A robust global and local mixture distance based non-rigid point set registration

Yang, Yang; Ong, Sim Heng; Foong, Kelvin Weng Chiong

doi:10.1016/j.patcog.2014.06.017

Cited by 124 publications

(82 citation statements)

References 29 publications

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“…SIFT [11], SURF [13], CPD [25], GLMDTPS [30], RSOC [21], totally five state-of-the-art methods are compared against our method in the experiments. These methods can mainly categorize into three types based on the methods of feature extraction.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Recently, Ma et al proposed a robust L 2 -minimizing estimate (L 2 E) [28] for non-rigid point set registration, they later proposed a flexible and general algorithm called locally linear transforming (LLT) [29] for both rigid and non-rigid registration on remote sensing images. More recently, Yang et al [30] proposed a new method named GLMDTPS, which considers global and local structural differences as a linear assignment problem.…”

Section: Introductionmentioning

confidence: 99%

“…For example, methods for image registration which use only SIFT [11,12] or its variants [6,[14][15][16][17][18][19][20] suffer from inaccurate registration of SIFT. In addition, the performances of the methods [23][24][25][26][27][28][29][30] which consider only the geometric structure features is limited by the assumption that the corresponding points have similar structures. (ii) Using a rigid or affine transformation model is infeasible for registering remote sensing images with different viewpoints since the images usually contain local non-rigid distortion.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Image Registration Using Multiple Image Features

et al. 2017

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Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Image Registration Using Multiple Image Features

et al. 2017

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“…The extracted feature points contain a large number of outliers that limit the performance of current non-rigid point set registration algorithms [29][30][31]. For this issue, a robust multi-feature guided model is designed-given two point sets A = {a n } N n=1 (i.e., the source point set) and B = {b m } M m=1 (i.e., the target point set) which are extracted from the sensed image and the reference image, respectively.…”

Section: Multi-feature Guided Point Set Registration Modelmentioning

confidence: 99%

“…CPD (coherent point drift) [29], GLMDTPS (global and local mixture distance with thin plate spline transformation) [30], SIFT (scale invariant feature transform) [38] and SURF (speeded-up robust features) [39], four state-of-the-art methods, are compared against our method in the following experiments. SIFT and SURF methods used the open source VLFeat toolbox with the threshold 1 and the Matlab open source OpenSURF function with the default setting, respectively.…”

Section: Experiments Designmentioning

confidence: 99%

A Small UAV Based Multi-Temporal Image Registration for Dynamic Agricultural Terrace Monitoring

Wei

Han

et al. 2017

Remote Sensing

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Terraces are the major land-use type of agriculture and support the main agricultural production in southeast and southwest China. However, due to smallholder farming, complex terrains, natural disasters and illegal land occupations, a light-weight and low cost dynamic monitoring of agricultural terraces has become a serious concern for smallholder production systems in the above area. In this work, we propose a small unmanned aerial vehicle (UAV) based multi-temporal image registration method that plays an important role in transforming multi-temporal images into one coordinate system and determines the effectiveness of the subsequent change detection for dynamic agricultural terrace monitoring. The proposed method consists of four steps: (i) guided image filtering based agricultural terrace image preprocessing, (ii) texture and geometric structure features extraction and combination, (iii) multi-feature guided point set registration, and (iv) feature points based image registration. We evaluated the performance of the proposed method by 20 pairs of aerial images captured from Longji and Yunhe terraces, China using a small UAV (the DJI Phantom 4 Pro), and also compared against four state-of-the-art methods where our method shows the best alignments in most cases.

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Retinal fundus image registration framework using Bayesian integration and asymmetric Gaussian mixture model

Zhang

Jia

Zhuo

et al. 2022

Int J Imaging Syst Tech

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Retinal image registration, which is applied in diagnosing and treating eye diseases, plays an important role in medical image analysis. Existing methods suffer from problems due to different imaging viewpoints, times, quality, modalities, and retinal disasters. In this paper, we propose an efficient retinal images registration framework that overcomes these challenges without supervision. We present a layer-wise matching method to achieve a uniform distribution of features in both image-space and scale-space. Then, a novel method called Bayesian integration is generated to accumulate more meaningful inputs. We use the results of different matches as priors, assign a score to each match, and categorize them using a dynamic threshold. Finally, in accordance with previous work, we transform the problem into a probabilistic model, with the asymmetric Gaussian mixture model representing the distribution. A robust estimation is performed on a non-rigid transformation. The experimental results demonstrate that our proposed framework is robust to kinds of retinal image degradation and produces a more stable and accurate result than state-of-the-art methods.

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A robust global and local mixture distance based non-rigid point set registration

Cited by 124 publications

References 29 publications

Remote Sensing Image Registration Using Multiple Image Features

Remote Sensing Image Registration Using Multiple Image Features

A Small UAV Based Multi-Temporal Image Registration for Dynamic Agricultural Terrace Monitoring

Retinal fundus image registration framework using Bayesian integration and asymmetric Gaussian mixture model

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