In this paper, an improved contour detection method based on level set and watershed transform is proposed. It is
performed on coarse-to-fine approach: 1) primary contours detected by using level set evolution; 2) accurate contours
detected by watershed transform. The combined method utilizes the advantages of level set method and watershed
transform. Experimental results show that our method could detect accurate contours and gain great improvement
compared with level set evolution and watershed transform.
A novel synthetic aperture radar (SAR) automatic target recognition (ATR) approach based on Curvelet Transform is
proposed. However, the existing approaches can not extract the more effective feature. In this paper, our method is
concentrated on a new effective representation of the moving and stationary target acquisition and recognition (MSTAR)
database to obtain a more accurate target region and reduce feature dimension. Firstly, MSTAR database can be
extracted feature through the optimal sparse representation by curvelets to obtain a clear target region. However,
considering the loss of part of edges of image. We extract coarse feature, which is to compensate fine feature error
brought by segmentation. The final features consisting of fine and coarse feature are classified by SVM with Gaussian
radial basis function (RBF) kernel. The experiments show that our proposed algorithm can achieve a better correct
classification rate.
Synthetic aperture radar (SAR) image segmentation is a fundamental problem in SAR image interpretation. SAR images
often contain non-texture object and texture object. Level set method, known as deformable model, is a powerful image
segmentation technique. It can get accurate contours of non-texture object, but has poor performance in getting contours
of texture object. In this paper, a new modified model of level set based on clonal selection algorithm is proposed. We
use clonal selection algorithm to choose some pixels near the contour, and then perform a neighborhood modification on
the level set function during its evolution. The region texture information, supervising the modification process, is
incorporated into the level set framework. This new method is particularly well adapted to detection of texture object of
interesting. We illustrated the performance of the new method on SAR images. Furthermore, we compared our method
with level set method and the modified model of level set based on standard genetic algorithm (SGA) in texture object
detection results and image segmentation results. The experimental results show that incorporating region texture
information into the level set framework, consistent texture objects are obtained, and accurate and robust segmentations
can be achieved.
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