Publication | Closed Access
Sparse pixel vectorization: an algorithm and its performance evaluation
137
Citations
17
References
1999
Year
Geometric ModelingSparse RepresentationMachine VisionImage AnalysisEngineeringImage CodingPattern RecognitionHigher Level ProcessingEfficient VectorizationNatural SciencesSpecialized Data StructureMedical Image ComputingComputational GeometrySparse Pixel VectorizationComputer VisionVectorization
Accurate and efficient vectorization of line drawings is essential for their higher level processing. We present a thinningless sparse pixel vectorization (SPV) algorithm. Rather than visiting all the points along the wire's black area, SPV sparsely visits selected medial axis points. The result is a crude polyline, which is refined through polygonal approximation by removing redundant points. Due to the sparseness of pixel examination and the use of a specialized data structure, SPV is both time efficient and accurate, as evaluated by our proposed performance evaluation criteria.
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