Pascal Matsakis’s research while affiliated with University of Guelph and other places

A relative position descriptor is a quantitative representation of the relative position of two spatial objects. It is a low-level image descriptor, like colour, texture, and shape descriptors. A good amount of work has been carried out on relative position description. Application areas include content-based image retrieval, remote sensing, medical imaging, robot navigation, and geographic information systems. This paper reviews the existing work. It identifies the approaches that have been used as well as the properties that can be expected from relative position descriptors. It compares and provides a brief overview of various descriptors, including their main properties, strengths and limitations, and it suggests areas for future work.

Spatial prepositions, like above, inside, near, denote spatial relationships. A relative position descriptor is a basis from which quantitative models of spatial relationships can be derived. It is an image descriptor, like colour, texture, and shape descriptors. Various relative position descriptors can be found in the literature. In this paper, we introduce a new relative position descriptor-the O-descriptor-that has about all the strengths of each and every one of its competitors, and none of the weaknesses. Our approach is based on the concept of the F-histogram and on an original categorization of pairs of consecutive boundary points on a line.

In daily conversation, people use spatial prepositions to denote spatial relationships and describe relative positions. Various quantitative relative position descriptors can be found in the literature. However, they all have been designed with 2D objects in mind, most of them cannot be extended to handle 3D objects in vector form, and there is currently no implementation able to process such objects. In this paper, we build on a descriptor called the histogram of forces, and we present the first algorithm for quantitative relative position descriptor calculation in the case of 3D vector objects. Experiments validate the approach.

Both quantitative and qualitative measures of directional spatial relationships (e.g., left, right, above) between two raster objects in a digital image are important for high-level computer vision tasks such as scene analysis and robot navigation. The histogram of forces can provide such measures, but cannot be computed in real-time. A new approach for real-time computation, based on a vector representation of raster objects, is presented. The performance of the proposed approach is examined in an extensive experiment. Considering processing time and accuracy, optimal assessments of directional spatial relationships for use in real-time applications can be obtained.

The histogram of forces is a generic relative position descriptor with remarkable properties, and it has found many applications, in various domains. So far, however, the applications involve objects in raster form. The fact is that several general algorithms for the computation of force histograms when dealing with such objects have been developed; on the other hand, there is no general algorithm available for objects in vector form, and the algorithms for raster objects cannot be adapted to vector objects. Here, the first general algorithm for calculating force histograms using vector data is presented.

The goal of this research is to evaluate the use of English stop word lists in Latent Semantic Indexing (LSI)-based Information Retrieval (IR) systems with large text datasets. Literature claims that the use of such lists improves retrieval performance. Here, three different lists are compared: two were compiled by IR groups at the University of Glasgow and the University of Tennessee, and one is our own list developed at the University of Northern British Columbia. We also examine the case where stop words are not removed from the input dataset. Our research finds that using tailored stop word lists improves retrieval performance. On the other hand, using arbitrary (non-tailored) lists or not using any list reduces the retrieval performance of LSI-based IR systems with large text datasets.

How to satisfactorily model spatial relationships between 2D or 3D objects? If the objects are far enough from each other, they can be approximated by their centers. If they are not too far, not too close, they can be approximated by their minimum bounding rectangles or boxes. If they are close, no such simplifying approximation should be made. Two concepts are at the core of the approach described in this paper: the concept of the F\mathcal{F}-histogram and that of the F\mathcal{F}-template. The basis of the former was laid a decade ago; since then, it has naturally evolved and matured. The latter is much newer, and has dual characteristics. Our aim here is to present a snapshot of these concepts and of their applications. It is to highlight (and reflect on) their duality–a duality that calls for a clear distinction between the terms spatial relationship, relationship to a reference object, and relative position. Finally, it is to identify directions for future research.