Deep Learning is dominant in the field of computer vision, thanks to its high performance. This high performance is driven by large annotated datasets and proper evaluation benchmarks. However, two important areas in computer vision, depth-based hand segmentation, and local features, respectively lack a large well-annotated dataset and a benchmark protocol that properly demonstrates its practical performance. Therefore, in this thesis, we focus on these two problems. For hand segmentation, we create a novel systematic way to easily create automatic semantic segmentation annotations for large datasets. We achieved this with the help of traditional computer vision techniques and minimal hardware setup of one RGB-D camera and two distinctly colored skin-tight gloves. Our method allows easy creation of large-scale datasets with high annotation quality. For local features, we create a new modern benchmark, that reveals their different aspects. Specifically wide-baseline stereo matching and Multi-View Stereo (MVS), of keypoints in a more practical setup, namely Structure-from-Motion (SfM). We believe that through our new benchmark, we will be able to spur research on learned local features to a more practical direction. In this respect, the benchmark developed for the thesis will be used to host a challenge on local features. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/10689 |
| Date | 03 April 2019 |
| Creators | Malireddi, Sri Raghu |
| Contributors | Yi, Kwang Moo |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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