Open AccessDevelopment of depth map from stereo images using sum of absolute differences and edge filters
Author(s) -
Rostam Affendi Hamzah,
Muhd Nazmi Zainal Azali,
Zarina Mohd Noh,
Madiha Zahari,
Adi Irwan Herman
Publication year - 2022
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v25.i2.pp875-883
Subject(s) - artificial intelligence , computer vision , depth map , robustness (evolution) , computer science , enhanced data rates for gsm evolution , block (permutation group theory) , stereopsis , matching (statistics) , process (computing) , 3d reconstruction , pattern recognition (psychology) , image (mathematics) , mathematics , gene , operating system , biochemistry , chemistry , statistics , geometry
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.