Markov Random Field Image Models and Their Applications to Computer Vision

1. Introduction. Computer vision refers to a variety of applications involving a sensing device, a computer, and software for restoring and possibly interpreting the sensed data. Most commonly, visible light is sensed by a video camera and converted to an array of measured light intensities, each element corresponding to a small patch in the scene (a picture element, or "pixel"). The image is thereby "digitized," and this format is suitable for computer analysis. In some applications, the sensing mechanism responds to other forms of light, such as in infrared imaging where the camera is tuned to the invisible part of the spectrum neighboring the color red. Infrared light is emitted in proportion to temperature, and thus infrared imaging is suitable for detecting and analyzing the temperature profile of a scene. Applications include automated inspection in industrial settings, medical diagnosis, and targeting and tracking of military objects. In single photon emission tomography, as a diagnostic tool, individual photons, emitted from a "radiopharmaceutical" (isotope combined with a suitable pharmaceutical) are detected. The object is to reconstruct the distribution of isotope density inside the body from the externally-collected counts. Depending on the pharmaceutical, the isotope density may correspond to local blood flow ("perfusion") or local metabolic activity. Other applications of computer vision include satellite imaging for weather and crop yield prediction, radar imaging in military applications, ultrasonic imaging for industrial inspection and a host of medical applications, and there is a growing role for video imaging in robotics. The variety of applications has yielded an equal variety of algorithms for restoration and interpretation. Unfortunately, few general principals have emerged and no common foundation has been layed. Algorithms are by and-large-ad=hoet=they=are=typic^^ ically tuned to the particulars of the environment (lighting, weather conditions, magnification, and so on) in which they are implemented. It is likely that a