Computer vision and artificial intelligence (original) (raw)

1996, XRDS: Crossroads, The ACM Magazine for Students

One of the monolithic goals of computer vision is to automatically interpret general digital images of arbitrary scenes. This goal has produced a vast array of research over the last 35 years, yet a solution to this general problem still remains out of reach. A reason for this is that the problem of visual perception is typically under-constrained. Information like absolute scale and depth is lost when the scene is projected onto an image plane. In fact, there are an infinite number of scenes that can produce the exact same image, which makes direct computation of scene geometry from a single image impossible. The difficulty of this ``traditional goal'' of computer vision has caused the field to focus on smaller, more constrained pieces of the problem. The hope is that when the pieces are put back together, a successful scene interpreter will have been created. Digital filtering, motion analysis , image registration, segmentation, and model matching schemes are all examples of areas where progress has been made in the field. Other research has focused on the general problem through the use of knowledge and context. The use of external knowledge both about the world and about the current visual task reduces the number of plausible scene interpretations and may make the problem solvable. This approach is referred to as knowledge-based vision. Work in the area of knowledge-based vision incorporates methods from the field of AI in order to focus on the influence of context on scene understanding, the role of high level knowledge, and appropriate knowledge representations for visual tasks. The importance of computer vision to the field of AI is fairly obvious: intelligent agents need to acquire knowledge of the world through a set of sensors. What is not so obvious is the importance that AI has to the field of computer vision. Indeed, I believe that the study of perception and intelligence are necessarily intertwined. This article will look at the role that knowledge plays in computer vision and how the use of reasoning, context, and knowledge in visual tasks reduces the complexity of the general problem. The importance of context and knowledge in vision has been pointed out by psychologists many times, and these ideas have driven computer vision studies as well. Take the example in Figure 1.

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