检测、三维重构、图像拼接和运动分割等.鲁棒几何模型拟合的主要任务是从包含离群点和噪声的多结构数据中估计模型实例的参数和数量.然而,当前的模型拟合方法在拟合精度和计算速度上仍然无法满足实际场景中应用的需求.为此,该文提出一种基于非负矩阵欠逼近和剪枝技术的模型拟合方法,以提升模型拟合的性能.该文所提出的模型拟合方法包含误匹配剪枝算法、模型假设剪枝算法和改进的非负矩阵欠逼近算法.我们首先将误匹配移除技术引入到模型拟合中,以减少离群点对数据点采样过程的影响,进而减少生成无效模型假设的数量;接着我们利用模型假设剪枝算法来修剪无效的模型假设并选择有意义的模型假设,以构建一个高质量的非负偏好矩阵;最后,我们将空间约束和稀疏约束引入到非负矩阵欠逼近的优化问题中,并采用结构合并策略自适应地估计模型实例的参数和数量.在合成数据和真实图像上的实验结果表明,该文所提出的方法比当前一些有代表性的模型拟合方法具有更好的拟合性能和鲁棒性.在拟合精度上,该方法比T-Linkage和RS-NMU分别提升了约197.2%和47.7%.在拟合速度上,该方法比T-Linkage和RS-NMU分别快了约2.3倍和1.9倍,而且在三维重建任务的运行速度上比最新的拟合方法MCT快了约42.5倍.Robust geometric model fitting is an important and challenging research problem in computer vision.It has been widely used in many artificial intelligence related applications,such as lane detection,3D reconstruction,image stitching and motion segmentation,etc.With the rapid development of the artificial intelligence,the data processed by artificial intelligence systems inevitably contain outliers or noise generated by sensors,environment or human factors.The main task of robust geometric model fitting is to estimate the parameters and the number of model instances from multi-structural data contaminated with outliers and noise.However,the performance of current model fitting methods are far from being satisfactory in practical applications in terms of fitting accuracy and computational speed.In this paper,we propose an efficient model fitting method(NPMF)based on nonnegative matrix underapproximation and pruning techniques,to obtain more accurate fitting results from multi-structural data.The propose NPMF includes a mismatch pruning algorithm,a model hypothesis pruning algorithm and an improved nonnegative matrix underapproximation algorithm.Specifically,a mismatch pruning algorithm is proposed to alleviate the influence of outliers on the data point sampling process by using a mismatch removal technique,thereby reducing the number of insignificant model hypotheses.After retaining significant model hypotheses by using the weighting scores of model hypotheses,a model hypothesis pruning algorithm is introduced to prune insignificant model hypotheses,and a high-quality nonnegative preference matrix is then constructed.Finally,both the spatial constraint and the sparsity constraint are integrated into the optimization problem of nonnegative matrix underapproximation,and the number and parameters of model instances are adaptively estimated by using a structure merging strategy.The comparison experiments on several representative model fitting methods show that the proposed NPMF obtains better fitting performance on b

关 键 词：计算机视觉 鲁棒几何模型拟合 多结构数据 非负矩阵欠逼近 离群点剪枝 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]