All Outputs (6)

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.

Enhancing supervised classifications with metamorphic relations (2018)
Conference Proceeding
Xu, L., Towey, D., French, A. P., Benford, S., Zhou, Z. Q., & Chen, T. Y. (2018). Enhancing supervised classifications with metamorphic relations. In MET '18: Proceedings of the 3rd International Workshop on Metamorphic Testing (46-53). https://doi.org/10.1145/3193977.3193978

We report on a novel use of metamorphic relations (MRs) in machine learning: instead of conducting metamorphic testing, we use MRs for the augmentation of the machine learning algorithms themselves. In particular, we report on how MRs can enable enha... Read More about Enhancing supervised classifications with metamorphic relations.

Recognizing the Presence of Hidden Visual Markers in Digital Images (2017)
Conference Proceeding
Xu, L., French, A. P., Towey, D., & Benford, S. (2017). Recognizing the Presence of Hidden Visual Markers in Digital Images. In Thematic Workshops '17: Proceedings of the on Thematic Workshops of ACM Multimedia 2017 (210-218). https://doi.org/10.1145/3126686.3126761

As the promise of Virtual and Augmented Reality (VR and AR) becomes more realistic, an interesting aspect of our enhanced living environment includes the availability — indeed the potential ubiquity — of scannable markers. Such markers could represen... Read More about Recognizing the Presence of Hidden Visual Markers in Digital Images.

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.

Selective labeling: identifying representative sub-volumes for interactive segmentation (2016)
Conference Proceeding
Luengo, I., Basham, M., & French, A. P. (2016). Selective labeling: identifying representative sub-volumes for interactive segmentation. In Patch-based Techniques in Medical Imaging (17-24). https://doi.org/10.1007/978-3-319-47118-1_3

Automatic segmentation of challenging biomedical volumes with multiple objects is still an open research field. Automatic approaches usually require a large amount of training data to be able to model the complex and often noisy appearance and struct... Read More about Selective labeling: identifying representative sub-volumes for interactive segmentation.

SMURFS: Superpixels from multi-scale refinement of super-regions (2016)
Conference Proceeding
Luengo, I., Basham, M., & French, A. P. (2016). SMURFS: Superpixels from multi-scale refinement of super-regions. In Proceedings of the British Machine Vision Conference 2016. https://doi.org/10.5244/C.30.4

Recent applications in computer vision have come to rely on superpixel segmentation as a pre-processing step for higher level vision tasks, such as object recognition, scene labelling or image segmentation. Here, we present a new algorithm, Superpixe... Read More about SMURFS: Superpixels from multi-scale refinement of super-regions.