Multivariate orthogonal polynomials to extract singular points.
ABSTRACT In fluid motion analysis, the extraction of singularity is an important step. This points are crucial for the analysis of physic phenomenas. For instance in meteorology these singularities might represent the center of depression.The objective of this paper is to present an original method for extraction of singularities in a vector field. We study the affine model of the motion to extract potential singularities. The originality of our method reside in the computation of the affine model by projection of the vector field onto multivariate orthogonal polynomials basis. We use a one degree basis so this method is enough computationally efficient to be included in a multiscale scheme. We have tested this method on synthetic and experimental vector field. It provides significant results. Moreover this technique is robust to noise.
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ABSTRACT: We present a method for detecting and describing features in vector ow elds. Our method models ow elds locally using a linear combination of complex monomials. These monomials form an orthog- onal basis for analytic ows with respect to a correlation-based inner- product. We investigate the invariance properties of the coecients of the approximation polynomials under both rotation and scaling opera- tors. We then propose a descriptor for local ow patterns, and developed a method for comparing them invariantly against rigid transformations. Additionally, we propose a SIFT-like detector that can automatically de- tect singular ow patterns at dierent scales and orientations. Promising detection results are obtained on dierent uid ow data.Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings; 01/2010
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ABSTRACT: In this paper, we present the first participation of a consortium of French laboratories, IRIM, to the TRECVID 2008 BBC Rushes Summarization task. Our approach resorts to video skimming. We propose two methods to reduce redundancy, as rushes include several takes of scenes. We also take into account low and mid-level semantic features in an ad-hoc fusion method in order to retain only significant content
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ABSTRACT: In this paper, we propose a method for detecting patterns of interest in vector fields. Our method detects patterns in a scale- and rotation-invariant manner. It works by approximating the vector-field data locally using a Laurent polynomial weighted by radial basis functions. The proposed representation is able to model both analytic and non-analytic flow fields. Invariance to scale and rotation is achieved by combining the linearity properties of the model coefficients and a scale-space parameter of the radial basis functions. Promising detection results are obtained on a variety of fluid-flow sequences.Pattern Recognition 11/2012; 45(11):3912–3925. DOI:10.1016/j.patcog.2012.04.025 · 2.58 Impact Factor