Connected-component labeling based on hypercubes for memory constrained scenarios |
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| Affiliation: | 1. INESC TEC - INESC Technology and Science. FEUP Campus, Dr. Roberto Frias 4200 - 465. Porto, Portugal;2. Faculdade de Engenharia, Universidade do Porto. FEUP Campus, Dr. Roberto Frias 4200 - 465. Porto, Portugal;3. University of A Coruña, Department of Computer Science. Campus de Elviña 15071. A Coruña, Spain;1. TUBITAK BILGEM, Information Technologies Institute, Kocaeli-Turkey\n;2. Department of Computer Engineering, Faculty of Computer and Information Science, Sakarya University, Sakarya-Turkey\n;1. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 - Porto, Portugal;2. Departamento de Ciências de Computação e Estatística, Instituto de Biociências, Letras e Ciências Exatas, Universidade Estadual Paulista, Rua Cristóvão Colombo, 2265, CEP: 15054-000, São José do Rio Preto, SP, Brazil;1. Department of Industrial Engineering, Faculty of Engineering, Gazi University, Maltepe, 06570, Ankara, Turkey;2. Machine Intelligence Institute, Iona College, New Rochelle, NY 10805, USA |
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| Abstract: | The extraction and labeling of connected components in images play an important role in a wide range of fields, such as computer vision, remote sensing, medicine, biometrics, document analysis, robotics, among others. The automatic identification of relevant image regions allows for the development of intelligent systems to address complex problems for segmentation, classification and interpretation purposes. In this work, we present novel algorithms for labeling connected components that do not require any data structure on the labeling process. The algorithms are derived from other based upon independent spanning trees over the hypercube graph. Initially, the image coordinates are mapped to a binary Gray code axis, such that all pixels that are neighbors in the image are neighbors on the hypercube and each node that belongs to the hypercube represents a pixel in the image. We then use the algorithm proposed by Silva et al. (2013) to generate the log N independent spanning trees over the image. The proposed methods for connected-component labeling are applied to a number of images to demonstrate its effectiveness. |
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