Stereo Matching Low-Textured Survey 1.Stereo Matching-Based Low-Textured Scene Reconstruction for Autonomous Land Vehicles (ALV) Navigation 2.A Robust Stereo Matching Method for Low Texture Stereo Images 1

Outline Introduction Proposed Paper 1 Proposed Paper 2 Conclusion Result 2

Introduction Low-textured – Matching costs of the stereo pairs are almost similar. In low-textured regions – Local algorithms are guaranteed to fail. – Global algorithms are too time-consuming. 3

Introduction Solution of Local Approach – Bigger window size. Low-textured regions are larger than the size of the aggregation window. 4

Introduction The size of aggregation windows should be – large enough to include intensity variation. – small enough to avoid disparity variation. An adaptive method for selecting the optimal aggregation window for stereo pairs. 5

Introduction Low computation time and high quality of disparity map. Different strategies are applied in the well- textured and low-textured regions. 6

Introduction 7

Outline Introduction Proposed Paper 1 – Proposed Method – Texture Detection – Approaches Proposed Paper 2 Conclusion Result 8

Stereo Matching-Based Low-Textured Scene Reconstruction for Autonomous Land Vehicles (ALV) Navigation Image Analysis and Signal Processing (IASP), 2011 International Conference on Mechatronics & Automation School, National University of Defense Technology, Changsha, Hunan, China Tingbo Hu Tao Wu Hangen He 9

Proposed Method Local algorithms are used to matching the pixels in well-textured regions. A new matching algorithm combining plane priors and pixel dissimilarity is designed for the low-textured regions. 10

Proposed Method In low-textured regions, the intensities of the pixels are almost identical. – Material and the Normal Vectors are consistent. 11

Proposed Method A low-textured region is likely to correspond to a 3D plane. 12

Texture Detection. 13

Approach - Local In the well-textured regions – Moravec Normalized Cross Correlation (MNCC) 14

Approach - Plane In the low-textured regions 15

Approaches. Low textured Well textured 16

Disparity Map 17

Outline Introduction Proposed Paper 1 Proposed Paper 2 – Proposed Method – Edge Detection – Aggregation Conclusion Result 18

A Robust Stereo Matching Method for Low Texture Stereo Images Computing and Communication Technologies, RIVF '09. International Conference on Department of Information Media Technology Faculty of Information Science and Technology, Tokai University Le Thanh SACH Kiyoaki ATSUTA Kazuhiko HAMAMOTO Shozo KONDO 19

Proposed Method Utilizes the edge maps computed from the stereo pairs to guide the cost aggregation. 20

Proposed Method 21

Edge Detection 22

Edge Map 23

Edge Detection 24

Aggregation Horizontal Aggregation 25

Aggregation Vertical Aggregation 26

Conclusion Different strategies are applied in different kinds of regions. The computational complexity of Paper 2 cost aggregation method is independent of the window size. 27

Result 28