Automatic orientation and 3D modelling from markerless rock art imagery

Lerma, José Luis; Vilanova, Santiago; Cabrelles, Miriam; Seguí, Ana Elena; Hernández, Danilo Cáceres

doi:10.1016/j.isprsjprs.2012.08.002

Cited by 33 publications

(19 citation statements)

References 25 publications

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“…), and spatial modeling (Lerma et al. ). The majority of the presented descriptors like SIFT are computed on gray scale images and cannot be used for our application in this article.…”

Section: Proposed Methodsmentioning

confidence: 99%

VGI and Reference Data Correspondence Based on Location‐Orientation Rotary Descriptor and Segment Matching

Mohammadi

Malek

2014

Transactions in GIS

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Volunteered geographic information (VGI) is spatial data that has been contributed by numerous volunteers in the form of user‐generated content. VGI provides the ability for non‐experts to collect and share geographic information. Comparison of a VGI dataset with a reference dataset in order to find data correspondence is a common process in some analyses of VGI data, such as quantitative analysis of its quality. This study addresses this case and presents an automated feature matching method for VGI linear data. The proposed method consists of two main steps. In the first step, a feature point matching method based on a novel descriptor, named Location‐Orientation Rotary Descriptor (LORD), is performed. The proposed LORD descriptor uses the location and orientation information of the linear data in a log polar structure and in a rotary manner. In the second step, a new segment matching strategy based on the buffered linear features is performed to reject the outliers of the previous step. The usefulness of the proposed method was demonstrated by applying it to the OpenStreetMap (OSM) dataset, as a VGI dataset, using an official reference dataset.

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“…), and spatial modeling (Lerma et al. ). The majority of the presented descriptors like SIFT are computed on gray scale images and cannot be used for our application in this article.…”

Section: Proposed Methodsmentioning

confidence: 99%

VGI and Reference Data Correspondence Based on Location‐Orientation Rotary Descriptor and Segment Matching

Mohammadi

Malek

2014

Transactions in GIS

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“…() and Lerma et al. (). It can also be used to compute dense point clouds (Cabrelles et al., ) and to integrate laser scanning datasets and image data for texture mapping (Lerma et al., ).…”

Section: D Modellingmentioning

confidence: 98%

“…Afterwards, the image coordinates are improved using normalised cross‐correlation and least squares matching (LSM) as suggested in Remondino () and Lerma et al. (). A robust estimation of the fundamental matrix with the random sample consensus (RANSAC) algorithm follows the orientation workflow and helps to delete the number of false corresponding features between images.…”

Section: D Modellingmentioning

confidence: 99%

Range‐Based Versus Automated Markerless Image‐Based Techniques For Rock Art Documentation

Lerma

Vilanova

Seguí

et al. 2014

The Photogrammetric Record

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Nowadays there is a huge proliferation of fully automatic image‐based solutions producing either three‐dimensional (3D) point clouds or 3D models. However, the reliability of the output is not usually reported and clarified. This paper presents a comparison of the 3D modelling results achieved on two rock art shelters at separate archaeological sites using a high‐resolution digital camera. The 3D point clouds were produced using automatic image‐based photogrammetric and computer vision software running either locally (FOTOGIFLE and VisualSFM) or through a web‐based reconstruction service (Autodesk 123D Catch). The first two automatic approaches are compared with a manual bundle block adjustment. Dense image matching was undertaken to densify the point clouds prior to 3D modelling. The derived 3D models are compared with a time‐of‐flight laser scanning dataset that was used as ground truth. Statistical analysis shows significant metric differences on the digital surface models for the different solutions.

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“…It has been widely used in the field of remote sensing, including image fusion, change detection [5][6][7], disaster analysis [8], semantic labelling of images [9] and mapping sciences [10][11][12]. Classical methods generally fall into two categories: area-based methods and feature-based methods [1].…”

Section: Introductionmentioning

confidence: 99%

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

Shi

Jiang

2016

Remote Sensing

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Abstract:Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood). Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges) is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines), which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, such as Landsat7, QuickBird, WorldView, and so on, demonstrate that the proposed algorithm is automatic, fast (4 ms faster than the second fastest method, i.e., the rotation-and scale-invariant shape context) and can achieve a recall of 79.7%, a precision of 89.1% and a root mean square error (RMSE) of 1.0 pixels on average for remote sensing images with large background variations.

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Automatic orientation and 3D modelling from markerless rock art imagery

Cited by 33 publications

References 25 publications

VGI and Reference Data Correspondence Based on Location‐Orientation Rotary Descriptor and Segment Matching

VGI and Reference Data Correspondence Based on Location‐Orientation Rotary Descriptor and Segment Matching

Range‐Based Versus Automated Markerless Image‐Based Techniques For Rock Art Documentation

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

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