Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency

Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series or string of symbols, which can be seen as an input for various subsequence matching approaches. The variety of data types, specific tasks and their partial or full solutions is so wide that the choice, implementation and parametrization of a suitable solution for a given task might be complicated and time-consuming; a possibly fruitful combination of fragments from different research areas may not be obvious nor easy to realize. The leading authors of this field also mention the implementation bias that makes difficult a proper comparison of competing approaches. Therefore we present a new generic Subsequence Matching Framework (SMF) that tries to overcome the aforementioned problems by a uniform frame that simplifies and speeds up the design, development and evaluation of subsequence matching related systems. We identify several relatively separate subtasks solved differently over the literature and SMF enables to combine them in straightforward manner achieving new quality and efficiency. This framework can be used in many application domains and its components can be reused effectively. Its strictly modular architecture and openness enables also involvement of efficient solutions from different fields, for instance efficient metric-based indexes. This is an extended version of a paper published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201

Granular temperature in a gas fluidized bed

In this paper, we present an innovative approach coupling active contours with an ontological representation of knowledge, in order to understand scenes acquired by a moving camera and containing multiple non-rigid objects evolving over space and time. The developed active contours enable both segmentation and tracking of multiple targets in each captured scene over a video sequence with unknown camera calibration. Hence, this active contour technique provides information on the objects of interest as well as on parts of them (e.g. shape and position), and contains simultaneously low-level characteristics such as intensity or color features. The ontology we propose consists of concepts whose hierarchical levels map the granularity of the studied scene and of a set of inter- and intra-object spatial and temporal relations defined for this framework, object and sub-object characteristics e.g. shape, and visual concepts like color. The system obtained by coupling this ontology with active contours can study dynamic scenes at different levels of granularity, both numerically and semantically characterize each scene and its components i.e. objects of interest, and reason about spatiotemporal relations between them or parts of them. This resulting knowledge-based vision system was demonstrated on real-world video sequences containing multiple mobile highly-deformable objects

Granular temperature in a gas fluidized bed

© Springer-Verlag 2007Mark J. Biggs, Don Glass, L. Xie, Vladimir Zivkovic, Alex Buts and M. A. Curt Kounder