Proceso Fernandez

MINING POSETS FROM LINEAR ORDERS

There has been much research on the combinatorial problem of generating the linear extensions of a given poset. This paper focuses on the reverse of that problem, where the input is a set of linear orders, and the goal is to construct a poset or set of posets that generates the input. Such a problem finds applications in computational neuroscience, systems biology, paleontology, and physical plant engineering. In this paper, two algorithms are presented for efficiently finding a single poset, if, such a poset exists whose linear extensions are exactly the same as the input set of linear orders. The variation of the problem where a minimum set of posets that cover the input is also explored. This variation is shown to be polynomially solvable for one class of simple posets (kite(2) posets) but NP-complete for a related class (hammock(2,2,2) posets).

Performance comparison of the Teknomo-Fernandez algorithm on the RGB and HSV colour spaces

Segmentation of the foreground objects is the primary step in many video analysis applications. The accuracy of the segmentation is dependent on an accurate background image that is used for background subtraction. The Teknomo-Fernandez (TF) algorithm is an efficient algorithm that quickly generates a good background image. A previous study showed the extendibility of the TF algorithm to higher number of frames per tournament, with the original 3 frames TF3L to be the most efficient and best configuration for actual implementation. In this study, we examine the performance of the TF algorithm on both RGB and HSV colour spaces using the TF3, 4 configuration and the Wallflower dataset. A simple background subtraction with threshold is implemented. The performances are measured numerically using the number of false negative and false positive pixel count against the provided ideal foreground image. The results show that the TF algorithm implemented using both RGB and HSV generates accurate background images in a wide range of video settings. The HSV implementation exhibits higher accuracies than the RGB implementation for majority of the test videos with the cost of an increase in processing time.