Projection-Based Spatial Morphology for Extracting Ridge and Valley Profiles of Mountains from 3D Amorphous Data

Maurya, Rajesh K.; Kulkarni, Sushil T.; Kalita, Nehal

doi:10.1109/ccaa.2018.8777680

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…As per the results of an experiment, this method outperformed Canny edge detection algorithm and AGPN (analyzing geometric properties of neighborhoods) algorithm in accuracy, completeness, and overall quality. A point cloud data-based method was also introduced that relies on a projection-based approach [21] to detect ridges and valleys of a terrain. Before attempting to analyse any ridge or valley, the method interpolates missing data in grids that are expected to have projected point cloud height values.…”

Section: Related Workmentioning

confidence: 99%

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Projection-based terrain feature line extraction from point cloud data

Kalita,

Maurya

2024

Preprint

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Feature lines of an object represent its surface characteristics along a specific plane or direction. They are essential for understanding and analysing the object's shape, geometry, and features. Depending upon the details stored in an object or data, feature line extraction can be a complex task. In terrain data, these details can be slopes for ridges and valleys, artefacts or some other forms of noise. If the data for analysis consists of point clouds with only positional values, one of the most common methods to detect patterns is to rely on the Euclidean distance between neighbouring points. In this paper, a method to extract terrain feature lines on point clouds has been presented that relies only on coordinate values. The feature line points are initially identified with the help of a projection of point clouds on 2D grids, and then these are used to extract feature lines or breaklines with the help of a spanning tree algorithm. The method can generate output with high accuracy for data with less noise and moderate accuracy for data with more noise.

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Section: Related Workmentioning

confidence: 99%

“…al. [21] makes use of a projection-based approach that can reduce complexity in extracting these lines. Their method refers to the X and the Y values of point clouds and projects the Z values (height values) on a 2D grid along XY plane.…”

Section: Related Workmentioning

confidence: 99%

Projection-based terrain feature line extraction from point cloud data

Kalita,

Maurya

2024

Preprint

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“…Due to DEM solution and production errors many of problems for automated feature recognition algorithms are minimized by manual feature detection to overrule local inconsistencies for preserving the global trend to avoid false truncations and fragmented lineaments. For extracting ridge and valley pro les of mountains from amorphous point cloud data, a projection-based spatial morphological extraction framework is proposed for detecting mountain pro le (Maurya et al, 2018). They consider that the membership, neighborhood, and cohesion between points are fundamental problems and should be examined to extract ridge and valley pro les from the point cloud surface data.…”

Section: Introductionmentioning

confidence: 99%

Extraction of terrain ridge lines and valley lines based on agglomeration analysis of terrain points: a cluster analysis method

2023

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Terrain feature extraction is one of the critical issues in geographic information science. As important terrain feature lines, ridge lines and valley lines, play an important role in hydrological analysis, terrain reconstruction and automatic integration of contour lines. But, the extraction of terrain feature lines is complicated and time-consuming task. In this paper, a terrain feature line extraction method is proposed based on clustering technique. The terrain feature points are automatically extracted according to the agglomeration of terrain points, and the similar points are automatically identi ed according to the DBSCAN clustering algorithm. The points with high similarity are clustered along the direction of ridge or valley, and the whole terrain will be clustered into multiple sub-regions. The nearest sub-regions are found by calculating the minimum distance between these sub-regions, and the adjacent sub-regions are connected orderly by their center line to obtain terrain feature lines. Compared with other methods, the cluster analysis method in this paper has simple process and high e ciency.

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“…These tools have been successfully applied to white adipose tissue, however, other organs like liver, pancreas, lungs have been challenging due to hetergeneous cell types. Previously, we have used tile rendering approach for profile analysis from three dimensional spatial data [11] as it reduces time complexity by considering only a portion of an image at a time. In the current study, we have developed an automated tool, Fatquant, in which the fat cells were identified in processed images by calculating the diagonal of a square circumscribed by circle.…”

Section: Introductionmentioning

confidence: 99%

Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images

Naik¹,

Rajan²,

Kalita

2021

Preprint

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Hematoxylin and eosin (H and E) is one of the common histological staining techniques that provides information on the tissue cytoarchitecture. Adipose (fat) cells accumulation in pancreas has been shown to impact beta cell survival and its endocrine function. The current automated tools available for fat analysis are suited for white adipose tissue which is homogeneous and easier to segment unlike heterogeneous tissues such as pancreas where fat cells continue to play critical physiopathological functions. In the current study, we present an automated fat analysis tool, Fatquant, where mathematical formula to calculate diagonal of a square drawn inside circle is utilized for identification and analysis of fat cells in heterogeneous H and E tissue sections. Using histological images of pancreas from a publicly available database, we show an area accuracy overlap of 89-93% between manual versus automated algorithm based fat cell detection.

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Projection-Based Spatial Morphology for Extracting Ridge and Valley Profiles of Mountains from 3D Amorphous Data

Cited by 5 publications

References 10 publications

Projection-based terrain feature line extraction from point cloud data

Projection-based terrain feature line extraction from point cloud data

Extraction of terrain ridge lines and valley lines based on agglomeration analysis of terrain points: a cluster analysis method

Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images

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