Browsing by Subject "Sequence matching"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Content-based video copy detection based on motion vectors estimated using a lower frame rate
    (Springer U K, 2014-09) Taşdemir K.; Çetin, A. Enis
    We propose a motion vector-based video content-based copy detection method. One of the signatures of a given video is motion vectors extracted from image sequences. However, when consecutive image frames are used, the resulting motion vectors are not descriptive enough because most vectors are either too small or they appear to scatter in all directions. We calculate motion vectors in a lower frame rate than the actual frame rate of the video to overcome this problem. As a result, we obtain large vectors and they represent a given video in a robust manner. We carry out experiments for various parameters and present the results. © 2014 Springer-Verlag London.
  • Thumbnail Image
    ItemOpen Access
    Matching Islamic patterns in Kufic images
    (Springer-Verlag London Ltd, 2015) Arifoglu, D.; Sahin, E.; Adiguzel, H.; Duygulu, P.; Kalpakli, M.
    In this study, we address the problem of matching patterns in Kufic calligraphy images. Being used as a decorative element, Kufic images have been designed in a way that makes it difficult to be read by non-experts. Therefore, available methods for handwriting recognition are not easily applicable to the recognition of Kufic patterns. In this study, we propose two new methods for Kufic pattern matching. The first method approximates the contours of connected components into lines and then utilizes chain code representation. Sequence matching techniques with a penalty for gaps are exploited for handling the variations between different instances of sub-patterns. In the second method, skeletons of connected components are represented as a graph where junction and end points are considered as nodes. Graph isomorphism techniques are then relaxed for partial graph matching. Methods are evaluated over a collection of 270 square Kufic images with 8,941 sub-patterns. Experimental results indicate that, besides retrieval and indexing of known patterns, our method also allows the discovery of new patterns.