%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2023/08.15.20.46
%2 sid.inpe.br/sibgrapi/2023/08.15.20.46.46
%@doi 10.1109/SIBGRAPI59091.2023.10347172
%T Unsupervised Image Segmentation by Oriented Image Foresting Transform in Layered Graphs
%D 2023
%A Kleine, Felipe A. S.,
%A Santos, Luiz F. D.,
%A Cappabianco, Fábio A. M.,
%A Miranda, Paulo A. V.,
%@affiliation IPT - Institute for Technological Research of the State of São Paulo, Brazil
%@affiliation University of São Paulo, Institute of Mathematics and Statistics, São Paulo, SP, Brazil
%@affiliation Instituto de Ciência e Tecnologia, São José dos Campos, SP, Brazil
%@affiliation University of São Paulo, Institute of Mathematics and Statistics, São Paulo, SP, Brazil
%E Clua, Esteban Walter Gonzalez,
%E Körting, Thales Sehn,
%E Paulovich, Fernando Vieira,
%E Feris, Rogerio,
%B Conference on Graphics, Patterns and Images, 36 (SIBGRAPI)
%C Rio Grande, RS
%8 Nov. 06-09, 2023
%S Proceedings
%K unsupervised image segmentation, image foresting transform, nested objects.
%X In this work, we address the problem of unsupervised image segmentation, subject to high-level constraints expected for the objects of interest. More specifically, we handle the segmentation of a hierarchy of objects with nested boundaries, each with its own expected boundary polarity constraint. To this end, this work successfully extends Hierarchical Layered Oriented Image Foresting Transform (HLOIFT), with the inclusion of nested object relations, to the unsupervised segmentation paradigm. On the other hand, this work can also be seen as an extension of Unsupervised OIFT (UOIFT) to include structural relationships of nested objects. The method is demonstrated in the segmentation of three datasets of colored images with superior performance compared to other existing techniques in graphs, requiring a smaller number of connected partitions to isolate the objects of interest in the images.
%@language en
%3 Kleine-103.pdf