Clinton Fookes mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Biometrics and Facial recognition system. His research on Artificial intelligence often connects related topics like Machine learning. His work carried out in the field of Machine learning brings together such families of science as Network architecture and Layer.
Clinton Fookes combines subjects such as Local binary patterns and Robustness with his study of Pattern recognition. His Biometrics research includes themes of Human visual system model, Iterative reconstruction, Authentication and Digital signature. His Feature extraction research includes elements of Feature and Gaze.
Clinton Fookes spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Feature extraction. His study in Artificial intelligence concentrates on Deep learning, Facial recognition system, Segmentation, Video tracking and Feature. The Deep learning study combines topics in areas such as Recurrent neural network and Epilepsy.
His research in Pattern recognition focuses on subjects like Artificial neural network, which are connected to Speech recognition. His Machine learning research incorporates themes from Domain and Robustness. His Biometrics research incorporates elements of Authentication and Identification.
Clinton Fookes focuses on Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Segmentation. His studies deal with areas such as Domain and Computer vision as well as Artificial intelligence. His studies in Pattern recognition integrate themes in fields like Artificial neural network, Object, Feature and Filter.
The concepts of his Machine learning study are interwoven with issues in Emotion recognition, Inference, Component and Epilepsy. Clinton Fookes studied Deep learning and Identification that intersect with Question answering. His Segmentation research focuses on Phonocardiogram and how it connects with Feature extraction, Wavelet and Mel-frequency cepstrum.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Deep learning, Pattern recognition and Recurrent neural network. His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Component, with regards to Ranking. His work on Artificial neural network as part of general Machine learning research is frequently linked to Generalization and Meaning, bridging the gap between disciplines.
The study incorporates disciplines such as Electrophysiology, Identification, Video tracking, Computer vision and Convolutional neural network in addition to Deep learning. As part of his studies on Pattern recognition, he often connects relevant subjects like Feature. While the research belongs to areas of Recurrent neural network, Clinton Fookes spends his time largely on the problem of Electroencephalography, intersecting his research to questions surrounding Cognitive psychology and Epilepsy.
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Crowd Counting Using Multiple Local Features
David Ryan;Simon Denman;Clinton Fookes;Sridha Sridharan.
digital image computing: techniques and applications (2009)
Soft + Hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection.
Tharindu Fernando;Simon Denman;Sridha Sridharan;Clinton Fookes.
Neural Networks (2018)
Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective
Kien Nguyen;Clinton Fookes;Arun Ross;Sridha Sridharan.
IEEE Access (2018)
A Database for Person Re-Identification in Multi-Camera Surveillance Networks
Alina Bialkowski;Simon Denman;Sridha Sridharan;Clinton Fookes.
digital image computing techniques and applications (2012)
Gait energy volumes and frontal gait recognition using depth images
Sabesan Sivapalan;Daniel Chen;Simon Denman;Sridha Sridharan.
international conference on biometrics (2011)
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Thomas Schaffter;Diana S. M. Buist;Christoph I. Lee;Yaroslav Nikulin.
JAMA Network Open , 3 (3) , Article e200265. (2020) (2020)
Long range iris recognition
Kien Nguyen;Clinton Fookes;Raghavender Jillela;Sridha Sridharan.
Pattern Recognition (2017)
A Mask-Based Approach for the Geometric Calibration of Thermal-Infrared Cameras
S. Vidas;R. Lakemond;S. Denman;C. Fookes.
IEEE Transactions on Instrumentation and Measurement (2012)
An evaluation of crowd counting methods, features and regression models
David Ryan;Simon Denman;Sridha Sridharan;Clinton Fookes.
Computer Vision and Image Understanding (2015)
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition
Harshala Gammulle;Simon Denman;Sridha Sridharan;Clinton Fookes.
workshop on applications of computer vision (2017)
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