Clinton Fookes
Queensland University of Technology
526 Papers
2.1K Citations
Clinton Fookes is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 40, co-authored 455 publications. Previous affiliations of Clinton Fookes include University of Queensland.
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Papers
Crowd Counting Using Multiple Local Features
David Ryan,Simon Denman,Clinton Fookes,Sridha Sridharan +3 more
- 01 Dec 2009
TL;DR: An approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes is proposed.
Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective
TL;DR: It is shown that the off-the-shelf CNN features, while originally trained for classifying generic objects, are also extremely good at representing iris images, effectively extracting discriminative visual features and achieving promising recognition results on two iris datasets: ND-CrossSensor-2013 and CASIA-Iris-Thousand.
387
Soft + Hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection.
TL;DR: This work proposes a novel method to predict the future motion of a pedestrian given a short history of their, and their neighbours, past behaviour using a combined attention model which utilises both "soft attention" as well as "hard-wired" attention in order to map the trajectory information from the local neighbourhood to the future positions of the pedestrian of interest.
364
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Thomas Schaffter,Diana S. M. Buist,Christoph I. Lee,Yaroslav Nikulin,Dezső Ribli,Yuanfang Guan,William Lotter,Zequn Jie,Hao Du,Sijia Wang,Jiashi Feng,Mengling Feng,Hyo-Eun Kim,F. Albiol,Alberto Albiol,Stephen Morrell,Zbigniew Wojna,Mehmet Eren Ahsen,Umar Asif,Antonio Jimeno Yepes,Shivanthan A.C. Yohanandan,Simona Rabinovici-Cohen,Darvin Yi,Bruce Hoff,Thomas Yu,Elias Chaibub Neto,Daniel L. Rubin,Peter Lindholm,Laurie R. Margolies,Russell B. McBride,Joseph H. Rothstein,Weiva Sieh,Rami Ben-Ari,Stefan Harrer,Andrew D. Trister,Stephen H. Friend,Thea Norman,Berkman Sahiner,Fredrik Strand,Fredrik Strand,Justin Guinney,Gustavo Stolovitzky,Lester Mackey,Joyce Cahoon,Li Shen,Jae Ho Sohn,Hari Trivedi,Yiqiu Shen,Ljubomir Buturovic,Jose Costa Pereira,Jaime S. Cardoso,Eduardo Castro,Karl Trygve Kalleberg,Obioma Pelka,Imane Nedjar,Krzysztof J. Geras,Felix Nensa,Ethan Goan,Sven Koitka,Sven Koitka,Luis Caballero,David D. Cox,Pavitra Krishnaswamy,Gaurav Pandey,Christoph M. Friedrich,Dimitri Perrin,Clinton Fookes,Bibo Shi,Gerard Cardoso Negrie,Michael Kawczynski,Kyunghyun Cho,Can Son Khoo,Joseph Y. Lo,A. Gregory Sorensen,Hwejin Jung +74 more
- 02 Mar 2020
TL;DR: This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.
356
Deep Learning for Medical Anomaly Detection – A Survey
TL;DR: A coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms and a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions are provided.
284