Multifeature energy optimization framework and parameter adjustment-based nonrigid point set registration

Dan, Tingting; Yang, Yang; Xing, Lin; Yang, Kun; Zhang, Yaying; Ong, Sim Heng; Song, Fei; Gao, Xueyan

doi:10.1117/1.jrs.12.035006

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…Moreover, Some probabilistic methods [19]- [21] were developed for point registration. Recently, a multi-feature energy optimization framework and parameter adjustment-based nonrigid point set registration was proposed in [22]. More recently, Ma et al [23] proposed an efficient feature matching approach based on maintaining the local neighborhood structures of potential true matches.…”

Section: Bi Et Al: Multiple Image Features-based Retinal Image Rementioning

confidence: 99%

Multiple Image Features-Based Retinal Image Registration Using Global and Local Geometric Structure Constraints

et al. 2019

IEEE Access

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Retinal image registration is a key step in treating hypertension, diabetes and various retinal global diseases. In current methods of retinal image registration, they generally suffer from a lack of reliable features, missing true correspondences and geometric distortion. To address above problem, we propose a robust non-rigid retinal image registration method using multi-image features and dual constraints (i.e. the global and local geometric structure constraints). Our method contains the following main contributions. (i) A finite mixture model based on multi-feature is constructed for handling different types of image features. (ii) A combination of three features is substituted into the mixture model to improve the complementarities of different features. (iii) Dual constraints are proposed for ensuring the stability of the global and local structures of feature sets in the process of spatial transformation and updating. The performance of our method is evaluated by four main types of retinal images, which shows our method outperforms five stateof-the-art methods in most scenarios, especially when the retinal image has a large angle change. INDEX TERMS Retinal image, image registration, multiple image features, global and local geometric structure constraints.

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Section: Bi Et Al: Multiple Image Features-based Retinal Image Rementioning

confidence: 99%

Multiple Image Features-Based Retinal Image Registration Using Global and Local Geometric Structure Constraints

et al. 2019

IEEE Access

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“…Multisource images acquired using different platforms and sensors for Earth observation can acquire different characteristics and information regarding target ground objects, and the integrated use of multisource remote sensing data can achieve complementary advantages, which can help improve the interpretation accuracy of target ground objects and obtain rich and complete comprehensive information. [1][2][3][4] Image matching is the basis for the good integration of multisource images, and its accuracy significantly impacts subsequent applications. 5 In recent years, researchers have extensively studied multisource remote sensing image matching.…”

Section: Introductionmentioning

confidence: 99%

Multisource remote sensing image matching based on expanded phase consistency

Zheng,

Yang,

et al. 2023

J. Appl. Rem. Sens.

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.To better cope with significant nonlinear radiation distortions and severe geometric distortions in multisource remote sensing image matching, we propose a high-precision image matching method for multisource remote sensing images based on extended phase consistency (EPC). First, feature detection was performed based on an anisotropic weighted moment equation. We used the EPC model to construct multi-orientation weighted phase sharpness map and the multiscale phase orientation index map and built a gradient location orientation histogram-like local feature descriptor to obtain the corresponding features based on the feature maps. The spatial relationship constraint function was introduced to screen the obtained initial matches to reduce the negative effects caused by the high proportion of outliers. The remaining outliers were removed using the marginalizing sample consensus (MAGSAC) method, and the transformation parameters of the sensed image were calculated based on the iteratively optimized homography matrix model. Based on the feature detection results obtained from two distinct groups of synthetic data, it is evident that the proposed detector outperforms the three classical detectors in terms of repeatability and uniformity. The registration performance experiment data were divided into six groups, and the proposed method was compared with the log-Gabor histogram descriptor, radiation-variation insensitive feature transform, histogram of absolute phase consistency gradients, and local phase sharpness orientation methods. The experimental results showed that the proposed method is significantly better than the existing algorithms in terms of image matching performance and achieves high registration accuracy.

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“…classification assigns a specific label based on the image content of a random scene [1][2][3]. The relevant research results are widely used in many important fields, such as national defense security, climate change monitoring, environmental monitoring and management, land classification for different purposes, ground target identification and detection, loss assessment in natural disasters, and other important fields [4][5][6][7][8].…”

mentioning

confidence: 99%

Branch Feature Fusion Convolution Network for Remote Sensing Scene Classification

Shi

Wang

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Convolutional neural networks (CNNs) have outstanding advantages in the classification of remote sensing scenes. Deep CNN models with better classification performance typically have high complexity, while shallow CNN models with low complexity rarely achieve good classification performance for remote sensing images with complex spatial structures. In this paper, we proposed a new lightweight CNN classification method based on branch feature fusion (LCNN-BFF) for remote sensing scene classification. In contrast to a conventional single linear convolution structure, the proposed model had a bilinear feature extraction structure. The branch feature fusion (BFF) method was utilized to fuse the feature information extracted from the two branches, which improved the classification accuracy. In addition, combining depthwise separable convolution (DSC) and conventional convolution (CConv) to extract image features greatly reduced the complexity of the model on the premise of ensuring the accuracy of classification. We tested the method on four standard data sets. The experimental results showed that, compared with recent classification methods, the number of weight parameters of the proposed method only accounted for less than 5% of the other methods; however, the classification accuracy was equivalent to or even superior to certain high-performance classification methods. The python code for the paper can be downloaded from here: https://github.com/scp19801980/Remote-sensing-scene-classificati on.

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Multifeature energy optimization framework and parameter adjustment-based nonrigid point set registration

Cited by 9 publications

References 53 publications

Multiple Image Features-Based Retinal Image Registration Using Global and Local Geometric Structure Constraints

Multiple Image Features-Based Retinal Image Registration Using Global and Local Geometric Structure Constraints

Multisource remote sensing image matching based on expanded phase consistency

Branch Feature Fusion Convolution Network for Remote Sensing Scene Classification

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