TY - GEN
T1 - Quantitative measurement of the performance of raster-to-vector conversion algorithms
AU - Hori, Osamu
AU - Doermann, David S.
N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 1996.
PY - 1996
Y1 - 1996
N2 - This paper presents a methodology for measuring the performance of application-specific raster-to-vector conversion algorithms. In designing and building image analysis systems, comparison of several algorithms is often required. Unfortunately, many methods of comparison do not give quantitative performance measurements, but rather qualitative, and often subjective, evaluations. Our key observation is that there is a need for domain, or task-dependent evaluation of the output. By specifying the input data in the same parameter space as the intended output of the system, we are able to evaluate the quality of the output and how well it conforms to the intended representation. We provide a set of basic metrics, but we emphasize that in general, such metrics may be task-specific. In this paper, the performance of three approaches to raster-to-vector conversion - thinning, medial line finding, and line fitting - are compared using this methodology.
AB - This paper presents a methodology for measuring the performance of application-specific raster-to-vector conversion algorithms. In designing and building image analysis systems, comparison of several algorithms is often required. Unfortunately, many methods of comparison do not give quantitative performance measurements, but rather qualitative, and often subjective, evaluations. Our key observation is that there is a need for domain, or task-dependent evaluation of the output. By specifying the input data in the same parameter space as the intended output of the system, we are able to evaluate the quality of the output and how well it conforms to the intended representation. We provide a set of basic metrics, but we emphasize that in general, such metrics may be task-specific. In this paper, the performance of three approaches to raster-to-vector conversion - thinning, medial line finding, and line fitting - are compared using this methodology.
UR - https://www.scopus.com/pages/publications/84948968048
U2 - 10.1007/3-540-61226-2_6
DO - 10.1007/3-540-61226-2_6
M3 - Conference contribution
SN - 3540612262
SN - 9783540612261
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 57
EP - 68
BT - Graphics Recognition
A2 - Kasturi, Rangachar
A2 - Tombre, Karl
PB - Springer Verlag
T2 - 1st International Workshop on Graphics Recognition, 1995
Y2 - 10 August 1995 through 11 August 1995
ER -