Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation
نویسندگان
چکیده
منابع مشابه
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...
متن کاملA 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...
متن کاملa 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 this pa...
متن کاملLow-Rank and Eigenface Based Sparse Representation for Face Recognition
In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC). Firstly, the low-rank images of the face images of each individual in training subset are extracted by the Robust Principal Component Analysis (Robust PCA) to alleviate the influence of noises (e.g., illumination difference and o...
متن کاملSparse Representation for Tumor Classification Based on Feature Extraction Using Latent Low-Rank Representation
Accurate tumor classification is crucial to the proper treatment of cancer. To now, sparse representation (SR) has shown its great performance for tumor classification. This paper conceives a new SR-based method for tumor classification by using gene expression data. In the proposed method, we firstly use latent low-rank representation for extracting salient features and removing noise from the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Intelligence and Neuroscience
سال: 2019
ISSN: 1687-5265,1687-5273
DOI: 10.1155/2019/9378014