Learn More
Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in(More)
Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic segmentation of biological volumes is still a very challenging research field, and current methods usually(More)
Recent applications in computer vision have come to heavily rely on superpixel over-segmentation as a pre-processing step for higher level vision tasks, such as object recognition, image labelling or image segmentation. Here we present a new superpixel algorithm called Hierarchical Piecewise-Constant Super-regions (HPCS), which not only obtains superpixels(More)
Here we present a new superpixel algorithm: Superpixels from MUlti-scale ReFinement of Super-regions (SMURFS), which not only obtains state of the art superpixels, but can also be applied hierarchically to form what we call nth order super-regions. In essence, starting from a uniformly distributed set of super-regions, the algorithm iteratively alternates(More)
  • 1