Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - WoS: 7Citation - Scopus: 6Semantic Pose Verification for Outdoor Visual Localization With Self-Supervised Contrastive Learning(IEEE, 2022) Guerrero, Jose J.; Orhan, Semih; Baştanlar, YalınAny city-scale visual localization system has to overcome long-term appearance changes, such as varying illumination conditions or seasonal changes between query and database images. Since semantic content is more robust to such changes, we exploit semantic information to improve visual localization. In our scenario, the database consists of gnomonic views generated from panoramic images (e.g. Google Street View) and query images are collected with a standard field-of-view camera at a different time. To improve localization, we check the semantic similarity between query and database images, which is not trivial since the position and viewpoint of the cameras do not exactly match. To learn similarity, we propose training a CNN in a self-supervised fashion with contrastive learning on a dataset of semantically segmented images. With experiments we showed that this semantic similarity estimation approach works better than measuring the similarity at pixel-level. Finally, we used the semantic similarity scores to verify the retrievals obtained by a state-of-the-art visual localization method and observed that contrastive learning-based pose verification increases top-1 recall value to 0.90 which corresponds to a 2% improvement.Article Citation - WoS: 2Citation - Scopus: 2Enhancing Stereo Matching Performance by Colour Normalisation and Specularity Removal(Institution of Engineering and Technology, 2011) Ozan, Şükrü; Gümüştekin, ŞevketA method to enhance the performance of stereo matching is presented. The position of the specular light reflection on an object surface varies due to the change in the position of the camera, light source, object or all combined. Additionally, there may be situations exhibiting a colour shift owing to a change in the light source chromaticity or camera white balance settings. These variations cause misleading results when stereo matching algorithms are applied. In this reported work, a single-image-based statistical method is used to normalise source images. This process effectively eliminates non-saturated specularities regardless of their positions on the object. The effect of specularity removal is tested on stereo image pairs. © 2011 The Institution of Engineering and Technology.