Disparity map production: an architectural proposal and a refinement method design

• Autor(a) principal: Vieira, Gabriel da Silva
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Institucional da UFG
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/9088
• topic: Stereo vision; Disparity map; Software design; Disparity methods; Software architecture; Visão estéreo; Mapa de disparidade; Projeto de software; Métodos de cálculo de disparidade; Arquitetura de software; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Disparity maps are key components of a stereo vision system. Autonomous navigation, 3D reconstruction, and mobility are examples of areas of research which use disparity maps as an important element. Although a lot of work has been done in the stereo vision field, it is not easy to build stereo systems with concepts such as reuse and extensible scope. In this study, we explore this gap and it presents a software architecture that can accommodate different stereo methods through a standard structure. Firstly, it introduces some scenarios that illustrate use cases of disparity maps and it shows a novel architecture that foments code reuse. A Disparity Computation Framework (DCF) is presented and we discuss how its components are structured. Then we developed a prototype which closely follows the proposal architecture and we prepared some test cases to be performed. Furthermore, we have implemented disparity methods for validation purposes and to evaluate our disparity refinement method. This refinement method, named as Segmented Consistency Check (SCC), was designed to increase the robustness of stereo matching algorithms. It consists of a segmentation process, statistical analysis of grouping areas and a support weighted function to find and to fill in unknown disparities. The experimental results show that the DCF can satisfy different scenarios on-demand. Besides, they show that SCC method is an efficient approach that can make some enhancements in disparity maps, as reducing the disparity error measure.