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Object extration as a basic process for content-based image retrieval (CBIR) system

Jaworska T.

Opto-Electronics Review

2007

Vol. 15, No. 4

184-195

This article describes the way in which image is prepared for content-based image retrieval system. Automated image extraction is crucial; especially, if we take into consideration the fact that the feature selection is still a task performed by human domain experts and represents a major stumbling block in the process of creating fully autonomous CBIR systems. Our CBIR system is dedicated to support estate agents. In the database, there are images of houses and bungalows. We put all our efforts into extracting elements from an image and finding their characteristic features in the unsupervised way. Hence, the paper presents segmentation algorithm based on a pixel colour in RGB colour space. Next, it presents the method of object extraction applied to obtain separate objects prepared for the process of introducing them into database and further recognition. Moreover, we present a novel method of texture identification which is based on wavelet transformation. Due to the fact that the majority of texture is geometrical (such as bricks and tiles) we have used the Haar wavelet. After a set of low-level features for all objects is computed, the database is stored with these features.

Extraction of data from low quality photographs to aid the construction of virtual reality models of archeological sites

Williams J. G., Blake E. H., Ruther H.

Machine Graphics and Vision

2001

Vol. 10, No. 3

353-368

A tool has been constructed to use information extracted from photographs captured using uncalibrated cameras to fill occlusions which occur in three-dimensional models of photogrammetrically captured sites. Capturing the geometry of archeological sies by photogrammetric means is relatively expensive and, because of the typical layouts of such sites, usually results in a degree of occulusion. The essential philosophy underlying the tool is to segment each occlusion into surfaces which may be approximated using curves, and then use the known geometry in the region of the occlusion to calculate the most probable locations of the junctions of such surface segments. Texture of the surface segments is then applied to the tree-dimensional model. The tool has been applied to occulations of various configurations thatb are expected to be typical for archeological sites, and has been found to deal well with such features and to provide accurate patches from typical data sets. It is also shown that the tree-dimensional geometric model is clearly improved by the filling-in of the occlusion.