Search

Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 1994. Simultaneously published in the Technical Report series., This dissertation studies the problem of searching for a target object with a visual sensor. In particular, it studies the task of selecting a sequence of viewpoints, viewing directions, and fields of view that efficiently examines the area being searched. This is made difficult by two problems, namely the need for high image resolution and the presence of obstacles that occlude portions of the search area from certain viewpoints. Searches for objects that require high image resolution to be recognizable can potentially require the examination of a large number of images; high resolution requires a narrow field of view, and hence more images are necessary to span a given visual angle. This dissertation considers a method for increasing search efficiency by searching only those subregions that are especially likely to contain the object. Searches that use this method, called indirect searches, repeatedly find a cheaply-locatable "intermediate" object that commonly participates in a spatial relationship with the target object, and then look for the target in the restricted region specified by this relationship. A decision-theoretic model of search efficiency is developed. The model identifies desiderata for useful intermediate objects and predicts that, in typical indoor situations, indirect search provides up to an eight-fold increase in efficiency. The model is also suitable for use in an on-line system for selecting intermediate objects. The second problem facing a searcher is that portions of the area being searched are often hidden from view. Multiple viewpoints are therefore often necessary. This dissertation examines the selection of such viewpoints. Traditional viewpoint selection methods involve detailed maps of the scene portions viewed so far. Simpler model-free methods are presented that, though less selective about their viewpoints, find objects without significantly more effort than map-based methods. They suggest that the main requirement for selecting

Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 1994. Simultaneously published in the Technical Report series., This dissertation studies the problem of searching for a target object with a visual sensor. In particular, it studies the task of selecting a sequence of viewpoints, viewing directions, and fields of view that efficiently examines the area being searched. This is made difficult by two problems, namely the need for high image resolution and the presence of obstacles that occlude portions of the search area from certain viewpoints. Searches for objects that require high image resolution to be recognizable can potentially require the examination of a large number of images; high resolution requires a narrow field of view, and hence more images are necessary to span a given visual angle. This dissertation considers a method for increasing search efficiency by searching only those subregions that are especially likely to contain the object. Searches that use this method, called indirect searches, repeatedly find a cheaply-locatable "intermediate" object that commonly participates in a spatial relationship with the target object, and then look for the target in the restricted region specified by this relationship. A decision-theoretic model of search efficiency is developed. The model identifies desiderata for useful intermediate objects and predicts that, in typical indoor situations, indirect search provides up to an eight-fold increase in efficiency. The model is also suitable for use in an on-line system for selecting intermediate objects. The second problem facing a searcher is that portions of the area being searched are often hidden from view. Multiple viewpoints are therefore often necessary. This dissertation examines the selection of such viewpoints. Traditional viewpoint selection methods involve detailed maps of the scene portions viewed so far. Simpler model-free methods are presented that, though less selective about their viewpoints, find objects without significantly more effort than map-based methods. They suggest that the main requirement for selecting