Estimation Of Three-dimensional Shape Research Articles

Three-dimensional shape estimation of wires from three-dimensional X-ray computed tomography images of electrical cables

Three-dimensional shape estimation of wires from three-dimensional X-ray computed tomography images of electrical cables

Impairment of cyclopean surface processing by disparity-defined masking stimuli.

Binocular disparity signals allow for the estimation of three-dimensional shape, even in the absence of monocular depth cues. The perception of such disparity-defined form depends, however, on the linkage of multiple disparity measurements over space. Performance limitations in cyclopean tasks thus inform us about errors arising in disparity measurement and difficulties in the linkage of such measurements. We used a cyclopean orientation discrimination task to examine the perception of disparity-defined form. Participants were presented with random-dot sinusoidal modulations in depth and asked to report whether they were clockwise or counter-clockwise rotated. To assess the effect of different noise structures on measurement and linkage processes, task performance was measured in the presence of binocular, random-dot masks, structured as either antiphase depth sinusoids, or as random distributions of dots in depth. For a fixed number of surface dots, the ratio of mask-to-surface dots was varied to obtain thresholds for orientation discrimination. Antiphase masks were found to be more effective than random depth masks, requiring a lower mask-to-surface dot ratio to inhibit performance. For antiphase masks, performance improved with decreased cyclopean frequency, increased disparity amplitude, and/or an increase in the total number of stimulus dots. Although a cross-correlation model of disparity measurement could account for antiphase mask performance, random depth masking effects were consistent with limitations in relative disparity processing. This suggests that performance is noise-limited for antiphase masks and complexity-limited for random masks. We propose that use of differing mask types may prove effective in understanding these distinct forms of impairment.

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Deflection and deformation occur easily in structures with long length, such as bridges and pipelines. Shape monitoring is required for ensuring their structural health. A fiber Bragg grating (FBG) sensor can be used for monitoring a large-scale structure because of its advantage of multiplexing. In this study, FBG sensors were used for monitoring a composite beam structure, and its strains were measured at multiple points. Thereafter, a shape estimation technique based on the strains was studied. Particularly, a three-dimensional shape estimation technique was proposed for accurate structural health monitoring. A simple experiment was conducted to verify the performance of the shape estimation technique. The result revealed that the estimated shape of the composite beam structure was in agreement with the actual shape obtained after the deformation of the specimen. Additionally, the deflection at a specific point was verified by comparing the estimated and actual deformations measured using a micrometer.

Selective image analysis for roughness characterisation

Atomic force microscopy images supply precise information on real surface topography, but an automated method allowing accurate description of roughness through an asperity based approach is necessary for tribological applications. The present paper describes an original selective image analysis algorithm enabling confident indexation and analysis of surface asperities. First, the original image undergoes noise filtering followed by edge detection and thresholding. In the resulting binary image, clusters of asperities are separated by a selective treatment depending on objects' size and convexity. Finally, a regrowth of identified objects is applied to ensure their accurate three-dimensional shape estimation using a second order polynomial least square approximation. The identification performance of the method is hereby verified on an artificial image and illustrated with surface images of sputtered gold thin film alone and with an additional layer of ruthenium.

A three-dimensional shape estimation approach for tracking of phase interfaces in sedimentation processes using electrical impedance tomography

Sedimentation monitoring provides information about the properties of a sedimentation process based on non-invasive measurements. The information obtained from sedimentation monitoring can be used in the control and optimization of industrial sedimentation processes. In this paper we introduce a novel three-dimensional computational method for sedimentation monitoring using electrical impedance tomography (EIT). EIT measurements consist of electrical current and voltage measurements on the surface of the sedimentation tank and the objective is to estimate the electrical properties of the sedimentation process based on these boundary data. The method we propose is based on three-dimensional shape estimation and state estimation formulation of the EIT problem. We parametrize the sedimentation using the locations of the phase interfaces and conductivities of the phase layers. The state estimation problem is solved using the extended Kalman filter algorithm. The performance of the method is evaluated using simulated and experimental data. From the results a promising performance of the method can be seen.

Shape estimation from topographic primal sketch

In this paper, a technique for estimating object shape from topographic primal sketch is investigated. Given a gray tone image of a three-dimensional object, a topographic labeling of the image indicates the peaks and pits, ridges and valleys, and flats and hillsides of the underlying, continuous, gray tone surface. The patterns of these topographic labels capture information about the original three-dimensional object in the scene and about the illumination. In order to determine if estimation of three-dimensional shape from a topographic labeling is feasible, we have both analytically and experimentally determined the topographic labelings for images of some mathematically generated surfaces with varied directions of illumination. Our results indicate that such patterns do exist and will be useful in determining three-dimensional shape from two-dimensional images. A scheme for partial classification of three-dimensional object surface is proposed. Preliminary results are illustrated.

Finding the illuminant direction

Shading is important for estimation of three-dimensional shape from the two-dimensional image, for instance, for distinguishing between the smooth occluding contour generated by the edge of a sphere and the sharp occluding contour generated by the edge of a disk. In order to use shading information to solve such problems, one must know the illuminant direction L. This is because variations in image intensity (shading) are caused by changes in surface orientation relative to the illuminant. Each illuminant direction L has a unique effect on the distribution of changes in image intensity dI, potentially permitting the estimation of L. However, because dI is a function of bothL and the surface curvature, L can be estimated from the image only by making an assumption about the imaged surface curvature. One assumption that is sufficient to disentangle L and surface curvature is that changes in surface orientation are isotropically distributed. This condition is true of images of convex objects bounded by a smooth occluding contour and is true on average over all scenes. Estimates made by using this assumption agree with estimates of illuminant direction given by human subjects for images of natural objects, even when both are objectively wrong. Further, there is a significant correlation between the variance of these estimates and the variance of the human subjects’ estimates.