Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identification of Man-Made Regions in Unmanned Aerial Vehicle Imagery and Videos
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Using Models to Detect Man-Made Objects
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
A Two-Stage Level Set Evolution Scheme for Man-Made Objects Detection in Aerial Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multiresolution direction filterbanks: theory, design, and applications
IEEE Transactions on Signal Processing - Part I
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Wavelet-based level set evolution for classification of textured images
IEEE Transactions on Image Processing
A Kernel Matching Pursuit Approach to Man-Made Objects Detection in Aerial Images
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
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This paper describes a new aerial images segmentation algorithm. The algorithm is based upon the knowledge of image multi-scale geometric analysis which can capture the image’s intrinsic geometrical structure efficiently. The Contourlet transform is selected to represent the maximum information of the image and obtain the rotation invariant features of the image. A modified Mumford-Shah model is built to segment the aerial image by a necessary level set evolution. To avoid possible local minima in the level set evolution, we control the value of weight numbers of features in different evolution periods in this algorithm, instead of using the classical technique which evolve in a multi-scale fashion.