Underwater vehicle visual servo and target grasp control

Huang, Hai; Zhou, Hao; Qin, Hongde; Sheng, Mingwei

doi:10.1109/robio.2016.7866559

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…On the other hand, high accuracy/quality interest point detectors should have the ability to detect interest points in real-time with acceptable memory and storage demands. The reason is that real-time processing is an indispensable requirement in many interest point based computer vision tasks such as autonomous driving [7] and image-guided robotic-assisted surgery [11], [41].…”

Section: Machine Learning Basedmentioning

confidence: 99%

“…I NTEREST points have been found very important in human perception of shapes [1] and have been one of the key image features which are widely used as critical cues for various image processing and image understanding tasks such as camera calibration [2], image registration [3], object recognition [4], 3D reconstruction [5], human-computer interaction [6], autonomous driving [7], face recognition [8], traffic analysis [9], multi-object detection and tracking [10], image-guided robotic-assisted surgery [11], surveillance [12], and augmented reality [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Gao

Zhang

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for fifteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated.

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Section: Machine Learning Basedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Gao

Zhang

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Section: B Main Challengesmentioning

confidence: 99%

“…I NTEREST points have been found very important in human perception of shapes [1] and have been one of the key image features which are widely used as critical cues for various image processing and image understanding tasks such as camera calibration [2], image registration [3], object recognition [4], 3D reconstruction [5], human-computer interaction [6], autonomous driving [7], face recognition [8] and traffic analysis [9] as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Ji¹,

Gao²,

Zhang³

et al. 2021

Preprint

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“…C ORNERS are one of the most important local features in an image, which have been widely applied in many computer vision and image processing tasks such as 3D reconstruction [1], autonomous driving [2], object recognition [3], and image registration [4]. The existing corner detection methods can be divided into three categories [5]: contour-based [6]- [11], template-based [12]- [15], and intensity-based [5], [16]- [27].…”

Section: Introductionmentioning

confidence: 99%

Fast Corner Detection Using Approximate Form of Second-Order Gaussian Directional Derivative

Gao

Ji²,

Liu³

et al. 2020

IEEE Access

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High-efficiency image corner detection, one of the most important and critical basic technology in industrial image processing, is to detect point features from an input image in real-time. In this paper, we propose a new corner detection method which has both good performance of corner detection and real-time processing abilities. Firstly, the integral image and the box filter are combined to obtain the secondorder derivative response in each direction of the image. Secondly, a new coarse screening mechanism for candidate corners is presented to reduce the complexity of the corner metric. Thirdly, a non-maximum suppression operation is utilized to obtain corners. Finally, the performance evaluation on accuracy of corner detection, localization error, average repeatability, region repeatability, different lighting conditions, and execution time are used to assess the proposed method against twelve state-of-the-art methods. The experimental results show that our proposed detector has good corner detection performance and achieves the requirement of real-time processing. INDEX TERMS Corner detection, real-time processing, directional derivative, detection performance.

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Underwater vehicle visual servo and target grasp control

Cited by 8 publications

References 11 publications

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Fast Corner Detection Using Approximate Form of Second-Order Gaussian Directional Derivative

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