Dataset Information

Analysis of the Selection Impact of 2D Detectors on the Accuracy of Image-Based TLS Data Registration of Objects of Cultural Heritage and Interiors of Public Utilities.

ABSTRACT: The aim of this article is to present the influence of detector selection for the image-based Terrestrial Laser Scanning (TLS) registration method. The presented results are the extended continuation of investigations presented in the article, 'The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration'. In order to obtain the correct results of the TLS registration process, it is necessary to detect and match the correct tie points, which are evenly distributed across the entire area. Commonly, for TLS data registration manually or semi-manually corresponding points are detected. However, when large, complicated cultural heritage objects are investigated, it is sometimes impossible to place marked control points. The only possibility of resolving this problem is the use of image-based TLS data registration. One of the most important factors that influences the quality and ability to use it correctly, is accurate selection. For this purpose, the authors decided to test three blob detectors ASIFT, SURF, CenSurE, and two point detectors FAST and BRISK. The results indicated that selection depends on two factors: if the time required for data processing is not important, the ASIFT algorithm should be used, which allows for full registration, but if not, a combination of other algorithms with results supervision should be considered.

SUBMITTER: Markiewicz J

PROVIDER: S-EPMC7309106 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Analysis of the Selection Impact of 2D Detectors on the Accuracy of Image-Based TLS Data Registration of Objects of Cultural Heritage and Interiors of Public Utilities.

Markiewicz Jakub J Zawieska Dorota D

Sensors (Basel, Switzerland) 20200609 11

The aim of this article is to present the influence of detector selection for the image-based Terrestrial Laser Scanning (TLS) registration method. The presented results are the extended continuation of investigations presented in the article, 'The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration'. In order to obtain the correct results of the TLS registration process, it is necessary to detect and match the correct tie points, which are evenl ...[more]

PMID: 32527053

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Project description:With the introduction of multi-camera systems in modern plant phenotyping new opportunities for combined multimodal image analysis emerge. Visible light (VIS), fluorescence (FLU) and near-infrared images enable scientists to study different plant traits based on optical appearance, biochemical composition and nutrition status. A straightforward analysis of high-throughput image data is hampered by a number of natural and technical factors including large variability of plant appearance, inhomogeneous illumination, shadows and reflections in the background regions. Consequently, automated segmentation of plant images represents a big challenge and often requires an extensive human-machine interaction. Combined analysis of different image modalities may enable automatisation of plant segmentation in "difficult" image modalities such as VIS images by utilising the results of segmentation of image modalities that exhibit higher contrast between plant and background, i.e. FLU images. For efficient segmentation and detection of diverse plant structures (i.e. leaf tips, flowers), image registration techniques based on feature point (FP) matching are of particular interest. However, finding reliable feature points and point pairs for differently structured plant species in multimodal images can be challenging. To address this task in a general manner, different feature point detectors should be considered. Here, a comparison of seven different feature point detectors for automated registration of VIS and FLU plant images is performed. Our experimental results show that straightforward image registration using FP detectors is prone to errors due to too large structural difference between FLU and VIS modalities. We show that structural image enhancement such as background filtering and edge image transformation significantly improves performance of FP algorithms. To overcome the limitations of single FP detectors, combination of different FP methods is suggested. We demonstrate application of our enhanced FP approach for automated registration of a large amount of FLU/VIS images of developing plant species acquired from high-throughput phenotyping experiments.

Dataset Information

Analysis of the Selection Impact of 2D Detectors on the Accuracy of Image-Based TLS Data Registration of Objects of Cultural Heritage and Interiors of Public Utilities.

Publications

Analysis of the Selection Impact of 2D Detectors on the Accuracy of Image-Based TLS Data Registration of Objects of Cultural Heritage and Interiors of Public Utilities.

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