Evaluating the robustness of feature correspondence using different feature extractors

El-Mashad, Shady Y.; Shoukry, Amin

doi:10.1109/mmar.2014.6957371

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…In the first two cases, different types of feature descriptors can be paired with different kinds of feature detectors. [27], [28]. Therefore, it is considered a viable choice for complementing those algorithms.…”

Section: Feature Detectors and Descriptorsmentioning

confidence: 99%

Towards a Fair Evaluation of Feature Extraction Algorithms Robustness in Structure from Motion

Taha¹,

Zayed²,

El-Mashad³

2022

IJACSA

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Structure from Motion is a pipeline for 3D reconstruction in which the true geometry of an object or a scene is inferred from a sequence of 2D images. As feature extraction is usually the first phase in the pipeline, the reconstruction quality depends on the accuracy of the feature extraction algorithm. Fairly evaluating the robustness of feature extraction algorithms in the absence of reconstruction ground truth is challenging due to the considerable number of parameters that affect the algorithms' sensitivity and the tradeoff between reconstruction size and error. The evaluation methodology proposed in this paper is based on two elements. The first is using constrained 3D reconstruction, in which only fixed numbers of extracted and matched features are passed to subsequent phases. The second is comparing the 3D reconstructions using size-error curves (introduced in this paper) rather than the value of reconstruction size, error, or both. The experimental results show that the proposed methodology is more transparent.

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Section: Feature Detectors and Descriptorsmentioning

confidence: 99%

Towards a Fair Evaluation of Feature Extraction Algorithms Robustness in Structure from Motion

Taha¹,

Zayed²,

El-Mashad³

2022

IJACSA

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“…In [34], authors compared combinations of well-known feature detectors and descriptors including Harris-FREAK, Hessian-SURF, maximally stable extremal regions (MSER)-SURF, and Fast-FREAK to identify the optimal detector and descriptor pair that best suits the matching procedure. Experiments conducted on 50 images revealed that the Harris-FREAK combination outperformed the other combinations.…”

Section: Feature-based Matching Using Local Templatesmentioning

confidence: 99%

A Double Epipolar Resampling Approach to Reliable Conjugate Point Extraction for Accurate Kompsat-3/3A Stereo Data Processing

Han

2020

Remote Sensing

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Kompsat-3/3A provides along-track and across-track stereo data for accurate three-dimensional (3D) topographic mapping. Stereo data preprocessing involves conjugate point extraction and acquisition of ground control points (GCPs), rational polynomial coefficient (RPC) bias compensation, and epipolar image resampling. Applications where absolute positional accuracy is not a top priority do not require GCPs, but require precise conjugate points from stereo images for subsequent RPC bias compensation, i.e., relative orientation. Conjugate points are extracted between the original stereo data using image-matching methods by a proper outlier removal process. Inaccurate matching results and potential outliers produce geometric inconsistency in the stereo data. Hence, the reliability of conjugate point extraction must be improved. For this purpose, we proposed to apply the coarse epipolar resampling using raw RPCs before the conjugate point matching. We expect epipolar images with even inaccurate RPCs to show better stereo similarity than the original images, providing better conjugate point extraction. To this end, we carried out the quantitative analysis of the conjugate point extraction performance by comparing the proposed approach using the coarsely epipolar resampled images to the traditional approach using the original stereo images. We tested along-track Kompsat-3 stereo and across-track Kompsat-3A stereo data with four well-known image-matching methods: phase correlation (PC), mutual information (MI), speeded up robust features (SURF), and Harris detector combined with fast retina keypoint (FREAK) descriptor (i.e., Harris). These matching methods were applied to the original stereo images and coarsely resampled epipolar images, and the conjugate point extraction performance was investigated. Experimental results showed that the coarse epipolar image approach was very helpful for accurate conjugate point extraction, realizing highly accurate RPC refinement and sub-pixel y-parallax through fine epipolar image resampling, which was not achievable through the traditional approach. MI and PC provided the most stable results for both along-track and across-track test data with larger patch sizes of more than 400 pixels.

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“…Consequently, the accuracy of the 2D location of the detected features (the responsibility of the feature detector) as well as the accuracy of the description and matching of the detected features (the responsibility of the feature descriptor) significantly affect the accuracy of the estimated 3D location. Therefore, a lot of attention has been paid during the past decade to the problem of deciding good feature detectors and descriptors combinations for SfM and it has become a hot area of research [4][5][6][7][8][9][10][11][12]. When using synthetic data (like SfM Flow does in [13]) is not an option, and the ground truth of the real data is not available, evaluation of the 3D reconstruction becomes very challenging as relying on the value of the reconstruction density (number of 3D points in the sparse point cloud) and accuracy (average reprojection error of the 3D points on the images) can be misleading [14].…”

Section: Introductionmentioning

confidence: 99%

SfM ToAEval: Trade-off Aware Evaluation of Feature Extraction Algorithms in Structure from Motion

Taha,

El-mashad,

Zayed

2024

Engineering Research Journal (Shoubra)

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This paper presents SfM ToAEval, a framework for evaluating different feature extraction algorithms in Structure from Motion (SfM) pipelines. SfM ToAEval allows automatically evaluating the effect of using different feature detectors and descriptors combinations on the quality of the 3D reconstruction from a given collection of image sequences. In addition, SfM ToAEval evaluates the 3D reconstruction without the need for ground truth. Moreover, SfM ToAEval is aware of the reconstruction density-accuracy trade-off, and it supports visualizing it in order to allow deciding the "best" reconstruction transparently. Furthermore, SfM ToAEval allows quantifying the quality of each 3D reconstruction compared to others. SfM ToAEval was used to evaluate 98 feature detectors and descriptors combinations on six image sequences, and it was able to identify four promising combinations. Experimental results comparing the proposed combinations with related work are presented in this research. The complete source code of the proposed framework as well as a minimal Jupyter Notebook demonstrating how different functionalities can be used are released under the MIT license.

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Evaluating the robustness of feature correspondence using different feature extractors

Cited by 6 publications

References 14 publications

Towards a Fair Evaluation of Feature Extraction Algorithms Robustness in Structure from Motion

Towards a Fair Evaluation of Feature Extraction Algorithms Robustness in Structure from Motion

A Double Epipolar Resampling Approach to Reliable Conjugate Point Extraction for Accurate Kompsat-3/3A Stereo Data Processing

SfM ToAEval: Trade-off Aware Evaluation of Feature Extraction Algorithms in Structure from Motion

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