II- Digital Repository for the Faculty of Education

Permanent URI for this communityhttps://ds.uofallujah.edu.iq/handle/123456789/7

Welcome to the Digital Repository for the Faculty of Education

The Digital Repository for the Faculty of Education is a dedicated platform for preserving and providing access to academic and research resources for faculty members, students, and researchers. This repository aims to promote knowledge sharing, facilitate access to studies and academic projects, and support research collaboration.

The faculty comprises four academic departments and postgraduate programs in the near future

Explore a wealth of studies and research, and become a part of advancing scientific knowledge and contributing to innovation through our digital repository.

News

Latest News

Annual Research Conference Scheduled

March 2025

The University of Fallujah will host its Annual Research Conference, inviting researchers and students to present their findings and discuss emerging trends in various fields.

Digital Repository Launch

November 15, 2024

The Digital Repository of the University of Fallujah has been launched, offering open access to research papers, academic publications, and other scholarly resources.

Postgraduate Program Admissions Open

December 1, 2024

Applications are now open for the University of Fallujah's postgraduate programs. Interested candidates can apply through the admissions portal on the university website.

University Launches New Learning Management System

October 10, 2024

A new Learning Management System (LMS) has been introduced at the University of Fallujah to enhance the learning experience for students and faculty.

Browse

Search Results

Now showing 1 - 1 of 1

Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
(CVPR, 2013) Jamie Shotton; Ben Glocker,
We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image. Our approach employs a regres sion forest that is capable of inferring an estimate of each pixel’s correspondence to 3D points in the scene’s world coordinate frame. The forest uses only simple depth and RGB pixel comparison features, and does not require the computation of feature descriptors. The forest is trained to be capable of predicting correspondences at any pixel, so no interest point detectors are required. The camera pose is inferred using a robust optimization scheme. This starts with an initial set of hypothesized camera poses, con structed by applying the forest at a small fraction of image pixels. Preemptive RANSAC then iterates sampling more pixels at which to evaluate the forest, counting inliers, and refining the hypothesized poses. We evaluate on several var ied scenes captured with an RGB-D camera and observe that the proposed technique achieves highly accurate relo calization and substantially out-performs two state of the art baselines.