This paper describes the fusion of information extracted from multispectral digital aerial images for land use classification. The proposed approach integrates spectral classification techniques and spatial information. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and NIR channels and form the basis for information ext...

Hidden object imaging is challenging problem in the fields of computer vision and image processing, and it’s a key step in many application fields, include intelligent video surveillance, visual tracking and scene understanding. Recently, the camera array synthetic aperture imaging has been proved to be a powerful technology for hidden object detection, and the state-of-art synthetic imaging me...

Mediators are used for integration of heterogeneous information sources. In this paper we present a system for declaratively specifying mediators. It is targeted for integration of sources with unstructured or semi-structured data, or sources with changing schemas. In the paper we illustrate the main features of the Mediator Speciication Language (MSL), show how they facilitate integration, and...

While recent advances in computer vision have caused object recognition rates to spike, there is still much room for improvement. In this paper, we develop an algorithm to improve object recognition by integrating human-generated contextual information with vision algorithms. Specifically, we examine how interactive systems such as robots can utilize two types of context information–verbal desc...

This paper presents research conducted into the automatic recognition of features and objects on topographic maps (for example, buildings, roads, land parcels etc.) using a selection of shape description methods developed mostly in the field of computer vision. In particular the work here focuses on the proposal and evaluation of fusion techniques (at the decision level of representation) for t...

We describe an object classification method based on weighted score-level feature fusion using learned weights. Our method is able to recognize 20 object classes in a customized fruit dataset. Although the fusion of multiple features is commonly used to distinguish variable object classes, the optimal combination of features is not well defined. Moreover, in these methods, most parameters used ...

Given two geographic databases, a fusion algorithm should produce all pairs of corresponding objects (i.e., objects that represent the same real-world entity). Four fusion algorithms, which only use locations of objects, are described and their performance is measured in terms of recall and precision. These algorithms are designed to work even when locations are imprecise and each database repr...

In this paper we introduce a novel sensor fusion algorithm based on the cooperative coevolutionary paradigm. We develop a multisensor robust moving object detection system that can operate under a variety of illumination and environmental conditions. Our experiments indicate that this evolutionary paradigm is well suited as a sensor fusion model and can be extended to different sensing modalities.

We address the task of ranking objects (such as people, blogs, or verticals) that, unlike documents, do not have direct term-based representations. To be able to match them against keyword queries, evidence needs to be amassed from documents that are associated with the given object. We present two design patterns, i.e., general reusable retrieval strategies, which are able to encompass most ex...

This paper addresses the problem of identity fusion, i.e. the problem of selecting one of several identity hypotheses concerning an observed object. Two problems are considered. Firstly the problem of representing and fusing measurements relating to identity hypotheses is treated. Here it is shown that if the hypotheses are chosen to be concerned with object properties rather than object identi...