Fast contact detection between moving deformable polyhedra

Abstract

ftp://ftp.inrialpes.fr/pub/sharp/publications/joukhadar:etal:iros:99.pdf.gz (not accepted here, non vectorial font)/http://www.ieee.orgThis paper presents an approach to detect and localize contact between deformable polyhedra, which can be convex or concave depending on the time step. Usual contact detection algorithms, defined for convex polyhedra, cannot be used efficiently as they would imply to compute the convex decomposition of the considered polyhedra at each time step, as it can change due to the deformability of these polyhedra. The computation of this convex decomposition being very expensive (in complexity and computation time), we propose an algorithm to detect and localize the contact in linear time wrt the number of vertices. This algorithm returns the direction of this contact and the value of the maximum intersection distance between the convex hulls of the two considered polyhedra. Experimental results, taken from a dynamic simulation application, are presented with their computation time to complete the complexity analysis

Real-time knot-tying simulation

“…However, less effort has been spent on collision detection between deformable objects [Brown et al 2002;Larsson and Akenine-Möller 2001;Joukhadar et al 1999;Smith et al 1995;van den Bergen 1997;Volino and Magnenat-Thalmann 1995], and even less on finding self-collisions in a deformable object [Guibas et al 2002;Lotan et al 2002;Volino and Magnenat-Thalmann 1995].…”

Object Tracking Using Local Multiple Features and a Posterior Probability Measure

“…Because of the background interference, the tracking result may easily get biased or be completely wrong. The location of the target obtained by the Bhattacharyya coefficient [ 7 ] or other similarity measures, such as normalized cross correlation, histogram intersection distance [ 13 ], and Kullback–Leibler divergence [ 14 ] may not be the ground truth. To improve the accuracy of object matching, a maximum posterior probability measure was proposed [ 15 ].…”

Word recognition in continuous speech with background noise based on posterior probability measure

“…The output of the probabilistic model is passed to the similarity measure which is the next component of SR. The most widely used similarity measure algorithms are Bhattacharyya coefficient [12][13][14], Kullback-Leibler divergence [15], Normalized Cross Correlation [16], Histogram intersection distance [17], Tanimoto coefficient [9], PPM for image processing [20]. Cross Correlation and Bhatacharraya Coefficient are most common pattern matching techniques applied in speech recognition [20].…”

“…The most common probability measure algorithms are [12,13,14,15,16,17,20]. [12], [13] and [14] are used extensively for similarity measure and probabilistic distribution.…”