Reliable fusion of ToF and stereo depth driven by confidence measures

In this paper we propose a framework for the fusion of depth data produced by a Time-of-Flight (ToF) camera and stereo vision system. Initially, depth data acquired by the ToF camera are upsampled by an ad-hoc algorithm based on image segmentation and bilateral filtering. In parallel a dense disparity map is obtained using the Semi- Global Matching stereo algorithm. Reliable confidence measures are extracted for both the ToF and stereo depth data. In particular, ToF confidence also accounts for the mixed-pixel effect and the stereo confidence accounts for the relationship between the pointwise matching costs and the cost obtained by the semi-global optimization. Finally, the two depth maps are synergically fused by enforcing the local consistency of depth data accounting for the confidence of the two data sources at each location. Experimental results clearly show that the proposed method produces accurate high resolution depth maps and outperforms the compared fusion algorithms

2-D edge feature extraction to subpixel accuracy using the generalized energy approach

Precision edge feature extraction is a very important step in vision, Researchers mainly use step edges to model an edge at subpixel level. In this paper we describe a new technique for two dimensional edge feature extraction to subpixel accuracy using a general edge model. Using six basic edge types to model edges, the edge parameters at subpixel level are extracted by fitting a model to the image signal using least-.squared error fitting technique.<br /

Subpixel Edge Localization with Statistical Error Compensation

Subpixel Edge Localization (EL) techniques are often affected by an error that exhibits a systematic character When this happens their performance can be improved through compensation of the systematic portion of the localization error In this paper we propose and analyze a method for estimating the EL characteristic of subpixel EL techniques through statistical analysis of appropriate test images The impact of the compensation method on the accuracy of a camera calibration procedure has been proven to be quite signicant, which can be crucial especially in applications of low-cost photogrammetry and 3D reconstruction from multiple views

Characterization of an in-vacuum PILATUS 1M detector

A dedicated in-vacuum X-ray detector based on the hybrid pixel PILATUS 1M detector has been installed at the four-crystal monochromator beamline of PTB at the electron storage ring BESSY II in Berlin. Due to its windowless operation, the detector can be used in the entire photon energy range of the beamline from 10 keV down to 1.75 keV for small-angle X-ray scattering (SAXS) experiments and anomalous SAXS (ASAXS) at absorption edges of light elements. The radiometric and geometric properties of the detector like quantum efficiency, pixel pitch and module alignment have been determined with low uncertainties. The first grazing incidence SAXS (GISAXS) results demonstrate the superior resolution in momentum transfer achievable at low photon energies.Comment: accepted by Journal of Synchrotron Radiatio

Computer Tomography 3D Edge Detection Comparative for Metrology Applications

AbstractThe CT process for metrology applications is very complex because has many factors that influence the loss of accuracy during CT measurements. One of the most critical is the edge detection also called surface extraction or image segmentation, which is the process of surface formation from the CT‘s volume data that represents a grey value corresponding to the mass attenuation coefficient of the object material. This paper presents different edge detection methods commonly used in areas like machine and computer vision and they are analyzed as an alternative to the common methods used in CT for metrology applications. Each method is described and analyzed separately in order to highlight its advantages and disadvantages from a metrological point of view. An experimental comparative between two of them is also shown

J-integral analysis: an EDXD and DIC comparative study for a fatigue crack

Synchrotron Energy Dispersive X-ray Diffraction (EDXD) and Digital Image Correlation (DIC) have been applied to map simultaneously the 2D elastic strain and displacement fields of a propagating fatigue crack in the HAZ of a welded Cr2Ni4MoV bainitic steel. The position of the crack tip was tracked via a phase congruency analysis of the displacement field, and also by detection of its cyclic plastic zone. Both types of full field data provided independent inputs to finite element/J-integral analyses that directly quantified the elastic cyclic stress intensity factor range applied to the crack. No knowledge was required of the specimen geometry, crack length or applied loads. The agreement between the two analyses in this controlled study shows that strain mapping by synchrotron EDXD can provide a reliable method to study the crack fields in more complex problems, such as interactions between crack closure, residual stresses and applied loading

The hessian blob algorithm : precise particle detection in atomic force microscopy imagery

"Received: 20 October 2017; Accepted: 29 December 2017; Published online: 17 January 2018."Imaging by atomic force microscopy (AFM) offers high-resolution descriptions of many biological systems; however, regardless of resolution, conclusions drawn from AFM images are only as robust as the analysis leading to those conclusions. Vital to the analysis of biomolecules in AFM imagery is the initial detection of individual particles from large-scale images. Threshold and watershed algorithms are conventional for automatic particle detection but demand manual image preprocessing and produce particle boundaries which deform as a function of user-defined parameters, producing imprecise results subject to bias. Here, we introduce the Hessian blob to address these shortcomings. Combining a scalespace framework with measures of local image curvature, the Hessian blob formally defines particle centers and their boundaries, both to subpixel precision. Resulting particle boundaries are independent of user defined parameters, with no image preprocessing required. We demonstrate through direct comparison that the Hessian blob algorithm more accurately detects biomolecules than conventional AFM particle detection techniques. Furthermore, the algorithm proves largely insensitive to common imaging artifacts and noise, delivering a stable framework for particle analysis in AFM

Automatic Particle Image Velocimetry Uncertainty Quantification

The uncertainty of any measurement is the interval in which one believes the actual error lies. Particle Image Velocimetry (PIV) measurement error depends on the PIV algorithm used, a wide range of user inputs, flow characteristics, and the experimental setup. Since these factors vary in time and space, they lead to nonuniform error throughout the flow field. As such, a universal PIV uncertainty estimate is not adequate and can be misleading. This is of particular interest when PIV data are used for comparison with computational or experimental data. A method to estimate the uncertainty due to the PIV calculation of each individual velocity measurement is presented. The relationship between four error sources and their contribution to PIV error is first determined. The sources, or parameters, considered are particle image diameter, particle density, particle displacement, and velocity gradient, although this choice in parameters is arbitrary and may not be complete. This information provides a four-dimensional uncertainty surface for the PIV algorithm used. After PIV processing, our code measures the value of each of these parameters and estimates the velocity uncertainty for each vector in the flow field. The reliability of the methodology is validated using known flow fields so the actual error can be determined. Analysis shows that, for most flows, the uncertainty distribution obtained using this method fits the confidence interval. The method is general and can be adapted to any PIV analysis