Geometric Estimation via Robust Subspace Recovery

Fan, Aoxiang; Jiang, X. Y.; Yang, Wang; Jiang, Junjun; Ma, Jiayi

doi:10.1007/978-3-030-58542-6_28

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Wang and Chen 29 propose a mismatch removing method called guided local outlier factor (GLOF) to match features under multigranularity neighborhood structure-preserving. Fan et al 30,31 propose a novel solution for geometric model fitting which is applied to estimate the intrinsic structure of image point correspondence. A class of algorithms are generated to make the homography and fundamental matrix estimation in his work.…”

Section: Feature-based Methodsmentioning

confidence: 99%

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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Section: Feature-based Methodsmentioning

confidence: 99%

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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“…A typical single-lens camera can only capture a single object area. If we want to capture all in-focus objects in a large field of view in one image frame, we need to capture them simultaneously with multiple cameras, or multiple captures with a single camera [2]. Gigapixel images can theoretically be captured by ultra-large and ultra-high-performance sensors.…”

Section: Introductionmentioning

confidence: 99%

“…The traditional panoramic image stitching is the violent collection of the entire area, which requires a lot of acquisition and storage costs, and the later image processing is expensive. However, natural scenes are highly compressible, often with large expanses of sky, foliage, and ground [2]. Similarly, the perspective of humans concentrated in some areas.…”

Section: Introductionmentioning

confidence: 99%

A gigapixel image mosaicking approach based on SURF and color transfer

Zhang

Yao

et al. 2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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In order to solve the problem that a single camera cannot take into consideration the broad imaging field and highresolution details, we propose a novel cross-resolution image mosaic framework that embeds local images into global images to synthesize gigapixel images with wide field-of-view and high-definition details. Firstly, the framework adopts a color transfer algorithm to maintain the color consistency between images; secondly, it adopts multi-level feature point matching and homography transformation for image registration; thirdly, the registered local images are segmented into small patches and fused into the global image to provide detailed information. The experimental results show that the framework proposed in this paper can alleviate color and content inconsistency between images and generate large-field images with excellent visual effects when the resolution gap is huge.

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