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 would solve the image understanding problem. In practice, such a model would be intractable for current inference methods. The open challenge for machine learning and machine vision researchers is to create a model which captures the imaging process as accurately as possible, whilst remaining tractable for accurate inference. To illustrate this challenge, I will show how different aspects of the imaging process can be incorporated into models for object detection and segmentation, and discuss techniques for making inference tractable in such models.

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