scholarly journals Three-Dimensional Transfer Functions of Interference Microscopes

Metrology ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 122-141
Author(s):  
Peter Lehmann ◽  
Sebastian Hagemeier ◽  
Tobias Pahl
Keyword(s):  
Spatial Frequency ◽  
Transfer Functions ◽  
Three Dimensional ◽  
High Spatial Frequency ◽  
Temporal Coherence ◽  
Measuring Instruments ◽  
Phase Detection ◽  
Point Scatterer ◽  
Practical Applications ◽  
Transfer Characteristics

Three-dimensional transfer functions (3D TFs) are generally assumed to fully describe the transfer behavior of optical topography measuring instruments such as coherence scanning interferometers in the spatial frequency domain. Therefore, 3D TFs are supposed to be independent of the surface under investigation resulting in a clear separation of surface properties and transfer characteristics. In this paper, we show that the 3D TF of an interference microscope differs depending on whether the object is specularly reflecting or consists of point scatterers. In addition to the 3D TF of a point scatterer, we will derive an analytical expression for the 3D TF corresponding to specular surfaces and demonstrate this as being most relevant in practical applications of coherence scanning interferometry (CSI). We additionally study the effects of temporal coherence and disclose that in conventional CSI temporal coherence effects dominate. However, narrowband light sources are advantageous if high spatial frequency components of weak phase objects are to be resolved, whereas, for low-frequency phase objects of higher amplitude, the temporal coherence is less affecting. Finally, we present an approach that explains the different transfer characteristics of coherence peak and phase detection in CSI signal analysis.

scholarly journals Epifluorescence-based three-dimensional traction force microscopy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Lauren Hazlett ◽  
Alexander K. Landauer ◽  
Mohak Patel ◽  
Hadley A. Witt ◽  
Jin Yang ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Open Source ◽  
Single Particle ◽  
Three Dimensional ◽  
Traction Force ◽  
High Spatial Frequency ◽  
Epifluorescence Microscopy ◽  
Traction Force Microscopy ◽  
Force Microscopy ◽  
Out Of Plane

Abstract We introduce a novel method to compute three-dimensional (3D) displacements and both in-plane and out-of-plane tractions on nominally planar transparent materials using standard epifluorescence microscopy. Despite the importance of out-of-plane components to fully understanding cell behavior, epifluorescence images are generally not used for 3D traction force microscopy (TFM) experiments due to limitations in spatial resolution and measuring out-of-plane motion. To extend an epifluorescence-based technique to 3D, we employ a topology-based single particle tracking algorithm to reconstruct high spatial-frequency 3D motion fields from densely seeded single-particle layer images. Using an open-source finite element (FE) based solver, we then compute the 3D full-field stress and strain and surface traction fields. We demonstrate this technique by measuring tractions generated by both single human neutrophils and multicellular monolayers of Madin–Darby canine kidney cells, highlighting its acuity in reconstructing both individual and collective cellular tractions. In summary, this represents a new, easily accessible method for calculating fully three-dimensional displacement and 3D surface tractions at high spatial frequency from epifluorescence images. We released and support the complete technique as a free and open-source code package.

scholarly journals Sharpening coarse-to-fine stereo vision by perceptual learning: asymmetric transfer across the spatial frequency spectrum

2016 ◽  
Vol 3 (1) ◽  
pp. 150523 ◽  
Author(s):  
Roger W. Li ◽  
Truyet T. Tran ◽  
Ashley P. Craven ◽  
Tsz-Wing Leung ◽  
Sandy W. Chat ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Perceptual Learning ◽  
Frequency Spectrum ◽  
Three Dimensional ◽  
High Spatial Frequency ◽  
Visual Image ◽  
Spatial Frequencies ◽  
Early Visual Cortex ◽  
Coarse To Fine ◽  
Spatial Frequency Spectrum

Neurons in the early visual cortex are finely tuned to different low-level visual features, forming a multi-channel system analysing the visual image formed on the retina in a parallel manner. However, little is known about the potential ‘cross-talk’ among these channels. Here, we systematically investigated whether stereoacuity, over a large range of target spatial frequencies, can be enhanced by perceptual learning. Using narrow-band visual stimuli, we found that practice with coarse (low spatial frequency) targets substantially improves performance, and that the improvement spreads from coarse to fine (high spatial frequency) three-dimensional perception, generalizing broadly across untrained spatial frequencies and orientations. Notably, we observed an asymmetric transfer of learning across the spatial frequency spectrum. The bandwidth of transfer was broader when training was at a high spatial frequency than at a low spatial frequency. Stereoacuity training is most beneficial when trained with fine targets. This broad transfer of stereoacuity learning contrasts with the highly specific learning reported for other basic visual functions. We also revealed strategies to boost learning outcomes ‘beyond-the-plateau’. Our investigations contribute to understanding the functional properties of the network subserving stereovision. The ability to generalize may provide a key principle for restoring impaired binocular vision in clinical situations.

Density-based discrimination of protein and RNA in the ribosome

1992 ◽  
Vol 50 (1) ◽  
pp. 464-465
Author(s):  
Joachim Frank
Keyword(s):  
Transfer Function ◽  
Spatial Frequency ◽  
Three Dimensional ◽  
Wiener Filter ◽  
Dark Field Image ◽  
Dark Field ◽  
Frequency Range ◽  
Bright Field ◽  
Contrast Transfer Function ◽  
Pass Filter

Cryo-electron microscopy combined with single-particle reconstruction techniques has allowed us to form a three-dimensional image of the Escherichia coli ribosome.In the interior, we observe strong density variations which may be attributed to the difference in scattering density between ribosomal RNA (rRNA) and protein. This identification can only be tentative, and lacks quantitation at this stage, because of the nature of image formation by bright field phase contrast. Apart from limiting the resolution, the contrast transfer function acts as a high-pass filter which produces edge enhancement effects that can explain at least part of the observed variations. As a step toward a more quantitative analysis, it is necessary to correct the transfer function in the low-spatial-frequency range. Unfortunately, it is in that range where Fourier components unrelated to elastic bright-field imaging are found, and a Wiener-filter type restoration would lead to incorrect results. Depending upon the thickness of the ice layer, a varying contribution to the Fourier components in the low-spatial-frequency range originates from an “inelastic dark field” image. The only prospect to obtain quantitatively interpretable images (i.e., which would allow discrimination between rRNA and protein by application of a density threshold set to the average RNA scattering density may therefore lie in the use of energy-filtering microscopes.

Three-dimensional Graphene Coated Shape Memory Polyurethane Foam with Fast Responsive Performance

2021 ◽  
Author(s):  
Tianjiao Wang ◽  
Jun Zhao ◽  
Chuanxin Weng ◽  
Tong Wang ◽  
Yayun Liu ◽  
...  
Keyword(s):  
Shape Memory ◽  
Polyurethane Foam ◽  
Three Dimensional ◽  
Shape Memory Polymers ◽  
Practical Applications ◽  
External Stimuli ◽  
Shape Memory Polyurethane

Shape memory polymers (SMPs) that change shapes as designed by external stimuli have become one of the most promising materials as actuators, sensors, and deployable devices. However, their practical applications...

scholarly journals Automatic Detection Technology of Sonar Image Target Based on the Three-Dimensional Imaging

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Wanzeng Kong ◽  
Jinshuai Yu ◽  
Ying Cheng ◽  
Weihua Cong ◽  
Huanhuan Xue
Keyword(s):  
Data Storage ◽  
3D Imaging ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Automatic Detection ◽  
Classification Model ◽  
Image Detection ◽  
Practical Applications ◽  
Sonar Image ◽  
Detection Technology

With 3D imaging of the multisonar beam and serious interference of image noise, detecting objects based only on manual operation is inefficient and also not conducive to data storage and maintenance. In this paper, a set of sonar image automatic detection technologies based on 3D imaging is developed to satisfy the actual requirements in sonar image detection. Firstly, preprocessing was conducted to alleviate the noise and then the approximate position of object was obtained by calculating the signal-to-noise ratio of each target. Secondly, the separation of water bodies and strata is realized by maximum variance between clusters (OTSU) since there exist obvious differences between these two areas. Thus image segmentation can be easily implemented on both. Finally, the feature extraction is carried out, and the multidimensional Bayesian classification model is established to do classification. Experimental results show that the sonar-image-detection technology can effectively detect the target and meet the requirements of practical applications.

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