Multi-spectral camera set-ups may generally allow for creating surveillance applications even under unfavorable conditions, such as low-light environments or scenes involving vastly different lighting conditions. A high- resolution color camera, a high-dynamic-range camera and an infrared thermal camera were combined into a self-sufficient platform for continuous outdoor operation. The sheer amount of produced data poses a serious challenge, both in terms of available bandwidth and processing power, because self-sufficiency requires using relatively low-power components, and privacy, as high-resolution, multi-spectral image data are sensitive information. Thus, relevant objects of interest had to be efficiently extracted, tracked and georeferenced on the sensor platform. These data, from one or more sensorheads, are then sent via WLAN or mobile data link to a central control unit, possibly anonymized, e.g. prompting immediate action by a human operator in a disaster response use case, or stored for further offline analysis when used in the framework of "Smart City". Applying the classic stereo vision approach would require calibrating both intrinsic and extrinsic parameters of all cameras. The input data's multi-spectral nature complicates the correspondence problem for extrinsic parameter calibration and subsequent stereo matching, while intrinsic parameter calibration according to the pinhole camera model is made difficult due to the cameras having to be focused at infinity. However, by making certain reasonable assumptions about the observed scene in typical use cases, accepting a possible loss in localization accuracy, camera calibration could be limited to the bare minimum and less computational power was required at run-time.

Optically modulated elastic waves enter the surface of the inspected material by absorption of thermal radiation. An inhomogeneous disturbance in the material causes locally enhanced losses. Such material defects heat up at a different rate than the surrounding more homogenous material and therefore generate differences in thermal contrast. Modulating the amplitude of the optical stimulus turns defects into thermal wave transmitters. The frequency of the stimulus signal must be matched to the specific thermal conductivity and mass density of the inspected material. It is possible to locate defects at different depths below the surface by varying the amplitude of the stimulus wave. Proper detection of thermal waves resulting from material defects near the surface requires a measurement setup that allows the recording of thermal images from an infrared camera. The recorded images can then be compared against the phase of the stimulus signal. This photo-thermal lock-in thermography method not only allows the evaluation of the amplitudal thermal wave information but more importantly it also allows for the extraction of the phase information. The phase shift between the stimulus signal and the captured thermal wave is directly correlated to the thermal propagation time.

This paper deals with applications of angle resolved light scatter (ARS) measurements as well as with the discussion of design and application problems of ARS sensors. The first section gives a description of the experimental sensor setup. In the second section of the paper two applications will be outlined, firstly particle detection on smooth Si surfaces: and secondly defect detection in small Si v-grooves. In the third section of the paper principal drawbacks of the experimental ARS sensor and their elimination will be discussed.

For quality inspection of polished surfaces as applied in semi conductor and optical industry, various methods are used for a fast detection of microroughness, defects, and contaminations. With the aid of stray light sensors the intensity distribution of the reflected and scattered light, i.e. the BRDF,(1-3) is measured. The probability distribution of values of a BRDF is parametrized(4,7,5) to obtain a measure for roughness and for classes of defects. There is still need for justifying the choice of statistical moments to characterize and finally to classify different surfaces. Of course, a basic quantitative, i.e. metrological understanding of stray light sensors is necessary. The power spectrum of surface topographies sufficiently smooth to obey Rayleigh-Rice approximation is proportional to the BRDF. Therefore a comparison was only carried out with sample surfaces obeying this approximation. Defects and contaminations with lateral sizes smaller than the wavelength of the illuminating light employed in the stray light sensor, however, could not be analyzed within this investigation. We have measured the topography of large areas up to 600 mu m x 100 mu m with an AFM by patching several scans (up to 8) with overlap. BRDFs evaluated from AFM measurements agree well with BRDFs measured with a stray light sensor.

To measure microroughness, defects and contamination on surfaces such as wafers or optical instruments stray light sensors are a fast means. In order to obtain a traceable quantitative, i.e. metrological, measure of roughness (rms) the relation between rms from BRDF of a stray light sensor and rms from topography has to be given.The quantification of stray light sensor signals can well be done with smooth surfaces that have no defects, since forward simulation of the bidirectional reflectance distribution function (BRDF) from smooth surfaces obeying Rayleigh-Rice approximation is possible.We have measured the topography of large areas up to 315 x 315 mu m(2) with an atomic force microscope (AFM) by patching several scans (up to 25) with overlap to obtain bandwidth limits compatible to our stray light sensor. In profilometry roughness usually is evaluated after detrending, i.e. subtraction of surface figures. Hence for an evaluation of the roughness parameter rms by integrating the BRDF of a stray light measurement, the integration limits need to be chosen carefully.

It is pointed out that the von-Mises distribution can replace the Gaussian distribution for circular or spherical vector fields, i.e. BRDF data obtained from a variety of technical surfaces by stray light measuring or sensing. For the purpose of in line quality control formulae for the parameters corresponding to mean and variance in Gaussian distributions as well as parameter tests and confidence intervals for circular unimodal vector fields will be given. A family of scatter sensors is introduced. Finally, measurement results will be compared to circular statistical inference.

A major problem of in-situ surface characterization by using angle resolved light scattering (ARS) is the contradiction of speed and accurate detection of the scatter signal. During the last years several fast and compact ARS sensors have been developed, namely a fiber optic stray light sensor (FOSSIL) with 516 fibers in 3 azimuths, an integrated optics stray light sensor with 120 waveguides in one azimuth and a planar silicon scatter sensor (PSS) with 8013 detector elements.This paper deals with the evaluation of these sensors and their employment to characterize smooth and rough surfaces, After introducing the sensor setups the theoretical performance of the various sensors is compared. This is done by modeling the properties of the radiation detectors and the arrangements of the sampling points. The real performance is obtained by applying the sensors to surfaces with a known BRDF and comparing the measurements with the expected scatter distributions, i.e. from Lambertian reflection standards and preliminary scatter standards of NIST *. Furthermore a set of smooth surfaces (polished silicon and steel) was scanned by an atomic force microscope (AFM) and the computed surface statistics is compared to the values obtained from scatter measurements. Finally, the sensors ability to characterize rough surfaces is shown by using pattern recognition methods.

Especially for wafers! hard disks and flat panel displays fast and accurate technical means for roughness characterization are needed. However, speed and accuracy are contradictory. Generally speaking, fast roughness sensors are not accurate, and precise instruments are slow. It turned out in the last years that with multi aperture fiber optic sensors which acquire ARS/TIS data a very fast estimation of surface roughness is possible. But it is rather difficult to convince e.g. chip manufacturers that the results of such sensors are reliable, because there are no accepted international standards for these kinds of optical measurements. Therefore we decided to establish a setup of our ARS/TIS sensor for roughness characterization and an instrument for roughness measurement in a cleanroom consisting of the following parts: (i) 200x200 mm stages, speed 0.4 ms(-1), +/-1 micron accuracy, acceleration 1 g (ii) visual inspection head consisting of 50 x objective and CCD camera, (iii) AFM scan head, (iv) ARS/TIS fiber optic sensor, (v) laminar bos.Topics of the paper are measurement philosophy, specs of the setup, architecture of the fiber optic ARS/TIS head, as well as data processing algorithms and software.

Mit der hier vorgeschlagenen Variante des Lasertriangulationsverfahrens ist es möglich, ein einfaches und genaues Meßgerät für die Profilometrie zu realisieren, welches gleichzeitig eine 3D-Formerfassung in einem Meßvolumen von (50×50×5)mm 3 mit einer Standardabweichung der Einzelmeßpunkte von ca. 1.5 μm und eine Rauheitsklassifikation im Bereich von 0.1 bis 2.5 μm ermöglicht. Folglich kann ein Verhältnis von Meßbereich zu Standardabweichung der Koordinatenmessung von > 3000: 1 erreicht werden. Die rms-Rauheitswerte der Oberfläche werden aus dem für die Triangulation gemessenen Strahlprofil durch ein statistisches Verfahren der Mustererkennung gewonnen.

Near or even beyond the Rayleigh-limit it is not possible to compute the surface PSD analytically. Since stray light measurements are an affordable approach for quality control in industry it has to be investigated, in which case they can be applied. Therefore a statistical method is proposed to determine, whether there is a significant correlation between roughness and scattering, or not. The method is proposed to determine, whether there is a significant correlation between roughness and scattering, or not. The method is tested with a newly developed rugged stray light sensor, several samples, and comparative measurements with an optical profiler.

This paper reports on the development of a fully optoelectronic optical sensor that has been used for investigations concerning quality control of the high velocity oxygen fuel (HVOF) flame spraying process. The authors focused on the following parameters: WoC with Co, CoCr, Ni at T < 500 C, and Cr{sub 3}C{sub 2} with Ni-Cr, Ni at T < 900 C. Until recently, there was no non-destructive testing (NDT) approach for flame spraying available, which is a major drawback for many critical applications, like in the aerospace industry. For instance, while cooling down after spraying, cracks in the coatings may occur caused by strain. Furthermore, edges may cause similar damage to the coating. The idea was to provide the flame spraying industry with a measurement technology that is applicable in the workshop, has a contactless principle of operation and is fast compared to the commonly used metallography or scanning electron microscopy. The approach is designed for use close to the process, i.e. coating morphology was not the focus of the investigations. On the contrary, they tried to extract quality related information from surface microtopography which can be obtained in a non-destructive and affordable manner. Main points covered in this paper aremore » theoretical considerations, the operational principles and construction of the sensor head, and quantitative metrology of surface damage such as variations in rms-roughness and cracks.« less

This paper reports on (1) the development of a fully optoelectronic BRDF sensor, and (2) first experimental results obtained with an automated setup consisting of the sensor, stages for specimen scanning, as well as PC hard- and software for the control of the setup and the evaluation of data. Main points covered are the construction of the sensor head, problems with the alignment of the hundreds of fibers needed, how to reach the necessary high dynamic range of the CCD camera employed, and the control of the laser diode.

In single-shot laser radar speckle noise, drop-outs, outliers, and eventually, non-cooperative targets are to be considered in order to ensure high accuracy and reliability of measurement data. The most common approach for the stabilization of laser radar data is temporal averaging over several shots. This, however, is not in all cases the best method for the reconstruction of noisy imagery data. It is shown that principal-component filtering can yield a remarkable improvement of accuracy and robustness of range data.

A commonly used estimator for the microtopography of a surface is its RMS-roughness. Raw surface profile data may contain trending components. Therefore they should be subjected to a detrending procedure before estimating the RMS-value. This procedure is limited in most cases to the removal of piston, slope and curvature. Consequently, undesired artifacts may arise, which negatively influence the precision of RMS-roughness estimation. In scanning surface metrology, the principal surface computed by using eigenvalues and eigenvectors of the covariance matrix of the surface data under consideration can be used for a robust and precise multivariate estimation of RMS-roughness.