260 research outputs found
The Complexity of Reasoning about Spatial Congruence
In the recent literature of Artificial Intelligence, an intensive research
effort has been spent, for various algebras of qualitative relations used in
the representation of temporal and spatial knowledge, on the problem of
classifying the computational complexity of reasoning problems for subsets of
algebras. The main purpose of these researches is to describe a restricted set
of maximal tractable subalgebras, ideally in an exhaustive fashion with respect
to the hosting algebras. In this paper we introduce a novel algebra for
reasoning about Spatial Congruence, show that the satisfiability problem in the
spatial algebra MC-4 is NP-complete, and present a complete classification of
tractability in the algebra, based on the individuation of three maximal
tractable subclasses, one containing the basic relations. The three algebras
are formed by 14, 10 and 9 relations out of 16 which form the full algebra
Integrated region- and pixel-based approach to background modelling
In this paper a new probabilistic method for background modelling is proposed, aimed at the application in video surveillance tasks using a monitoring static camera. Recently, methods employing Time-Adaptive, Per Pixel, Mixture of Gaussians (TAPPMOG) modelling have become popular due to their intrinsic appealing properties. Nevertheless, they are not able per se to monitor global changes in the scene, because they model the background as a set of independent pixel processes. In this paper, we propose to integrate this kind of pixel-based information with higher level region-based information, that permits to manage also sudden changes of the background. These pixel- and regionbased modules are naturally and effectively embedded in a probabilistic Bayesian framework called particle filtering, that allows a multi-object tracking. Experimental comparison with a classic pixel-based approach reveals that the proposed method is really effective in recovering from situations of sudden global illumination changes of the background, as well as limited non-uniform changes of the scene illumination.
ACOUSTIC RANGE IMAGE SEGMENTATION BY EFFECTIVE MEAN SHIFT
Image perception in underwater environment is a difficult task for a human operator, and data segmentation becomes a crucial step toward an higher level interpretation and recognition of the observing scenarios. This paper contributes to the related state of the art, by fitting the mean shift clustering paradigm to the segmentation of acoustical range images, providing a segmentation approach in which whatever parameter tuning is absent. Moreover, the method exploits actively the connectivity information provided by the range map, by using reverse projection as acceleration technique. Therefore, the method is able to produce, starting from raw range data, meaningful segmented clouds of points in a fully automatic and efficient fashion. 1
A knowledge-intensive methodology for explainable sales prediction
Sales prediction in food market is a complex issue that has been addressed in the recent past with machine learning techniques. Although some promising results, an experimental work that we describe in this paper shows some drawbacks of the above mentioned data-driven method and habilitates the definition of a novel methodology, strongly involving a piori knowledg
Lipid peroxidation and protein oxidation in patients affected by Hodgkin's lymphoma.
A dysregulation of the redox homoeostasis has been reported in various neoplastic disorders. Malondialdehyde/4-hydroxy-2,3-nonenal (MDA/HNE) and protein carbonyl groups represent in vivo indexes of lipid peroxidation and protein oxidation, respectively, suitable to investigate radical-mediated physio-pathological conditions. We evaluated MDA/HNE and protein carbonyl groups in sera of untreated Hodgkin's lymphoma (HL) patients in advanced disease stages, in order to quantify the oxidative stress. HL patients displayed significantly higher levels of both MDA/HNE and protein carbonyl groups as compared with healthy controls. This is the first evidence that a strong increase in HL is one of the most common haematological malignancies, representing approximately 30% of all lymphomas in the circulating protein carbonyl content in HL. These findings may contribute to a better definition of the redox homoeostasis dysregulation in HL
Dynamics of Crossover from a Chaotic to a Power Law State in Jerky Flow
We study the dynamics of an intriguing crossover from a chaotic to a power
law state as a function of strain rate within the context of a recently
introduced model which reproduces the crossover. While the chaotic regime has a
small set of positive Lyapunov exponents, interestingly, the scaling regime has
a power law distribution of null exponents which also exhibits a power law. The
slow manifold analysis of the model shows that while a large proportion of
dislocations are pinned in the chaotic regime, most of them are pushed to the
threshold of unpinning in the scaling regime, thus providing insight into the
mechanism of crossover.Comment: 5 pages, 3 figures. In print in Phy. Rev. E Rapid Communication
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