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70 results for “Optical Flow”
Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography</em>”</strong> in Optics Express (doi.org/10.1364/OE.474279<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 10 minutes and 2D flow profile analysis may take up to one hour.</p> <p>For 1D depth-resolved measurements each dataset includes diffusion, focus (beam shape) calibration, and flow measurements for different discharge rates, <em>Q</em>. For all measurements time series length is 31000 points and the sampling rate is 5.5 kHz. Diffusion measurements are performed on a static sample with a stationary beam. Focus (waist) calibration measurements are performed by moving the OCT beam over the static sample with a known velocity. Flow measurements are performed on the flowing sample with the stationary beam. Each measurement is averaged 6 times. The analysis process is as follows: Firstly, the beam waist (focus) calibration is performed using the script ‘Beam Shape.py’. For improved accuracy it is preferable to perform several measurements and average beam waist values at every depth. Secondly, the Doppler angle is determined using a flow measurement with the largest discharge rate using the script ‘Doppler Angle.py’. Thirdly, the flow profiles are obtained with predetermined calibration parameters using the script ‘Flow Profile.py’. Finally, the particle number density is calculated using the script ‘Number Density.py’. This requires knowledge of particle size for calculating the theoretical number density values. The particle size can be determined using the script ‘Diffusion.py’. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement.</p> <p>For 2D depth and laterally resolved measurements each dataset includes diffusion, focus (beam shape) calibration, M-scan and B-scan flow measurements for different discharge rates, <em>Q</em>. Diffusion and focus calibration measurements are same as in 1D. M-scan flow measurements are performed on a flowing sample with a stationary beam. They are same as flow measurements in 1D and are only used for determining the Doppler angle. B-scan flow measurements are performed by moving the OCT beam over the flowing sample with a known velocity. 2D flow profiles can be determined using the script ‘2D Flow Profile.py’. The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Usability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 15-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 0.34 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.74 deg and alignment angle of 2.3 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 22-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.00 deg and alignment angle of 1.15 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 08-07-2022.zip</p> </td> <td> <p>2D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.84 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>All measurements</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>Processing.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines particle size from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Shape.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines axial beam shape from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Doppler Angle.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow Profile.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Number Density.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines particle number density raw OCT spectra.</p> </td> </tr> <tr> <td> <p>2D Flow Profile.py</p> </td> <td> <p>2D measurements</p> </td> <td> <p>This script determines 2D flow profiles from raw OCT spectra.</p> </td> </tr> </tbody> </table> <p> </p>
Prospective, Multicenter, Self-controlled Clinical Trial to Validate Optical Ultrasonic Flow Ratio (OUFR)
ClinicalTrials.gov study NCT06726252. IPD Sharing: NO. Countries: 1. Publications: 3.
Postural stability and optic flow sensitivity following sight restoration from congenital bilateral cataracts
Open the record for dataset details and reuse information.
Spatio-temporal dynamics of impulse responses to figure motion in optic flow neurons
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Optic flow and odometry data from intelrealsense camera
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Data from: Persistent firing and adaptation in optic-flow-sensitive descending neurons
<p>A general principle of sensory systems is that they adapt to prolonged stimulation by reducing their response over time. Indeed, in many visual systems, including higher-order motion sensitive neurons in the fly optic lobes and the mammalian visual cortex, a reduction in neural activity following prolonged stimulation occurs. In contrast to this phenomenon, the response of the motor system controlling flight maneuvers persists following the offset of visual motion. It has been suggested that this gap is caused by a lingering calcium signal in the output synapses of optic lobe neurons. However, whether this directly affects the responses of the post-synaptic descending neurons, leading to the observed behavioral output, is not known. We use extracellular electrophysiology to record from optic flow sensitive descending neurons in response to prolonged wide-field stimulation. We find that, as opposed to most sensory and visual neurons, and in particular to the motion vision sensitive neurons in the brains of flies and mammals, the descending neurons show little adaption during stimulus motion. In addition, we find that the optic flow sensitive descending neurons display persistent firing, or an after-effect, following the cessation of visual stimulation, consistent with the lingering calcium signal hypothesis. However, if the difference in after-effect is compensated for, subsequent presentation of stimuli in a test-adapt-test paradigm reveals adaptation to visual motion. Our results thus show a combination of adaptation and persistent firing, in the neurons that project to the thoracic ganglia, and thereby control behavioral output.</p>
Data for the effect of optic flow cues on honeybee flight control in wind
<p><span>To minimise the risk of colliding with the ground or other obstacles, flying animals need to control both their ground speed and ground height. This task is particularly challenging in wind, where head winds require an animal to increase its airspeed to maintain a constant ground speed and tail winds may generate negative airspeeds, rendering flight more difficult to control. In this study, we investigate how head and tail winds affect flight control in the honeybee <i>Apis mellifera</i>, which is known to rely on the pattern of visual motion generated across the eye – known as optic flow – to maintain constant ground speeds and heights. We find that, when provided with optic flow cues in both the longitudinal and transverse directions of flight, honeybees maintain a constant ground speed but fly lower in head winds and higher in tail winds, a response that is also observed when longitudinal optic flow cues are minimised. This change in height with wind does not appear to result in a constant rate of optic flow in the ventral visual field, suggesting that honeybees may rely on a combination of mechanosensory and visual information when controlling flight in wind. We also find that, when the transverse component of optic flow is minimised, or when all optic flow cues are minimised, the effect of wind on ground height is abolished. We propose that the regular sidewards oscillations that the bees make as they fly may be used to extract information about the distance to the ground, independently of the longitudinal optic flow that they use for ground speed control. This computationally simple strategy could have potential uses in the development of lightweight and robust systems for guiding autonomous flying vehicles in natural environments.</span></p>
Video Data: Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation
<p>Video dataset accompanying "Spatial Biases in Optic-Flow Sampling for Self-Motion Estimation in Natural Environments." See accompanying <a href="https://github.com/eacooper/AlexanderOpticFlow">Github repository</a> for more documentation and analysis code.</p>
Data for Transparent Porous Medium for Optical Fluid Flow Measurement using Refractive Index Matching
<p>This data repository contains shadowgraph images acquired during the design of three transparent porous media using refractive index matching. The images were captured through various liquid mixtures, including toluene/1-hexanol, potassium thiocyanate (KSCN), and cyclohexanol/toluene, both with (PM) and without glass beads (FM). The images were processed and analysed using ImageJ, an open-source software tool.</p>
Debris Flow Dataset for Debris Flow Velocity Inversion based on Farneback Optical Flow
<p>A velocity inventory of large-scale debris flow flume experimental data, published by USGS (Logan, 2018), was generated using the Debris Flow Velocity Inversion Method based on optical flow model (Farnebäck<span>, 2003</span>). This dataset includes raw data from three debris flow experiments conducted in 2007, 2015, and 2017. Each dataset corresponds to three relevant results: perspective transformation, optical flow analysis, and front position detection.</p>
Facilitation of neural responses to targets moving against optic flow
<p>For the human observer, it can be difficult to follow the motion of small objects, especially when they move against background clutter. In contrast, insects efficiently do this, as evidenced by their ability to capture prey, pursue conspecifics, or defend territories, even in highly textured surrounds. We here recorded from target selective descending neurons (TSDNs) which likely subserve these impressive behaviors. To simulate the type of optic flow that would be generated by the pursuer's own movements through the world, we used the motion of a perspective corrected sparse dot field. We show that hoverfly TSDN responses to target motion are suppressed when such optic flow moves syn-directional to the target. Indeed, neural responses are strongly suppressed when targets move over either translational sideslip or rotational yaw. More strikingly, we show that TSDNs are facilitated by optic flow moving counter-directional to the target, if the target moves horizontally. Furthermore, we show that a small, frontal spatial window of optic flow is enough to fully facilitate or suppress TSDN responses to target motion. We argue that such TSDN response facilitation could be beneficial in modulating corrective turns during target pursuit.</p>
Visual pursuit behavior in mice maintains the pursued prey on the retinal region with least optic flow
<p>Mice have a large visual field that is constantly stabilized by vestibular ocular reflex (VOR) driven eye rotations that counter head-rotations. While maintaining their extensive visual coverage is advantageous for predator detection, mice also track and capture prey using vision. However, in the freely moving animal quantifying object location in the field of view is challenging. Here, we developed a method to digitally reconstruct and quantify the visual scene of freely moving mice performing a visually based prey capture task. By isolating the visual sense and combining a mouse eye optic model with the head and eye rotations, the detailed reconstruction of the digital environment and retinal features were projected onto the corneal surface for comparison, and updated throughout the behavior. By quantifying the spatial location of objects in the visual scene and their motion throughout the behavior, we show that the prey image consistently falls within a small area of the VOR-stabilized visual field. This functional focus coincides with the region of minimal optic flow in the visual field and consequently minimal motion-induced image blur during pursuit. The functional focus lies in the upper-temporal part of the retina and coincides with the reported high density-region of Alpha-ON<sub> </sub>sustained retinal ganglion cells.</p>
Mental comparison of 3D objects is based on 2D optical flow computations: human data
<p>Human behavioural data for manuscript:</p> <p><strong>Mental comparison of 3D objects is based on 2D optical flow computations.</strong></p>
Role of Nitric Oxide in Optic Nerve Head Blood Flow Regulation During Isometric Exercise in Healthy Humans
ClinicalTrials.gov study NCT00806741. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Postural Control During Concurrent Cognitive Tasks During Optic Flow Stimulation
ClinicalTrials.gov study NCT05117463. IPD Sharing: NO. Countries: 1. Publications: 5.
A Comparison of the Effect of Dorzolamide and Timolol on Optic Disk Blood Flow in Patients With Open Angle Glaucoma
ClinicalTrials.gov study NCT00991822. IPD Sharing: Not stated. Countries: 1. Publications: 1.
An Optical Neuro-monitor of Cerebral Oxygen Metabolism and Blood Flow for Neonatology
ClinicalTrials.gov study NCT02815618. IPD Sharing: UNDECIDED. Countries: 2. Publications: 1.
Intraocular Pressure, Optic Nerve Sheath Diameter and Optic Perfusion Pressure of Minimal Low and High-Flow Anesthesia
ClinicalTrials.gov study NCT06684704. IPD Sharing: NO. Countries: 1. Publications: 2.
Optical Coherence Tomography Morphologic and Fractional Flow Reserve Assessment in Diabetes Mellitus Patients
ClinicalTrials.gov study NCT02989740. IPD Sharing: NO. Countries: 1. Publications: 7.
Comparison of Optical Coherence Tomography-derived Minimal Lumen Area, Invasive Fractional Flow Reserve and FFRCT
ClinicalTrials.gov study NCT03820492. IPD Sharing: NO. Countries: 4. Publications: 5.
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