• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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    • 专利摘要:
    A sensor unit (100) for determining control information for use in a daylight-dependent lighting control comprises an image sensor (10) for acquiring digital image information and a controller (20) for evaluating the image information and creating a brightness signal representing the daylight, the controller ( 20) is designed to take greater account of image areas of the image information acquired by the image sensor, which are influenced more strongly by the daylight, when the brightness signal is generated.
    公开号:AT14229U1
    申请号:TGM55/2014U
    申请日:2014-02-07
    公开日:2015-06-15
    发明作者:Gerd Zeidler
    申请人:Zumtobel Lighting Gmbh；
    IPC主号:
    专利说明:

    description
    SELF-ADJUSTING SENSOR FOR CAPTURING DAYLIGHT
    The present invention relates to a light sensor for measuring the room brightness, in particular for detecting the e.g. passing through windows into a room of daylight.
    Modern building lighting, in particular lighting of offices or work places high demands on the chosen lighting solution. The lighting should always be sufficiently available, seamlessly integrated into the building concept and easy to operate. At the same time, the maintenance effort should be low and the power consumption should be low in order to save costs and protect the environment. In order to fulfill these requirements, often a so-called daylight-dependent lighting control is used, in which the artificial light is thus tuned to the light incident from outside into a room. Thus, the indoor lighting can be kept constant over long distances depending on the current external light situation, the artificial lighting is always needed only when there is not enough daylight available. Apart from the fact that the support from the daylight leads to a more natural lighting and thus to a more pleasant so-called room climate, this procedure can also save energy.
    A solution known from the prior art for realizing a daylight-dependent lighting control, which is schematically illustrated in FIG. 1, is based on the use of a light sensor 210 mounted on the ceiling of a room 200 to be illuminated, which brightness is directed to a window area 201 the external light situation measures. On the basis of this external light measured value, a control unit 215 connected to the sensor 210 can then dim the room lamps 220, ie adjust their brightness so that the illuminance in the area to be illuminated remains approximately constant. Such light sensors 210 work on the basis of a specially adapted photodiode, which is adapted to the brightness perception of the human eye.
    This known solution for daylight-dependent lighting control has been working reliably for many years and is installed in many buildings. Despite the undeniable success of this concept, however, there are efforts to improve this further. Namely, the light sensors known from the prior art provide a good or meaningful measured value, corresponding installation instructions are to be followed exactly. For example, no interference sources, such as suspended luminaires, lintels, window crosses or columns, may be located in the optical axis or in the measuring field of the sensor. Even a direct incidence of sunlight is not permitted because it can falsify the measured value. Venetian blinds or shadowing in front of the window, however, do not influence the measured value or are less negative, as long as these sources of interference occur identically in all windows of the room. Accordingly, therefore, the window to be evaluated must be selected representative of the room and the light sensor must be arranged exactly in the position provided for this purpose.
    Practical experience from the past has shown that the correct mounting of the light sensor is a major problem with the prior art solutions. Often, the system is retrofitted into existing buildings, the existing cabling usually only reach up to the bulbs, which are usually mounted in the central area of the room. For an electrician, it is relatively easy to retighten the signal lines for the light sensor into the existing plumbing tubes, however, it is relatively expensive to lay new tubes. For this reason, the light sensor is often mounted where it is possible with little effort, but not where the light sensor according to installation instructions should actually be. As a result, the signal of the light sensor can not provide the correct measured value and the illumination control does not work as intended.
    In addition, it would be desirable to be able to filter out sources of interference that falsify the measured value of the sensor efficiently from the signal. Such disturbances are, for example, artifical sources of light, reflections, shadows or short-term disturbances, such as passers-by on the window, vehicles or small clouds.
    The present invention is therefore based on the object to provide a neuarti¬gen sensor for detecting the daylight situation available that independently of the mounting situation as independently as possible adjusts the signal to the correct value. The sensor should preferably be seamlessly integrated into existing systems and ideally be able to replace existing sensors.
    The object is achieved by a sensor unit for determining control information for use in a daylight-dependent lighting control with the features of claim 1. Advantageous developments of the invention are the subject of the dependent claims.
    The solution according to the invention is based initially on the idea of using an image sensor for detecting the daylight situation, with the aid of which digital image information can be detected. Compared to a classical light sensor, which does not offer the possibility of spatial resolution, an image sensor allows to obtain a variety of information regarding the spatial area detected by the image sensor. In turn, in the evaluation of the brightness information, it is possible to hide or at least take less account of those areas of the acquired image which contain less information with regard to the daylight. According to the invention, a controller is provided for this purpose, which evaluates the digital image information captured by the image sensor and ultimately produces a brightness signal representing the daylight, wherein the controller takes greater account of image areas of the image information acquired by the image sensor, which are more strongly influenced by the daylight when the brightness signal is generated.
    The invention therefore proposes a sensor unit for determining control information for use in daylight-dependent lighting control, the sensor unit comprising an image sensor for acquiring digital image information and a controller for evaluating the image information and producing a brightness signal representing the daylight and wherein the controller is designed to take greater account of image areas of the image information acquired by the image sensor, which are more strongly influenced by the daylight, when the brightness signal is generated.
    The inventive design of the sensor unit, in particular the special evaluation of the image information by the controller allows the sensor unit to be mounted more freely within a room. In particular, no strict assembly rules must be maintained, which stipulate that the image sensor exclusively detects a suitable window surface. Instead, the image sensor can now also detect additional, disturbing objects, which are then ignored or at least less taken into account when the brightness signal is generated. As a result, the mounting of the erfindungs¬gemäßen sensor unit compared to previously known solutions significantly simplified. On the other hand, the sensor unit according to the invention ultimately emits only a single brightness signal representing the daylight, as does a conventional light sensor. This in turn means that the unit according to the invention within a lighting system that already exists can easily replace a classic light sensor and perform its function.
    It is a particular advantage of the sensor unit according to the invention is that the controller is designed to independently recognize image areas, which are influenced more strongly by the daylight. The brightness signal is preferably produced by multiplying brightness information, in particular brightness values of individual image areas or pixels of the image captured by the image sensor, by a weighting factor and then representing a value representing these weighted brightness values as a whole, e.g. an average or the sum of these is calculated. The controller is designed to independently determine these weighting factors for the various image areas or pixels and thereby automatically to prefer areas which are relevant with regard to the detection of daylight. On the other hand, the other areas that are not or hardly affected by daylight are automatically assigned weighting factors by the controller so that they do not contribute, or hardly contribute, to the final output signal representing daylight.
    The determination of the weighting factors for the different image areas is thereby preferably carried out in the context of a special optimization algorithm carried out independently by the sensor unit, for which purpose the brightness information of temporally successively acquired images is compared with one another. This algorithm is based on the assumption that similar light components behave similarly. This is understood to mean that image areas, which are primarily influenced by the daylight, more or less follow the course of the total daylight, while on the other hand image areas, which are primarily exposed to artificial light, rather follow the temporal course of the art light. By observing the brightness changes in the various Bildberei¬chen while these different light classes can be isolated from each other and accordingly those areas are identified that contain information regarding the daylight.
    In the context of the above-mentioned optimization algorithm, according to a first variant, the controller can accordingly be designed to increase the weighting factor associated with an image area, if the temporal change of the brightness information of this image area is similar to the temporal change of the overall brightness of the image, and if so the temporal change of the brightness information of this image area is not similar to the temporal change of the overall brightness of the image.
    In the context of various test series, it has been found that alternatively to the variant described above, it is also possible to increase the weighting factor associated with an image area if the brightness information of this image area is not similar to the current change in the overall brightness of the image and to reduce it if the brightness information of this image area is similar to the temporal change of the overall brightness of the image. Also in this case, the controller is enabled, after observing the time course of the brightnesses in the image information, to automatically identify those areas that primarily contain information regarding the daylight.
    It can be provided in both variants that the weighting factors changed by the algorithm can only be changed within a certain range of values. In this way, borderline cases can be excluded in which individual image areas excessively influence the measurement signal or fail completely from the evaluation.
    According to an advantageous development can also be provided that - in the case of using color image sensors - in addition to a brightness signal, the controller also outputs a color information or a color temperature information. In this case, it would be conceivable in particular that the controller makes use of the knowledge gained in the context of the optimization algorithm and, on the one hand, generates a color information or color temperature information representing the daylight and, on the other hand, a color information or color temperature information representing the artificial light. This additional information could then be used, for example, to match the color of the artificial light to the color of the natural daylight.
    Further measures can contribute to reducing the effort in carrying out the above-mentioned optimization algorithm or when creating the finally output Sig¬nals. A first measure may be to first reduce the resolution of the image captured by the image sensor and then to perform the optimization algorithm only on the basis of the images with reduced resolution. As a result, relatively simple designed processors can be used and the memory requirement is kept very low.
    Another measure that contributes to the improvement in terms of the quality of the brightness signal is to first combine a plurality of different exposure levels of the image sensor captured images into an image with sog.erhöhter brightness dynamics when evaluating the image information.
    Background of this measure is that a classic digital image sensor in comparison to a specialized light sensor has a lower dynamic range. That is, a single image captured by the image sensor will typically provide information only within a limited range of brightness. In order to finally be able to produce a brightness signal which corresponds in terms of its circumference to the value range of a classical light sensor, a plurality of images are preferably acquired in the context of an exposure series at different exposure levels and then combined to form a so-called HDR image, ie an image with increased brightness dynamics. Appropriate methods for this purpose are already known from the prior art. The optimization algorithm itself is then performed on the basis of these images with increased brightness dynamics.
    Finally, it has been found that, by the measures described above, the information captured by the image sensor can be evaluated by the controller so efficiently that the finally obtained brightness signal reproduces the daylight very well and corresponds in quality to the signal of an optimally mounted classical light sensor , The great advantage, however, is that the new sensor unit no longer has to be mounted in accordance with strict regulations and, despite everything, is able to identify the image areas relevant to the detection of daylight.
    The invention will be explained in more detail with reference to the accompanying drawings. In the drawings: Figure 1: the components of a known from the prior art
    Systems for daylight-dependent lighting control; Figure 2: a system for daylight-dependent lighting control with a sensor unit according to the invention; FIG. 3 shows the basic structure of the sensor unit according to the invention; FIG. 4 shows conceivable weighting functions for merging a plurality of LDR images into an HDR image; FIG. 5: the processing of the data acquired by the image sensor; FIG. 6 shows the merging of three LDR images into an HDR image, and FIGS. 7a to 7h show the structure of the weighting matrix using the optimization algorithm. [0029] FIG.
    FIG. 2 shows an example of a lighting system in which a sensor unit 100 according to the invention is used.
    1, the sensor unit 100 is preferably mounted on the ceiling of a surface 200 to be illuminated, the sensor unit 100 being directed onto a window surface 201 for detecting the daylight is. The brightness signal output by the sensor unit 100 is transmitted to a control unit 215 for controlling the lights 220 in order to control them with regard to their brightness in accordance with the daylight.
    The peculiarity of the solution according to the invention is that the sensor unit 100 according to the invention does not necessarily have to be arranged in such a way that the area covered by it coincides 100% with the window area 201 or is directed exclusively at the window area 201 and no longer interfering objects includes. Instead, the detected area may well be larger, or the window itself may have disturbing objects 202, such as rungs or the like, as illustrated, which would normally interfere with the measurement signal of a classical light sensor. The sen¬soreinheit 100 invention is immune to such intrinsically disturbing effects, so able to automatically suppress such disturbing objects or to ignore and despite all a meaningful signal that provides information about the daylight, to the control unit 215zuzuzu.
    According to the schematic representation of FIG. 3, the sensor unit 100 according to the invention essentially consists of two components, firstly an image sensor 10 and secondly a microcontroller 20, wherein preferably both units are arranged within a common housing, but theoretically also be arranged separately from one another could. The task of the image sensor 10 is to create a digital image of the area to which the sensor unit 100 is aligned. The image sensor 10 thus serves to determine a two-dimensional image of the light situation by electronic means.
    The results are digitally stored in pictures, a matrix of picture elements (pixels). Depending on the type of image data, a pixel consists of a brightness value in the case of black-and-white images or of a plurality of brightness values in the case of color images. In any case, the image data is spread over a matrix of photosensitive components, e.g. a CCD sensor or a CMOS sensor, detected, digitized and passed on to the subsequent processing unit, ie the controller 20. In the simplest case, the image sensor 10 may be a simple digital camera.
    The task of the controller 20 is, on the one hand, to control the digital camera or the image sensor 10 in order to initiate the creation of the digital images. On the other hand, the controller 20 analyzes the obtained image data and derives therefrom the finally desired brightness signal. This brightness signal is a single numerical value which, comparable to the signal of a classical light sensor, provides information about the intensity of the daylight entering the room. For this purpose, the controller 20 contains a microcontroller 21 for carrying out the calculations for the image evaluation and a memory 22, which is usually integrated in the microcontroller 21, in which at least some of the images transmitted by the image sensor 10 can be temporarily stored.
    The value output by the controller 20 is thus derived from the information of the images captured by the image sensor 10. As mentioned above, since this image sensor 10 is not necessarily directed only to areas affected by daylight, it is necessary to make a statement as to which image areas contain daylight-relevant information and in which image areas this is not the case when creating the final brightness signal. Ideally, therefore, in the situation shown schematically in FIG. 2, the signal should primarily be generated on the basis of information relating to the window areas, that is to say the areas shown in dashed lines. All other areas of the image captured by the image sensor 10 should be ignored, or at least taken less into account.
    According to the present invention, this evaluation of the image areas is carried out by the controller 20 itself, which evaluates the digital image data in the context of a special brightness measurement algorithm to be described in more detail below and then determines the brightness signal based thereon. In this case, the data transmitted by the image sensor 10 are preferably first processed, as described in more detail below.
    Namely, in view of the fact that the effort for performing the brightness measurement algorithm according to the invention should be relatively low and, ideally, also be carried out by a microcontroller having limited resources, it is advantageous to use a comparatively low image resolution for the images to be finally evaluated. However, a reduction of the image resolution additionally leads to a further advantage. Thus, by lowering the resolution, the luminance signal is locally low-pass filtered, so to speak. High spatial frequencies, which are characterized by high Kontrastebzw. Gray value differences in the image express are automatically suppressed in this way. Local highlights in the image caused, for example, by reflections can be minimized by the averaging performed to reduce image resolution. It has been found that such highlights do not contribute significantly to room brightness, but can significantly falsify the measurement, so that the reduction in resolution is automatic an improvement of the finally created brightness signal leads. On the other hand, of course, the resolution should still permit a sufficient differentiation between different areas of the image. As a sensible compromise between these different requirements, a resolution of 40x30 pixels has emerged here, in which case a storage consumption of 1.2 kB per image frame or pixel at 8 bit / pixels. 2.4 kB per picture at 16 bit / pixel. Of course, depending on the available resources, another, in particular a higher resolution, can be selected for the images to be evaluated.
    A further problem with the use of an image sensor for determining a brightness signal is to be able to achieve a sufficiently high measurement range since the so-called dynamic range of an image sensor is limited, in particular, in comparison to a classical light sensor. The finally realizable measuring range for the brightness signal to be output should be able to cover a range of 0-5000 lux. In this case, the output value corresponds in a first approximation to the averaged brightness recorded over the entire image, whereby individual image parts can also clearly exceed 5000 lux.
    At this point it should be noted that strictly speaking, the image sensor just like the human eye only a luminance (in the unit candela / m2) can measure directly, but not the specified in the unit Lux illuminance. The relationship between the luminance and the illuminance is the so-called reflection factor. If this is considered to be constant, as assumed below, then the signal can be output in lux. However, this lux value is then not regarded as an absolute value but rather correctly represents a value proportional to the illuminance. In this sense, the following Lux values are to be understood.
    For the reasons mentioned above, it is therefore essential to extend the dynamic range of the image sensor, which is achieved according to a preferred embodiment of the invention by the procedure described in more detail below. In this case, a so-called high-dynamic-range (HDR) image, ie an image with increased dynamic range, is determined from a series of "usual" so-called low-dynamic-range (LDR) images acquired by the image sensor 10. Alternatively, however, there are also other possibilities for obtaining an HDR image, as will be explained in more detail later.
    An exposure series consists of two or more images of the same scene, taken with different exposure settings. It is optional whether the sensor sensitivity, aperture and / or exposure time is varied. From the different images it is then possible to calculate pixel by pixel an image with an increased contrast range. For example, in a whole overexposed photograph, dark spots are correctly exposed, but in underexposed images, correct exposure to bright spots, such as light, is difficult. Clouds in the sky or the like in front. In this way image parts are found in each exposure stage, which ultimately contribute together to the HDR image.
    Several methods of prior art are known for merging the images of different exposure levels. Basically, all known methods are based on linearizing the pixel brightness values, scaling them to the new output range, and weighting them according to the correctness of the measured value. The main difference lies in the choice of the weighting function. Various possibilities for this are described, for example, in Fode A: Robust Generation of High Dynamic Range Images (Diploma Thesis), Institute for Computational Visualistics, University of Koblenz Landau, 2004. According to a preferred embodiment of the invention, a simple weighting function optimized with regard to an implementation on the microcontroller is used, as will be described in more detail below.
    A prerequisite for obtaining an HDR image in the context of the present combination of multiple LDR images relates to the immobility of the scene and camera during the exposure sequence. Most of the shots of an exposure series are made in quick succession. In order that the individual pixels from the different recordings can be correctly offset with each other, they represent the same picture point with different brightnesses, should neither the scene change nor the camera move. Any change of any of these factors worsens the result. Since time immutable scenes are rather rare, methods have been developed that can tolerate Ver¬ changes in the individual images, with appropriate procedures are again known from the above-mentioned prior art. Such supplementary measures may possibly be used, although for the present field of light measurement the prerequisites for recording HDR images are relatively good since the image sensor is permanently mounted, usually on the ceiling, and is predominantly stationary Scene photographed. Smaller movements in the picture, e.g. Bäu¬men or clouds, but also by passing people in this case can be accepted as Störgrö¬.
    Cameras or the image sensors of digital cameras usually do not display brightness values in an ideal linear manner. These nonlinearities are intentionally inserted by the manufacturer, in order to convey a, as natural as possible, impression of the image matched to the receiving chip. For the HDR image calculation, however, linear brightness values are needed in order to be able to compose the individual exposure levels. The curve shape of the so-called camera curve usually has a so-called gamma curve-like course in order to adapt the original linear brightness signal of the image sensor to the logarithmic brightness sensation of the human eye. In this case, the curve of the camera curve can also deviate from an ideal gamma curve and be differently configured for each color component. If the image information is output by the image sensor 10 in this modified manner, it is first necessary to correct the values in order to obtain the desired linear shape again, which can generally be achieved by means of an inverse gamma function.
    After determining the linear brightness values of the image data, these are then scaled appropriately to the target area. Each exposure level requires its own scaling factor, which allows the values to be comparable and to be netted with each other.
    For example, the data is mapped to the lux scale, with the following example assuming that the data of the LDR images is in the form of a 8-bit / pixel black-and-white image while the HDR target image is 16-bit / pixel should. The minimum value is set at 0 lux and the maximum value at 10000 lux, resulting in a brightness resolution of 0.15 lux / digit. Observations have shown that even though the total output value of the sensor has not yet reached its maximum of 5000 lux, individual parts of the image can clearly turn bright above 5000 lux. A maximum value of 10000 lux accordingly represents a practicable size, where appropriate, the range can also be adjusted accordingly.
    Experiments have shown that for an exposure series three exposure levels, separated by two so-called F-stops (the difference from one exposure level to the next is referred to as F-stop or often referred to as a time step or aperture step) are sufficient to increase the dynamic range expand so that the desired measurement range can be covered. In the individual exposure steps, due to the previously performed scaling, image parts whose measured values overlap are found. It must therefore be decided which measurement is taken in the finally formed HDR image. This can be done in accordance with a preferred embodiment of the invention, shown schematically in Figure 4, by the use of several piecewise continuous weighting functions, which are multiplied by the measurements of the LDR images. The weighting functions of the LDR images overlap in the transition areas, and it must be ensured that the total weighting factor is 1 at each location. The scaling factors and weighting functions depend on the image sensor used and can be determined experimentally, for example, by calibrating the finally obtained brightness signal with the signal of a classical brightness sensor.
    The complete process of HDR image calculation is schematically summarized in FIG. The frames of the exposure steps are thus first subjected to a gamma correction (S101), converted to gray values (S102) and scaled to the target resolution of 40x30 pixels (S103). The resulting 8-bit brightness images then preferably undergo a filtering (S104) in which values at the edges of the range of values are taken out. Since the scaling is done by averaging the pixels of the high-resolution image, it can be assumed that many information from overexposed and underexposed pixels are contained in the boundary values. These distort the reading since the size of the mis-exposure is unknown. For example, values 32 and 224 are used as threshold values for the filtering. In the following step (S105), the brightness values are scaled to the lux scale and finally (S106) weighted is entered into the HDR image via the appropriate function. This eventually results in a 16-bit HDR image from several 8-bit gray scale images, which forms the starting point for the subsequent optimization algorithm.
    Figure 6 shows, by way of example, combining three LDR images into a 16-bit HDR image. Three Gaust images formed at different exposure levels are shown in the reduced resolution of 40x30 pixels which are eventually combined into the HDR image shown on the right. This includes brightness information over the entire desired range of 0 lux to 10,000 lux.
    As already mentioned above, however, the desired HDR image could also be maintained elsewhere. For example, image sensors could be used in which the described method of combining a plurality of LDR images is already internally implemented and which, accordingly, already output an HDR image on its own. Furthermore, image sensors with a logarithmic characteristic may also be used, or several image sensors are used which at the same time record the same subject with different exposure settings, in which case the images of the sensors are again combined into an HDR image.
    With the aid of the HDR image obtained in the manner described above, the brightness can therefore be determined at any point of the region detected by the image sensor over the full range of measurement. For the desired lighting control, however, it is necessary to produce from this a single brightness value, which corresponds to the signal of a light sensor mounted correctly above the window. That is, the brightness values of the various pixels can not be easily averaged but must be properly weighted in their relevance to the desired luminance signal, so that interfering elements located in the field of view of the image sensor are excluded from the evaluation or at least reduced in their influence on the measurement result.
    In order for the brightness sensor according to the invention to be able to recognize the relevance of the different image areas or pixels independently within the framework of a suitable algorithm, the following assumptions are made: The searched-for measured value becomes - Main - presented by the pixels of the window surface re¬. This corresponds to the assembly instructions of the classic LSD light sensor, which may only look at open window surfaces.
    · "Similar light components behave the same way". This means that pixels representing daylight components follow more or less the course of the (total) daylight. Pixels, on the other hand, which are exposed to artificial light, instead follow the artificial light curve.
    By observing the pixel values over a relatively long period of time, the individual light classes can be isolated from one another.
    Based on these considerations, according to the invention, a weighting matrix can be created with the aid of which a correct measurement signal can be obtained. The special feature lies in the fact that the sensor unit is able to independently optimize this weighting matrix. For this purpose, the brightness profile of the images detected by the image sensor 10 is observed over a relatively long period of time. The observation of the course of brightness over a longer period of time shows which image components are subject to strong changes and which image components remain relatively constant.
    The calculation of the correct measured value on the basis of an HDR image then takes place via the already mentioned weighting matrix. A value is stored per pixel of the image in this matrix, with which the respective brightness value of the associated pixel of the HDR image is multiplied. The sum of these products then gives the total measured value, which is why the sum of all elements of the weighting matrix should always be 1. Otherwise, the weighted pixel brightness values must be summed again and the corresponding result normalized.
    In the case of the preferred embodiment, each element or entry of the weighting matrix is initialized with the value 1 / (number of pixels). At 40x30 pixels image size is 1/1200 or 0.000833. Thus, all pixels are equally weighted and an application to the HDR image initially corresponds to averaging.
    The algorithm described below, which starts from the above-formulated assumption of the similar behavior of similar light components, is then applied to this initial form of the weighting matrix. This algorithm has the following steps: Step 1: Initialize weighting matrix (factor = 1/1200 in the present example).
    Step 2: Determination of the gradient of the brightness change of the overall image (relative to the previous image); that is, it determines how the overall brightness of the image has changed.
    Step 3: Loop over all pixels i) Comparison of the pixel gradient, ie the change of the pixel brightness with the overall gradient • if the pixel gradient is close to the overall gradient (between A * gradient and B * gradient): good pixel, ie Increase pixel weighting (by factor C); Otherwise: bad pixel: decrease weighting (factor D); ii) restrict minimum and maximum pixel weight so that individual pixels can not attract the entire weight and low weighted pixels have a chance to regain weight if they represent "good" daylight - maximum weight: E, mini-weight: F.
    Step 4: normalize weighting matrix (set the total weight of the matrix to 1).
    The variables A to F mentioned in the above algorithm can be chosen within certain limits, with the following example values having shown to work well: A: 0.1 B: 10 C: 1.1 D: 0.91 E: 0.003 (or 0.3%) F: 0.00001 (or 0.001%) [0066] In particular, the third point of the algorithm is based on the above formulated assumptions with regard to the similar behavior of the light components. If the change in a pixel value corresponds approximately to the change in the total brightness, then it can be assumed that the corresponding image area is influenced by the daylight and, accordingly, should be taken into greater consideration overall when creating the brightness signal. Image areas or pixels, on the other hand, are different behavior, eg are affected by the artifact or otherwise, behave differently and are therefore reduced in their contribution to the total brightness value.
    It has been found that as an alternative to comparing the brightness change of a pixel with the total brightness change, it would also be possible to compare the current pixel brightness value with the change in the total brightness. In this case, point 3 of the optimization algorithm described above changes as follows: Step 3 (alternative): loop over all pixels i) comparison of the current pixel value with the gradient • if the pixel value is NOT in the vicinity of the gradient (between A * gradient and B * gradient): good pixel, ie Increase pixel weighting (by factor C); Otherwise: bad pixel: decrease weighting (factor D); ii) restrict minimum and maximum pixel weight so that individual pixels can not attract the entire weight and low weighted pixels have a chance to regain weight if they represent "good" daylight - maximum weight: E, mini-weight: F.
    Again, the above example values for the variables A to F are well suited for performing the algorithm, although other values would be conceivable.
    The efficiency of the optimization algorithm according to the invention can be seen from the various illustrations of FIGS. 7a to 7h, which illustrate the structure of the weighting matrix over several hours. It can easily be seen that the window areas of the image captured by the image sensor excite after a comparatively short time. The initial weightings on the inner walls will be further reduced over time. After a few days, mis-weighted locations are almost completely faded and the window areas of the captured image are uniquely weighted in determining the brightness signal.
    It has been shown that the time interval between the individual recordings plays a rather minor role in the formation of the weighting matrix. In the example shown, the images were created and evaluated at 5 minute intervals. However, intervals of one minute or only 20 seconds lead to a comparable or the same result. In addition, it has also been shown that the start time does not affect the
    However, the optimization algorithm has the result that, after a certain adjustment phase, the sensor according to the invention can independently subdivide the data acquired by the image sensor into daylight-relevant information and irrelevant information on the basis of the relevant data Information to a signal representing the daylight erstel¬len.
    It should be mentioned that the two described variants of the optimization algorithm represent only two exemplary embodiments by means of which the sensor is put into the position to distinguish daylight-relevant information from daylight-irrelevant information. However, other algorithms would also be conceivable with the aid of which the evaluation of the information captured by the image sensor can be carried out in order to identify those areas which are characteristic of the daylight. For example, it would also be possible to use algorithms for object recognition, in which case the associated computational effort would possibly be higher here.
    It should also be mentioned that - in the case of the use of color image sensors - in addition to a brightness signal, the controller could also output color information or color temperature information. In this case, it would be conceivable, in particular, for the controller to fall back on the findings obtained in the context of the optimization algorithm and to create color information or color temperature information representing the daylight on the one hand, and color information or color temperature information representing the artificial light on the other hand. For this purpose, for example, a threshold value for the values of the weighting matrix could be defined in order to distinguish between image regions attributable to daylight and image regions attributable to artificial light. The two color or color temperature information items are then respectively determined using the ent ¬responding image areas determined. Alternatively, as in the calculation of the brightness signal, the entire image could also be used, the color information then being multiplied once again with the entries of the weighting matrix with respect to the daylight, and inverted entries of the weighting matrix used in the calculation of the information relating to the artifact, so that Now those areas that are less affected by daylight, have a stronger impact on the result.
    The two color or color temperature information can then be used, for example, in the lighting control as the set value (color of the daylight) and as the actual value (color of the art light) to adjust the artificial light depending on the color of the daylight. Again, this results in the advantageous effect that all necessary information is provided by the sensor unit according to the invention and beyond this, no further sensor is required.
    Finally, with the aid of the present invention, a sensor is finally created for the first time which is enabled to independently evaluate the information detected by an image sensor in order to be able to establish a brightness signal representing the daylight based thereon. The sensor can preferably replace existing or used daylight sensors without additional measures, however, with regard to the type of mounting and mounting has a significantly higher flexibility, which leads to significant improvements in many applications.
    权利要求:
    Claims (22)
    [1]
    Claims 1. A sensor unit (100) for determining control information for use in daylight-dependent light control, comprising: an image sensor (10) for acquiring digital image information and a controller (20) for evaluating the image information and creating a brightness signal representing daylight; characterized in that the controller (20) is designed to take into greater account when creating the brightness signal Bildbe¬reich the captured by the image sensor image information, which are more affected by the Ta¬licht.
    [2]
    2. Sensor unit according to claim 1, characterized in that the controller (20) is adapted to independently recognize image areas which are more strongly influenced by the daylight.
    [3]
    3. Sensor unit according to claim 2, characterized in that for generating the brightness signal, brightness information, in particular brightness values of individual image areas, are multiplied by a weighting factor, wherein the controller (20) is designed to determine the weighting factors for the image areas independently.
    [4]
    4. Sensor unit according to claim 3, characterized in that the controller (20) is adapted to determine the weighting factors for the image areas in the context of an optimization algorithm.
    [5]
    5. Sensor unit according to claim 4, characterized in that in the context of the optimization algorithm, the brightness information of two temporally successively detected images are compared.
    [6]
    A sensor unit according to claim 5, characterized in that the controller (20), within the optimization algorithm, raises the weighting factor a) associated with an image area if the temporal change of the brightness information of that image area is similar to the current change in the overall brightness of the image, b) reduces if the temporal change of the brightness information of this image area is not similar to the current change in the overall brightness of the image.
    [7]
    A sensor unit according to claim 5, characterized in that the controller (20), in the optimization algorithm, raises the weighting factor a) associated with an image area if the brightness information of that image area is not similar to the temporal change in the overall brightness of the image, b) reduces if the brightness information this image area is similar to the temporal change in the overall brightness of the image.
    [8]
    8. Sensor unit according to claim 6 or 7, characterized in that the weighting factors are changed only within a predetermined range.
    [9]
    9. Sensor unit according to one of the preceding claims, characterized in that the controller (20) is adapted to additionally at least one Farbinformationbzw. provide color temperature information regarding the data captured by the image sensor.
    [10]
    10. Sensor unit according to claim 9, characterized in that the controller (20) is adapted to provide a first, the daylight representing color information or color temperature information and a second, the artificial light reprä¬sentierende color information or color temperature information available.
    [11]
    A sensor unit according to any preceding claim, characterized in that the controller (20) reduces the resolution of the image captured by the image sensor and performs the optimization algorithm based on the reduced resolution images.
    [12]
    12. Sensor unit according to one of the preceding claims, characterized in that the controller (20) is adapted to combine a plurality of different exposure levels of the image sensor (10) detected images to form an image with increased brightness Dyna¬¬mik and the optimization algorithm Base the images with increased brightness dynamics.
    [13]
    13. A method for determining control information for use in a daylight-dependent lighting control, comprising the following steps: acquiring digital image information using an image sensor and evaluating the image information and creating a daylight representing brightness signal, characterized in that when creating the brightness signal image areas of the image information acquired by the image sensor, which is more influenced by the daylight, is taken more into account.
    [14]
    14. The method according to claim 13, characterized in that for generating the brightness signal, brightness information, in particular brightness values of individual image areas, are multiplied by a weighting factor.
    [15]
    15. The method according to claim 14, characterized in that the weighting factors for the image areas are optimized in the context of an optimization algorithm.
    [16]
    16. The method according to claim 15, characterized in that in the context of the optimization algorithm, the brightness information of two temporally successively detected images are compared.
    [17]
    17. Method according to claim 16, characterized in that within the scope of the optimization algorithm the weighting factor a) associated with an image area is raised if the temporal change of the brightness information of this image area is similar to the temporal change of the overall brightness of the image, b) is reduced is, if the temporal change of the brightness information of this Bildbe¬reichs the temporal change of the overall brightness of the image is not similar.
    [18]
    18. Method according to claim 16, characterized in that within the scope of the optimization algorithm the weighting factor a) associated with an image area is raised, if the brightness information of this image area is not similar to the temporal change of the total brightness of the image, b) is reduced, if the brightness information of this image area is similar to the temporal change in the overall brightness of the image.
    [19]
    A method according to claim 17 or 18, characterized in that the weighting factors are changed only within a predetermined range.
    [20]
    20. The method according to any one of claims 13 to 19, characterized in that in addition at least one color information or a color temperature information bzgl.der the data detected by the image sensor is provided, preferably a first, the daylight representing color information or Farbtemperaturinformati¬on and a second, the artificial light representing color information or Farbtempera¬turinformation be provided.
    [21]
    21. The method according to any one of claims 13 to 20, characterized in that the resolution of the image detected by the image sensor is reduced and the optimization algorithm is carried out on the basis of the images with reduced resolution.
    [22]
    22. The method according to any one of claims 13 to 21, characterized in that a plurality of different exposure levels of the image sensor (10) captured images are combined into an image with increased brightness dynamics and the optimization algorithm based on the images with increased brightness dynamics performed , For this 7 sheets drawings
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    DE102014201652A1|2015-07-30|
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    法律状态:
    2018-10-15| MM01| Lapse because of not paying annual fees|Effective date: 20180228 |
    优先权:
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    DE102014201652.5A|DE102014201652A1|2014-01-30|2014-01-30|Self-adjusting sensor for detecting daylight|
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