Representing space for practical reasoning

This paper describes a new approach to representing space and time for practical reasoning, based on space-filling cells. Unlike R n, the new models can represent a bounded region of space using only finitely many cells, so they can be manipulated directly. Unlike Z n, they have useful notions of function continuity and region connectedness. The topology of space is allowed to depend on the situation being represented, accounting for sharp changes in function values and lack of connectedness across object boundaries. Algorithms based on this model of space are neither purely region-based nor purely boundary-based, but a blend of the two. This new style of algorithm design is illustrated by a new program for finding edges in grey-scale images. Although the program is based on a relatively conventional second directional difference operator, it can detect fine texture in the presence of camera noise, produce connected boundaries around sharp corners, and return thin boundaries without "feathering. " New algorithms are presented for combining directional differences, suppressing the effects of camera noise, reconstructing image intensities from the second difference values and merging results from different scales (including suppression of spurious boundaries in staircase patterns).

Target tracking and image interpretation in natural open world scenes

This thesis is concerned with tracking man made objects moving in natural open world scenes and based on the tracking data, construct a structural representation of that scene, frame by frame. The system developed uses a static camera and a statistical frame differencing technique for detecting motion in an image that has a relatively static background. Objects with a measured temporal consistency are tracked across successive image frames. Based on the tracking data, regions in the scene are associated with particular types of dynamic event. For example regions containing movement (could be roads) and regions where objects seem to disappear or partially disappear (could be hedges). Because of the sensitivity of the motion estimator to changes in scene illumination and environmental conditions, a tile-based method is used to detect scene motion based on the estimations of statistical variations within the tiles. An updating process is used to ensure that a reliable estimate of the background reference image is maintained by the system. Motion cues are matched against tracked objects from a previous frame using an estimate of the temporal continuity of an object. A spatial-temporal reasoning process is used to infer the structure in the image. This inference mechanism is implemented using a semantic network. The system has been tested on several open world sequences and in each case has demonstrated that it can identify and track vehicles moving in the scene. Based on the motion of these vehicles regions in the image were identified and scene maps constructed for each scene. The map identified regions where vehicles can be expected to be observed moving and regions where they could become occluded. A CD-ROM is included with this thesis that contains the results obtained by the system for the two image sequences used in chapter seven. These results incorporate some of the enhancements outlined in chapter 8, section 8.3. A windows movie player is included on the CD-ROM and appendix d provides information on the contents of the CD-ROM together with installation and operating instructions

Boundaries and Topological Algorithms

This thesis develops a model for the topological structure of situations. In this model, the topological structure of space is altered by the presence or absence of boundaries, such as those at the edges of objects. This allows the intuitive meaning of topological concepts such as region connectivity, function continuity, and preservation of topological structure to be modeled using the standard mathematical definitions. The thesis shows that these concepts are important in a wide range of artificial intelligence problems, including low-level vision, high-level vision, natural language semantics, and high-level reasoning