This map shows the geographic impact of Simon Denman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Simon Denman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simon Denman more than expected).
This network shows the impact of papers produced by Simon Denman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Simon Denman. The network helps show where Simon Denman may publish in the future.
Co-authorship network of co-authors of Simon Denman
This figure shows the co-authorship network connecting the top 25 collaborators of Simon Denman.
A scholar is included among the top collaborators of Simon Denman based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Simon Denman. Simon Denman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Fernando, Tharindu, Simon Denman, Sridha Sridharan, & Clinton Fookes. (2020). Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis. QUT ePrints (Queensland University of Technology).21 indexed citations
6.
Fernando, Tharindu, et al.. (2020). Heart Sound Segmentation using Bidirectional LSTMs with Attention. QUT ePrints (Queensland University of Technology).60 indexed citations
7.
Dissanayake, Theekshana, Tharindu Fernando, Simon Denman, et al.. (2020). Understanding the Importance of Heart Sound Segmentation for Heart Anomaly Detection. arXiv (Cornell University).3 indexed citations
Fernando, Tharindu, et al.. (2020). Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection. QUT ePrints (Queensland University of Technology).7 indexed citations
Fernando, Tharindu, Sridha Sridharan, Clinton Fookes, & Simon Denman. (2018). Deep decision trees for discriminative dictionary learning with adversarial multi-agent trajectories. QUT ePrints (Queensland University of Technology).1 indexed citations
13.
Fernando, Tharindu, Simon Denman, Sridha Sridharan, & Clinton Fookes. (2018). Task specific visual saliency prediction with memory augmented conditional generative adversarial networks. QUT ePrints (Queensland University of Technology).16 indexed citations
14.
Fernando, Tharindu, Simon Denman, Sridha Sridharan, & Clinton Fookes. (2018). Tracking by prediction: A deep generative model for multi-person localisation and tracking. QUT ePrints (Queensland University of Technology).39 indexed citations
Denman, Simon, et al.. (2016). Detecting rare events using Kullback-Leibler divergence: A weakly supervised approach. QUT ePrints (Queensland University of Technology).1 indexed citations
17.
Yang, Shuai, Edward Chung, Marc Miska, et al.. (2013). An analysis of the KEEP CLEAR pavement markings effects on queuing vehicles dynamic performance at urban signalised intersections. QUT ePrints (Queensland University of Technology).
18.
Denman, Simon, Sridha Sridharan, & Vinod Chandran. (2008). Abandoned object detection using multi-layer motion detection. QUT ePrints (Queensland University of Technology).9 indexed citations
19.
Denman, Simon, et al.. (2007). Robust Real Time Multi-Layer Foreground Segmentation. QUT ePrints (Queensland University of Technology). 496–499.2 indexed citations
20.
Denman, Simon, et al.. (2007). Automatic Tracking, Super-Resolution and Recognition of Human Faces from Surveillance Video. QUT ePrints (Queensland University of Technology). 37–40.3 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.