Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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2012 - 20192

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Author: search.filters.author.Özuysal, MustafaWoS Q: search.filters.wosQuartile.Q4

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  • Article
    Citation - Scopus: 1
    Curve Description by Histograms of Tangent Directions
    (Institution of Engineering and Technology, 2019) Köksal, Ali; Özuysal, Mustafa
    The authors propose a novel approach for the description of objects based on contours in their images using real-valued feature vectors. The approach is particularly suitable when objects of interest have high contrast and texture-free images or when the texture variations are high so textural cues are nuisance factors for classification. The proposed descriptor is suitable for nearest neighbour classification still popular in embedded vision applications when the power considerations outweigh the performance requirements. They describe object outlines purely based on the histograms of contour tangent directions mimicking many of the design heuristics of texture-based descriptors such as scale-invariant feature transform (SIFT). However, unlike SIFT and its variants, the proposed approach is directly designed to work with contour data and it is robust to variations inside and outside the object outline as well as the sampling of the contour itself. They show that relying on tangent direction estimation as opposed to gradient computation yields a more robust description and higher nearest neighbour classification rates in a variety of classification problems.
  • Article
    Citation - WoS: 11
    Citation - Scopus: 12
    Passenger Flows Estimation of Light Rail Transit (lrt) System in Izmir, Turkey Using Multiple Regression and Ann Methods
    (Faculty of Transport and Traffic Sciences, University of Zagreb, 2012) Özuysal, Mustafa; Tayfur, Gökmen; Tanyel, Serhan
    Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.