caa a22 4500 46905140X CHVBK 20180323132831.0 cr unu---uuuuu 170328e19921201xx s 000 0 eng 10.1007/BF01213527 doi (NATIONALLICENCE)springer-10.1007/BF01213527 The background primal sketch: An approach for tracking moving objects [Elektronische Daten] [Yee-Hong Yang, Martin Levine] In this paper we present an algorithm that integrates spatial and temporal information for the tracking of moving nonrigid objects. In addition, we obtain outlines of the moving objects. Three basic ingredients are employed in the proposed algorithm, namely, the background primal sketch, the threshold, and outlier maps. The background primal sketch is an edge map of the background without moving objects. If the background primal sketch is known, then edges of moving objects can be determined by comparing the edge map of the input image with the background primal sketch. A moving edge point is modeled as an outlier, that is, a pixel with an edge value differing from the background edge value in the background primal sketch by an amount larger than the threshold in the threshold map at the same physical location. The map that contains all the outliers is called the outlier map. In this paper we present techniques based on robust statistics for determining the background primal sketch, the threshold, and outlier maps. In an ideal situation the outlier map would contain the complete outlines of the moving objects. In practice, the outliers do not form closed contours. The final step of the algorithm employs an edge-guided morphological approach to generate closed outlines of the moving objects. The proposed approach has been tested on sequences of moving human blood cells (neutrophil) as well as of human body motion with encouraging results. Springer-Verlag New York Inc, 1992 active contour nationallicence motion tracking nationallicence moving edges nationallicence primal sketch nationallicence robust statistics nationallicence snakes nationallicence Yang Yee-Hong Department of Computational Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada aut Levine Martin McGill Research Center for Intelligent Machines, McGill University, Montréal, Québéc, Canada aut Machine Vision and Applications Springer-Verlag 5/1(1992-12-01), 17-34 0932-8092 5:1<17 1992 5 138 https://doi.org/10.1007/BF01213527 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF01213527 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yang Yee-Hong Department of Computational Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada aut NATIONALLICENCE 700 1- Levine Martin McGill Research Center for Intelligent Machines, McGill University, Montréal, Québéc, Canada aut NATIONALLICENCE 773 0- Machine Vision and Applications Springer-Verlag 5/1(1992-12-01), 17-34 0932-8092 5:1<17 1992 5 138 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer