Direct Curvature Scale Space: Theory and Corner Detection

Zhong, Baojiang; Liao, Wenhe

doi:10.1109/tpami.2007.50

Cited by 74 publications

(39 citation statements)

References 15 publications

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“…DCSS was presented by Zhong, et al [23] and used to recognize corners (curvature extreme point) in contours. See Figure 11, a DCSS consists of curves, one of which is a track curve of a corner under a different contour C t (t = 0,1,…).…”

Section: Dcssmentioning

confidence: 99%

“…Automatically or semi-automatically extracting them from discrete data, such as bone surface data reconstructed from CT images, is a challenging task, because of noises and sampling errors [18][19][20][21]. The task of the present work is to automatically extract AFC of hip bones using a computer graphics technique (DCSS-direct curvature scale space) [22,23] and the anatomical structure of the hip bone as prior knowledge. To the best of our knowledge, there are seldom papers that incorporate shape prior information in the process of extracting feature curves.…”

Section: Introductionmentioning

confidence: 99%

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Automatic extraction of 3D anatomical feature curves of hip bone models reconstructed from CT images

Liu

Qian

Zhao

2015

BME

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Abstract. 3D anatomical feature curves (AFC) on bone models reconstructed from CT/MRI images are important in some fields, such as preoperative planning, intra-operative navigation, patient-specific prosthesis design, etc. Interactive extraction of feature curves on patient-specific bone models is time-consuming, has low repeatability and accuracy. This paper presents a computer graphics method to automatically extract AFC from 3D hip bone models reconstructed from CT images. A DCSS (direct curvature scale space)-based technique is firstly used to extract anatomical feature points (AFP) in every contour, using anatomical structure information as prior knowledge so that AFP are extracted and only extracted. Then, corresponding AFP are linked in different contours and AFC is generated. AFC obtained by our method were compared with those interactively extracted by three surgeons, which showed that our method is feasible (Dice coefficient: 0.94; Average symmetric surface distance: 3.97 mm). The method was also applied to identify anatomical landmarks, which showed that our method is superior to the curvature-based methods that fail to identify landmark regions or have too many redundant regions, which results in failures to subsequently label landmark regions using pre-defined spatial adjacency matrices.

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Section: Dcssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic extraction of 3D anatomical feature curves of hip bone models reconstructed from CT images

Liu

Qian

Zhao

2015

BME

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“…One of the most popular multiscale curvature representation of 2D curves is the curvature scalespace [17], which has been improved and applied in different works [1,3,11,18,19,20,25,33,35,36].…”

Section: The Curvature Space-scale Techniquementioning

confidence: 99%

“…The CSS technique is suitable for recovering invariant geometric features, curvature zerocrossing points [17] and/or extreme [35], of a planar curve at multiple scales. To compute it, the curve Γ is first parameterized by the contour point t:…”

Section: The Curvature Space-scale Techniquementioning

confidence: 99%

Multiscale Corner Detection in Planar Shapes

Paula

Medeiros

Bezerra³

et al. 2012

J Math Imaging Vis

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This paper presents a multiscale corner detection method in planar shapes, which applies an undecimated Mexican hat wavelet decomposition of the angulation signal to identify significant points on a shape contour. The advantage of using this wavelet is that it is well suited for detecting singularities as corners and contours due to its excellent selectivity in position. Thus, this wavelet plays an important role in our approach because it identifies changes in non-stationary angulation signals, and it can be extended to multidimensional approaches in an efficient way when approximating this wavelet by difference of Gaussian. The proposed algorithm detects peaks on a correlation signal which is generated from different wavelet scales and retains relevant points on the decomposed angulation signal while discards poor information. Our approach assumes that only peaks which persist through several scales correspond to corners. Furthermore, we introduce a novel procedure to tune parameters for the corner detection algorithms that corresponds to the best relation between Precision and Recall measures. This technique guides the parameter adjustment of the algorithms according to the image database and it improves their performance with regard to true corner detection. Concerning the performance assessment of the algorithms, we compare the proposed one to other corner detectors by using Precision and Recall measures which are based on ground-truth information. Tests were carried out using more than a hundred images from a non-homogenous database that contains noisy and non-noisy binary shapes 1

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“…These methods continue to be refined and has also been applied to image retrieval of shapes, e.g. (Dudek and Tsotsos, 1997;Zhong and Liao, 2007).…”

Section: Previous Contour Approachesmentioning

confidence: 99%

An Approach to the Parameterization of Structure for Fast Categorization

Rasche

2009

Int J Comput Vis

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A decomposition is described, which parameterizes the geometry and appearance of contours and regions of gray-scale images with the goal of fast categorization. To express the contour geometry, a contour is transformed into a local/global space, from which parameters are derived classifying its global geometry (arc, inflexion or alternating) and describing its local aspects (degree of curvature, edginess, symmetry). Regions are parameterized based on their symmetric axes, which are evolved with a wave-propagation process enabling to generate the distance map for fragmented contour images. The methodology is evaluated on three image sets, the Caltech 101 set and two sets drawn from the Corel collection. The performance nearly reaches the one of other categorization systems for unsupervised learning.

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Direct Curvature Scale Space: Theory and Corner Detection

Cited by 74 publications

References 15 publications

Automatic extraction of 3D anatomical feature curves of hip bone models reconstructed from CT images

Automatic extraction of 3D anatomical feature curves of hip bone models reconstructed from CT images

Multiscale Corner Detection in Planar Shapes

An Approach to the Parameterization of Structure for Fast Categorization

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