Hierarchical Shape Fitting using an Iterated Linear Filter

1996 ◽  
Author(s):  
A. Baumberg
Keyword(s):  
Linear Filter ◽  
Shape Fitting

Recent Progress and Plans in Computer-Controlled High Resolution Electron Microscopy

1990 ◽  
Vol 48 (1) ◽  
pp. 154-155
Author(s):  
W.J. de Ruijter ◽  
Peter Rez ◽  
David J. Smith
Keyword(s):  
Electron Microscopy ◽  
Digital Processing ◽  
High Resolution Electron Microscopy ◽  
Objective Lens ◽  
Linear Filter ◽  
Contrast Transfer Function ◽  
Image Displacement ◽  
Spatially Resolved ◽  
Wide Range ◽  
On Line

Digital computers are becoming widely recognized as standard accessories for electron microscopy. Due to instrumental innovations the emphasis in digital processing is shifting from off-line manipulation of electron micrographs to on-line image acquisition, analysis and microscope control. An on-line computer leads to better utilization of the instrument and, moreover, the flexibility of software control creates the possibility of a wide range of novel experiments, for example, based on temporal and spatially resolved acquisition of images or microdiffraction patterns. The instrumental resolution in electron microscopy is often restricted by a combination of specimen movement, radiation damage and improper microscope adjustment (where the settings of focus, objective lens stigmatism and especially beam alignment are most critical). We are investigating the possibility of proper microscope alignment based on computer induced tilt of the electron beam. Image details corresponding to specimen spacings larger than ∼20Å are produced mainly through amplitude contrast; an analysis based on geometric optics indicates that beam tilt causes a simple image displacement. Higher resolution detail is characterized by wave propagation through the optical system of the microscope and we find that beam tilt results in a dispersive image displacement, i.e. the displacement varies with spacing. This approach is valid for weak phase objects (such as amorphous thin films), where transfer is simply described by a linear filter (phase contrast transfer function) and for crystalline materials, where imaging is described in terms of dynamical scattering and non-linear imaging theory. In both cases beam tilt introduces image artefacts.

Trombone Transfer Functions: Comparison Between Frequency-Swept Sine Wave and Human Performer Input

2012 ◽  
Vol 37 (4) ◽  
pp. 447-454
Author(s):  
James W. Beauchamp
Keyword(s):  
Transfer Function ◽  
Transfer Functions ◽  
Cutoff Frequency ◽  
Sine Wave ◽  
Nonlinear Propagation ◽  
Linear Filter ◽  
Wave System ◽  
General Effect ◽  
Filter Characteristic ◽  
High Pass

Abstract Source/filter models have frequently been used to model sound production of the vocal apparatus and musical instruments. Beginning in 1968, in an effort to measure the transfer function (i.e., transmission response or filter characteristic) of a trombone while being played by expert musicians, sound pressure signals from the mouthpiece and the trombone bell output were recorded in an anechoic room and then subjected to harmonic spectrum analysis. Output/input ratios of the signals’ harmonic amplitudes plotted vs. harmonic frequency then became points on the trombone’s transfer function. The first such recordings were made on analog 1/4 inch stereo magnetic tape. In 2000 digital recordings of trombone mouthpiece and anechoic output signals were made that provide a more accurate measurement of the trombone filter characteristic. Results show that the filter is a high-pass type with a cutoff frequency around 1000 Hz. Whereas the characteristic below cutoff is quite stable, above cutoff it is extremely variable, depending on level. In addition, measurements made using a swept-sine-wave system in 1972 verified the high-pass behavior, but they also showed a series of resonances whose minima correspond to the harmonic frequencies which occur under performance conditions. For frequencies below cutoff the two types of measurements corresponded well, but above cutoff there was a considerable difference. The general effect is that output harmonics above cutoff are greater than would be expected from linear filter theory, and this effect becomes stronger as input pressure increases. In the 1990s and early 2000s this nonlinear effect was verified by theory and measurements which showed that nonlinear propagation takes place in the trombone, causing a wave steepening effect at high amplitudes, thus increasing the relative strengths of the upper harmonics.

Smoothing of stationary random signal in continuous flow mixer with gamma-distribution of residence times

1985 ◽  
Vol 50 (11) ◽  
pp. 2545-2557
Author(s):  
Pavel Hasal ◽  
Vladimír Kudrna ◽  
Jitka Vyhlídková
Keyword(s):  
Theoretical Analysis ◽  
Gamma Distribution ◽  
Continuous Flow ◽  
Random Signal ◽  
Linear Filter ◽  
Residence Times ◽  
Statistical Parameters ◽  
Basic Parameters

The paper is focused on a theoretical analysis of the function of continuous flow mixer with the so-called gamma-distribution of fluid residence times, used as a linear filter smoothing undesirable fluctuations of input properties. A relation is derived expressing the degree of smoothing of the signal passing through the system, as a function of statistical parameters of this signal and of gamma-distribution of fluid-residence times in the mixer. The analysis of this relation leads to conclusions concerning the prediction of the operation of smoothing mixers or the design of their basic parameters.

scholarly journals A Two-Step Phenotypic Parameter Measurement Strategy for Overlapped Grapes under Different Light Conditions

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4532
Author(s):  
Yubin Miao ◽  
Leilei Huang ◽  
Shu Zhang
Keyword(s):  
Edge Detection ◽  
Parameter Measurement ◽  
Average Error ◽  
Projection Area ◽  
Shape Fitting ◽  
Multi Scale ◽  
Image Pyramids ◽  
Phenotypic Parameter ◽  
Contour Fitting ◽  
Image Edge

Phenotypic characteristics of fruit particles, such as projection area, can reflect the growth status and physiological changes of grapes. However, complex backgrounds and overlaps always constrain accurate grape border recognition and detection of fruit particles. Therefore, this paper proposes a two-step phenotypic parameter measurement to calculate areas of overlapped grape particles. These two steps contain particle edge detection and contour fitting. For particle edge detection, an improved HED network is introduced. It makes full use of outputs of each convolutional layer, introduces Dice coefficients to original weighted cross-entropy loss function, and applies image pyramids to achieve multi-scale image edge detection. For contour fitting, an iterative least squares ellipse fitting and region growth algorithm is proposed to calculate the area of grapes. Experiments showed that in the edge detection step, compared with current prevalent methods including Canny, HED, and DeepEdge, the improved HED was able to extract the edges of detected fruit particles more clearly, accurately, and efficiently. It could also detect overlapping grape contours more completely. In the shape-fitting step, our method achieved an average error of 1.5% in grape area estimation. Therefore, this study provides convenient means and measures for extraction of grape phenotype characteristics and the grape growth law.

Random filters which preserve the stability of random inputs

1988 ◽  
Vol 20 (2) ◽  
pp. 275-294 ◽  
Author(s):  
Stamatis Cambanis
Keyword(s):  
Transfer Function ◽  
Moving Average ◽  
Random Processes ◽  
Time Shift ◽  
Linear Filter ◽  
Random Inputs ◽  
The Stability ◽  
Non Gaussian ◽  
Random Gain

A stationary stable random processes goes through an independently distributed random linear filter. It is shown that when the input is Gaussian or harmonizable stable, then the output is also stable provided the filter&s transfer function has non-random gain. In contrast, when the input is a non-Gaussian stable moving average, then the output is stable provided the filter&s randomness is due only to a random global sign and time shift.

scholarly journals Spatial and Temporal Integration of Visual Motion Signals for Smooth Pursuit Eye Movements in Monkeys

2009 ◽  
Vol 102 (4) ◽  
pp. 2013-2025 ◽  
Author(s):  
Leslie C. Osborne ◽  
Stephen G. Lisberger
Keyword(s):  
Random Walk ◽  
Eye Movements ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Target Motion ◽  
Linear Filter ◽  
Smooth Pursuit Eye Movements ◽  
Spatial Averaging ◽  
Spatial Integration ◽  
Pursuit Eye Movements

To probe how the brain integrates visual motion signals to guide behavior, we analyzed the smooth pursuit eye movements evoked by target motion with a stochastic component. When each dot of a texture executed an independent random walk such that speed or direction varied across the spatial extent of the target, pursuit variance increased as a function of the variance of visual pattern motion. Noise in either target direction or speed increased the variance of both eye speed and direction, implying a common neural noise source for estimating target speed and direction. Spatial averaging was inefficient for targets with >20 dots. Together these data suggest that pursuit performance is limited by the properties of spatial averaging across a noisy population of sensory neurons rather than across the physical stimulus. When targets executed a spatially uniform random walk in time around a central direction of motion, an optimized linear filter that describes the transformation of target motion into eye motion accounted for ∼50% of the variance in pursuit. Filters had widths of ∼25 ms, much longer than the impulse response of the eye, and filter shape depended on both the range and correlation time of motion signals, suggesting that filters were products of sensory processing. By quantifying the effects of different levels of stimulus noise on pursuit, we have provided rigorous constraints for understanding sensory population decoding. We have shown how temporal and spatial integration of sensory signals converts noisy population responses into precise motor responses.

Stochastic Dynamic Analysis and Reliability of a Vessel Rolling in Random Beam Seas

2015 ◽  
Vol 59 (02) ◽  
pp. 113-131
Author(s):  
Wei Chai ◽  
Arvid Naess ◽  
Bernt J. Leira
Keyword(s):  
Dynamic System ◽  
Markov Property ◽  
Planck Equation ◽  
Linear Filter ◽  
Stochastic Dynamic ◽  
Theoretical Principle ◽  
Pi Method ◽  
Beam Seas ◽  
Stochastic Dynamic Analysis ◽  
High Level

This article presents a four-dimensional (4D) path integration (PI) approach to study the stochastic roll response and reliability of a vessel in random beam seas. Specifically, a 4D Markov dynamic system is established by combing the single-degree-of freedom model used to represent the ship rolling behavior in random beam seas with a second-order linear filter used to approximate the stationary roll excitation moment. On the basis of the Markov property of the coupled 4D dynamic system, the response statistics of roll motion can be obtained by solving the Fokker-Planck equation of the dynamic system via the 4D PI method. The theoretical principle and numerical implementation of the current state of the art 4D PI method are presented. Moreover, the numerical robustness and accuracy of the 4D PI method are evaluated by comparing with the results obtained by the application of Monte Carlo simulation (MCS). The influence of the restoring terms and the damping terms on the stochastic roll response are investigated. Furthermore, based on the well-known Poisson assumption and the response statistics yielded by the 4D PI technique, evaluation of the reliability associated with high-level response is performed. The performance of the Poisson estimate for different levels of external excitations is evaluated by the versatile MCS technique.

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