Ossama El Badawy

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We propose a new shape-based, query-by-example, image database retrieval method that is able to match a query image to one of the images in the database, based on a whole or partial match. The proposed method has two key components: the architecture of the retrieval and the features used. Both play a role in the overall retrieval efficacy. The proposed(More)
A concavity tree is a data structure for hierarchically representing the shape of two-dimensional silhouettes using convex polygons. In this paper, we present a new algorithm for concavity tree extraction. The algorithm is fast, works directly on the pixel grid of the shape, and uses exact convex hull computations. We compare our method to the morphological(More)
The purpose of this paper is to allow for high level shape representation and matching in multi-object images by detecting and extracting the envelope of object groupings in the image. The proposed algorithm uses hierarchical clustering to find object groupings based on spatial proximity as well as low-level shape features of objects in the image. Each(More)
In this paper, we present a framework for clustering and classifying cheque images according to their payee-line content. The features used in the clustering and classification processes are extracted from the wavelet domain by means of thresholding and counting of wavelet coeilficients. The feasibility of this framework is tested on a database of 2620(More)
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