Ground segmentation from outdoor environments in rural areas

Abstract

The understanding of the complexity of outdoor environments is an essential issue for the development of efficient processes of autonomous mobility, especially in areas with uneven illumination and without a well-defined road. In this context, the detection of ground and obstacles plays a relevant role in giving the first impressions of the external surroundings to a machine. Furthermore, it can guide independent movements and decisions. In this study, we introduce a segmentation method that detects ground and non-ground points of complex scenes under different exposures to illumination, textures, and shading. We prepared a dataset with images collected from some environments in which trees are prominent obstacles. The proposed method uses contrast templates, statistical measures, and morphological operators to reach the ground segmentation. Experiments showed satisfactory results in which trees were well detected and the ground was efficiently segmented with the maintenance of the structure of the image.

Publication
In Proceedings of 32nd IEEE Canadian Conference of Electrical and Computer Engineering (CCECE 2019)
Gabriel Vieira
Gabriel Vieira
PhD Student
and former Master’s Student

Fabrizzio Soares is bla bla bla.

Fabrizzio Soares
Fabrizzio Soares
Associate Professor and CS Chair

Fabrizzio Soares is a professor of Computer Science, Information Systems and Software Engineering at INF/UFG. His research interests include Computer Vision, Human Computer Interaction, Machine Learning and Programming topics. He is the leader of the Pixellab group, which develops solutions for accesibilty, Precision Agriculture, and Interactive Systems.

Thamer Nascimento
Thamer Nascimento
PhD Student
and former Master’s Student

Fabrizzio Soares is bla bla bla.

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