Abstract This paper presents a learning-based method for parameter tuning of object recognition systems and its application to automatic road extraction from high resolution remotely sensed (HRRS) images. Our approach is based on region growing using fast marching level set method (FMLSM), and machine learning for automatically tuning its parameters. FMLSM is used to extract the shape of objects in images. Parameters are introduced into the speed function of the FMLSM to improve flexibility and reflect the variety of images. The parameters are tuned using machine learning and utilizing background knowledge. The primary contribution of our approach is the ability to learn the parameters for a FMLSM model for object extraction. Experimental results on 11 HRRS image datasets, 1024*1024 pixels each with ground resolution of 1.3 meters, demonstrate the validity of the proposed algorithm. We are able to extract the roads without the use of heuristic parameters and other manual intervention.
Learning Parameter Tuning for Object Extraction
Computer Vision – ACCV 2006 ; 10 ; 868-877
Lecture Notes in Computer Science ; 3851 , 10
2006-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Texture Feature , Training Image , Road Segment , Active Contour Model , Seed Selection Computer Science , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Image Processing and Computer Vision , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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