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Lindenbergh R.c. - One of the best experts on this subject based on the ideXlab platform.

  • Building outline extraction from als Point clouds using medial axis transform descriptors
    'Elsevier BV', 2020
    Co-Authors: Widyaningrum E., Peters R.y., Lindenbergh R.c.
    Abstract:

    Automatic building extraction and delineation from airborne LiDAR Point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or Skeleton structure instead of its boundary. Notably, end Points of the MAT in principle coincide with corner Points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne Point clouds challenging. We propose a robust MAT-based method for detecting building corner Points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge Points acquired by the alpha-shape algorithm to derive a so-called building roof Skeleton. We then propose a hierarchical corner-aware segmentation to cluster Skeleton Points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge Point indices. From each segment, a corner Point is then estimated by extrapolating the position of the zero radius inscribed circle based on the Skeleton Point positions within the segment. Our experiment uses Point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that Skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical Point cloud data.Optical and Laser Remote Sensing3D Geo-Informatio

  • Building outline extraction from als Point clouds using medial axis transform descriptors
    'Elsevier BV', 2020
    Co-Authors: Widyaningrum E., Peters R.y., Lindenbergh R.c.
    Abstract:

    Automatic building extraction and delineation from airborne LiDAR Point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or Skeleton structure instead of its boundary. Notably, end Points of the MAT in principle coincide with corner Points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne Point clouds challenging. We propose a robust MAT-based method for detecting building corner Points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge Points acquired by the alpha-shape algorithm to derive a so-called building roof Skeleton. We then propose a hierarchical corner-aware segmentation to cluster Skeleton Points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge Point indices. From each segment, a corner Point is then estimated by extrapolating the position of the zero radius inscribed circle based on the Skeleton Point positions within the segment. Our experiment uses Point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that Skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical Point cloud data.

Widyaningrum E. - One of the best experts on this subject based on the ideXlab platform.

  • Building outline extraction from als Point clouds using medial axis transform descriptors
    'Elsevier BV', 2020
    Co-Authors: Widyaningrum E., Peters R.y., Lindenbergh R.c.
    Abstract:

    Automatic building extraction and delineation from airborne LiDAR Point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or Skeleton structure instead of its boundary. Notably, end Points of the MAT in principle coincide with corner Points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne Point clouds challenging. We propose a robust MAT-based method for detecting building corner Points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge Points acquired by the alpha-shape algorithm to derive a so-called building roof Skeleton. We then propose a hierarchical corner-aware segmentation to cluster Skeleton Points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge Point indices. From each segment, a corner Point is then estimated by extrapolating the position of the zero radius inscribed circle based on the Skeleton Point positions within the segment. Our experiment uses Point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that Skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical Point cloud data.Optical and Laser Remote Sensing3D Geo-Informatio

  • Building outline extraction from als Point clouds using medial axis transform descriptors
    'Elsevier BV', 2020
    Co-Authors: Widyaningrum E., Peters R.y., Lindenbergh R.c.
    Abstract:

    Automatic building extraction and delineation from airborne LiDAR Point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or Skeleton structure instead of its boundary. Notably, end Points of the MAT in principle coincide with corner Points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne Point clouds challenging. We propose a robust MAT-based method for detecting building corner Points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge Points acquired by the alpha-shape algorithm to derive a so-called building roof Skeleton. We then propose a hierarchical corner-aware segmentation to cluster Skeleton Points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge Point indices. From each segment, a corner Point is then estimated by extrapolating the position of the zero radius inscribed circle based on the Skeleton Point positions within the segment. Our experiment uses Point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that Skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical Point cloud data.

Peters R.y. - One of the best experts on this subject based on the ideXlab platform.

  • Building outline extraction from als Point clouds using medial axis transform descriptors
    'Elsevier BV', 2020
    Co-Authors: Widyaningrum E., Peters R.y., Lindenbergh R.c.
    Abstract:

    Automatic building extraction and delineation from airborne LiDAR Point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or Skeleton structure instead of its boundary. Notably, end Points of the MAT in principle coincide with corner Points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne Point clouds challenging. We propose a robust MAT-based method for detecting building corner Points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge Points acquired by the alpha-shape algorithm to derive a so-called building roof Skeleton. We then propose a hierarchical corner-aware segmentation to cluster Skeleton Points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge Point indices. From each segment, a corner Point is then estimated by extrapolating the position of the zero radius inscribed circle based on the Skeleton Point positions within the segment. Our experiment uses Point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that Skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical Point cloud data.Optical and Laser Remote Sensing3D Geo-Informatio

  • Building outline extraction from als Point clouds using medial axis transform descriptors
    'Elsevier BV', 2020
    Co-Authors: Widyaningrum E., Peters R.y., Lindenbergh R.c.
    Abstract:

    Automatic building extraction and delineation from airborne LiDAR Point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or Skeleton structure instead of its boundary. Notably, end Points of the MAT in principle coincide with corner Points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne Point clouds challenging. We propose a robust MAT-based method for detecting building corner Points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge Points acquired by the alpha-shape algorithm to derive a so-called building roof Skeleton. We then propose a hierarchical corner-aware segmentation to cluster Skeleton Points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge Point indices. From each segment, a corner Point is then estimated by extrapolating the position of the zero radius inscribed circle based on the Skeleton Point positions within the segment. Our experiment uses Point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that Skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical Point cloud data.

Alexandru Telea - One of the best experts on this subject based on the ideXlab platform.

  • multiscale 2d medial axes and 3d surface Skeletons by the image foresting transform
    Skeletonization#R##N#Theory Methods and Applications, 2017
    Co-Authors: Alexandre X Falcao, Jacek Kustra, Cong Feng, Alexandru Telea
    Abstract:

    Skeletons are simplified shape representations with many applications involving image processing, analysis, and visualization. A fundamental problem in shape Skeletonization is the sensitivity of the Skeletons to small perturbations of the input shape, which leads to the appearance of spurious branches. By assigning an importance metric to every Skeleton Point, one encodes the scale of the shape details. Subsequently, simplified Skeletons can be obtained by simply thresholding at continuous values of that metric. Such a multiscale regularization procedure should ideally produce one-spel-wide Skeletons in all scales. In this chapter, we present a new method based on the image foresting transform framework, which achieves this result for medial axes of 2D shapes and for surface Skeletons of 3D shapes. Our approach relies on simple and efficient algorithms, faster than several methods based on the same importance metric, simpler than others, far less sensitive to numerical noise than a recent one, and attains similar quality of centeredness, smoothness, thinness, and ease to simplify the Skeleton. Such a conclusion is substantiated with a comparative analysis on a wide set of 2D and 3D real-word shapes against its multiscale counterparts.

Venkatesh R Babu - One of the best experts on this subject based on the ideXlab platform.

  • human gait recognition using depth camera a covariance based approach
    Indian Conference on Computer Vision Graphics and Image Processing, 2012
    Co-Authors: M Naresh S Kumar, Venkatesh R Babu
    Abstract:

    Gait is an important biometric modality for recognizing humans. Unlike other biometrics, human gait can be captured at a distance which makes it an unobtrusive method for recognition. In this paper, an unrestricted gait recognition algorithm is proposed which uses 3D Skeleton information and trajectory covariance of joint Points. 3-D Skeleton is generated from the depth images that are captured using Kinect sensor. The temporal tracking of Skeleton Points is used for gait analysis. The covariance measure between these Skeleton Point trajectories are computed and the covariance matrices form the gait model. The gait is recognized by computing the minimum dissimilarity measure between the gait models of the training data and the testing data. Recognition accuracy of over 90% has been achieved for a data set consisting of fixed and moving camera scenarios of 20 subjects.