Digital image processing
A variational level set approach to multiphase motion
Journal of Computational Physics
Identification of Man-Made Regions in Unmanned Aerial Vehicle Imagery and Videos
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpretation of urban surface models using 2D building information
Computer Vision and Image Understanding
Automatic object extraction from aerial imagery—a survey focusing on buildings
Computer Vision and Image Understanding
A Level Set Method for the Extraction of Roads from Multispectral Imagery
AIPR '02 Proceedings of the 31st Applied Image Pattern Recognition Workshop on From Color to Hyperspectral: Advancements in Spectral Imagery Exploitation
Using Models to Detect Man-Made Objects
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
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The detection of man-made objects is important for scene understanding, image retrieval and surveillance. A modified Chan-Vese model based level set method for detecting man-made objects in aerial images is presented. The method applies a fractal error metric with an additional constraint-texture edge descriptor to realize a more accurate segmentation. Man-made objects are extracted from the natural areas by changing the geometric active contours, which are governed by a partial differential equation based on the modified Chan-Vese model. Benefiting from the level set method, the extracted man-made object contours may change topology easily during the evolution. Our method does not need to select a threshold to separate the fractal error image in cases where segmentation errors may result from using an unsuitable threshold. We validate the proposed algorithm by numerical results of real aerial images.