Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skelet al flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.
We provide an algorithm that makes general assumptions: No strong physical model is applied, instead, we assume that a skelet al structure can be used to characterise broad appearance and skelet al matching can be used to characterise broad motion. This general idea has been used by @cite_29 for rain cloud tracking with radar image; we differ in the way in which both construct and match skeletons. Noted that the proposed method is much simpler compare to @cite_29 .
@cite_29
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We describe a novel method for searching and comparing 3D objects. The method encodes the geometric and topological information in the form of a skelet al graph and uses graph matching techniques to match the skeletons and to compare them. The skelet al graphs can be manually annotated to refine or restructure the search. This helps in choosing between a topological similarity and a geometric (shape) similarity. A feature of skelet al matching is the ability to perform part-matching, and its inherent intuitiveness, which helps in defining the search and in visualizing the results. Also, the matching results, which are presented in a per-node basis can be used for driving a number of registration algorithms, most of which require a good initial guess to perform registration. We also describe a visualization tool to aid in the selection and specification of the matched objects.