Computer vision is a challenging application area for learning algorithms. For instance, the task of object detection is a critical problem for many systems, like mobile robots, that remains largely unsolved. In order to interact with the world, robots must be able to locate and recognize large numbers of objects accurately and at reasonable speeds. Unfortunately, off-the-shelf computer vision algorithms do not yet achieve sufficiently high detection performance for these applications. A key difficulty with many existing algorithms is that they are unable to take advantage of large numbers of examples. As a result, they must rely heavily on prior knowledge and hand-engineered features that account for the many kinds of errors that can occur. In this chapter, we present two methods for improving performance by scaling up learning algorithms to large datasets: (1) using graphics processing units (GPUs) and distributed systems to scale up the standard components of computer vision algorithms and (2) using GPUs to automatically learn high-quality feature representations using deep belief networks (DBNs). These methods are capable of not only achieving high performance but also removing much of the need for hand-engineering common in computer vision algorithms.

The fragility of many vision algorithms comes from their lack of knowledge about the multitude of visual phenomena that occur in the real world. Whereas humans can intuit information about depth, occlusion, lighting, and even motion from still images, computer vision algorithms generally lack the ability to deal with these phenomena without being engineered to account for them in advance.