WO2017037248A1 - Method and device for spatial filter measurement - Google Patents

Abstract

The invention relates to a spatial filter measurement method for determining a relative speed between a spatial filter measurement device (120, 120') and at least one object (2) or object collective, wherein the spatial filter measurement device (120, 120') captures image data of the at least one object (2) or object collective, which moves through a stationary recording measurement field (60, 125) of the spatial filter measurement device in a reference system of the spatial filter measurement device (120, 120'), wherein at least one temporally modulated spatial filter signal is produced by applying at least one spatial filter (106) to at least one part of the captured image data, and a spatial filter measurement device (120, 120') comprising a recording device and an image data processing unit. With the claimed spatial filter measurement method, an observed portion of the at least one object (2) or object collective is constantly or at least intermittently moved with the moved object in order to produce the at least one spatial filter signal so the image region subjected to spatial filtering is kept approximately constant. The partial measurement field is determined on the basis of image signal analysis and can then be changed if the current partial measurement field has reached the edge of the recording measurement field.

Description

 Spatial Filter Measurement Method and Spatial Filter Measurement Device Description

The invention relates to a spatial filter measuring method for determining a relative speed between a spatial filter measuring device and at least one object or object collective, wherein the spatial filter measuring device captures image data of the at least one object or object collective moving through a recording measuring field of the spatial filter measuring device that is stationary in a reference system of the spatial filter measuring device, wherein at least a spatially modulated spatial filter signal is generated by applying at least one spatial filter to at least a portion of the acquired image data, and a spatial filter measuring apparatus comprising a recording device and an image data processing device.

Spatial filter measurement technology is an established, robust and efficient method for the non-contact determination of velocities of DUTs, such as gases, fluids or solids. Spatial filter technology works without mechanically moving parts, wear-free and reliable. There is no slippage yet Wear that affects the measurement or the equipment. In addition to a speed measurement, accelerations, the location or the position and the length of measurement objects or other application-specific derived variables such as a volume flow or a material flow, particle sizes or particle size distributions can be measured and derived.

Much of the signal processing that accompanies the conversion of motion into a (local) frequency is already realized in the construction of known spatial filter measuring devices or spatial filter measuring systems. For spatial frequency measurement, there is no need for an excellent point source. Image information is linked according to the spatial filter principle and a motion-equivalent frequency is formed. The signal evaluation provides a simple and strong data reduction.

Simplified, the spatial filter measurement technique is based on the principle that a moving object or a moving surface, which is moved past an optical grating with an optionally periodic structure of grating lines, generates a periodic signal whose frequency depends both on the speed of the grating Movement of the object as well as on the lattice parameters or on the lattice constants of the optical grating depends. In this dependence, for example, magnification factors of optical lenses, lens systems or other optical elements used, which are used in some cases. The grating structure can be realized by hardware grids, such as optical gratings with different transmission or specially arranged optical waveguides, by electronic grids, for example realized by weighting electrical signals, or by software grids, for example realized by weighting the pixel values of images. If the grating structure is known and it is known, if any, which magnification factor an optic used in the beam path of a spatial filterness arrangement is, the frequency of the observed signal can be used to deduce the movement speed of the measurement object. In this case, for example in the case of a linear grid with parallel grid lines, that component of the velocity which is perpendicular to the orientation of the grid lines of the local grid is detected. A velocity component that is parallel to the alignment of the gratings does not result in a modulation of the light transmitted through the grating. This component is therefore not measured.

In the case of a uniform movement, the result for a linear grating as the spatial filter is a signal spectrum with a significant maximum, which corresponds to the component of motion of the measuring object perpendicular to the orientation of the grating lines. From the dominant frequency of the spatial filter signal, the speed of movement can be determined. The accuracy and time resolution of the speed measurement depends on how exactly the frequency corresponds to the speed and how exactly and how fast the frequency in the signal is determined.

Essentially, two different approaches to spatial filter measurement are known. In a first approach, the spatial filter measurement is implemented using hardware spatial filters or hardware grids. Such spatial filters are discrete optical components, such as optical transmission grating, phase grating, reflection grating or specially arranged optical waveguides. The light, which passes from a moving object to be measured through an optical grating as a spatial filter, is modulated temporally in its entirety. The modulation frequency depends, among other things, on the speed ability of the measuring object and the orientation of the optical grating and its lattice parameters or lattice constant. If magnification optics are used, the modulation frequency also increases with the magnification factor.

The temporally modulated light is detected via an optic, for example by means of photodiodes, which have a high cutoff frequency, for example about 10 MHz or up to the GHz range. Due to the fast response time of the photodiodes can be processed according to this first approach in the spatial filter measurement very fast signals and very high frequencies, as long as enough light is available. This is the hardware, u. a. the optics used and the spatial filters used, adapted to the respective conditions of use, for example with regard to the speeds to be measured or the surface condition of the measurement objects. During the adaptation, it is achieved that optimum modulation signals are set for the respective operating conditions and the variables to be measured. This is intended to avoid ambiguities due to over- and undersampling caused, for example, by the grid parameters of the spatial filter, and to achieve a good signal-to-noise ratio.

This first approach with discrete optical components as a spatial filter offers a very high temporal resolution of the measurement due to the possible use of very fast receivers. However, the hardware adaptation is time-consuming, since the grating must be adapted to the hardware when changing the measurement conditions.

In a second approach, the use of hardware grids is dispensed with. Instead, the spatial filter measurement is achieved by a special signal generation for line or area receivers, for example cameras with CCD or CMOS chips, PD arrays or fiber optics. see grid implemented. In this case, no optical gratings are necessary, but the grid or matrix-like structuring of the optical receiver is utilized for spatial filter measurement.

In this case, the spatial filter measurement comprises the signal generation, for example with a so-called software grid via the weighting of the electrical signals or pixel values with the spatial filter function and subsequent summation from the individual pixels, whereby a spatial filter effect is produced. Thus, without changing the hardware, spatial filters with different orientations or lattice constants can be created or applied. This is similar to the replacement of optical gratings according to the first approach mentioned above.

Such an electronic or software-side realization of grid functions has the further advantage of the adaptability of the spatial filter system to the present process. For CCD and CMOS sensors, this is done by weighting pixel rows and columns according to a given grid function. Furthermore, grid functions can be generated with software grids that are not optically feasible or only with great effort. Thus, grids with negative and / or complex weights can be realized. These gratings offer the advantage that they provide mean-free modulated signals and / or orthogonal signals for determining instantaneous amplitude and instantaneous phase with high temporal resolution. Further, the weights are not limited to 0 and 1, but may take intermediate values and other values (gain, complex numbers). For example, it is very simple to realize complex harmonic functions as lattice functions in this way.

The resulting spatial filter signal is proportional to its frequency. proportional to the speed of movement. The estimation of the signal frequency can be carried out by means of a zero-crossing or threshold value detection or periodic duration measurement, the determination of the power density spectrum, the rotation-pointing method or the cross-correlation phase. These analysis methods are explained, for example, in the dissertation by M. Schaeper, "Mehrdimensionale Ortsfiltertechnik", University of Rostock 201 3, published in Springer Vieweg, Springer Fachmedien Wiesbaden 2014.

For the uncertainty of the speed measurement by means of a spatial filter measurement method with one of the aforementioned analysis methods, different error influences come into play. First, the frequency value does not necessarily correspond to the speed of the surface. This error is typically classified as a systematic error. Furthermore, the accuracy of the speed determination depends on the signal-to-noise ratio. Influences include, for example, temporal fluctuations in the lighting, noise processes in the measuring chain or quantization errors. These are typically random errors.

The systematic error in the spatial filter measurement is essentially based on phase jumps in the signal. In the case of the relative movement between the object or object collective and the spatial filter measuring device, a part of the viewed surface runs out of the viewed area or recording measuring field between successive shots and a new part runs into the observed area. Thus, the data base of the image information is continuously changing, since removed image parts are replaced by additional new image parts. This causes the variations in the phase position of the spatial filter signal.

Furthermore, the phase jumps do not occur for a short time, but the spatial filter signal changes from recording to recording continuously until a new frequency value has stabilized. Since these phase changes can not be unambiguously identified and sometimes can be present over a long period of time, they enter into the speed statistics and cause a systematic error that can not be quantified in the measurement process. Due to the constantly changing surface, the error in the measurement process occurs as a fluctuation in the frequency or broadening of the spectrum. The uncertainty due to the phase changes is greater than the uncertainty due to the noise and dominates the accuracy of the local filter technique. Previous spatial filter measuring systems therefore achieve measurement uncertainties of a maximum of 0.05% under laboratory conditions.

Furthermore, it is observed that the amplitude of the spatial filter signal in the range of phase jumps and frequency changes often breaks down and the signal-to-noise ratio is significantly reduced. At the same time with phase jumps, high random errors occur, which temporarily cause an additional uncertainty of the analysis method. Again, no quantification of the error is possible because low amplitudes may also occur outside of phase jumps.

The errors are counteracted, for example, by upper limits for phase changes, by means of plausibility tests between several signal pairs (see I. Menn, "Optical Measurement of the Flow Rate of Erythrocytes for Recording the Microcirculation", University of Rostock, 201 0) and by averaging over a plurality of measured values reduces the dynamics of the systems, since the measurement of fast speed and frequency changes is no longer possible. The present invention is based on the object of increasing the measurement accuracy of spatial filter measurement methods and, for this purpose, providing a spatial filter measurement method and a spatial filter measurement apparatus with improved accuracy.

This object is achieved by a spatial filter measuring method for determining a relative speed between a spatial filter measuring device and at least one object or object collective, the spatial filter measuring device detecting image data of the at least one object or object collective moving through a recording measuring field of the spatial filter measuring device that is stationary in a reference system of the spatial filter measuring device, wherein at least one temporally modulated spatial filter signal is generated by applying at least one spatial filter to at least a portion of the acquired image data, which is further developed in that for generating the at least one spatial filter signal a section of the at least one object or object collective at least temporarily or permanently at least is kept approximately constant.

The spatial filter measuring method according to the invention fundamentally eliminates the previously described problem that phase jumps and phase changes together with small amplitudes basically limit the accuracy of the spatial filter measurement. So far, all captured image information is usually used to generate a spatial filter signal and the signal content for the offset estimation has not been optimized.

In contrast, the present invention provides the already conceptually completely different approach to suppress at least temporarily phase fluctuations due to changes in the image database as completely as possible, by over one, in particular as long as possible, the same image information for the spatial filter measurement method can be used. As a result, the phase changes in the spatial filter signal are limited to the contributions of unavoidable image noise and possibly unavoidable small inaccuracies in keeping constant the considered section of the object or object being considered. In this way, the spectral coefficients of the generated spatial filter signal are kept constant or substantially constant. With these pure spatial filter signals thus a very accurate and phase-jump-free or low-speed speed measurement is possible. Furthermore, the constant maintenance of the image information results in further advantages for the evaluation and validation of the spatial filter signal. On the one hand, the keeping constant of the image information, in contrast to previous evaluation, at least temporarily temporally constant amplitudes of the complex spatial filter signals. These, in turn, can be used as a direct and unambiguous measure of the reliability of the speed estimate. This provides information about the local quality of the signal. On the other hand, although the image information can not be kept constant for any length of time, the signal points at which there is no constancy of the image contents and thus a phase jump are known and are reduced to a few measured values. In this way, the measurement values which are uncertain due to phase jumps can be reduced to a minimum number and identified and either iminiert or possibly replaced by valid measured values, eg other spatial filters. This results in an improvement in the accuracy or reduction of the phase noise by an order of magnitude compared to known methods of signal validation and plausibility check of spatial filter signals.

A further improvement results in the case that several Local filter signals are measured simultaneously, if advantageously the location filter signals are selected with the highest amplitudes in multiple spatial filter signals. In case of doubt these will have the highest phase stability and the highest signal-to-noise ratio

Regarding the constant maintenance of the image database, there are several possibilities. In an advantageous embodiment, which is easy to apply in particular in linear relative movements, image data from a series of successive partial measuring fields are used which move within the recording measuring field with the object or object collective. In this case, preferably, measured values of the spatial filter signal, which arise during the transition from a partial measuring field to a subsequent partial measuring field, are disregarded, wherein in particular such a measured value is replaced by a preceding measured value, a measured value interpolated from previous measured values or by a measured value from another spatial filter or another partial measuring field ,

In this advantageous procedure, a partial region of the observed region is selected in each case and moved within the recording measuring field with the relative movement and movement direction between the object and the spatial filter measuring device. Thus, in each subsequent exposure, in which the subregion is still completely within the acquisition measurement field, the same or almost the same image information is available, to which the spatial filter is used which is stationary in the reference system of the spatial filter measurement device. Since a constant image section is now guided past the spatial filter, the spatial filter signal is a substantially phase-pure signal, that is to say a spatial filter signal with frequency change disappearing as part of the image noise and the accuracy of the determination of the tracked partial measuring field. tion

In this procedure, the application of the spatial filter to the image data of each tracked sub-field generates a subset or section of the spatial filter signal limited in time to the duration of the "existence" of the sub-array However, the selection should be based on the length of the spatial filter signal sub-trains, for example, the rotation pointer method is in many cases suitable for evaluating the spatial filter signal even if the lengths of the spatial filter signal sub-trains are less than a complete period, for example sine period or cosine period In this case, two consecutive measurement points or recordings suffice to estimate the frequency from the temporal phase change, in which case it is also particularly e It is simply possible to replace or fill in the gaps that arise during the transition from one subfield to the next sub field. With longer locator signal trains, which pass through several oscillation periods, the zero-crossing detection with period duration measurement is also applicable, as well as the autocorrelation phase or the evaluation of the power density spectrum.

Advantageously, a new partial measuring field is generated as soon as a preceding partial measuring field has reached a limit of the recording measuring field. Thus, a new part of the measuring field is selected as soon as the observed part of the field runs out of the recording measuring field. This is already foreseeable, since the location of the observed sub-area in the recording measuring field is known in each recording. The new sub-field includes at least partially a newly added Coming image section in the recording panel. This is generally accompanied by an amplitude and phase jump in the spatial filter signal. However, this is expected or is known due to the change of the partial measuring field, so that this transition can be excluded when determining the speed. An advantage here is that only the measured value that is generated when changing the partial measuring field is subject to errors due to the phase jump. In contrast, the conventional evaluation involves a continuous transition over a large number of inaccurate measured values. The subsequent Teilsignalzug the spatial filter signal from the other part of the measuring field then again results in a substantially phase-pure signal, with only the phase and the Ampl itude of the signal differ from that of the previous signal section.

If a phase jump has been detected as a transition from one partial measuring field to another partial measuring field, then the measured value at this point can either be discarded or replaced by an interpolated or valid measured value. An interpolated measured value can be generated, for example, by a preceding measured value or by an average of preceding measured values. Another val ider measured value can be determined by means of another spatial filter, for which no phase jump occurred at this point, or by means of another partial measuring field within the recording measuring field.

To keep the image information in a partial measuring field constant, the partial measuring field is moved with the object or the object collective. For this, an estimation of the first still to be determined speed of the objects in the image section can be used. Typically, the estimation has an accuracy of one or a few pixels, which sufficient maintenance of the spectral image information. In contrast, the accuracy of offset estimation using spatial filters is negligible in the subpixel range, from less than one pixel to 0.01 pixels.

This means a two-stage or multi-stage process in which first of all a "provisional" spatial filter signal is generated and analyzed with respect to the image data of the entire acquisition measurement field.This spatial filter signal is subject to phase jumps, but provides a basis for an estimate of the relative velocity On this basis, a partial measuring field, in particular arbitrarily I, can be defined within the recording measuring field and moved along with the speed thus determined within the recording measuring field already subject to less fluctuations, so that already by this measure, the measurement is improved.The correspondingly improved measurement can also turn for the adaptation of Mitbewegungsgeschwindigkeit the sub-field in the receiving field d or the subsequent next part of the measuring field are used so that a continuous approach to the actual relative speed and direction of movement takes place.

In an additional or alternative advantageous development, it is provided that limits of the partial measuring fields are determined by at least one, in particular one-dimensional, image signal analysis in which at least one characteristic, in particular one-dimensional, projected image signal profile is generated for each image by the acquired image data from the recording measuring field , in particular in rows and / or rows, in be mulated and / or averaged. This image signal analysis makes it possible to estimate the relative movement of the object or object collective in the recording measuring field with little effort and with an accuracy of one or a few pixels on the basis of the original image data, in order to determine the co-movement speed of the partial measuring field in the recording measuring field. This can also be a follow-up of certain structures in the image signal or the recognition of certain structures in the image signal waveform, which are spatially offset in successive recordings.

For this purpose, the limits of the partial measuring fields are preferably set at significant points in the projected image signal waveform. Significance points are in particular extreme values or qualified threshold value passages. A qualified threshold pass is understood to be a threshold pass that has a sufficient change in amplitude, so that not every small image noise that happens to pass through the threshold is also recognized as a significance point. Even extreme values are conspicuous by their emphasis on the surrounding signal curve or by exceeding an upper threshold value or falling below a lower threshold value, wherein possibly also a sufficient stroke must be present. Threshold passes may be negative edge-down thresholds and / or rising-edge positive thresholds. Extreme values can be local maxima and / or local minima.

The significance points may also be subjected in their spatial relationship to each other under certain conditions. For example, a minimum distance between the selected significance points, which can be used to determine the start and end of a part tag field so that the image components used do not become too small. On the other hand, a maximum value for the distance can also be determined so that sufficiently long partial signal trains of the spatial filter signal are ensured, since too large partial measuring fields in the recording measuring field would very quickly run out of the recording measuring field. The thus defined maximum extent of a partial measuring field in the recording measuring field in the direction of movement can also be made dependent on the periodicity of the spatial filter function used in the entire recording measuring field, in order to ensure that when passing through the recording measuring field, the partial measuring field passes through at least one or more periods of the spatial filter.

In the definition of a new partial measuring field, the selection of the significance point recently entered into the recording measuring field as a trailing partial measuring field boundary and a leading significance point whose spatial distance to the trailing partial measuring field boundary lies between a minimum distance and a maximum distance is particularly preferred as a preceding partial measuring field boundary.

Likewise advantageously, the first and / or last occurrences of the significance points in the projected image signal course are used as partial measuring field delimitation in the case of a partial measuring field to be generated until the leading significance point runs out of the recording measuring field. Alternatively, it is also advantageous to generate a new partial measuring field as soon as the distance between the recognized first and last occurrences of significant points changes in the projected image signal waveform. Also alternatively, the strongest significant points can be used. The transition from one subfield to the next does not require replacing the entire subfield becomes . Successive subject fields may also overlap. In the case of change of a partial measuring field can advantageously be used also another part measuring field, which has no change. Thus, the non-valid spatial filter reading of one sub-field can be replaced by the spatial-filter reading of the other sub-field.

Preferably, a change of the partial measuring fields is determined on the basis of a change in a position, a distance and / or an amplitude of the significant points in the recording measuring field and / or partial measuring field.

Since the spatial relationship of the significance points in a partial measuring field does not shift or only slightly against one another, it is advantageously possible by means of the position or the pattern of the significance points and the length of the partial measuring field to identify and delimit a change of partial measuring fields and thus the phase jumps. Thus, in the local filter technique, the influence of the phase jumps can be completely eliminated.

Another way to keep the image database constant is through rotating movements. Thus, an alternative embodiment of the spatial filter measuring method according to the invention provides that during a rotating relative movement of the at least one object or object collective to the spatial filter measuring device, a circular or annular spatial filter is used with circumferentially modulated structure whose center is located on a center of rotation of the rotating relative movement. In a rotating relative movement, which is given for example in rotating rollers, wheels, etc., it is possible to keep the selected image section constant, as by the circular motion always the same surface structure of a moving object or Structure of a rotating object collective representing the image database. A circular or annular spatial filter with structure modulated in the circumferential direction has the advantage in the case that no image constituents run out of the circular or annular recording measuring field or run into it again, so that already in this way the section of the at least one considered Object or object collective is permanently kept constant. The spatial filter signal thus generated is permanently phase-stable.

The object on which the invention is based is also achieved by a spatial filter measuring device having a recording device and an image data processing device which is set up by means of image data processing software or as an FPGA to carry out a spatial filter measuring method according to the invention described above. Thus, the spatial filter measuring device according to the invention offers the same advantages, features and properties as the spatial filter measuring method according to the invention, which is carried out in the spatial filter measuring device.

Further features of the invention will be apparent from the description of embodiments according to the invention together with the claims and the accompanying drawings. Embodiments of the invention may satisfy individual features or a combination of several features.

The invention will be described below without limiting the general inventive idea by means of embodiments with reference to the drawings, reference being expressly made to the drawings with respect to all in the text unspecified details of the invention. Show it: a construction of a known classical optical spatial filter measuring system in a schematic representation, b) known implementations of a spatially orthogonal grating function using the example of a simple differential grating in a schematic representation, an example of an intensity distribution of image brightness, an example of signal generation at a given spatial filter and shift the intensity distribution Fig. 3, b), 5c) the exemplary time profile of a complex spatial filter signal with harmonic spatial filter with evaluated sizes, an exemplary embodiment of a spatial filter measuring method according to the invention, an exemplary representation of a resulting spatial filter signal in a spatial filter according to the invention with successive partial measuring fields, b), 8c) exemplary temporal course of a complex spatial filter signal with harmonic spatial filter in a spatial filter method according to the invention and the derived variables and an annular spatial filter with circumferentially modulated structure. In the drawings, the same or similar elements and / or parts are provided with the same reference numerals, so that apart from a new idea each.

In Fig. 1 shows a construction of a classical optical spatial filter measuring system with a spatial filter measuring device 100, as described, for example, in the abovementioned dissertation by M. Schaeber, "Mehrdimensionale Ortsfiltertechnik", Universität Rostock, 201 3.

The spatial filter measuring apparatus 100 includes a photoreceptor 102 which receives light from an object 2 moving in the moving direction 4 having an illuminated structure through an optical system. This comprises an imaging optical system 1 08, in the image plane of which a spatial filter formed as an optical grating 1 06 is arranged. The light transmitted through the spatial filter 1 06 is focused and integrated by means of an optical system 1 04 on the photoreceiver 1 02. The spatial filter 1 06 causes each pixel of the object 2 with the spatial filter function, here with the transmission function of the spatial filter 1 06, weighted. The fundamental frequency of each light spot is proportional to the speed of movement of the surface point. Since this applies to all pixels, there is a temporal modulation of the amount of light that passes through the spatial filter 1 06 and arrives at the photoreceiver 1 02. If all the pixels are moved at the same relative speed to the imaging optics 108, the result is a very pure frequency spectrum of the modulated light. The speed of movement of the relative movement perpendicular to the orientation of the optical grating of the spatial filter 1 06 is then the product of the carrier frequency of the recorded time signal and the grating period of the grating used, wherein even the magnification of the lens inversely proportional.

In Fig. 2 a), 2 b) are shown schematically implementations of two spatial filter measuring devices 120 (FIG. 2 a) and 1 20 '(FIG. 2 b) using the example of a simple differential grating, which is essentially also shown in the dissertation by M. Schaeper. In place of a combination of an optical grating as a spatial filter and a photoreceiver, a structured receiver in the form of a matrix receiver 1 22 or a line receiver 1 23 of the spatial filter measuring device 1 20, 1 20 ', the image information from the object 2, which in a Movement direction 4 moves, received by an imaging optics 1 24. The combination of imaging optics 1 24 and structured receiver 1 22, 1 23 defines on the surface of the object 2 a recording measuring field 1 25, which comprises a part of the surface of the object 2.

The structured matrix receiver 12, for example a CCD or CMOS image receiver, comprises image pixels arranged in columns and rows. In order to generate a spatial filter signal s (t) (reference numeral 14), the light brightness information of all image pixels multiplied by the weights 1 26 and added in columns 1 27 in electronically implemented spatial filter 1 06, wherein the spatial filter function 1 06 is defined by the weights 1 26. The corresponding selection paths 1 28 are indicated by solid lines.

In contrast to in Fig. 2a), in which the signals of the matrix elements within each column are summed up, the signal shown in FIG. 2b), the line receiver 1 23 already has only one line with matrix elements. Correspondingly, the corresponding recording measuring field 1 is substantially narrower than in the case of the matrix receiver 1 22. The periodicity and the weighting functions of the spatial filter 1 06 can also be changed. You can also apply weighting such as cosine or sine functions or complex weighting functions. Multiple spatial filters can also be applied to an image. This is possible either by software or by means of programmable FPGA, for example.

The spatial filter signals of the spatial filter measuring device 1 00, 1 20 of FIGS. 1 and 2 are necessarily phased or phase-biased, since, due to the relative movement between object 2 and the spatial filter measuring device 1 00, 1, 20 parts of the surface of the object 2 run out of the recording measuring field and new parts run in, so that the database on which is based on the local filter signal generation, constantly changes.

In order to illuminate the procedure according to the invention in more detail, in the following FIGS. 3 to 5, first the effect of the phase noise is shown. Figures 3 and 4 are also from the dissertation of M. Schaeper.

Fig. 3 shows an exemplary profile of an intensity distribution of an image brightness signal 10 of recorded image data along the longitudinal extent of an object in its movement direction 4. The movement direction or spatial extent is also designated by x as spatial coordinate. The intensity is represented in a normalized amplitude between 0 and 1 for intensity values between black (value 0) and white (value 1). The intensity distribution varies between about 0.4 and 0.7.

Along the x-extension of the image brightness signal, arrows or arrows indicate locations or location positions in which a outgoing image edge of the recording measuring field is at different times ti to t ₄ , wherein the object moves in the direction of movement 4 through the recording measuring field. Between times ti to t ₄ , the object moves relative to the recording measuring field in each case by the width of the recording measuring field, which thus has the extension, which is represented by the distance of the arrows at the times ti to t ₄ .

In the illustration of FIG. 3, the recording measuring field is shown as being moved with respect to the brightness data of the object. For the spatial filter measuring method, however, it is unimaginable whether the object moves or the recording measuring field, since it depends on the relative movement.

In Fig. FIG. 4 shows on the left side a sequence of representations which are staggered among one another and in which the image brightness signal 1 0 from FIG. 3 each moved to the width of the recording measuring field. There are thus snapshots at the times ti to t ₄ . The recording measuring field extends between x = 0 and XB, its extension in the direction of movement is thus x _B.

The image brightness signal 1 0 is a spatial filter 1 2 superimposed, which contains four periods of a cosine signal that alternates between the values -1 and +1. Only in this area of the recording measurement field between x = 0 and XB is the image brightness signal 1 0 with the spatial filter 1 2 folded or weighted summed. The image brightness information outside of the recording field is not included in the measurement because it is not visible to the image sensor. The resulting spatial filter signal s (t) is shown in FIG. 4 shown on the right with a vertical downwards timeline. The times ti to t ₄ are indicated by arrows and crosses on the spatial filter signal s (t) whose real part 14 is shown. It is in Fig. 4 that the spatial filter signal 14 between the times ti and t _{2 has} a very low Ampl itude and a relatively irregular structure having little in common with a pure sine or cosine function. Around the time t ₃ around the amplitude is very large and the signal is very regular. At time t ₄ , at medium signal amplitude, an irregularity in the alternating signal can be seen, indicating a phase change.

The in Fig. 4 relates to a cosine local grating having a periodicity μχ _Β of 4. However, spatial filter signals can also be produced for any other periodicities.

In the preceding Figures 3 and 4, only one cosine grating has been considered, which corresponds to a real part of a complex spatial filter signal. Fig. 5 shows the temporal behavior of a complex spatial filter signal. In the picture part Fig. 5a), the real part 14, the imaginary part 1 5 and the amount of Ampl itude 1 6 are shown. Real part 14 and imaginary part 1 5 are generated, each with a shifted by 90 ° spatial filter grids the same periodicity. The upper enveloping curve corresponds to the amount 1 6 of the complex spatial filter signal and can be calculated from real part 14 and imaginary part 15 in the evaluation.

Again, it can be seen that the magnitude of the amplitude 1 6 of the complex spatial filter signal is very small in parts of the course. In these areas, a comparatively bad signal and inaccurate measurement can be expected. The image part 5b) shows the phase 1 7 of the complex spatial filter signal calculated from the real part 14 and the imaginary part 1 5. In regions of high amplitude, it results in a nearly linear phase increase. In areas with ringer Ampl itude 1 6 are more pronounced deviations from the linearity.

In the image part 5c), the phase difference signal 1 8 is shown weighted with the spatial filter period and the magnification scale as an image offset or a specific velocity profile. The phase difference is the change of the phase 1 7 between two images and corresponds to the rotary pointer signal method for determining the object speed. There are deviations from the true image offset 19. The deviations increase the smaller the amplitude 16 of the spatial filter signal is. Furthermore, deviations from the true value are evident even at high amplitudes. A plausibility test is used to minimize the influence of noise and phase jumps in the signal by averaging and excluding unreliable measurements. For example, speed variations could be limited to a maximum value. It could be detected without any problems strong deviations 21. However, if smaller deviations 22 due to smaller phase jumps are to be detected, the threshold must be set very low and many signal values are invalid.

In the Figs. FIGS. 6 to 9 show characteristics of the spatial filter measuring method according to the invention, which cancel out the previously shown limitations of the conventional spatial filter measuring method.

Fig. 6 shows schematically the procedure in a first embodiment of the method according to the invention. In this exemplary case, a linear movement takes place in which an object 2 or object collective moves through a recording measurement field 60, as described above. The eight mutually imaged curves each relate to the one recorded on the spatial filter. formed object or object collective 2 at different continuous times. The dashed curves 61 correspond to the image brightness signal 1 0 in the recording measuring field 60, which receives, for example, the line sensor 1 23 of FIG. 2b).

Significance points 62 ^{1 "3} , 63 ^{1" 2 are} searched for in the image brightness signal within the respective recording measuring field. These are in the embodiment of FIG. 6 around the respectively first and last negative threshold value passages in the recording measuring field 60. Between these two respectively recognized significance points 62 ^{1 "3} , 63 ^{1" 2} , for example the first (62 ^{1 "3} ) and the last (63 ^{1" 2} ) within the interval , a considered patch is defined as a sub-field 64 ^{1 "4} that remains stationary with respect to the image brightness signal 61 until it emerges from the acquisition array 60. As this happens, new significance locations within the acquisition measurement field are searched to define a new sub-field 6, several times have also existed between the time points in which the partial measuring fields have been newly defined .. Differing from the selection criterion illustrated in FIG. 6, smaller or larger partial measuring fields may also be selected which remain longer or shorter in the recording measuring field 60.

The image brightness signals 61 of the partial measuring fields 64 ^{1 "4} are weighted by the spatial filter 1 06 and summation forms the spatial filter signal 14. The image brightness measured values which lie outside the partial measuring fields 64 ^{1" 4} do not flow into the formation of the spatial filter signal and advantageously occur the summation is set to zero or weighted with zero.

Fig. 7 shows by way of example schematically the result of the spatial filter signal thus generated. As long as a part measuring field 64 ^{1 "4} within the Recording field 60 remains, results for the spatial filter signal s (t) each a spatial filter signal section 74 to 74 "", which is a Teilzug a monofrequenten sine or cosine signal, if the spatial filter function was also a sine or cosine function. Deviations from this pure signal structure may arise from the existing image noise and possibly occurring inaccuracies in tracking the sub-fields 64 ^{1 "4.} These local filter signal sections 74 to 74""may comprise either fractions of a period or one or more periods, as the case may be minimum or maximum width of the partial measuring fields 64 ^{1 "4} within the recording measuring field 60. Each at the points 76 at which a partial measuring field 64 ^{1" 4 runs} out of the recording measuring field 60 and is replaced by a new partial measuring field 64 ^{1 "4} are dashed vertical lines represented, which characterize this transition. At these points, there is no valid measured value. This can be replaced by the last previous measurement, an average from previous measurements or other valid spatial filter measurements. Although the amplitudes and phases of the individual spatial filter signal sections 74 to 74 "" differ from each other, their fundamental frequency is always the same.

Fig. 8 shows the result for the same signal train and the same spatial filter weighting from FIG. 5. In the image part Fig 8a) turn the complex spatial filter signal with real part 14 and imaginary part 15 and the signal amplitude formed therefrom 1 6 can be seen. As shown in FIG. 8, the real part 14 and the imaginary part 1 5 are harmonic in sections, and the amplitude 16 is constant in sections.

In the picture part Fig. 8b), in turn, the phase 1 7 of the complex spatial filter signal is plotted. This is now, in contrast to the previous spatial filter technique, for a part of the measuring field 64 ^{1 "4} almost linear and has jumps 23 for changes in the partial measuring field 64 ^{1" 4} . These jumps 23 are indicated by horizontal lines and were derived from the change of the significance points 62 ^{1 "3} , 63 ^{1" 2} or the partial measuring fields 64 ^{1 "4.} Thus, the phase jumps in the spatial filter signal are localized to a measured value and via the changes of the Partial measuring fields 64 ^{1 "4} detectable.

In the picture part Fig. 8c) is again the image offset, the true value 1 9 and the now piecewise constant, Ampl itude 1 6 shown. Comparing to FIG. 5 it can be seen that the accuracy of the speed estimation is greatly improved. Even at high amplitudes no systematic deviations from the true value occur and the noise in the phase difference signal 18 is significantly reduced. For small amplitudes the measured value is less reliable and higher fluctuations occur. However, the signal amplitude in these areas can be used to identify the occurrence of uncertain readings. For this purpose, e.g. when falling below an amplitude threshold as well as at the locations of the phase jumps 23, the measured value is discarded, interpolated or replaced by a safer measured value of another spatial filter or partial measuring field.

Fig. 9 schematically shows an alternative embodiment of a spatial filter 82 having a ring shape with a circumferentially modulated characteristic. Its center coincides with the center 80 in a rotation in the direction of movement 4, which in this case is a direction of rotation. In this case, the viewed image section, namely the ring section, always remains the same, so that a quasi-infinite course of the spatial filter signal analogous to that in FIG. 9c). In this case, the detail 60 from the image brightness signal from FIG. 9a) and 9b) comparable to the course of the image brightness along a complete revolution through 360 ° in the ring section around the center 80. Through the Dre- hung in the direction of movement 4 corresponds to the annular grid 82 and the infinite spatial filter 62 of FIG. 9th

All these features, including the drawings to be taken alone as well as individual features that are disclosed in combination with other features are considered alone and in combination as erfindungswesentl I. Embodiments of the invention may be accomplished by individual features or a combination of several features. In the context of the invention, features which are identified by "particular" or "preferably" are to be understood as optional features.

LIST OF REFERENCES

 2 object

 4 direction of movement

 10 Image brightness signal of recorded image data

14 real part of the spatial filter signal s (t)

 15 imaginary part of the spatial filter signal s (t)

 16 amplitude of the spatial filter signal s (t)

 17 phase of the complex spatial filter signal s (t)

 18 phase difference signal of the spatial filter signal s (t)

19 true image offset

 21 strong deviations

 22 slight deviations

 23 jumps

 60 recording measurement field

 61 Image brightness signal

62 ^{1 "3} , 63 ^{1" 2} significance points

64 ^{1 "4} partial measuring field

 74 _ 74 "" Location filter signal section

 80 center of rotation

 82 spatial filters

 100 spatial filter measuring device

 102 photoreceptor

 104 optics

 106 spatial filter

 108 imaging optics

 120, 120 'spatial filter measuring device

 122 Matrix receiver

 123 line receiver

 124 imaging optics

 125 recording measurement field

 126 weights

 127 adding elements

Claims

Spatial filter measuring method and device for spatial filter measurement Claims 1 . Spatial filter measuring method for determining a relative speed between a spatial filter measuring device (120, 1 20 ') and at least one object (2) or object collective, wherein the spatial filter measuring device (1 20, 1 20') captures image data of the at least one object (2) or object collective that is moved by a in a reference system of the spatial filter measuring device (120, 1 20 ') stationary recording measuring field (60, 1 25) of the spatial filter measuring device, wherein at least one zeitl I modulated spatial filter signal by applying at least one spatial filter (1 06) on at least a part of the detected Image data is generated, characterized in that for generating the at least one spatial filter signal (s) a considered section of the at least one object (2) or object collective is kept at least temporarily or permanently at least approximately constant. 2. Spatial filter measuring method according to claim 1, characterized in that, in the case of a plurality of spatial filter signals, the spatial filter signal selected with the highest amplitudes. 3. spatial filter measuring method according to claim 1 or 2, characterized in that image data from a series of successive partial measuring fields (64 ^{1 "4} ) are used, which move within the recording measuring field (60, 1 25) with the object (2) or object collective. 4. spatial filter measuring method according to claim 3, characterized in that measured values of the spatial filter signal, which arise during the transition from a partial measuring field (64 ^{1 "4} ) to a subsequent partial measuring field (64 ^{1" 4} ), disregarded, in particular such a measured value by a preceding Measured value, a measured value interpolated from previous measured values, or replaced by a measured value of another spatial filter or another partial measuring field (64 ^{1 "4} ). 5. spatial filter measuring method according to claim 3 or 4, characterized in that a new part measuring field (64 ^{1 "4} ) is generated as soon as a preceding part measuring field (64 ^{1" 4} ) has reached a limit of the recording measuring field (60, 1 25). 6. spatial filter measuring method according to any one of claims 3 to 5, characterized in that a movement speed of the partial measuring fields (64 ^{1 "4} ) in the recording measuring field (60, 1 25) is extracted from a temporal modulation of at least one spatial filter signal on the image data of the entire recording measuring field (60, 1 25) or based on the image data of a current partial measuring field (64 ^{1 "4} ) and / or one or more earlier partial measuring fields (64 ^{1" 4} ). Spatial filter measuring method according to one of Claims 2 to 6, characterized in that boundaries of the partial measuring fields (64 ^{1 "4} ) are determined by at least one, in particular one-dimensional, image signal analysis, in which at least one characteristic, in particular one-dimensional, projected image signal course (1 0 , 61) is generated by the collected image data from the recording measuring field (60, 1 25), in particular in rows and / or rows, summed and / or averaged. Spatial filter measuring method according to claim 7, characterized in that the boundaries of the partial measuring fields (64 ^{1 "4} ) at significance points (62 ^{1" 3} , 63 ^{1 "2} ) in the projected image signal waveform (1 0, 61) are set, in particular at extreme values or qualified threshold value passages , Spatial filter measuring method according to claim 8, characterized in that in the determination of a new partial measuring field (64 ^{1 "4} ) the selection of a last in the receiving measuring field (60, 1 25) in-run significance point (63 ^{1" 2} ) as a trailing Teilmessfeldbegrenzung and a leading significance point ( 62 ^{1 "3} ), whose spatial distance to the trailing part of the measuring field boundary between a predetermined minimum distance and a predetermined maximum distance is, as a leading part of the measuring field boundary, takes place. Spatial filter measuring method according to claim 8 or 9, characterized in that a change of the partial measuring fields (64 ^{1 "4} ) on the basis of a change of a position, a distance and / or an amplitude of the significant points (62 ^{1" 3} , 63 ^{1 "2} ) in the recording measuring field ( 60, 1 25) and / or partial measuring field (64 ^{1 "4} ) is determined. 1 . Spatial filter measuring method according to claim 1, characterized in that in a rotating relative movement of the at least one object (2) or object collective for Ortsfilter- measuring device (1 20, 1 20 ') a circular or annular spatial filter (82) is used with modulated in the circumferential direction structure whose center lies on a rotation center (80) of the rotating relative movement. 2. Spatial filter measuring device (1 20, 1 20 ') with a recording device and an image data processing device which is set up by means of image data processing software or as an FPGA to perform a spatial filter measurement method according to one of claims 1 to 1 1.