Local image features matching for real-time seabed tracking applications

Żak, Bogdan; Hożyń, Stanisław

doi:10.1080/20464177.2017.1386266

Cited by 15 publications

(10 citation statements)

References 33 publications

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“…The results show the features matched from two images with different camera angles in a video frame from an underwater motion camera. Figure 6 shows a comparison between this model and another image matching method result called Oriented FAST and Rotated BRIEF (ORB) for underwater image [45] which is basically a fusion of the scale-invariant feature transform (FAST) key-point detector and the Binary Robust Independent Elementary Features (BRIEF) descriptor with many modifications to enhance the performance. As shown, the key-points extraction and matching in the proposed method is very accurate and this method is better than other methods for image mosaicking.…”

Section: Resultsmentioning

confidence: 99%

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Kazerouni

Dooly

Toal

2020

JMSE

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Feature extraction and matching is a key component in image stitching and a critical step in advancing image reconstructions, machine vision and robotic perception algorithms. This paper presents a fast and robust underwater image mosaicking system based on (2D)2PCA and A-KAZE key-points extraction and optimal seam-line methods. The system utilizes image enhancement as a preprocessing step to improve quality and allow for greater keyframe extraction and matching performance, leading to better quality mosaicking. The application focus of this paper is underwater imaging and it demonstrates the suitability of the developed system in advanced underwater reconstructions. The results show that the proposed method can address the problems of noise, mismatching and quality issues which are typically found in underwater image datasets. The results demonstrate the proposed method as scale-invariant and show improvements in terms of processing speed and system robustness over other methods found in the literature.

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Section: Resultsmentioning

confidence: 99%

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Kazerouni

Dooly

Toal

2020

JMSE

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“…Regarding the baseline image, the top of the distribution is located in the centre of the input picture. To find the filter template -, the algorithm uses the square of the sum of the output convolution, and determines the output error of the convolution using the following operation [9]:…”

Section: O S S E T R a C K E Rmentioning

confidence: 99%

“…The significant progress of computer vision technique has been demonstrated in many research areas, such as intelligent surveillance systems, autonomous vehicles, or industrial automation [7][8][9][10]. Cheaper cameras and faster computers, as well as more sophisticated algorithms, facilitate engaging computer vision in a wide range of real-time applications [11].…”

Section: Introductionmentioning

confidence: 99%

Tracking of Unmanned Aerial Vehicles Using Computer Vision Methods: A Comparative Analysis

Hożyń

Wierszyło

2020

Scientific Journal of Polish Naval Academy

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Tracking of small objects in any given airspace is an integral part of modern security systems. In these systems, there are embedded methods that employ the techniques based on either radio waves, or acoustic signals, or light radiation. The computer vision operation, springing from the light radiation-based technique, has prompted interest in its research. This operation has the advantage of being less expensive than radars and acoustic systems. In addition, it can solve complex security problems by detecting and tracking humans, vehicles, and flying objects. Therefore, this article evaluates the usefulness of the varying computer vision algorithms for tracking of small flying objects.

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“…The Biomimetic Underwater Vehicles BUVs can be used in a wide variety of underwater applications [1], such as monitoring [2], investigation of sea region [3], [4] pollution detection, military operation [5], [6], [7] and protection [8], [9], [10]. In comparison to propulsion systems with the rotary propeller, the energy effi ciency is limited to 70 % and is 20 % less than the swimming mechanism of real fi sh [11], [12].…”

Section: Introduction / Uvodmentioning

confidence: 99%

Analysis of a Fin Drag Force in a Biomimetic Underwater Vehicle

Piskur¹,

Szymak²,

Flis³

et al. 2020

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This article presents the analysis of fi n drag force in a Biomimetic Underwater Vehicle (BUV). To carry out numerical simulations Incompressible Computational Fluid Dynamics (ICFD) solver was used. The simulation model was verifi ed in the designed laboratory test stand. The laboratory test stand was equipped with specialized sensors for force measurements and control of fl uid velocity. An average value of fl uid velocity in the water tunnel was measured by specialized high accuracy ultrasonic fl owmeter. A vision system was used for analysis of laminar and turbulent fl ows in the fl uid structure interaction area. The research of fl uid-structure interaction phenomena was depicted using dimensional analysis. Sažetak U radu se daje analiza sile otpora biomimetičkog podvodnog vozila (BUV). Za provođenje numeričkih simulacija koristio se program Računalna dinamika fl uida (ICFD). Simulacijski model provjeren je u posebno izrađenom laboratoriju. Laboratorij je opremljen specijaliziranim senzorima za mjerenje sile i kontrole brzine fl uida. Prosječna vrijednost brzine fl uida u vodenom tunelu izmjerena je specijaliziranim i vrlo preciznim ultrasoničnim uređajem za mjerenje brzine strujanja. Sustav promatranja koristio se za analizu laminarnih i turbulencijskih strujanja u području interakcije u strukturi fl uida. Istraživanje fenomena interakcije u strukturi fl uida provedeno je uporabom dimenzionalne analize. KEY WORDS artifi cial fi sh fl uid structure interaction (FSI) computational fl uid dynamics (CFD) biomimetic underwater vehicle KLJUČNE RIJEČI umjetna riba interakcija u strukturi fl uida (FSI) računalna dinamika fl uida (CFD) biomimetičko podvodno vozilo

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Local image features matching for real-time seabed tracking applications

Cited by 15 publications

References 33 publications

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Tracking of Unmanned Aerial Vehicles Using Computer Vision Methods: A Comparative Analysis

Analysis of a Fin Drag Force in a Biomimetic Underwater Vehicle

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