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Article
Improved Feature Extraction Using Weightless Neural Networks(IWNC)

Author: Ikhlas Watan Ghindawi
Journal: Journal Of AL-Turath University College مجلة كلية التراث الجامعة ISSN: 20745621 Year: 2012 Issue: 12 Pages: 244-253
Publisher: Heritage College كلية التراث الجامعة

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Abstract

The weightless neural classifier (WNC) is based on the collective response of RAM-based neurons. The ability of producing prototypes, analog to unconstrained images, from learned categories, was first introduced in the (IWNC) model. By counting the frequency of write accesses at each RAM neuron during the training phase, it is possible to associate the most accessed addresses to the corresponding input field contents that defined them. This work is about extracting information from such frequency counting in the form of fuzzy rules as an alternative way to describe the same images produced by (IWNC) as logical prototypes.

المصنف العصبي العديم الوزن ( اللاموزون ) يعتمد على الاستجابة المشتركة للاعصاب المعتمدة على ذاكرة الوصول العشوائي ( الذاكرة المؤقتة) . ان امكانية انتاج نماذج اولية تناضرية للصور غير المقيدة من تصانيف معلومة قد تم تقديمها بنموذج تحسين ميزة الاستخراج باستخدام الشبكات العصبية اللاموزونة ( عديمة بحساب تردد الكتابة على كل ذاكرة وصول عشوائية خلال طور التدريب . بالامكان ربط معظم عناوين الوصول مع محتويات مجال الادخال المناضرة . هذا البحث حول استخراج معلومات من قياسات التردد بشكل قواعد غامضة كطريقة بديلة لوصف نفس الصور المنتجة بواسطة تحسين ميزة الاستخراج باستخدام الشبكات العصبية اللاموزونة ( عديمة الوزن ) كنموذج اولي منطقي .

Keywords

Weightless --- Neural --- Networks --- IWNC


Article
Enhancement the Augmented Reality Framework Using Proposed Registration Method

Authors: Ikhlas Watan Ghindawi --- Abdul Ameer Abdulla --- Yossra Hussain Ali
Journal: Al-Nahrain Journal of Science مجلة النهرين للعلوم ISSN: (print)26635453,(online)26635461 Year: 2017 Volume: 20 Issue: 2 Pages: 153-161
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

The augmented reality field is a visualization technique using the computers, it provides an additional information to support the work of multiple applications. This research presents a proposed method to solve the registration problem in the augmented reality field. This method dealing with a special case of the registration process, when the virtual object registered with the real object, which sometime has an addition above it. It ensures the continuance of the registration process when there is an object exist on or pass through a real object. This method converting the image colors combining with using the interest features principles in order to implement an accurate registration process. This method work accurately to detect the right position and monitor the motion in the used video, depending on finding the important common features and geometric transformation principles.


Article
A Proposed Registration Method Using Tracking Interest Features for Augmented Reality

Authors: Abdul Ameer Abdulla --- Yossra Hussain Ali --- Ikhlas Watan Ghindawi
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2016 Volume: 34 Issue: 6 Part (B) Scientific Pages: 831-841
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

An Augmented Reality (AR) incorporates a mix of genuine and PC created scene segments. AR frameworks enhance a client's impression of this present reality with information that is not entirely of the scene. A key test for making an expanded the truth is to keep up precise arrangement amongst genuine and virtual thing.This exploration delineates a method to build up the enlistment extent of a dream based enlarged reality (AR) framework, also explores a simple method for detecting and tracking natural features in video stream. In this method, a reference image has been used as a tool to find the a proper position of an object. This method first uses Harris Corner Detector to detect the interest features and find the correspondences using cross-correlation method then it used the Random Sample Consensus (RANSAC) algorithm to find the Homography matrix .After acquiring keypoints in the video frame, a Kanade–Lucas–Tomasi (KLT) feature tracker optical flow tracking algorithm has been used to track the motion of these keypoints frame-by-frame. By maintaining the correspondence between the tracked keypoints and those on the clean marker image, a new homography for every frame has been computed. This allows tracking the orientation of the marker as it moves in the video frames.Experiments for assessing the possibility of the technique are implemented in order to illustrate the potential benefits of the method, in which result's that to the target registration error ( TRE) reach 0.0020 , root mean square error (RMSE ) is 0.003 and average time for whole dataset is 2.5 s

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