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A. Pikaz, I. Disntein, "Using Simple Decomposition for Smoothing and Feature Point Detection of Noisy Digital Curves," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 8, pp. 808813, August, 1994.  
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@article{ 10.1109/34.308476, author = {A. Pikaz and I. Disntein}, title = {Using Simple Decomposition for Smoothing and Feature Point Detection of Noisy Digital Curves}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {16}, number = {8}, issn = {01628828}, year = {1994}, pages = {808813}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.308476}, 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  Using Simple Decomposition for Smoothing and Feature Point Detection of Noisy Digital Curves IS  8 SN  01628828 SP808 EP813 EPD  808813 A1  A. Pikaz, A1  I. Disntein, PY  1994 KW  computational complexity; feature extraction; image processing; decomposition; smoothing; feature point detection; noisy digital curves; smoothed polygonal approximation; curvature function; planar curves; separation point; smoothed approximation; inflection points; curve points; convexconcave sections; cusps; concaveconcave sections; convexconvex sections; local minimal curvature; total curvature graphs; complexity analysis VL  16 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
This correspondence presents an algorithm for smoothed polygonal approximation of noisy digital planar curves, and feature point detection. The resulting smoothed polygonal representation preserves the signs of the curvature function of the curve. The algorithm is based on a simple decomposition of noisy digital curves into a minimal number of convex and concave sections. The location of each separation point is optimized, yielding the minimal possible distance between the smoothed approximation and the original curve. Curve points within a convex (concave) section are discarded if their angle signs do not agree with the section sign, and if the resulted deviations from the curve are less than a threshold /spl epsi/, which is derived automatically. Inflection points are curve points between pairs of convexconcave sections, and cusps are curve points between pairs of convexconvex or concaveconcave sections. Corners and points of local minimal curvature are detected by applying the algorithm to respective total curvature graphs. The detection of the feature points is based on properties of pairs of sections that are determined in an adaptive manner, rather than on properties of single points that are based on a fixedsize neighborhood. The detection is therefore reliable and robust. Complexity analysis and experimental results are presented.
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