Abstract Aiming at the problems of local stereo matching methods, such as difficulty in selecting sizes of matching windows and low accuracy of stereo matching in weak texture or highlight region, a multi-scale fusion stereo matching method is proposed by combining convolutional neural network model (CNN) and image pyramid method in this paper. By training CNN, image features of the matched image pairs are learned automatically to complete the calculation of matching cost. Based on the construction of image pyramids, the matched image pairs are expressed in multiple scale. Grounded on the template construction of weak texture region, the matching images of each layer are divided into weak texture region and rich texture region. The image of weak texture region is transformed into small-scale image to calculate the matching degree and reduce the mismatching rate of weak texture image. Then, the image is transformed back to large-scale images and fused with the matching results of rich texture regions to maintain the matching accuracy. Experiments on KITTI dataset indicate that the proposed algorithm yields a better image matching result.
Fund:Supported by National Natural Science Foundation of China(No.61763012)
Corresponding Authors:
XU Xuesong, Ph.D., professor. His research interests include UAV vision navigation and fault tolerant control, intelligent traffic.
About author:: WU Junjie, master student. His research interests include pattern recognition and image processing.
[1] 刘 杰,张建勋,代 煜,等.基于跨尺度引导图像滤波的稠密立体匹配.光学学报, 2018, 38(1). DOI: 10.3788/AOD201838.0115004. (LIU J, ZHANG J X, DAI Y, et al. Dense Stereo Matching Based on Cross-Scale Guided Image Filtering. Acta Optica Sinca, 2018, 38(1). DOI: 10.3788/AOD201838.0115004.) [2] 马 宁,门宇博,门朝光,等.基于扩展相位相关的小基高比立体匹配方法.电子学报, 2017, 45(8): 1827-1835. (MA N, MENG Y B, MENG C G, et al. A Small Baseline Stereo Matching Method Based on Extended Phase Correlation. Acta Electronica Sinica, 2017, 45(8): 1827-1835.) [3] GEIGER A, ROSER M, URTASUN R. Efficient Large-Scale Stereo Matching // Proc of the Asian Conference on Computer Vision. Berlin, Germany: Springer, 2010: 25-38. [4] BOYKOV Y, VEKSLER O, ZABIH R. Markov Random Fields with Efficient Approximations // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 1998: 648-655. [5] SUN J, ZHANG N N, SHUM H Y. Stereo Matching Using Belief Propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(7): 787-800. [6] HIRSCHMULLER H. Accurate and Efficient Stereo Processing by Semi-global Matching and Mutual Information // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2005, II: 807-814. [7] YAO P, ZHANG H, XUE Y B, et al. As-Global-As-Possible Stereo Matching with Adaptive Smoothness Prior. IET Image Processing, 2019, 13(1): 98-107. [8] 尹宝才,王文通,王立春.深度学习研究综述.北京工业大学学报, 2015, 41(1): 48-59. (YIN B C, WANG W T, WANG L C. Review of Deep Learning. Journal of Beijing University of Technology, 2015, 41(1): 48-59.) [9] 周飞燕,金林鹏,董 军.卷积神经网络研究综述.计算机学报, 2017, 40(6): 1229-1251. (ZHOU F Y, JIN L P, DONG J. Review of Convolutional Neural Network. Chinese Journal of Computers, 2017, 40(6): 1229-1251.) [10] ŽAGORUYKO S, KOMODAKIS N. Learning to Compare Image Patches via Convolutional Neural Networks // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2015: 4353-4361. [11] ŽBONTAR J, LECUN Y. Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches. Journal of Machine Learning Research, 2016, 17(1): 2287-2318. [12] SHAKED A, WOLF L. Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 4641-4650. [13] ZHANG K, FANG Y Q, MIN D B, et al. Cross-Scale Cost Aggregation for Stereo Matching // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 1590-1597. [14] 张 欢,安 利,张 强,等.SGBM算法与BM算法分析研究.测绘与空间地理信息, 2016, 39(10): 214-216. (ZHANG H, AN L, ZHANG Q, et al. SGBM Algorithm and BM Algorithm Analysis and Research. Geomatics and Spatial Information Technology, 2016, 39(10): 214-216.)
2020, Vol. 33 
