Artwork painting identification method for panorama based on adaptive rectilinear projection and optimized ASIFT

Jiang, Dayou; Kim, Jongweon

doi:10.1007/s11042-019-07985-4

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…In this section, we have studied the different robust detectors using the (SIFT, PCA-SIFT, ASIFT) [21,22,23,24] and SURF methods by varying the expression of the faces of the same person. Then, we have measured the number of descriptors, the number of matches and the processing time by different detectors according to the change in facial expression.…”

Section: Some Simulation Results and Discussionmentioning

confidence: 99%

New approach to the identification of the easy expression recognition system by robust techniques (SIFT, PCA-SIFT, ASIFT and SURF)

Chater

Lasfar

2020

TELKOMNIKA

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In recent years, facial recognition has been a major problem in the field of computer vision, which has attracted lots of interest in previous years because of its use in different applications by different domains and image analysis. Which is based on the extraction of facial descriptors, it is a very important step in facial recognition. In this article, we compared robust methods (SIFT, PCA-SIFT, ASIFT and SURF) to extract relevant facial information with different facial posture variations (open and unopened mouth, glasses and no glasses, open and closed eyes). The simulation results show that the detector (SURF) is better than others at finding the similarity descriptor and calculation time. Our method is based on the normalization of vector descriptors and combined with the RANSAC algorithm to cancel outliers in order to calculate the Hessian matrix with the objective of reducing the calculation time. To validate our experience, we tested four facial images databases containing several modifications. The results of the simulation show that our method is more efficient than other detectors in terms of speed of recognition and determination of similar points between two images of the same face, one belonging to the base of the text and the other one to the base driven by different modifications. This method, which can be applied on a mobile platform to analyze the content of simple images, for example, to detect driver fatigue, human-machine interaction, human-robot. Using descriptors with properties important for good accuracy and real-time response.

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Section: Some Simulation Results and Discussionmentioning

confidence: 99%

New approach to the identification of the easy expression recognition system by robust techniques (SIFT, PCA-SIFT, ASIFT and SURF)

Chater

Lasfar

2020

TELKOMNIKA

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“…In this way, we can ensure that the same features can be extracted from different images of the same scene. Traditional feature extraction methods include scale-invariant feature transform (SIFT) [29], speeded up robust features (SURF) [30], oriented fast and rotated brief (ORB) [31], affine SIFT (ASIFT) [32], binary robust invariant scalable keypoints (BRISK) [33], and binary fisheye spherical distorted robust independent elementary features (FSD-BRIEF) [34]. These algorithms rely on handdesigned feature descriptors; thus, their real-time performance and robustness need to be further improved.…”

Section: Related Workmentioning

confidence: 99%

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

Fan,

Yang,

et al. 2023

International Journal of Aerospace Engineering

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The positioning function of unmanned aerial vehicles (UAVs) is a challenging and fundamental research topic and is the premise for UAVs to realize autonomous navigation. The disappearance of satellite signals makes it challenging to achieve accurate positioning. Thus, visual positioning algorithms based on computer vision have been proposed in recent years and these algorithms have produced good results. However, these algorithms have relatively simple functions and cannot perceive the environment. Their versatility is poor, and mismatching often occurs, which affects the positioning accuracy. Aiming to address the need for integrated target recognition, target matching, and positioning of UAVs, we propose an algorithm that integrates the target recognition, matching, and positioning functions by combining the single-shot multibox detector (SSD) algorithm with the deep feature matching algorithm. This algorithm is based on the idea of pseudo-Siamese networks and the SSD algorithm, introducing a deep feature matching method to directly calculate the correspondence between two images. The main idea is to use the VGG network trained by the SSD target recognition algorithm to extract deep features, without any special training for feature matching. Finally, by sharing neural network weights, the integrated design of target recognition and image-matching localization algorithms is achieved. Mismatches between the real-time and reference images are addressed by introducing the grid-based motion statistics algorithm to optimize the matching result and improve the correct matching efficiency of the target. The University-Release dataset was used to compare and analyze the performance of the proposed algorithm to verify its superiority and feasibility. The results show that the matching accuracy of the PSiamRML algorithm is generally good and that it significantly compensates for changes in the contrast, scale, brightness, blur, deformation, and so on, apart from improving the stability and robustness. Finally, a matching test scenario with aerial images captured by an S1000 six-rotor UAV served to verify the effectiveness and practicability of the PSiamRML algorithm.

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“…In feature-based image-matching methods, feature extraction is a very important component. Traditional feature extraction methods mainly include scale-invariant feature transform (SIFT) [18], oriented fast and rotated brief (ORB) [19], features from accelerated segment test (FAST) [20], histogram of orientated gradient (HOG) [21], affine-SIFT (ASIFT) [22], binary robust invariant scalable keypoints (BRISK), binary fisheye spherical distorted robust independent elemental features (FSD-BRIEF) [23], etc. Because traditional feature extraction methods do not fully utilize data, they can only extract certain aspects of image features.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Scene Matching Location Algorithm Based on M2Det

Fan,

Yang,

et al. 2023

CMC

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In recent years, many visual positioning algorithms have been proposed based on computer vision and they have achieved good results. However, these algorithms have a single function, cannot perceive the environment, and have poor versatility, and there is a certain mismatch phenomenon, which affects the positioning accuracy. Therefore, this paper proposes a location algorithm that combines a target recognition algorithm with a depth feature matching algorithm to solve the problem of unmanned aerial vehicle (UAV) environment perception and multi-modal image-matching fusion location. This algorithm was based on the single-shot object detector based on multi-level feature pyramid network (M2Det) algorithm and replaced the original visual geometry group (VGG) feature extraction network with the ResNet-101 network to improve the feature extraction capability of the network model. By introducing a depth feature matching algorithm, the algorithm shares neural network weights and realizes the design of UAV target recognition and a multi-modal image-matching fusion positioning algorithm. When the reference image and the real-time image were mismatched, the dynamic adaptive proportional constraint and the random sample consensus consistency algorithm (DAPC-RANSAC) were used to optimize the matching results to improve the correct matching efficiency of the target. Using the multi-modal registration data set, the proposed algorithm was compared and analyzed to verify its superiority and feasibility. The results show that the algorithm proposed in this paper can effectively deal with the matching between multi-modal images (visible image-infrared image, infrared image-satellite image, visible image-satellite image), and the contrast, scale, brightness, ambiguity deformation, and other changes had good stability and robustness. Finally, the effectiveness and practicability of the algorithm proposed in this paper were verified in an aerial test scene of an S1000 sixrotor UAV.

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Artwork painting identification method for panorama based on adaptive rectilinear projection and optimized ASIFT

Cited by 4 publications

References 25 publications

New approach to the identification of the easy expression recognition system by robust techniques (SIFT, PCA-SIFT, ASIFT and SURF)

New approach to the identification of the easy expression recognition system by robust techniques (SIFT, PCA-SIFT, ASIFT and SURF)

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

Multi-Modal Scene Matching Location Algorithm Based on M2Det

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