Chen J.-HCHU-SONG CHEN2023-06-092023-06-0920043029743https://www.scopus.com/inward/record.uri?eid=2-s2.0-35048822418&doi=10.1007%2f978-3-540-24670-1_9&partnerID=40&md5=cb386ae62ad446933bc8822eb878f5a8https://scholars.lib.ntu.edu.tw/handle/123456789/632548An image sequence-based framework for appearance-based object recognition is proposed in this paper. Compared with the methods of using a single view for object recognition, inter-frame consistencies can be exploited in a sequence-based method, so that a better recognition performance can be achieved. We use the nearest feature line method (NFL) [8] to model each object. The NFL method is extended in this paper by further integrating motion-continuity information between features lines in a probabilistic framework. The associated recognition task is formulated as maximizing an a posteriori probability measure. The recognition problem is then further transformed to a shortest-path searching problem, and a dynamic-programming technique is used to solve it. © Springer-Verlag 2004.Computer vision; Dynamic programming; Image processing; A-posteriori probabilities; Appearance-based object recognition; Dynamic programming techniques; Feature lines; Image sequence; Nearest feature line method; Probabilistic framework; Shortest path searching; Object recognitionjournal article10.1007/978-3-540-24670-1_92-s2.0-35048822418