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| T.W. Pai, J.H.L. Hansen, "Boundary-Constrained Morphological Skeleton Minimization and Skeleton Reconstruction," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 2, pp. 201-208, February, 1994. | |||
| BibTex | x | ||
| @article{ 10.1109/34.273731, author = {T.W. Pai and J.H.L. Hansen}, title = {Boundary-Constrained Morphological Skeleton Minimization and Skeleton Reconstruction}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {16}, number = {2}, issn = {0162-8828}, year = {1994}, pages = {201-208}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.273731}, 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 - Boundary-Constrained Morphological Skeleton Minimization and Skeleton Reconstruction IS - 2 SN - 0162-8828 SP201 EP208 EPD - 201-208 A1 - T.W. Pai, A1 - J.H.L. Hansen, PY - 1994 KW - image processing; mathematical morphology; computational complexity; boundary-constrained morphological skeleton minimization; boundary-constrained skeleton reconstruction; data storage requirement reduction; binary morphological skeleton; morphological boundary structure; computational complexity; image analysis; image coding VL - 16 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
A new algorithm for minimizing a morphological skeleton entitled boundary-constrained skeleton minimization (BCSM), as well as a new algorithm for reconstructing an original image from its minimized skeletal structure termed boundary-constrained skeleton reconstruction (BCSR), are proposed. The new algorithms are shown to reduce data storage requirements from (N+1) binary images represented as separate skeleton subsets with their corresponding indices, to 2 binary images composed of a binary morphological skeleton and its corresponding morphological boundary structure. In addition to a reduction in memory storage, BCSM and BCSR result in substantial savings in computational complexity. The proposed algorithms are evaluated in the context of image analysis and coding, and their performance is compared to previous algorithms proposed by Serra (1982, 1988) and by Maragos and Schafer (1986). Sample evaluations indicate a greater than 22-fold savings in computational requirements and 11-fold reduction in memory requirements.
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