An Efficient Descriptor-Based Approach for Dominant Point Detection in Shape Contours
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2023, Vol 2, Issue 3
Abstract
Dominant points, or control points, represent areas of high curvature on shape contours and are extensively utilized in the representation of shape outlines. Herein, we introduce a novel, descriptor-based approach for the efficient detection of these pivotal points. Each point on a shape contour is evaluated and mapped to an invariant descriptor set, accomplished through the use of point-neighborhood. These descriptors are then harnessed to discern whether a point qualifies as a dominant one. Our proposed methodology eliminates the need for costly computations typically associated with evaluating candidate dominant points. Furthermore, our algorithm significantly outperforms its predecessors in terms of speed, relying solely on integer operations and obviating the necessity for an optimization phase. Experimental outcomes, derived from the widely used MPEG7_CE-Shape-1_Part_B, denote a minimum enhancement of 2.3 times in terms of running time. This implies that the proposed methodology is particularly suitable for real-time applications or scenarios managing shapes comprising a substantial number of points.
Authors and Affiliations
Mohammad T. Parvez
Keywords
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- EP ID EP731891
- DOI https://doi.org/10.56578/ataiml020303
- Views 55
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