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- W2597660848 abstract "Studies on computational neuroscience through functional magnetic resonance imagingand following human visual systems state that the mammalian brain pursues two distinctpathways in the model. These pathways are designed to analyze not only motion information(optical flow) but also the ventral processing stream in the brain that proceedswith form features, in which Gabor wavelet is widely used. The original model of themammalian visual system represents two independent pathways, which become a subjectof interest among researchers. Model development is performed via systematic organization,where the active basis model is added into the ventral processing stream. TheGabor wavelet-based and supervised method is efficient in terms of Gabor beam utilizationand object recognition-directed task through form pathway. In addition, the motioninformation that is generated via optical flow in motion pathway is stabilized through applyingthe fuzzy membership scoring, which delays the changes in optical flow outcomesand provides further robustness to the system. The interaction between these processingpathways is another substantial matter implied in the model. The cross-connection of thetwo pathways is implied throughout the present research via direct consideration, suchas shared sketch algorithm and optical flow information, fuzzy max-product involvement,and scoring among each other. In addition, the model is considered a form informationthrough active basis model based on incremental slow feature analysis (denoted as slowfeatures). In this study, the motion perception in human visual system comprises fast andslow feature interactions, which render biological movement understandable. Primarily,a form feature is defined. This feature biologically follows the visual system throughapplying active basis model and incremental slow feature analysis for extraction of theslowest form features of human object for ventral stream. The interaction is consideredwithin the time that provides valuable features to recognize biological movements. Incrementalslow feature analysis provides a chance for fast action prototypes and bag-of-wordtechniques, and opens a new perspective to recognize the original biological movementmodel. Episodic observation is required to extract the slowest features. However, fast features of dorsal processing pathway through episodic ventral analysis update the processingof motion information. Experimental results in the development of the biologicalmovement model indicate promising accuracies for proposed improvements and favorableperformance on different datasets (KTH and Weizmann). The results also providepromising direction on this area." @default.
- W2597660848 created "2017-04-07" @default.
- W2597660848 creator A5086309547 @default.
- W2597660848 date "2016-09-06" @default.
- W2597660848 modified "2023-09-27" @default.
- W2597660848 title "Human action recognition using slow feature analysis / Bardia Yousefi" @default.
- W2597660848 hasPublicationYear "2016" @default.
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