Iris Segmentation Using Interactive Deep Learning
نویسندگان
چکیده
منابع مشابه
Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment
Existing iris recognition systems are heavily dependent on specific conditions, such as the distance of image acquisition and the stop-and-stare environment, which require significant user cooperation. In environments where user cooperation is not guaranteed, prevailing segmentation schemes of the iris region are confronted with many problems, such as heavy occlusion of eyelashes, invalid off-a...
متن کاملLearning Text Segmentation Using Deep Lstm
We train an LSTM-based model to predict structure in Wikipedia articles. This results in a model that is capable of segmenting any English text, is not constrained to a limited number of topics, and has much better runtime characteristics than previous methods. Finally, we introduce a new dataset which is much more extensive than current ones, and compare our method with previous methods in ter...
متن کاملSimultaneous Multiple Surface Segmentation Using Deep Learning
The task of automatically segmenting 3-D surfaces representing boundaries of objects is important for quantitative analysis of volumetric images, and plays a vital role in biomedical image analysis. Recently, graph-based methods with a global optimization property have been developed and optimized for various medical imaging applications. Despite their widespread use, these require human expert...
متن کاملIris segmentation using game theory
Robust segmentation of an iris image plays an important role in iris recognition. However, the nonlinear deformations, pupil dilations, head rotations, motion blurs, reflections, nonuniform intensities, low image contrast, camera angles and diffusions, and presence of eyelids and eyelashes often hamper the conventional iris/pupil localization methods,whichutilize the region-basedor the gradient...
متن کاملInteractive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes. To address these problems, we propose a...
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: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3041519