Open AccessPOOR TEXTURAL IMAGE MATCHING BASED ON GRAPH THEORY
Author(s) -
Shiyu Chen,
Xia Yuan,
Yuan Wang,
Yong Cai
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b3-741-2016
Subject(s) - scale invariant feature transform , artificial intelligence , matching (statistics) , computer science , computer vision , graph , image matching , discontinuity (linguistics) , homogeneous , pattern recognition (psychology) , blossom algorithm , mathematics , image (mathematics) , theoretical computer science , statistics , mathematical analysis , combinatorics
Image matching lies at the heart of photogrammetry and computer vision. For poor textural images, the matching result is affected by low contrast, repetitive patterns, discontinuity or occlusion, few or homogeneous textures. Recently, graph matching became popular for its integration of geometric and radiometric information. Focused on poor textural image matching problem, it is proposed an edge-weight strategy to improve graph matching algorithm. A series of experiments have been conducted including 4 typical landscapes: Forest, desert, farmland, and urban areas. And it is experimentally found that our new algorithm achieves better performance. Compared to SIFT, doubled corresponding points were acquired, and the overall recall rate reached up to 68%, which verifies the feasibility and effectiveness of the algorithm.