IEEE Transactions on Circuits and Systems for Video Technology · 1998 · 250 citations · 34 references
Scene AnalysisImportant Motion SegmentationEngineeringVideo ProcessingImage Sequence AnalysisImage AnalysisPattern RecognitionVideo Content AnalysisComputational GeometryAutomatic SegmentationGeometric ModelingMachine VisionVideo Object PlanesVideo UnderstandingStructure From MotionComputer VisionVideo SegmentationStandard Mpeg-4Natural SciencesMotion Analysis
MPEG‑4 video coding uses video object planes (VOPs) to represent individual moving objects, and recent reviews highlight key motion‑segmentation techniques for generating these VOPs. This work introduces an automatic video‑sequence segmentation algorithm that extracts moving objects for VOP generation. The algorithm employs a 2‑D binary object model tracked across frames with the Hausdorff distance, updating the model each frame to accommodate translation, rotation, and shape changes, and uses a moving‑connected‑components update rule and background filtering to handle large shape variations. The binary model sequence successfully guides extraction of VOPs, and experimental results confirm the algorithm’s effectiveness.
The new video coding standard MPEG-4 is enabling content-based functionalities. It takes advantage of a prior decomposition of sequences into video object planes (VOPs) so that each VOP represents one moving object. A comprehensive review summarizes some of the most important motion segmentation and VOP generation techniques that have been proposed. Then, a new automatic video sequence segmentation algorithm that extracts moving objects is presented. The core of this algorithm is an object tracker that matches a two-dimensional (2-D) binary model of the object against subsequent frames using the Hausdorff distance. The best match found indicates the translation the object has undergone, and the model is updated every frame to accommodate for rotation and changes in shape. The initial model is derived automatically, and a new model update method based on the concept of moving connected components allows for comparatively large changes in shape. The proposed algorithm is improved by a filtering technique that removes stationary background. Finally, the binary model sequence guides the extraction objects of the VOPs from the sequence. Experimental results demonstrate the performance of our algorithm.
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Robust Regression and Outlier Detection
Gregory F. Piepel, Peter J. Rousseeuw, Annick M. Leroy · Technometrics · 1989 · 6.2K citations