PROCEDURE OF EVALUATION OF THE CLARITY OF A MATERIAL, ESPECIALLY NON-HOMOGENEOUS

Abstract

The invention relates to a method for evaluating the clarity of a material, such as a lignocellulosic material, comprising the steps of: - providing a sample of said material, - partially backlighting the sample with a light source so as to form on said sample an unlit area, an illuminated area, and an edge separating the unlit and illuminated areas, - acquiring a first digital image, by an image sensor, of the partially backlit sample, characterized in that it further comprises the steps of: - generating a digital image corrected by a function for homogenizing the brightness level of the illuminated area of ​​said first digital image, and - determining a rise distance from the corrected digital image to evaluate the clarity of said material. Figure for the abstract: Fig. 1

Description

Title of the invention: METHOD FOR EVALUATING THE CLARITY OF A MATERIAL, IN PARTICULAR NON-HOMOGENIC TECHNICAL FIELD OF THE INVENTION

[0001] The present invention relates to the field of optical characterization of materials intended for use, for example, in luminous display devices.

[0002] According to a preferred application, the invention is described below with reference to the optical characterization of so-called "non-homogeneous" materials, such as lignocellulosic materials. It should be noted, however, that the invention is applicable to any other material, in particular homogeneous materials, whether diffusive or not. PRIOR TECHNOLOGY

[0003] In various industrial fields, particularly the automotive industry, the readability of human-machine interfaces (illuminated pictograms, screens, etc.) is of paramount importance, especially when it comes to signaling an alert.

[0004] Manufacturers of such interfaces therefore pay particular attention to the sharpness (referred to as resolution for screens) of the displayed information and ensure that it is legible under all circumstances. This involves, among other things, controlling the sharpness of this information, which typically defines patterns or images.

[0005] In the case of screens, the resolution of the displayed information is primarily related to the size of the pixels used. For luminous display devices, such as illuminated pictograms, sharpness is more closely related to the architecture of the device, particularly the materials used and the arrangement of the device's constituent layers.

[0006] The use of diffusive materials, particularly for aesthetic or camouflage purposes, to cover illuminated pictograms can degrade the sharpness of these pictograms. In particular, the edges of the pictogram may appear blurry due to the diffusive nature of the material, thus impairing its legibility.

[0007] Known methods exist for evaluating sharpness by assessing clarity. Clarity corresponds to the notion of "clarity" in English and qualitatively defines the sharpness of light information, such as a pattern or image, when passing through a material.

[0008] A material with low clarity will tend to degrade the sharpness of the display device, while a material with high clarity will have only a minimal, or even no, impact on sharpness.

[0009] Among the known methods for evaluating clarity, the so-called rise distance method is classically used to provide a quantitative evaluation of the capability of a system, such as a camera or A camera can reproduce the fine details and edges of a digital image. This method is also used to quantitatively assess the clarity of a material.

[0010] In this context, the rise distance method requires the provision of a digital image 11, as illustrated in [Fig.1], representing a material backlit by a light source and a mask partially occulting the light source so that the digital image 11 includes an unlit part 12, an illuminated part 13 and an edge 14 separating the unlit part 12 and the illuminated part 13.

[0011] The digital image typically consists of a grid of pixels aligned in columns and rows. The edge 14 is generally aligned with the columns or with the rows to facilitate the determination of an edge spread function, as illustrated in [Fig.2] in graphical form.

[0012] The edge spreading function here represents the light intensity of a line of the digital image 11 (referenced II-II on [Fig.1]) as a function of a distance, which is usually expressed in millimeters.

[0013] The rise distance Dm is the distance over which the edge spreading function goes from a predefined lower threshold of light intensity Sinf, typically equal to 10% of the maximum light intensity on the digital image 11, to a predefined upper threshold of light intensity Ssup, typically equal to 90% of the maximum light intensity on this digital image 11.

[0014] In practice, a rise distance is determined for each line (or column, depending on the edge angle) of the digital image, and an average of these rise distances is calculated. It should be noted that the greater the rise distance, the less sharp the image will be when viewed through the material.

[0015] The material represented by the digital image 11 in [Fig. 1] is a non-homogeneous material, here a lignocellulosic material exhibiting local variations in transmittance or diffusion. Due to these variations, the brightness level varies within the illuminated area 13, which generates noise in the edge spreading function of certain lines. The edge spreading functions can then vary significantly from one line to another, particularly when the brightness level varies near the edge 14.

[0016] When the edge spreading functions are perturbed, as shown in [Fig. 2], the light intensity thresholds Sinf and Ssup can be reached at random points, which makes it impossible to reliably evaluate the material's brightness. The rise distance Dm determined on the basis of these edge spreading functions 14 is not representative of the material's actual brightness.

[0017] Thus, although the climb distance method is relatively effective for homogeneous materials, such as glass, it does not give reliable results for non-homogeneous materials, such as lignocellulosic materials. Description of the invention

[0018] The present invention aims to remedy all or part of the disadvantages of the prior art mentioned above.

[0019] To this end, the present invention relates, according to a first aspect, to a method for evaluating the clarity of a material, such as a lignocellulosic material, comprising the steps of: - provision of a sample of said material, - partial backlighting of the sample by a light source so as to form on said sample an unlit part, an illuminated part and an edge separating the unlit part and the illuminated part, - acquisition of a first digital image, by an image sensor, of the partially backlit sample. The process also includes the following steps: - generation of a digital image corrected by a function for homogenizing the brightness level of the illuminated part of said first digital image, and - determination of a rise distance from the corrected digital image to evaluate the clarity of said material.

[0020] The term "sample" means a portion of material whose clarity is to be evaluated or a sub-part of that portion.

[0021] The term "partial backlighting" means backlighting a part of the sample so that a brightness contrast between the unlit and lit parts formed on the sample is sufficient to be detected by the image sensor.

[0022] The term "brightness level homogenization function" means an image processing or post-processing adapted to even out the brightness level over at least one area of ​​a digital image.

[0023] In the evaluation method according to the invention, the rise distance method is not applied directly to a digital image representing a partially backlit sample (the first digital image), but to a (corrected) digital image generated from said first digital image and in which the brightness level of the illuminated part is homogenized.

[0024] By homogenizing the brightness level in the illuminated area, it is possible to mitigate any local variations in the transmittance or diffusion of the material while preserving the clarity information at the edge. In this way, the method makes it possible to evaluate the clarity of a material, including a non- homogeneous, taking advantage of both the ease of implementation and the reliability of the climb distance method.

[0025] The material may be a non-homogeneous material. In particular, the material may be a lignocellulosic material. Such a lignocellulosic material may, for example, be partially delignified and impregnated with a resin. The material may also be wood, in particular natural wood, artificial wood, or plastic-coated wood. The material may also be a plastic material, for example, a plastic film. The plastic film may be coated with a printing layer, for example, a printing layer giving a wood-like appearance. The material may also be glass, in particular diffusing glass, such as frosted glass or glass with colloids. The material may be paper. The material may be a composite material, for example, glass-reinforced plastic. The material may be formed by a combination of several of the materials mentioned, in particular by an assembly of layers of different materials.

[0026] In a preferred embodiment, the step of generating a corrected digital image includes a substep of fully backlighting the sample and a substep of acquiring a second digital image, by the image sensor, of the fully backlit sample, the corrected digital image being generated from the first and second digital images. The use of a second digital image, in addition to the first digital image, to generate the corrected digital image improves the accuracy of the results obtained by the climb distance method.

[0027] The function for homogenizing the brightness level of the illuminated area can consist of dividing the characteristic information for each pixel at a given position in the first digital image by the same information for each pixel at the same given position in the second digital image, said characteristic information being representative of the brightness. Determining representative brightness information for each pixel in the first and second digital images, as well as dividing this information, is relatively simple to implement and does not require complex processing. The corrected digital image can thus be generated quite quickly, on the order of a second or even less. Furthermore, this method provides satisfactory homogenization of the brightness level in the illuminated area without disturbing the clarity information at the edge.

[0028] The characteristic information of the pixel representing brightness can be luminance. Luminance measures the light intensity perceived by the human eye on a given surface. It allows for a rigorous evaluation of the material's clarity.

[0029] The step of generating a corrected digital image may further include a substep of digitally aligning the first digital image and the second digital image. The spatial alignment between the first digital image and the A second digital image helps to limit noise that could be generated by a slight misalignment between these digital images. Digital alignment can be performed using an image alignment algorithm. Furthermore, digital alignment can only be implemented if mechanical alignment is not correctly achieved, for example, if there is a misalignment greater than one pixel.

[0030] The method may further include, after the determination step, a step for correcting the rise distance by subtracting a reference rise distance from the rise distance determined in the determination step. The reference rise distance can be determined from a first digital reference image representing the light source partially illuminating it so as to form an unilluminated area, an illuminated area, and an edge separating the unilluminated area from the illuminated area. Since the image sensor used is not perfect, the rise distance determined in the determination step may sometimes be overestimated. The rise distance correction step thus makes it possible to overcome inaccuracies due to the physical limitations of the image sensor. The value obtained allows for an even more precise evaluation of the material's clarity.

[0031] The first digital image may comprise several distinct edges, the determination step being implemented for each edge, and the evaluation process further comprising, after the determination step, a step of calculating an average of the rise distances measured for each edge. Having several distinct edges makes it possible to evaluate the clarity at different locations on the sample and to estimate, via the averaging step, an average clarity value for the material. It is then possible to evaluate the overall clarity of a material.

[0032] The invention also relates, according to a second aspect, to a use of the method as described above for controlling the brightness of a display device comprising a material backlit by a light source and a mask situated between the material and the light source so as to partially obscure the light source. The material may be non-homogeneous, such as a lignocellulosic material.

[0033] The invention also relates, according to a third aspect, to a device for evaluating the clarity of a material. This evaluation device has advantages similar to those described above in relation to the evaluation method. The device comprises a sample of said material, a light source configured to back-illuminate, in particular partially and / or totally, the sample, an image sensor configured to provide digital images of said sample, and a digital processing device configured to implement the evaluation method according to the first aspect of the invention, using digital images acquired by the image sensor.

[0034]

[0035]

[0036]

[0037]

[0038] The material sample can be a sample of non-homogeneous material, such as a lignocellulosic material, and preferably wood. The sample may have a general plate shape with two main opposing faces. The light source can be a flat surface that diffuses light evenly. In this case, one of the main faces of the material sample can be placed against the flat surface of the light source. This allows for even backlighting of the sample and facilitates easy sample positioning, as the light source can then serve as a support. BRIEF DESCRIPTION OF THE FIGURES Other advantages, purposes and particular features of the present invention will become apparent from the following non-limiting description of at least one particular embodiment of the devices and methods of the present invention, with reference to the accompanying drawings, in which: • [Fig.1] illustrates a digital image representing, in a manner known in the prior art, a sample of a non-homogeneous material partially backlit by a light source so as to form on the sample an unlit part, an illuminated part and an edge separating the unlit part and the illuminated part; • [Fig.2] represents, in a manner known in the prior art, a spreading function of the edge of the first digital at the level of a line ILII represented on [Fig.l]; • [Fig.3] represents, according to a schematic three-dimensional view, one embodiment of a device for evaluating the clarity of a material; • [Fig.4] is an exploded view of a control assembly of the device in [Fig.3]; • [Fig.5] represents, in the form of a synoptic diagram, the steps of a method of implementing a process for evaluating the clarity of a material; • [Fig.6] illustrates a second digital image representing the sample from [Fig.1] backlit totally by the light source; • Figure 7 illustrates a corrected digital image generated from the first digital image in Figure 1; and • [Fig.8] represents a spreading function of an edge of the digital image corrected at the level of a line VIII-VIII shown on [Fig.7]. DETAILED DESCRIPTION OF THE INVENTION Figures 3 and 4 depict a device 1 for evaluating the clarity of a material according to an embodiment of the invention. The device 1 mainly comprises a control assembly 2 comprising a sample 4 of a material and a digital image sensor 3 configured to acquire images of the control assembly 2 or of an area of ​​that assembly.

[0039] The device 1 optionally includes a digital processing device 20 configured to digitally process images acquired by the image sensor 3, according to a method described later in the description.

[0040] The control assembly 2 includes a light source 5 configured to back-illuminate the sample 4. The light source 5 is located opposite the image sensor 3 with respect to the sample 4. Thus, the light emitted by the light source 5 passes through the sample 4 before being received by the image sensor 3. The sample 4 has a general plate shape whose thickness is such that at least a portion of the light emitted by the light source 5 is transmitted through the sample 4. The maximum thickness of the sample 4 depends in particular on the level of light transmission and scattering in the material.

[0041] The light source 5 is located on the side of a first principal face 6 of the sample 4 in order to illuminate by transmission a second principal face 7, opposite the first principal face 6. The light source 5 here presents a flat surface that is homogeneously illuminated. This ensures that any variations in brightness on the second principal face 7 result solely from local variations in transmittance or diffusion within the material of the sample 4.

[0042] By way of example, the light source 5 is an LED panel. The homogeneity of the light source 5 can be further improved by covering its flat surface with a diffusing film, such as a translucent layer of plastic material (PMMA, PET).

[0043] In the illustrated embodiment, the device 1 includes a mask 8 intended to be disposed between the sample 4 and the light source 5. The mask 8 serves to partially occult the light source 5 (i.e. only a part of the surface of the light source) in order to form, on the second main face 7, an unlit (occulted) part in which at least a part of the light rays emitted by the light source 5 is absorbed by the mask 8, an illuminated (unocculted) part in which the light rays are transmitted by the sample 4, and an edge separating the unlit and illuminated parts.

[0044] As can be seen in [Fig. 4], the mask 8 comprises at least one occultation zone and at least one transmission zone. The mask here comprises six transmission zones, designated 8a. Each transmission zone 8a is separated from an occultation zone by at least one edge, which is preferably straight. Each transmission zone here has a square shape, thus forming four edges.

[0045] The occultation zone can be opaque so as not to transmit any rays luminous emitted by the light source or generally opaque so as to transmit a portion of the light rays, which portion is relatively small compared to the portion of the light rays transmitted by the transmission zone. Each transmission zone 8a can be transparent or semi-transparent.

[0046] In the illustrated example, the mask 8 is produced by printing on a transparent or translucent plate, so as to define at least one occultation zone and at least one transmission zone. The transmission zone is, for example, devoid of a printing layer. The printing can be by screen printing, inkjet printing (with one or more layers), physical deposition of a material, for example chromium, in vapor phase, or by microlithography.

[0047] The mask 8 is preferably located as close as possible to the first main face 6 of the sample 4. The mask 8 can, for example, be in contact with the first main face 6 of the sample 4. This makes it possible to limit the diffusion of light rays in the sample 4, and to display on the second main face 7 an edge that is as sharp as possible.

[0048] Since the mask 8 is generally only used to evaluate the clarity of the material of the sample 4, the device 1 includes a temporary holding means 10 configured to temporarily hold the mask 8 in contact with or as close as possible to the first main face 6 of the sample 4. The mask 8 can thus be removed after being positioned to occult the light source 5. This avoids in particular permanently fixing a mask on a sample whose clarity turns out not to be suitable for a display device.

[0049] It is also preferable that the position of the sample 4 relative to the image sensor 3 be maintained, for example when the mask 8 is positioned or removed, between different acquisitions by the image sensor 3. The device 1 includes in this respect a means for maintaining the sample 4 in position relative to the image sensor 3, such as a lateral edge against which the sample 4 rests, or an adhesive that fixes the sample 4 to a support fixed relative to the image sensor 3. A slight displacement of the sample 4 relative to the image sensor 3 may nevertheless occur.

[0050] To prevent such displacement from affecting the evaluation of the material's clarity, the evaluation device 1 preferably includes an alignment marker 9 placed on the sample 4 to be detected by the image sensor 3. The alignment marker 9 comprises, for example, two distinct opaque patterns fixed to the second principal face 7 of the sample 4 so that both are detected by the image sensor 3. The patterns can be of different shapes and sizes, and are here represented as squares arranged at opposite corners of the sample 4. The alignment marker 9 can be fixed by gluing or clamping, in particular to the using known clamps or fasteners.

[0051] As can be seen in [Fig. 4], the alignment marker 9 preferably includes a transparent support on which the patterns are arranged. This makes it easier to position the alignment marker 9 on the sample 4.

[0052] In an unillustrated embodiment, the positioning marker consists of two distinct motifs fixed directly, for example by gluing, onto the second main face of the sample. In this embodiment, the motifs may be opaque or generally opaque stickers.

[0053] The digital processing device 20 is configured to digitally align digital images acquired by the image sensor 3 from the alignment marker 9. The image sensor 3 is in fact configured to acquire a digital image of the second principal face 7 of the sample 4.

[0054] The digital processing device 20 is configured to implement, from digital images acquired by the image sensor 3, a method for evaluating the clarity of the material.

[0055] A particular embodiment of the method for evaluating 100 the clarity of the material of the sample 4 is shown in [Fig. 5]. The evaluation method 100 first comprises a step 110 of supplying the sample 4.

[0056] The evaluation method 100 includes a step of partially backlighting 120 the sample 4 by the light source 5. To backlight the sample 4, the latter is first positioned and held in a predefined position relative to the light source 5 by the temporary holding means 10. When the light source includes a flat illuminated surface, the sample 4 is for example held in contact with this surface.

[0057] The partial backlighting step 120 is implemented by partially occulting the light source 5 with a mask 8 so as to form on the sample 4, in particular on its second main face 7, an unlit (masked) part, an illuminated (unmasked) part and an edge separating the unlit part and the illuminated part.

[0058] The occlusion is implemented by positioning the mask 8 between the sample 4 and the light source 5. To do this, the sample 4 is, for example, moved away from the light source 5. Once the mask 8 is correctly positioned, it is held in a fixed position relative to the sample 4. The sample 4 is, for example, brought closer to the light source 5 via the temporary holding means 10 until the mask 8 is clamped between the sample 4 and the light source 5.

[0059] Following the partial backlighting step 120, the evaluation process 100 includes an acquisition step 130, via the image sensor 3, of a first digital image of the partially backlit sample 4.

[0060] The first digital image 11 acquired in acquisition step 130 is illustrated in the [Fig.l].

[0061] Sample 4 in this figure is a wooden board of sufficient thickness to render the sample translucent. The sample or material may, for example, be transparent, semi-transparent, or translucent. The intensity of the light source 5 can be adjusted according to the type of material or the thickness of the board so that the image sensor 3 detects sufficient light.

[0062] The first digital image 11 represents the second main face 7 of the sample and includes an unlit part 12, an illuminated part 13 and an edge 14 separating the unlit part 12 and the illuminated part 13.

[0063] In practice, the first digital image represents several distinct edges. Thresholding this first digital image makes it possible to identify and label the unlit and lit areas, and then to define a sub-image for each edge. Each sub-image is preferably centered on a respective edge and is then used in the subsequent steps of the process.

[0064] The unlit area 12 shown in the first digital image 11 is homogeneous and generally exhibits a black or near-black color. The lit area shown in the first digital image 11 is not homogeneous and displays color variations representative of variations in brightness. These variations result, for example, from local variations in transmittance or diffusion within the sample material 4. Since these variations are detrimental to the evaluation of the material's brightness, it is necessary to eliminate them while preserving the material's brightness information, in particular the brightness level along the edge 14.

[0065] To do this, the evaluation process 100 includes a generation step 140, by the digital processing device 20, of a digital image corrected by a function for homogenizing the brightness level of the illuminated part of said first digital image 11. The corrected digital image 16 is illustrated in [Fig.7].

[0066] The corrected digital image 16 comprises a dark area 17 corresponding to the unlit area 12 of the first digital image 11, a bright area 18 corresponding to the lit area 13 of the first digital image 11, and an edge 19 corresponding to the edge 14 of the first digital image 11. Unlike the lit area 13 of the first digital image 11, the bright area 18 of the corrected digital image 16 does not exhibit any variation in brightness level. In other words, the bright area 18 of the corrected digital image 16 is homogeneous, which allows for a reliable assessment of brightness, particularly using the rise-distance method.

[0067] In a preferred embodiment of the invention, the acquisition step 140 comprises a substep of total backlighting 141 of the sample 4. The re-step total backlighting 141 is implemented in a similar manner to the partial backlighting step 120 described previously.

[0068] The full backlighting step 141 is implemented by removing the mask 8 so that the light source 5 is no longer obscured by it. Removing the mask 8 can be done in the same way as positioning it for obscuration as described previously.

[0069] The evaluation method 100 then includes a substep of acquiring 142, by the image sensor 3, a second digital image 15, illustrated in [Fig.6], representing the totally backlit sample 4.

[0070] The second digital image 15 represents the second main face 7 of the sample 4 and includes a part (on the right) which is identical to the illuminated part 13 of the first digital image 11.

[0071] The substep of acquiring the second digital image can be implemented before the step of acquiring the first digital image. The evaluation process can then include, between the substep of acquiring the second digital image and the step of acquiring the first digital image, a step of positioning the mask instead of a step of removing the mask.

[0072] The first digital image 11 and the second digital image 15 each consist of a grid of pixels arranged in columns and rows, the number of pixels depending on the resolution of the image sensor 3. Each pixel contains information that is representative of the brightness, that is, varying according to the light transmitted by the material. The information representing the brightness is preferably the luminance. In this case, the image sensor 3 is calibrated so as to be able to determine the luminance. Of course, any other information proportional to the luminance, or behaving in the same way, can be considered.

[0073] The generation step 140 further includes, for each pixel at a given identical position (defined for example by a pair of x and y coordinates) of the first digital image 11 and the second digital image 15, a sub-step of determination 144 of a characteristic pixel information representative of the brightness.

[0074] The characteristic information associated with a pixel located in the unlit part 12 of the first digital image, for example, has a low value, in particular close to zero. Indeed, the brightness is low or even zero in this part since the light rays are blocked by the mask 8. Conversely, the characteristic information associated with a pixel located in the lit part 13 of the first digital image or with a pixel of the second digital image 15 has, for example, a value greater than zero.

[0075] Following determination substep 144, the evaluation process 100 includes a calculation substep 145 of a value of at least one pixel of the corrected digital image 16 by dividing the characteristic information of each pixel at said given position (x, y) of the first digital image 11 by the characteristic information of each pixel at said given position (x, y) of the second digital image 15. In particular, the luminance value for each pixel of the first digital image 11 is denoted Em(x,y), and Ev(x,y) for each pixel of the second digital image 15. The value of the pixels of the corrected digital image 16, denoted Rf(x,y), is generated according to the equation: Rf(x,y) = Em(x,y) / Ev(x,y).

[0076] By dividing the characteristic information associated with a pixel located in the unlit area 12 of the first digital image 11 by the characteristic information associated with a pixel located in the same position on the second digital image 15, the resulting value will be close to or equal to zero. Indeed, the information associated with the pixel of the second digital image 15 necessarily has a higher value than the characteristic information associated with the pixel of the first digital image 11, due to the absence of the mask 8. This digital processing thus makes it possible to reconstruct the unlit area 12 of the first digital image 11 on the corrected digital image 16.

[0077] Furthermore, by dividing the characteristic information associated with a pixel located in the illuminated portion 13 of the first digital image 11 by the characteristic information associated with a pixel located at an identical position in the second digital image 15, the value obtained will be close to or equal to one. Indeed, since the second digital image 15 represents the illuminated portion 13 of the first digital image 11, the characteristic information associated with each pixel for a given position is identical, provided that the images are correctly aligned. Thus, this determination substep 172 makes it possible to overcome the inhomogeneities of the illuminated portion 13 of the first digital image 11, while preserving the information on the clarity of the material.

[0078] If the sample 4 is not positioned in the same place on the first digital image 11 and on the second digital image 15, the value obtained may be different or far from one, which may impair the homogeneity of the bright part 18 on the corrected digital image 16. When the first digital image 11 and the second digital image 15 are not aligned, for example when there is a misalignment of at least one pixel, the evaluation process preferably includes a digital alignment substep 143 of these digital images 11, 15. This digital alignment substep 143 is implemented before the determination substep 144.

[0079] Digital alignment 143 is implemented, for example, using an image alignment algorithm, such as the ORB method (for "Oriented FAST and Rotated BRIEF (in English, FAST stands for "Features from Accelerated Segment Test" and BRIEF for "Binary Robust Independent Elementary Features"). The alignment algorithm 143 then uses the alignment coordinate system as a key point to align the first digital image 11 and the second digital image 15 with respect to each other. Of course, digital alignment can be implemented using any other known technique, and in particular the method known as "feature-based image alignment."

[0080] Following the generation step 140 of the corrected digital image 16, the evaluation process 100 includes a step 150 of determining the rise distance Dm2 from the corrected digital image 16. The rise distance Dm2 is determined by establishing a spreading function of the edge 19 of the corrected digital image 16.

[0081] Figure 8 represents a spreading function of the edge along line VIII-VIII in Figure 7. Unlike the spreading function of edge 14 illustrated in Figure 2, the spreading function of edge 19 is noise-free and corresponds to a spreading function of a homogeneous material. The rise distance Dm² can thus be reliably measured along the entire length of edge 19, making it possible to assess the clarity of the material along edge 19.

[0082] In order to evaluate the clarity of the material more globally, for example over a larger area of ​​the sample 4, the rise distance can be determined at different locations on the sample 4. In particular, the rise distance can be measured for several distinct edges of the mask located at different positions on the sample 4. This makes it possible to evaluate the clarity at different positions of the sample 4.

[0083] After determining the rise distances at different locations of the sample, the evaluation process may include a step of calculating the average of the rise distances measured for each edge, in order to obtain an average rise distance used to evaluate the overall clarity of the sample material.

[0084] To further improve the accuracy of the rise distance of an edge or the average rise distance, the evaluation method 100 preferably includes a correction step 160 of the rise distance determined directly from the corrected digital image 16.

[0085] The climb distance may indeed be overestimated due to measurement noise from the image sensor 3, which is generally not perfect. The climb distance is therefore likely to vary from one image sensor to another.

[0086] To eliminate this noise, a reference climb distance is determined from a corrected reference digital image, obtained in the same way as the corrected digital image 16 but in which sample 4 has been removed. In particular, the corrected reference digital image is generated from a first a digital reference image, representing the illuminated light source (without sample) and partially obscured by the mask so as to form on this digital image an illuminated part, an unlit part and an edge, and from a second digital image representing the unobscured light source.

[0087] In theory, in the case of a perfect image sensor, the reference rise distance should be zero since, in the first digital reference image, there is no material other than the mask between the light source and the image sensor. Since the image sensor 3 is generally not perfect, the reference rise distance allows us to measure the noise originating from this sensor.

[0088] To overcome this, and thus correct the climb distance measured in the determination step 150, it is necessary to subtract the reference climb distance from this climb distance.

[0089] It may also be envisaged, in order to obtain a corrected value of the average climb distance of sample 4, to subtract from the average climb distance an average reference climb distance.

[0090] The clarity evaluation method according to the invention is thus particularly well suited to reliably and accurately evaluate the clarity of non-homogeneous materials, such as ligno-cellulosic materials and in particular wood.

[0091] Clarity assessment makes it possible to assign a specific use to each material or sample based on its clarity. It also allows for the evaluation of material performance, particularly for the production of illuminated display devices. Furthermore, it enables the reliable and precise optical characterization of non-homogeneous materials, for example, those with diffusive properties, as well as illuminated display devices camouflaged by such materials.

[0092] The evaluation method can also be used to check the clarity of a display device comprising a non-homogeneous material backlit by a light source and a mask positioned between the material and the light source so as to partially obscure the light source. The light source of the illuminated display device is used to backlight the material whose clarity is to be evaluated. It is, of course, preferable that the illuminated display device be designed so that the mask can be easily removed or repositioned.

[0093] Variants not illustrated are described below.

[0094] In one variant, the mask is made directly on the sample, in particular by printing.

[0095] In another embodiment, the mask consists of a substantially opaque plate in which openings are provided to form the transmission zones. The mask may comprise a plate of vinyl, metal, plastic, or any other opaque or generally opaque material. The openings may be made by cutting, by laser scraping, by engraving, particularly laser.

[0096] In one embodiment, the light source is configured to partially backlight the sample and thus form on the sample an unlit part, an illuminated part and an edge separating the unlit part and the illuminated part.

[0097] In this embodiment, it is no longer necessary to position a mask between the light source and the sample to partially backlight the sample. This embodiment therefore eliminates the need for a mask. Naturally, the light source is then configured to partially illuminate (i.e., only a portion of the surface of the light source) or to fully backlight the sample. The light source can be a screen of the LCD, TFT, LED, OLED, or any other similar technology, with a portion of the screen illuminated and a portion of the screen turned off.

[0098] Thus, the partial backlighting step of the evaluation process is implemented by partially illuminating the light source, for example, by illuminating only a portion of the light source. In other words, the unlit portion, the illuminated portion, and the edge are formed directly by the light source, without the need to obscure said light source with the mask. The same applies to the determination of the reference rise distance.

[0099] In this variant, the total backlighting step of the evaluation process is implemented by total illumination of the light source, without the need to remove the mask since the latter is not necessary for occultation.

[0100] In one variant, the light source can be a video projector.

[0101] In one embodiment, the step of generating the corrected digital image is put into The work involves blurring at least the illuminated portion of the first digital image 11. This has the effect of homogenizing the brightness level of that portion. A brightness threshold can be established to distinguish between the unlit and illuminated portions.

[0102] A blur function can thus be implemented in various ways. For example, the blur function can be implemented, in particular, with a sliding blur kernel (or convolution matrix) of variable shape and dimension, or with various known blurring methods such as the mean, the median, or the Gaussian. This homogenization function is relatively simple to implement, but does not offer as much precision as the function described in the preferred embodiment.

[0103] Of course, the examples of implementation described above are by no means limiting.

Claims

Claims

1. Method for evaluating (100) the clarity of a material, such as a lignocellulosic material, comprising the steps of: - providing (110) a sample (4) of said material, - partially backlighting (120) the sample (4) by a light source (5) so as to form on said sample (4) an unlit portion (12), an illuminated portion (13) and an edge (14) separating the unlit portion (12) and the illuminated portion (13), - acquiring (130) a first digital image (11), by an image sensor (3), of the partially backlit sample (4), characterized in that it further comprises the steps of: - generating (140) a corrected digital image (16) by a function for homogenizing the brightness level of the illuminated portion of said first digital image (11), and - determining (150) of a rise distance (Dm2) from the corrected digital image (16) to assess the clarity of said material.

2. Method according to claim 1, in which the step of generating (140) a corrected digital image (16) comprises a sub-step of total backlighting (141) of the sample (4) and a sub-step of acquiring (142) a second digital image (15), by the image sensor (3), of the totally backlit sample (4), the corrected digital image (16) being generated from the first digital image (11) and the second digital image (15).

3. Method according to claim 2, in which the function of homogenizing the brightness level of the illuminated part consists of dividing characteristic information of each pixel at a given position of the first digital image (11) by said information of each pixel at said given position of the second digital image (15), said characteristic information being representative of the brightness.

4. Method according to claim 3, in which the characteristic information of the pixel representative of the brightness is the luminance.

5. A method according to any one of claims 2 to 4, wherein the generating step (170) further comprises a sub-step of digitally aligning (143) the first digital image (11) and the second digital image (15).

6. A method according to any one of claims 1 to 5, comprising in furthermore, after the determining step (150), a step of correcting (160) the rise distance by subtracting from the rise distance (Dm2) determined in the determining step (150) a reference rise distance, the reference rise distance being determined from a first reference digital image representing the light source (5) partially illuminating so as to form an illuminated part, an unilluminated part and an edge separating the unilluminated part from the illuminated part.

7. Method according to any one of claims 1 to 6, in which the first digital image (11) comprises several distinct edges (14), the determining step (150) being implemented for each edge (14), and the evaluation method (100) further comprising, after said determining step (150), a step of calculating the average of the ascent distances (Dm2) determined for each edge (14).

8. A method according to any one of claims 1 to 7, wherein said material is a non-homogeneous material, such as a lignocellulosic material, and preferably wood.

9. Use of the method according to any one of claims 1 to 8 for controlling the clarity of a display device comprising a non-homogeneous material backlit by a light source and a mask located between the material and the light source so as to partially obscure the light source.

10. Device for evaluating the clarity of a material, the device (1) comprising a sample (4) of said material, a light source (5) configured to backlight the sample (4), an image sensor (3) configured to provide digital images of said sample (4), and a digital processing device (20) configured to implement the evaluation method according to any one of claims 1 to 8, from digital images acquired by the image sensor (3).

Images