IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
Finding Edges and Lines in Images
Finding Edges and Lines in Images
Identification of Roads in Satellite Imagery Using Artificial Neural Networks: A Contextual Approach
Identification of Roads in Satellite Imagery Using Artificial Neural Networks: A Contextual Approach
Finding ridges and valleys in a discrete surface using a modified MLS approximation
Computer-Aided Design
An alternative curvature measure for topographic feature detection
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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We describe a new approach for extracting crest lines and thin nets. The key point of our approach is to model thin nets as the crest lines of the image surface. Crest lines are the lines where one of the two principal curvatures is locally extremal. We define these lines using first, second and third derivatives of the image. We compute the image derivatives using recursive filters approximating the Gaussian filter and its derivatives. Using an adapted scale factor, we apply this approach to the extraction of roads in satellite data and blood vessels in medical images. We also apply this method to the extraction of the crest lines in depth maps of human faces.