Magnetostatic Active Contour Model with Classification Method of Sparse Representation

Correction to "MAC: Magnetostatic Active Contour Model"

Image Classification via Sparse Representation and Subspace Alignment

Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...

A New IRIS Segmentation Method Based on Sparse Representation

Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...

A New IRIS Segmentation Method Based on Sparse Representation

Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...

Magnetostatic Field for the Active Contour Model: A Study in Convergence

A new external velocity field for active contours is proposed. The velocity field is based on magnetostatics and hypothesised magnetic interactions between the active contour and image gradients. In this paper, we introduce the method and study its convergence capability for the recovery of shapes with complex topology and geometry, including deep, narrow concavities. The proposed active contou...