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Improved image based localization using semantic descriptors için kapak resmi
Improved image based localization using semantic descriptors
Improved image based localization using semantic descriptors
Çınaroğlu, İbrahim, author.
Fiziksel Tanımlama:
xiv, 98 leaves: color illustrations, charts;+ 1 computer laser optical disc.
Place recognition and Visual Localization (VL) for autonomous driving are the topics that keep their popularity in the field of Computer Vision. In this study, semantically improved Hybrid-VL approaches, that use localization aware semantic information in street-level driving images are proposed. Initially, Semantic Descriptor (SD) is extracted from semantically segmented images with a Convolutional Neural Network (CNN) trained for localization task. Then, image retrieval based VL task is performed using the approximate nearest neighbor search (ANNS) in 2D-2D matching context. This proposed method is named as SD-VL and its success is compared with the success of the state-of-the-art Local Descriptor (LD) based VL method (LD-VL) which is frequently used in the literature. Furthermore, with the aim of alleviating the shortcomings of both two methods, a novel decision-level Hybrid-VL (Hybrid-VLDL) method is proposed by combining SD-VL and LD-VL in post-processing stage. Also feature-level Hybrid-VL (Hybrid-VLFL) method is proposed in order to produce automatically tuned hybrid result. These proposed VL methods are examined on two challenging benchmarks; RobotCar Seasons and Malaga Downtown Data Sets. Moreover, a new VL data set Malaga Streetview Challenge is generated by collecting Google Streetview images on the same path of Malaga Downtown in order to observe impact of environmental and wide-baseline changes. This newly generated test set will be useful for researchers studying in this field. After all, the proposed semantically boosted Hybrid-VLDL method is able to increase localization performance on both RobotCar Seasons and Malaga Streetview Challenge data sets by 11.6% and 4.5% Top-1 recall@5, and 4% and 5.4% recall@1 scores respectively. Additionally, reliability of our hyper-parameter (W) based Hybrid-VLDL approach is supported by very close performance of the Hybrid-VLFL method.
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Tek Biçim Eser Adı:
Thesis (Doctoral)--İzmir Institute of Technology:Computer Engineering.

İzmir Institute of Technology:Computer Engineering. --Thesis (Doctoral).
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