Enhanced Image Segmentation by a Novel Test Time Augmentation and Super-Resolution

• Iván García-Aguilar ^[1] ; Jorge García-González ^[1] ; Rafael Marcos Luque-Baena ^[1] ; Ezequiel López-Rubio ^[1] ; Enrique Domínguez-Merino ^[1]
  1. [1] Universidad de Málaga

     Universidad de Málaga

     Málaga, España

     
• Localización: Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 – June 3, 2022, Proceedings, Part II / José Manuel Ferrández Vicente (dir. congr.) , José Ramón Álvarez Sánchez (dir. congr.) , Félix de la Paz López (dir. congr.) , Hojjat Adeli (aut.), 2022, ISBN 978-3-031-06527-9, págs. 153-162
• Idioma: inglés
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• Resumen

  • Image segmentation in computer vision applications plays a critical role in the video processing workflow. In real applications, where interesting elements are moving in the presence of moving objects in the background, complex models are required in the segmentation process to obtain better results. In this paper, a methodology based on super-resolution and test time augmentation is proposed to improve the precision and effectiveness of the segmentation process. Our proposal avoids both modification and retraining of the model. Experiments show that our approach can increase the mean average precision of images segmentation in sequences from well-known benchmark datasets with a significant improvement.