All Outputs (4)

Towards low-cost image-based plant phenotyping using reduced-parameter CNN (2018)
Conference Proceeding
Atanbori, J., Chen, F., French, A. P., & Pridmore, T. (2018). Towards low-cost image-based plant phenotyping using reduced-parameter CNN

Segmentation is the core of most plant phenotyping applications. Current state-of-the-art plant phenotyping applications rely on deep Convolutional Neural Networks (CNNs). However, these networks have many layers and parameters, increasing training a... Read More about Towards low-cost image-based plant phenotyping using reduced-parameter CNN.

Deep learning for multi-task plant phenotyping (2017)
Conference Proceeding
Pound, M. P., Atkinson, J. A., Wells, D. M., Pridmore, T. P., & French, A. P. (2017). Deep learning for multi-task plant phenotyping. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017 (2055-2063). https://doi.org/10.1109/ICCVW.2017.241

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recent... Read More about Deep learning for multi-task plant phenotyping.

TRIC-track: tracking by regression with incrementally learned cascades (2015)
Conference Proceeding
Wang, X., Valstar, M. F., Martinez, B., Khan, M. H., & Pridmore, T. (2015). TRIC-track: tracking by regression with incrementally learned cascades. In 2015 IEEE International Conference on Computer Vision (ICCV) (4337-4345). https://doi.org/10.1109/ICCV.2015.493

This paper proposes a novel approach to part-based track- ing by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations. We propose to use cascaded regression with incre... Read More about TRIC-track: tracking by regression with incrementally learned cascades.

The spatial character of sensor technology (2006)
Conference Proceeding
Reeves, S., Pridmore, T., Crabtree, A., Green, J., Benford, S., & O'Malley, C. (2006). The spatial character of sensor technology.

By considering the spatial character of sensor-based interactive systems, this paper investigates how discussions of seams and seamlessness in ubiquitous computing neglect the complex spatial character that is constructed as a side-effect of deployin... Read More about The spatial character of sensor technology.