Advances in Homotopy Applied to Object Deformation. Salazar-Castro, J., A., Umaquinga-Criollo, A., C., Cruz-Cruz, L., D., Alpala-Alpala, L., O., González-Castaño, C., Becerra-Botero, M., A., Peluffo-Ordóñez, D., H., & Castellanos-Domínguez, C., G. Volume 10814 LNBI. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 231-242. 2018. ![Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 3 downloads This work explores novel alternatives to conventional linear homotopy to enhance the quality of resulting transitions from object deformation applications. Studied/introduced approaches extend the linear mapping to other representations that provides smooth transitions when deforming objects while homotopy conditions are fulfilled. Such homotopy approaches are based on transcendental functions (TFH) in both simple and parametric versions. As well, we propose a variant of an existing quality indicator based on the ratio between the coefficients curve of resultant homotopy and that of a less-realistic, reference homotopy. Experimental results depict the effect of proposed TFH approaches regarding its usability and benefit for interpolating images formed by homotopic objects with smooth changes.
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chapter = {Advances in Homotopy Applied to Object Deformation},
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