This paper analyzes how the natural scales of the shapes in 2D images can be extracted. Spatial information is analyzed by multiple units sensitive to both spatial and spatial-frequency variables. Scale estimates of the relevant shapes are constructed only from strongly responding detectors. The meaningful structures in the response of a detector (computed through 2-D Gabor ltering) are, at their natural level of resolution, relatively sharp and have well-de ned boundaries. A natural scale is so de ned as a level producing local minimum of a function that returns the relative sharpness of the detector response ltered over a range of scales. In a second stage, to improve a rst crude estimate of the local scale, the criterion is also rewritten to directly select scales at locations of signi cant features of each activated detector.
Here we study the relationship between journal quartile rankings of ISI impact factor (at the 2010) and journal classification in four impact classes, i.e., highest impact, medium highest impact, medium lowest impact, and lowest impact journals in subject category computer science artificial intelligence. To this aim, we use fuzzy maximum likelihood estimation clustering in order to identify groups of journals sharing similar characteristics in a multivariate indicator space. The seven variables used in this analysis are: (1) Scimago Journal Ranking (SJR); (2) H-Index (H); (3) ISI impact factor (IF); (4) 5-Year Impact Factor (5IF); (5) Immediacy Index (II); (6) Eigenfactor Score (ES); and (7) Article Influence Score (AIS). The fuzzy clustering allows impact classes to overlap, thereby accommodating for uncertainty related to the confusion about the impact class attribution for a journal and vagueness in impact classes definition. This paper demonstrates the complex relationship between quartiles of ISI impact factor and journal impact classes in the multivariate indicator space. And that several indicators should be used for a distinct analysis of structural changes at the score distribution of journals in a subject category. Here we propose it can be performed in a multivariate indicator space using a fuzzy classifier.
In this paper, a new frequency-domain approach to represent motions is proposed. The new scheme is based on a band-pass filtering with a set of logGabor spatio-temporal filters. It is well known that one of the main problems of these approaches is that a filter response varies with the spatial orientation of the underlying signal. To solve this spatial dependency, the proposed model allows to recombine information of motions that has been separated in several filter responses due to its spatial structure. For this purpose, motion patterns are detected as invariance in statistical structure across a range of spatio-temporal frequency bands. This technique is illustrated on real and simulated data sets, including sequences with occlusion and transparencies.
Abstract. In this paper, a new frequency-based approach to motion segmentation is presented. The proposed technique represents the sequence as a spatio-temporal volume, where a moving object corresponds to a three-dimensional object. In order to detect the "3D volumes" corresponding to significant motions, a new scheme based on a band-pass filtering with a set of logGabor spatio-temporal filters is used. It is well known that one of the main problems of these approaches is that a filter response varies with the spatial orientation of the underlying signal. To solve this spatial dependency, the proposed model allows to recombine information of motions that has been separated in several filter responses due to its spatial structure. For this purpose, motions are detected as invariance in statistical structure across a range of spatio-temporal frequency bands. This technique is illustrated on real and simulated data sets, including sequences with occlusion and transparencies.
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