Browsing by Subject "Motion vectors"
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Item Open 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. EnisWe 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.Item Open Access HMM based method for dynamic texture detection(IEEE, 2007) Töreyin, Behçet Uğur; Çetin, A. EnisA method for detection of dynamic textures in video is proposed. It is observed that the motion vectors of most of the dynamic textures (e.g. sea waves, swaying tree leaves and branches in the wind, etc.) exhibit random motion. On the other hand, regular motion of ordinary video objects has well-defined directions. In this paper, motion vectors of moving objects are estimated and tracked based on a minimum distance based metric. The direction of the motion vectors are then quantized to define two threestate Markov models corresponding to dynamic textures and ordinary moving objects with consistent directions. Hidden Markov Models (HMMs) are used to classify the moving objects in the final step of the algorithm.Item Open Access Wavelet based detection of moving tree branches and leaves in video(IEEE, 2006-05) Töreyin, B. Uğur; Çetin, A. EnisA method for detection of tree branches and leaves in video is proposed. It is observed that the motion vectors of tree branches and leaves exhibit random motion. On the other hand regular motion of green colored objects has well-defined directions. In this paper, the wavelet transform of motion vectors are computed and objects are classified according to the wavelet coefficients of motion vectors. Color information is also used to reduce the search space in a given image frame of the video. Motion trajectories of moving objects are modeled as Markovian processes and Hidden Markov Models (HMMs) are used to classify the green colored objects in the final step of the algorithm. © 2006 IEEE.