Bioimage Computing @ CVPR 2019

The detailed program of this workshop can be found here…

Bio-image computing (BIC) is a rapidly growing field at the interface of engineering, biology and computer science. Advanced light microscopy can deliver 2D and 3D image sequences of living cells with unprecedented image quality and ever increasing resolution in space and time. The emergence of novel and diverse microscopy modalities has provided biologists with unprecedented means to explore cellular mechanisms, embryogenesis, and neural development, to mention only a few fundamental biological questions. Electron microscopy provides information on the cellular structure at nanometer resolution. Here, correlating light microscopy and electron microscopy at the subcellular level, and relating both to animal behavior at the macroscopic level, is of paramount importance. The enormous size and complexity of these data sets, which can exceed multiple TB per volume or video, requires state-of-the-art computer vision methods.

Relevance to the computer vision community. This workshop will bring the latest challenges in bio-image computing to the computer vision community. It will showcase the specificities of bio-image computing and its current achievements, including issues related to image modeling, denoising, super-resolution, multi-scale instance- and semantic segmentation, motion estimation, image registration, tracking, classification, event detection — important topics that appertain to the computer vision field.

Topics of interest include:

  • Light Microscopy (LM) and Electron Microscopy (EM) image restoration and reconstruction
  • Reproducible image analysis over terabyte-sized images
  • Deep learning for bioimaging
  • 3D registration
  • Segmentation of subcellular objects, cells, and animals
  • Learning in the face of little to no training data
  • Multimodal image analysis (correlative LM and EM, various LM modalities)
  • Analysis of motion: tracking of cells, tissues, organisms, and particles
  • Automated behavior recognition
  • Dense motion estimation in 2D and 3D LM image sequences
  • Diffusion computation
  • Statistical shape analysis
  • Image-based phenotyping
  • Evaluation and benchmarking methodologies of automated image algorithms/pipelines
  • Interactive image analysis (on gigapixel images)

 


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