Suction system vapour velocity map estimation through SIFT-based alghoritm

Abstract

Measurement of velocity fields is a fundamental topic in fluid dynamics. Image-based analysis methods such as Particle Image Velocimetry or Laser Doppler Velocimetry are usually used. However, these techniques need complex instrumentation and particular test conditions. In this work, a computer vision-based approach is developed in order to obtain vapour velocity field map in effective, robust and economic way. Moreover, iterative filtering algorithm is applied to improve the results. The implemented method is tested on a suction system for domestic use, and the obtained velocity maps are validated by hot-wire anemometry, leading to totally comparable results, both in terms of profile and mean velocity. Uncertainty analysis shows acceptable results, considering the random nature of the phenomenon.

Frequency response function identification using fused filament fabrication-3D-printed embedded ArUco markers

“…One of the most promising approaches for deformation, displacement and motion detection involves markers, either physical or virtual [17]- [19]. Virtual markers are often employed as they allow tracking objects in subsequent acquired frames without introducing physical targets [20], [21].…”

Optical-Flow based Analysis for Range Hoods captured Flow Measurement

“…However, one of the limits imposed by this technique is the complexity of the measurement and the high Optical-Flow based Analysis for Range Hoods captured Flow Measurement costs of the instrumentation, due to theoretical considerations. For this reason, in last years, computer-vision based approaches have been largely implemented in fluid-dynamics measurements in order to establish reliable alternatives to the classic methods [5]. In literature, several researches are found as validation of the technique through the comparison with the PIV, such as Corpetti et al [6] and Liu et al [7].…”

“…In literature, several researches are found as validation of the technique through the comparison with the PIV, such as Corpetti et al [6] and Liu et al [7]. Among all the computer-vision methods, the optical-flow technique is one of the mostly employed approach in this field [8,9,10], but in general, it has been widely applied to research displacement fields in solid bodies [11,12,13,14], liquid flows [15] and gas flows [16,17,18]. Optical-flow is related to the motion of visualfeatures, such as corners, edges, ridges and textures in two consecutive frames of a video scene [19,20].…”

Thermoelasticity and ArUco marker-based model validation of polymer structure: application to the San Giorgio’s bridge inspection robot

“…One of the more promising approach for displacement and motion detection involves markers, either they are physical or virtual. Virtual markers are directly generated through computer-vision algorithms, such as Scale Invariant Feature Transform (SIFT) [31], [32], and Speeded Up Robust Features (SURF) [33]. These algorithms are able to detect and describe local characteristics (i.e., features) in images.…”