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P.F.M. Nacken, "A Metric for Line Segments," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 12, pp. 13121318, December, 1993.  
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@article{ 10.1109/34.250848, author = {P.F.M. Nacken}, title = {A Metric for Line Segments}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {15}, number = {12}, issn = {01628828}, year = {1993}, pages = {13121318}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.250848}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Metric for Line Segments IS  12 SN  01628828 SP1312 EP1318 EPD  13121318 A1  P.F.M. Nacken, PY  1993 KW  line segments; metric; collinearity; nearness; neighborhood functions; clustering algorithm; edge detection; groupability measure; geometry; image recognition VL  15 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
This correspondence presents a metric for describing line segments. This metric measures how well two line segments can be replaced by a single longer one. This depends for example on collinearity and nearness of the line segments. The metric is constructed using a new technique using socalled neighborhood functions. The behavior of the metric depends on the neighborhood function chosen. In this correspondence, an appropriate choice for the case of line segments is presented. The quality of the metric is verified by using it in a simple clustering algorithm that groups line segments found by an edge detection algorithm in an image. The fact that the clustering algorithm can detect long linear structures in an image shows that the metric is a good measure for the groupability of line segments.
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