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] Year Filters: Author is O'Connor, N. [Clear All Filters]
. Object Retrieval with Deep Convolutional Features. In Deep Learning for Image Processing Applications. Amsterdam, The Netherlands: IOS Press; 2017.
. Assessing Knee OA Severity with CNN attention-based end-to-end architectures. In International Conference on Medical Imaging with Deep Learning (MIDL) 2019. London, United Kingdom: JMLR; 2019. 
(3.1 MB)
(3.1 MB). Bags of Local Convolutional Features for Scalable Instance Search. In ACM International Conference on Multimedia Retrieval (ICMR). New York City, NY; USA: ACM; 2016. (3.73 MB)
(3.73 MB). Demonstration of an Open Source Framework for Qualitative Evaluation of CBIR Systems. In ACM Multimedia. Seoul, South Korea: ACM; 2018. (11.05 MB)
(11.05 MB). The DICEMAN description schemes for still images and video sequences. In Workshop on Image Analysis for Multimedia Application Services, WIAMIS’99. Berlin, Germany; 1999. pp. 25–34. (718.69 KB)
(718.69 KB). Dublin City University and Partners' Participation in the INS and VTT Tracks at TRECVid 2016. In TRECVID Workshop 2016. Gaithersburg, MD, USA; 2016.
. Exploring EEG for Object Detection and Retrieval. In ACM International Conference on Multimedia Retrieval (ICMR) . Shanghai, China; 2015. (5.37 MB)
(5.37 MB). Hierarchical visual description schemes for still images and video sequences. In 1999 IEEE International Conference on Image Processing, ICIP 1999. Kobe, Japan; 1999. (375.21 KB)
(375.21 KB). Improving Spatial Codification in Semantic Segmentation. In IEEE International Conference on Image Processing (ICIP), 2015. Quebec City: IEEE; 2015. (18.81 MB)
(18.81 MB). Insight Centre for Data Analytics (DCU) at TRECVid 2014: Instance Search and Semantic Indexing Tasks. In 2014 TRECVID Workshop. Orlando, Florida (USA): National Institute of Standards and Technology (NIST); 2014. (2.45 MB)
(2.45 MB). Insight DCU at TRECVID 2015. In TRECVID 2015 Workshop. Gaithersburg, MD, USA: NIST; 2015. (2.13 MB)
(2.13 MB). Object segmentation in images using EEG signals. In ACM Multimedia. Orlando, Florida (USA); 2014.
. PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks. In ECCV 2018 Workshop on Egocentric Perception, Interaction and Compution (EPIC). Munich, Germany: Springer; 2018. (3.78 MB)
(3.78 MB). Region and object segmentation algorithms in the QIMERA segmentation platform. In Third International Workshop on Content-Based Multimedia Indexing. 2003. pp. 95–103.
. SalGAN: Visual Saliency Prediction with Generative Adversarial Networks. In CVPR 2017 Scene Understanding Workshop (SUNw). Honolulu, Hawaii, USA; 2017. (1.85 MB)
(1.85 MB). Saliency Weighted Convolutional Features for Instance Search. In Content-Based Multimedia Indexing - CBMI. La Rochelle, France: IEEE; 2018. (3.8 MB)
(3.8 MB). SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes. In ICCV Workshop on Egocentric Perception, Interaction and Computing. Venice, Italy: IEEE; 2017. (2.34 MB)
(2.34 MB). Shallow and Deep Convolutional Networks for Saliency Prediction. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. Las Vegas, NV, USA: Computer Vision Foundation / IEEE; 2016. (466.13 KB)
(466.13 KB). Simple vs complex temporal recurrences for video saliency prediction. In British Machine Vision Conference (BMVC). Cardiff, Wales / UK.: British Machine Vision Association; 2019. (1.79 MB)
(1.79 MB). Description Schemes for Video Programs, Users and Devices. Signal processing: image communication. 2000;16:211–234. (879.4 KB)
(879.4 KB). Improving Object Segmentation by using EEG signals and Rapid Serial Visual Presentation. Multimedia Tools and Applications. 2015;. (3.86 MB)
(3.86 MB). Scanpath and Saliency Prediction on 360 Degree Images. Elsevier Signal Processing: Image Communication. 2018;. (2.61 MB)
(2.61 MB). Investigating EEG for Saliency and Segmentation Applications in Image Processing. . 2013. (332.54 KB)
(332.54 KB). An investigation of eye gaze tracking utilities in image object recognition. . 2014. (1.63 MB)
(1.63 MB)