Outliers rejection in similar image matching

Istead, Lesley; Istead, Joe; Pocol, Andreea; Kaplan, Craig S.

doi:10.1016/j.vrih.2023.02.004

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Image matching refers to establishing reliable feature correspondences between two or more images, it is widely used in tasks such as image alignment, 3D reconstruction, and target localization and recognition [11]. As a pre-processing step, a correct image matching result is crucial for 3D reconstruction and other computer vision tasks.…”

Section: Related Workmentioning

confidence: 99%

2chADCNN: A Template Matching Network for Season-Changing UAV Aerial Images and Satellite Imagery

Ren,

Liu,

Huang

et al. 2023

Drones

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Visual navigation based on image matching has become one of the most important research fields for UAVs to achieve autonomous navigation, because of its low cost, strong anti-jamming ability, and high performance. Currently, numerous positioning and navigation methods based on visual information have been proposed for UAV navigation. However, the appearance, shape, color, and texture of objects can change significantly due to different lighting conditions, shadows, and surface coverage during different seasons, such as vegetation cover in summer or ice and snow cover in winter. These changes pose greater challenges for feature-based image matching methods. This encouraged us to overcome the limitations of previous works, which did not consider significant seasonal changes such as snow-covered UAV aerial images, by proposing an image matching method using season-changing UAV aerial images and satellite imagery. Following the pipeline of a two-channel deep convolutional neural network, we first pre-scaled the UAV aerial images, ensuring that the UAV aerial images and satellite imagery had the same ground sampling distance. Then, we introduced attention mechanisms to provide additional supervision for both low-level local features and high-level global features, resulting in a new season-specific feature representation. The similarity between image patches was calculated using a similarity measurement layer composed of two fully connected layers. Subsequently, we conducted template matching to estimate the UAV matching position with the highest similarity. Finally, we validated our proposed method on both synthetic and real UAV aerial image datasets, and conducted direct comparisons with previous popular works. The experimental results demonstrated that our method achieved the highest matching accuracy on multi-temporal and multi-season images.

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Section: Related Workmentioning

confidence: 99%

2chADCNN: A Template Matching Network for Season-Changing UAV Aerial Images and Satellite Imagery

Ren,

Liu,

Huang

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…When considering problems in the field of computer vision, great attention is paid to special algorithms and a set of actions that help implement image matching [11,12]. The need to match images is currently a priority task for electronic vision.…”

Section: Introductionmentioning

confidence: 99%

Trainable Regularization in Dense Image Matching Problems

Kuklin,

Tatarkanov,

Umyskov

2023

HighTech. Innov. J.

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This study examines the development of specialized models designed to solve image-matching problems. The purpose of this research is to develop a technique based on energy tensor aggregation for dense image matching. This task is relevant within the framework of computer systems since image comparison makes it possible to solve current problems such as reconstructing a three-dimensional model of an object, creating a panorama scene, ensuring object recognition, etc. This paper examines in detail the key features of the image matching process based on the use of binocular stereo reconstruction and the features of calculating energies during this process, and establishes the main parts of the proposed method in the form of diagrams and formulas. This research develops a machine learning model that provides solutions to image matching problems for real data using parallel programming tools. A detailed description of the architecture of the convolutional recurrent neural network that underlies this method is given. Appropriate computational experiments were conducted to compare the results obtained with the methods proposed in the scientific literature. The method discussed in this article is characterized by better efficiency, both in terms of the speed of work execution and the number of possible errors. Doi: 10.28991/HIJ-2023-04-03-011 Full Text: PDF

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Directional region-based feature point matching algorithm based on SURF

Huang,

Xiang,

Zhao

et al. 2024

J. Opt. Soc. Am. A

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Feature point matching is one of the fundamental tasks in binocular vision. It directly affects the accuracy and quality of 3D reconstruction. This study proposes a directional region-based feature point matching algorithm based on the SURF algorithm to improve the accuracy of feature point matching. First, same-name points are selected as the matching reference points in the left and right images. Then, the SURF algorithm is used to extract feature points and construct the SURF feature point descriptors. During the matching process, the location relationship between the query feature point and the reference point in the left image is directed to determine the corresponding matching region in the right image. Then, the matching is completed within this region based on Euclidean distance. Finally, the grid-based motion statistics algorithm is used to eliminate mismatches. Experimental results show that the proposed algorithm can substantially improve the matching accuracy and the number of valid matched points, particularly in the presence of a large amount of noise and interference. It also exhibits good robustness and stability.

show abstract

Outliers rejection in similar image matching

Cited by 3 publications

References 28 publications

2chADCNN: A Template Matching Network for Season-Changing UAV Aerial Images and Satellite Imagery

2chADCNN: A Template Matching Network for Season-Changing UAV Aerial Images and Satellite Imagery

Trainable Regularization in Dense Image Matching Problems

Directional region-based feature point matching algorithm based on SURF

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