Automatic generation of high performance morphological filters to fix missing data in depth images on real-time embedded systems for visually impaired people

Autor(es)

Batista Dourado, Antonio Miguel ^[1] ; Pedrino, Emerson Carlos ^{[2, 3]}

Afiliação do(s) autor(es)

^[1] Fed Inst Sao Paulo, Av Zelia Lima Rosa 100, BR-18550000 Boituva, SP - Brazil

^[2] Univ Fed Sao Carlos, Sao Carlos, SP - Brazil

^[3] Univ Fed Sao Carlos, Dept Comp, Rod Washington Luis Km 235, BR-13565905 Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PRZEGLAD ELEKTROTECHNICZNY; v. 96, n. 1, p. 113-117, 2020.
• Citações Web of Science: 0

Resumo

The paper presents an evolutionary multi-objective approach to automatically generate morphological filters to solve unknown distances areas, found in depth images used by real-time embedded systems for visually impaired people, and to prevent accidents. It was used Cartesian Genetic Programming as base for the NSGAII multi-objective optimization algorithm proposed to optimize two objectives: low error rates for quality x low complexity for speed. Results showed this approach was able to deliver feasible solutions with good quality and speed to be used in real-time systems. (AU)

• Processo FAPESP: 17/26421-3 - Investigação do uso de Sistemas Inteligentes para Mapeamento Eficiente de Aplicações em Arquiteturas Many-Core
• Beneficiário: Emerson Carlos Pedrino
• Modalidade de apoio: Bolsas no Exterior - Pesquisa