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Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLARANS which

frames of measurements (range scans), together with the related issues of representation and manipulation of spatial uncertainties. Our approachistomaintain all the local frames of data as well as the relative spatial relationships between local frames. These spatial relationships are modeled

In this paper we will describe a representation for spatial relationships which makes explicit their inherent uncertainty. We will show ways to manipulate them to obtain estimates of relationships and associated uncertainties not explicitly given, and show how decisions to sense or act can be made

Öhman, K. 2001. Forest planning with consideration to spatial relationships. Doctor’s

Abstract—Scene matching is the process of recognizing two images as different views of the same scene captured using different sensor poses, and/or different types of sensors. In this work, each image contains the same objects and sensor pose parameters are not known. The spatial relationships

programming and spatial relationships

This paper describes the need for mining complex relationships in spatial data. Complex relationships are defined as those involving two or more of: multi-feature colocation, self-colocation, one-to-many relationships, self-exclusion and multi-feature exclusion. We demonstrate that even

In this paper, we describe an algorithm for object recognition that explicitly models and estimates the posterior probability function,. We have chosen a functional form of the posterior probability function that captures the joint statistics of local appearance and position on the object as well as the statistics of local appearance in the visual world at large. We use a discrete representation of local appearance consisting of approximately 106 P ( object image) patterns. We compute an estimate of P ( object image) in closed form by counting the frequency of occurrence of these patterns over various sets of training images. We have used this method for detecting human faces from frontal and profile views. The algorithm for frontal views has shown a detection rate of 93.0 % with 88 false alarms on a set of 125 images containing 483 faces combining the MIT test set of Sung and Poggio with the CMU test sets of Rowley, Baluja, and Kanade. The algorithm for detection of profile views has also demonstrated promising results.

and other research outputs Associative and spatial relationships in thesaurus-based retrieval