A computer vision system for the recognition of trees in aerial photographs

Increasing problems of forest damage in Central Europe set the demand for an appropriate forest damage assessment tool. The Vision Expert System (VES) is presented which is capable of finding trees in color infrared aerial photographs. Concept and architecture of VES are discussed briefly. The system is applied to a multisource test data set. The processing of this multisource data set leads to a multiple interpretation result for one scene. An integration of these results will provide a better scene description by the vision system. This is achieved by an implementation of Steven's correlation algorithm

Representations for Cognitive Vision : a Review of Appearance-Based, Spatio-Temporal, and Graph-Based Approaches

The emerging discipline of cognitive vision requires a proper representation of visual information including spatial and temporal relationships, scenes, events, semantics and context. This review article summarizes existing representational schemes in computer vision which might be useful for cognitive vision, a and discusses promising future research directions. The various approaches are categorized according to appearance-based, spatio-temporal, and graph-based representations for cognitive vision. While the representation of objects has been covered extensively in computer vision research, both from a reconstruction as well as from a recognition point of view, cognitive vision will also require new ideas how to represent scenes. We introduce new concepts for scene representations and discuss how these might be efficiently implemented in future cognitive vision systems

Consistent Visual Information Processing Applied to Object Recognition, Landmark Definition, and Real-Time Tracking. VMV'01

The handling of situations where multiple visual information occurs requires the fusion of visual information. This is a very common task found in the processing of multisource / multitemporal datasets, in sensor fusion, and in all kinds of active vision systems. A general approach to this problem is presented which goes beyond previous information theoretic investigations. Starting from the paradigm of ‘Active Fusion’, where entropy is used as a measure to evaluate the expected gain in information from a potential data source, we develop the concept of data ‘consistency’. In multisource visual information processing, consistency can be expressed by vicinity in space, by similarity of visual landmarks or by higher level constraints like smoothness of motion trajectories, rigid body, or continuity constraints. Several sample applications are presented, including an active object recognition system, the definition of salient landmarks, and an optical tracking system. In summary, consistency evaluation is a powerful method to reduce complexity and to resolve otherwise ill-posed problems like ambiguity in computer vision.