II-i- Department of Life Sciences

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Welcome to the Department of Life Sciences The Department of Life Sciences, part of the Faculty of Education at the University of Fallujah, offers a comprehensive program in biological sciences. The department focuses on teaching and research in various fields such as biology, ecology, and environmental science. Our aim is to equip students with the scientific knowledge and practical skills necessary to excel in both academic and professional fields related to life sciences. The department is committed to providing a high-quality education, fostering scientific inquiry, and promoting a deeper understanding of the living world. Students are encouraged to engage in hands-on learning through laboratory work, field studies, and research projects. Explore more about our programs and get involved in advancing scientific knowledge and environmental sustainability.

News

News Latest News - Department of Life Sciences New Research Lab Opened December 2024 The Department of Life Sciences is excited to announce the opening of a new research laboratory designed to enhance student and faculty research in biology and environmental sciences. New Curriculum Update November 2024 The Department of Life Sciences has introduced new courses in molecular biology and ecology as part of the updated curriculum for the 2024 academic year, aiming to expand learning opportunities for students. Annual Science Symposium October 2024 The Department of Life Sciences hosted its annual science symposium, where faculty and students presented their latest research in the fields of genetics, biochemistry, and environmental studies. For more updates, visit University of Fallujah Website Upcoming events: Department Events

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Start date: 2013End date: 2019

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    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.
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