Material Detail

Probabilistic models for understanding images

Probabilistic models for understanding images

This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. Getting a computer to understand an image is challenging due to the numerous sources of variability that influence the imaging process. The pixels of a typical photograph will depend on the scene type and geometry, the number, shape and appearance of objects present in the scene, their 3D positions and orientations, as well as effects such as occlusion, shading and shadows. The good news is that research into physics and computer graphics has given us a detailed understanding of how these variables affect the resulting image. This understanding can help us to build the right prior knowledge into our probabilistic models of images. In theory, building a model containing all of this knowledge... Show More


  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material


Log in to participate in the discussions or sign up if you are not already a MERLOT member.