A Designed &amp;#946;-Hairpin Forming Peptide Undergoes a Consecutive Stepwise Process for Self-Assembly into Nanofibrils

Wang, Chong; Yan, Sha

doi:10.2174/092986610790963690

Cited by 7 publications

(9 citation statements)

References 27 publications

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“…To evaluate the performance of our proposed GBE method, the publicly available reference benchmark of German data set is used [ 31 ], whereas the reference benchmark for Australian data set is obtained using the BARISTA [ 37 ]. In addition, the ISPRS adopted threshold-based evaluation system [ 38 ] and the threshold-free evaluation system [ 39 ] are employed to evaluate the performance.…”

Section: Methodsmentioning

confidence: 99%

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A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery

Siddiqui

Teng

Awrangjeb

et al. 2016

Sensors

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Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics.

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Section: Methodsmentioning

confidence: 99%

“…If the red colour trees are present in the site, then the red channel is replaced with the green channel to separate the red colour trees from buildings. The provided data sets, i.e., Australian and German data sets [ 31 ], do not have a infrared channel. Therefore, a pseudo-NDVI is used as an alternative index.…”

Section: Related Workmentioning

confidence: 99%

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery

Siddiqui

Teng

Awrangjeb

et al. 2016

Sensors

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“…The first dataset was collected in the city of Vaihingen, ISPRS dataset [34] and the other set, which has a higher point density, was collected on the Wuhan University campus, China. In the quantitative tests, the reference data were created manually based on the initial segmentation results and aerial images.…”

Section: Datasets and Fundamental Algorithmmentioning

confidence: 99%

Investigation on the Weighted RANSAC Approaches for Building Roof Plane Segmentation from LiDAR Point Clouds

et al. 2015

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RANdom SAmple Consensus (RANSAC) is a widely adopted method for LiDAR point cloud segmentation because of its robustness to noise and outliers. However, RANSAC has a tendency to generate false segments consisting of points from several nearly coplanar surfaces. To address this problem, we formulate the weighted RANSAC approach for the purpose of point cloud segmentation. In our proposed solution, the hard threshold voting function which considers both the point-plane distance and the normal vector consistency is transformed into a soft threshold voting function based on two weight functions. To improve weighted RANSAC's ability to distinguish planes, we designed the weight functions according to the difference in the error distribution between the proper and improper plane hypotheses, based on which an outlier suppression ratio was also defined. Using the ratio, a thorough comparison was conducted between these different weight functions to determine the best performing function. The selected weight function was then compared to the existing weighted RANSAC methods, the original RANSAC, and a representative region growing (RG) method. Experiments with two airborne LiDAR datasets of varying densities show that the various weighted methods can improve the segmentation quality differently, but the dedicated designed weight functions can significantly improve the segmentation accuracy and the topology correctness. Moreover, its robustness is much better when compared to the RG method.

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“…The authors would like to acknowledge the provision of the Downtown Toronto dataset by Optech Inc., First Base Solutions Inc., York University, and ISPRS WG III/4. The Vaihingen dataset was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [ 33 ]. This work was supported by 2014 Research Fund of Myongji University.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Segmentation of Planar Surfaces from Laser Scanning Data Using the Magnitude of Normal Position Vector for Adaptive Neighborhoods

Kim

Habib

Pyeon

et al. 2016

Sensors

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Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attribute similarity and the proximity of the laser point simultaneously; and (3) works well with both airborne and terrestrial laser scanning data. A neighborhood definition based on the shape of the surface increases the homogeneity of the laser point attributes. The magnitude of the normal position vector is used as an attribute for reducing the dimension of the accumulator array. The experimental results demonstrate, through both qualitative and quantitative evaluations, the outcomes’ high level of reliability. The proposed segmentation algorithm provided 96.89% overall correctness, 95.84% completeness, a 0.25 m overall mean value of centroid difference, and less than 1° of angle difference. The performance of the proposed approach was also verified with a large dataset and compared with other approaches. Additionally, the evaluation of the sensitivity of the thresholds was carried out. In summary, this paper proposes a robust and efficient segmentation methodology for abstraction of an enormous number of laser points into plane information.

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A Designed β-Hairpin Forming Peptide Undergoes a Consecutive Stepwise Process for Self-Assembly into Nanofibrils

Cited by 7 publications

References 27 publications

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery

Investigation on the Weighted RANSAC Approaches for Building Roof Plane Segmentation from LiDAR Point Clouds

Segmentation of Planar Surfaces from Laser Scanning Data Using the Magnitude of Normal Position Vector for Adaptive Neighborhoods

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