Moment-based alignment for shape prior with variational B-spline level set

Tran, Thi-Thao; Pham, Van-Truong; Shyu, Kuo-Kai

doi:10.1007/s00138-013-0504-2

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…where β m (•) is the uniform symmetric d-dimensional B-spline of degree m. As is commonly done in [58], we set m = 1. The knots of the B-spline lie in a grid α with regular spacing.…”

Section: Variational B-spline Level-set Methodsmentioning

confidence: 99%

Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images

Chen

2021

Remote Sensing

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Glaciers and numerous glacial lakes that are produced by glacier melting are key indicators of climate change. Often overlooked, supra-glacial lakes develop in the melting area in the low-lying part of a glacier and appear to be highly variable in their size, shape, and location. The lifespan of these lakes is thought to be quite transient, since the lakes may be completely filled by water and burst out within several weeks. Changes in supra-glacial lake outlines and other surface features such as supra-glacial rivers and crevasses on the glaciers are useful indicators for the direct monitoring of glacier changes. Synthetic aperture radar (SAR) is not affected by weather and climate, and is an effective tool for study of glaciated areas. The development of the Chinese GaoFen-3 (GF-3) SAR, which has high spatial and temporal resolution and high-precision observation performance, has made it possible to obtain dynamic information about glaciers in more detail. In this paper, the classical Canny operator, the variational B-spline level-set method, and U-Net-based deep-learning model were applied and compared to extract glacial lake outlines and other surface features using different modes and Chinese GF-3 SAR imagery in the Mount Everest Region of the Himalayas. Particularly, the U-Net-based deep-learning method, which was independent of auxiliary data and had a high degree of automation, was used for the first time in this context. The experimental results showed that the U-Net-based deep-learning model worked best in the segmentation of supra-glacial lakes in terms of accuracy (Precision = 98.45% and Recall = 95.82%) and segmentation efficiency, and was good at detecting small, elongated, and ice-covered supra-glacial lakes. We also found that it was useful for accurately identifying the location of supra-glacial streams and ice crevasses on glaciers, and quantifying their width. Finally, based on the time series of the mapping results, the spatial characteristics and temporal evolution of these features over the glaciers were comprehensively analyzed. Overall, this study presents a novel approach to improve the detection accuracy of glacier elements that could be leveraged for dynamic monitoring in future research.

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“…where β m (•) is the uniform symmetric d-dimensional B-spline of degree m. As is commonly done in [58], we set m = 1. The knots of the B-spline lie in a grid α with regular spacing.…”

Section: Variational B-spline Level-set Methodsmentioning

confidence: 99%

Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images

Chen

2021

Remote Sensing

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“…In prior models based on LSM, shape prior [20,21] is usually integrated into geometric active contour to segment or track objects. Shape prior has obtained robust results in segmenting objects with complex background [22][23][24][25]. However, the shape prior is usually learnt from a large set of annotated data, which is not always accessible in practice.…”

Section: Introductionmentioning

confidence: 99%

“…In ACGS, group similarity constraint is measured by low-rank, and low-rank property will not hold if the level set representation is used. Variational methods for image segmentation also have this issue [23][24][25][26][27][28][29]. us, parametric model is used to represent the contour in ACGS.…”

Section: Introductionmentioning

confidence: 99%

Active Contour Models Based on Block Similarity for Multiple Objects Segmentation

Liu

Wei

2019

Complexity

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For the model of active contours with group similarity (ACGS), a rank constraint for a group of evolving contours is defined to keep the shape similarity. ACGS obtains robust results in extracting a single object with missing or misleading features. However, with one initial contour, it could not extent to multiple objects segmentation because low-rank property will not hold in some image sequences. Besides, ACGS is affected by nontarget objects. In this paper, an active contour model based on block similarity of shapes is proposed to extend the ACGS model to realize multiple objects extraction. For a sequence of image with multiple objects, a model for multiple objects extraction is constructed by combining sparse decomposition and ACGS; second, a block low-rank constraint is proposed to constrain the similarity of these evolving contours in every block; finally, segmentation results are obtained through iterative evolutions. Experimental results show the proposed method could segment images with multiple targets, and it improves the robust segmentation performance of sequence of image when the features of multiobjects are missing or misleading.

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“…To extract the desired objects with geometric active contour, shape prior [26][27][28][29][30] is integrated into geometric active contour. The prior knowledge of the shape to be segmented is modelled based on a set of manually-annotated shapes to guide the segmentation.…”

Section: Introductionmentioning

confidence: 99%