Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

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...

Introduction: Various computer assisted medical procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries require automatic quantification and volumetric visualization of teeth. In this regard, segmentation is a major step. Material and Methods: In this paper, inspired by our previous experiences and considering the anatomical knowledge of teeth and jaws, we prop...

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...

We show that combining both bottom-up rule chunking and top-down rule segmentation search strategies in purely unsupervised learning of phrasal inversion transduction grammars yields significantly better translation accuracy than either strategy alone. Previous approaches have relied on incrementally building larger rules by chunking smaller rules bottomup; we introduce a complementary top-down...

Recent work on unsupervised speech segmentation has used self-supervised models with phone and word modules that are trained jointly. This paper instead revisits an older approach to segmentation: bottom-up phone-like unit discovery is performed first, symbolic then top of the discovered units (without influencing lower level). To do this, I propose a new model, chain two segment speech. Both u...

customers segmentation and analyzing their behavior at fast moving costumer goods (fmgs) industries which deal with a large number of customers with a variety of characteristics causes the marketing activities to be targeted and leads to effective communication with the customers. segmentation, a data mining approach, which leads to the discovery of similar groups of customers, is usually done ...

Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture. The top-down influence is especially effective when dealing with high noise or difficult segmentation problems. Our system is modular and extensible. It is also easy to train and cheap to run, and yet can accommodate complex behaviors. We obtain cl...