Title

Model Generation For Video-Based Object Recognition

Keywords

Morphing; Video-based object recognition

Abstract

This paper presents a novel approach to object recognition involving a sparse 2D model and matching using video. The model is generated on the basis of geometry and image measurables only. We first identify the underlying topological structure of an image dataset containing different views of the objects and represent it as a neighborhood graph. The graph is then refined by identifying redundant images and removing them using morphing. This gives a smaller dataset leading to reduced space requirements and faster matching. Finally we exploit motion continuity in video and extend our algorithm to perform matching based on video input and demonstrate that the results obtained using a video sequence are much robust than using a single image. Our approach is novel in that we do not require any knowledge of camera calibration or viewpoint while generating the model. We also do not assume any constraint on motion of object in test video other than following a smooth trajectory.

Publication Date

12-1-2006

Publication Title

Proceedings of the 14th Annual ACM International Conference on Multimedia, MM 2006

Number of Pages

715-718

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1180639.1180791

Socpus ID

34547198890 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/34547198890

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