Silhouette Extraction from Street View Images

Abstract

This study addresses the issue of silhouette extraction of a street, and proposes two novel approaches to overcome this problem. The first, namely hybrid-stitching, considers the silhouette extraction as an image stitching problem and aims to use 2D street view images. The algorithm used in this method integrates a new composition technique into a conventional image stitching pipeline. The developed software using the proposed hybrid approach results in better stitching performances when compared with the popular stitching tools in the literature. Despite the results of the proposed method are better than the state-of-the-art image stitching techniques in many cases, they are not reliable enough to handle all of the street view image sets. Accordingly, a second solution has been proposed, including 3D location information, namely, 3D Silhouette Extraction Pipeline. The pipeline involves several techniques and post-processing steps to handle both the transformation and projection of the obtained point cloud, and the elimination of misleading location information.The results reveal that compared with the 2D solutions, the proposed algorithm is very effective and more reliable in silhouette extraction of a street, which is critical in urban transformation and environmental protection.

A Hybrid Feature Extractor using Fast Hessian Detector and SIFT

“…Experiments were conducted with an Intel Core 2 Quad machine running at 1.8 GHz, with 2 Gbytes of RAM memory, under Windows 7 operating system. An image database has been obtained from the previous study of the author [10], including street view images, for experimental section.…”

“…Despite the recent developments, it is still a major challenge to extract features from images used in urban transformation and regeneration [10] that related tasks; including image stitching and silhouette extraction need as much as True Positive (TP) matches rather than computational performance. Accordingly, in order to boost TP matches, the SURF detector has been integrated into the SIFT algorithm that the all the interest points obtained from both detectors have been combined and employed as input to 128-elements SIFT descriptor, which surprisingly performs fairly well in off-line tasks as previously mentioned.…”

Multiagent Systems for 3D Reconstruction Applications

“…The 3D reconstruction pipeline includes tree main steps which is similar but its purpose and the steps not same with the work [5]. These steps are summarization of the videos as frames, extracting camera positions and creating point cloud, dense reconstruction.…”

A Comparative Study for Indoor Planar Surface Segmentation via 3D Laser Point Cloud Data

“…Thus, it is a very active research area in a wide range of applications from outdoor to indoor. The point cloud segmentation was used in different outdoor applications such as building roof plane segmentation with the Airborne Laser Scanning (ALS) point cloud data (Tarsha-Kurdi et al, 2007;Xu et al, 2016), building facade segmentation with the Terrestrial Laser Scanning (TLS) point cloud data (Ning et al, 2009;Vo et al, 2015), forest monitoring (Morsdorf et al, 2004;Ferraz et al, 2010), autonomous vehicles (Himmelsbach et al, 2010;Zermas et al, 2017), and 3D silhouette extraction of a street (Mutlu et al, 2014). The point cloud segmentation was also applied in indoor applications such as Building Information Modeling (BIM) (Anil et al, 2013;Qu et al, 2014), robotic applications (Rusu et al, 2008;Koppula et al, 2011), and object detection (Mattausch et al, 2014;Cadena and Košecka, 2015).…”