Recent Advances in Feature Detectors and Descriptors: A Survey

Lee, Haeseong; Jeon, Semi; Yoon, Inhye; Paik, Joonki

doi:10.5573/ieiespc.2016.5.3.153

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…A local descriptor is built from these features that should stay constant under numerous disturbances, like geometric transformation, noise, or photogrammetric changes. The SURF algorithm was selected as the feature extraction descriptor in this study due to its capability to reduce computational complexity [ 45 ], which mainly consists of four steps. First, the integral image that represents the input image is created.…”

Section: Computer Vision Procedures For Uav-based Seismic Structural ...mentioning

confidence: 99%

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures

Ngeljaratan,

Bas,

Moustafa

2024

Sensors

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Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity through an advanced SHM system. Thus, this study proposes a single UAV for the seismic monitoring and safety assessment of linear infrastructures along with their computer vision-aided procedures. The proposed procedures were implemented in a full-scale shake-table test of a natural gas pipeline assembly. The objectives were to explore the UAV potential for the seismic vibration monitoring of linear infrastructures with the aid of several computer vision algorithms and to investigate the impact of parameter selection for each algorithm on the matching accuracy. The procedure starts by adopting the Maximally Stable Extremal Region (MSER) method to extract covariant regions that remain similar through a certain threshold of image series. The feature of interest is then detected, extracted, and matched using the Speeded-Up Robust Features (SURF) and K-nearest Neighbor (KNN) algorithms. The Maximum Sample Consensus (MSAC) algorithm is applied for model fitting by maximizing the likelihood of the solution. The output of each algorithm is examined for correctness in matching pairs and accuracy, which is a highlight of this procedure, as no studies have ever investigated these properties. The raw data are corrected and scaled to generate displacement data. Finally, a structural safety assessment was performed using several system identification models. These procedures were first validated using an aluminum bar placed on an actuator and tested in three harmonic tests, and then an implementation case study on the pipeline shake-table tests was analyzed. The validation tests show good agreement between the UAV data and reference data. The shake-table test results also generate reasonable seismic performance and assess the pipeline seismic safety, demonstrating the feasibility of the proposed procedure and the prospect of UAV-based SHM for linear infrastructure monitoring.

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Section: Computer Vision Procedures For Uav-based Seismic Structural ...mentioning

confidence: 99%

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures

Ngeljaratan,

Bas,

Moustafa

2024

Sensors

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“…Various feature detection methods were proposed and widely applied to detect a common region in two images [22]. Harris et al proposed a seminal model to detect corner points where shifting a local window in any directions yields a large change in appearance [23].…”

Section: Feature Extraction and Matching For Robust Video Stabilizmentioning

confidence: 99%

“…Matching of features between temporally adjacent frames is very important to understand the geometric relationship of two frames and detect specific objects in video [ 11 , 21 ]. Various feature detection methods were proposed and widely applied to detect a common region in two images [ 22 ]. Harris et al proposed a seminal model to detect corner points where shifting a local window in any directions yields a large change in appearance [ 23 ].…”

Section: Feature Extraction and Matching For Robust Video Stabilizmentioning

confidence: 99%

Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path

Jeon

Yoon

Jang

et al. 2017

Sensors

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Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i) robust feature detection using particle keypoints between adjacent frames; (ii) camera path estimation and smoothing; and (iii) rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV). The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems.

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“…By far, numerous algorithms [4][5][6][7][8][9] dealing with keypoint extraction have been developed. Due to a large number of keypoint detectors and descriptors, many surveys [10,11] discussing their advantages and drawbacks have been reported. Furthermore, many works have evaluated and compared the performance of keypoint extraction and description techniques for various image matching tasks [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Using Information Content to Select Keypoints for UAV Image Matching

Mousavi¹,

Varshosaz²,

Remondino³

2021

Remote Sensing

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Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.

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Recent Advances in Feature Detectors and Descriptors: A Survey

Cited by 12 publications

References 15 publications

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures

Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path

Using Information Content to Select Keypoints for UAV Image Matching

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