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Neil Birkbeck
Researcher at Google
Publications - 101
Citations - 1638
Neil Birkbeck is an academic researcher from Google. The author has contributed to research in topics: Video quality & Computer science. The author has an hindex of 19, co-authored 88 publications receiving 1052 citations. Previous affiliations of Neil Birkbeck include Princeton University & University of Alberta.
Papers
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Proceedings ArticleDOI
3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
TL;DR: A variational brain tumor segmentation algorithm is proposed that extends current approaches from texture segmentation by using a high dimensional feature set calculated from MRI data and registered atlases and shows that using a conditional model to discriminate between normal and abnormal regions significantly improves the segmentation results compared to traditional generative models.
Journal ArticleDOI
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
TL;DR: This work conducts a comprehensive evaluation of leading no-reference/blind VQA (BVQA) features and models on a fixed evaluation architecture, yielding new empirical insights on both subjective video quality studies and objective V QA model design.
Journal ArticleDOI
RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content
TL;DR: In this paper, the Rapid and Accurate Video Quality Evaluator (RAPIQUE) model is proposed for video quality prediction, which combines and leverages the advantages of both quality-aware scene statistics features and semantics-aware deep convolutional features.
Journal ArticleDOI
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
TL;DR: In this article, the VIDeo quality EVALuator (VIDEVAL) is proposed to improve the performance of VQA models for UGC/consumer videos.
Book ChapterDOI
Rapid multi-organ segmentation using context integration and discriminative models
TL;DR: A novel framework for rapid and accurate segmentation of a cohort of organs that integrates local and global image context through a product rule to simultaneously detect multiple landmarks on the target organs and exploits sparsity in the global context for efficient detection.