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| F. Stein, G. Medioni, "Structural Indexing: Efficient 2D Object Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 12, pp. 1198-1204, December, 1992. | |||
| BibTex | x | ||
| @article{ 10.1109/34.177385, author = {F. Stein and G. Medioni}, title = {Structural Indexing: Efficient 2D Object Recognition}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {12}, issn = {0162-8828}, year = {1992}, pages = {1198-1204}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.177385}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Structural Indexing: Efficient 2D Object Recognition IS - 12 SN - 0162-8828 SP1198 EP1204 EPD - 1198-1204 A1 - F. Stein, A1 - G. Medioni, PY - 1992 KW - structural indexing; consecutive segment groups; scene segmentation; scale-insensitivity; rotation-insensitivity; translation-insensitivity; efficient 2D object recognition; multiple flat objects; cluttered environment; multiple line tolerances; robustness; super segments; database; polygonal approximation; noise; occlusion; weak perspective; complexity bound; computational complexity; database management systems; image recognition; image segmentation VL - 14 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
The problem of recognition of multiple flat objects in a cluttered environment from an arbitrary viewpoint is addressed. The models are acquired automatically and approximated by polygons with multiple line tolerances for robustness. Groups of consecutive segments (super segments) are then encoded and entered into a table. This provides the essential mechanism for indexing and fast retrieval. Once the database of all models is built, the recognition proceeds by segmenting the scene into a polygonal approximation; the code for each super segment retrieves model hypotheses from the table. Hypotheses are clustered if they are mutually consistent and represent the instance of a model. Finally, the estimate of the transformation is refined. This methodology makes it possible to recognize models despite noise, occlusion, scale rotation translation, and a restricted range of weak perspective. A complexity bound is obtained.
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