Disparity map production: an architectural proposal and a refinement method design
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2018-10-05
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Universidade Federal de Goiás
Resumo
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.
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Citação
VIEIRA, Gabriel da Silva. Disparity map production: an architectural proposal and a refinement method design. 2018. 81 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2018.