Emerging video standards addresses mainly content-based functionalities and interactive multimedia. This requires a prior decomposition of sequences into semantically meaningful physical objects. We formulate this problem as one of separating foreground objects from the background based on motion information. The algorithm begins with the motion estimation accomplished using ARPS (Adaptive Rood Pattern Search) algorithm on any two frames of image sequence. A binary object model representing edge features is derived using Canny edge detector and tracked in subsequent frames using motion vectors generated from ARPS algorithm. The object model is updated for each frame to accommodate for complex motions and shape changes. Motion based region merging is performed finally to merge regions with similar motion to extract moving objects. Experimental results demonstrate the performance of our proposed algorithm.
Autoimmune encephalitis is most commonly caused by autoantibodies against N-methyl-D-aspartate (NMDA) receptors, and the malignancy most often associated with anti-NMDA receptor autoimmune encephalitis is an ovarian teratoma. Here, we describe a case of autoimmune encephalitis caused by a newly discovered cerebrospinal fluid autoantibody that has not been previously described and is not anti-NMDA receptor-mediated, which has been associated with an ovarian teratoma. It was successfully treated with high-dose corticosteroids and plasmapheresis followed by rituximab and chemotherapy (paclitaxel, ifosfamide, and cisplatin) for her teratoma.
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