Method for determining the texture of an object and associated devices to aid in the visualization of surface roughness

Abstract

Method for determining the texture of an object and associated devices to aid in visualizing surface roughness. The present invention relates to a method for determining the texture of an object (12), the method being implemented by a computer (16) of a system for determining the texture of an object (12) and comprising a step of: - obtaining a set of images of the object (12), each image in the set being an image of the object (12) on a respective acquisition spectral band, each acquisition spectral band having at least one overlap with another acquisition spectral band, - selecting a reference image from among the images in the set obtained, - subtracting the reference image from an image obtained from the other image(s) in the image set, and - determining the texture image from the subtracted image. Figure for the abstract: Figure 1

Description

Title of the invention: Method for determining the texture of an object and associated devices to aid in the visualization of surface roughness

[0001] The present invention relates to a method for determining the texture of an object to aid in visualizing surface roughness. The present invention also relates to the devices involved in implementing such a determination method, namely a computer and a determination system.

[0002] In the field of geology, it is desirable to be able to know the geological composition of the subsoil in order to deduce in particular the presence of oil or gas reservoirs, fluid pressure, the presence of contaminants among other things.

[0003] For this purpose, it is known to analyze images taken on the surface to determine its texture.

[0004] The texture of a rock defines the size, shape and arrangement of the grains or crystals that compose it. This texture influences the surface roughness of the rock.

[0005] Knowledge of the texture thus makes it possible in particular to identify the presence of one or more specific minerals.

[0006] The determination of the texture is, in general, carried out by applying a statistical function to the image.

[0007] Typically, an entropy filter or a standard deviation filter is used.

[0008] All these functions are nevertheless complex to implement and, therefore, not very compatible with use at the image acquisition site.

[0009] There is therefore a need for a method of determining the texture of an object from images that is easier to implement.

[0010] To this end, the description relates to a method for determining the texture of an object, the method being implemented by a computer of a system for determining the texture of an object and comprising a step of:

[0011] - obtaining a set of images of the object, each image in the set being an image of the object on a respective spectral acquisition band, each spectral acquisition band having at least one overlap with another spectral acquisition band,

[0012] - selection of a reference image from among the images in the resulting set,

[0013] - subtraction of the reference image from an image obtained from the one or more other images from the image set, and

[0014] - determination of the texture image from the subtracted image.

[0015] According to other advantageous aspects, the determination method comprises one or more of the following features, taken individually or in all technically possible combinations:

[0016] - the image set comprises at least three images, the method comprising, In addition, a step of combining images other than the reference image to obtain a combined image in the spectral band of the reference image, the combined image being the image that is subtracted from the reference image during the subtraction step.

[0017] - each image corresponds to an observation channel of a camera used for To obtain the image, the combination step involves applying a square matrix comprising 9 elements to images derived from the images corresponding to an observation channel.

[0018] - the matrix has one column in which the value of each element is equal to 1, the other elements being harmful.

[0019] - the determination step is a scaling of the subtracted image.

[0020] - the scaling involves applying a scaling function different from one observation channel to another.

[0021] - scaling involves applying a scaling function common to all observation channels.

[0022] - the frequency bands are chosen from a visible channel, a red channel, a c blue anal and a green channel of a camera.

[0023] - the frequency bands are chosen from the ultraviolet and visible ranges.

[0024] The description also relates to a calculator for a system for determining the texture of an object, the calculator being specific to:

[0025] - obtain a set of images of the object, each image in the set being a image of the object on a respective spectral acquisition band, each spectral acquisition band having at least one overlap with another spectral acquisition band,

[0026] - select a reference image from the images in the resulting set,

[0027] - subtract the reference image from an image obtained from the other(s) images from the image set, and

[0028] - determine the texture image from the subtracted image.

[0029] The description also relates to a system for determining the texture of an object, the determination system comprising:

[0030] - a camera suitable for acquiring a set of images of the object, each image of the whole being an image of the object on a respective spectral acquisition band, each spectral acquisition band having at least one overlap with another spectral acquisition band, and

[0031] - a calculator as previously described.

[0032] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which: - [Fig. 1] [Fig. 1] is a schematic representation of an example of a system for determining the texture of an object, - [Fig.2] [Fig.2] is a flowchart of an example of the implementation of a method for determining the texture of an object, - [Fig.3] [Fig.3] is an example of a set of images that can be used at the input of the determination process of [Fig.2], - [Fig.4] [Fig.5] [Fig.6] [Fig.7] [Fig.8] [Fig.9] Figures 4 to 9 are images obtained at several stages of an example implementation of the determination process [Fig.2], and - [Fig.10][Fig.11][Fig.12][Fig.13][Fig.14] Figures 10 to 14 are images obtained at several stages of another example of implementation of the determination process of [Fig.2].

[0033] A system for determining the texture of an object 12 is schematically illustrated in [Fig.1].

[0034] The determination system 10 thus seeks to better see the surface of the object 12 in order to be able to clearly visualize the variations in the roughness of the surface.

[0035] Any object exhibiting a perceptible variation in surface roughness can be considered here.

[0036] The rest of the text focuses more on a geological object such as a rock, but it is quite conceivable to use this determination system 10 in other contexts, for example to observe pieces of wood.

[0037] The determination system 10 includes a camera 14 and a computer 16.

[0038] The camera 14 is capable of acquiring two or more images of the same object 12 in a fixed position of the camera 14 and according to several spectral bands (several channels) and this regardless of the lighting conditions.

[0039] By way of non-limiting example, spectral bands are visible and ultraviolet or visible and the red, green and blue channels of a visible camera 14.

[0040] It is assumed here that the camera 14 is a visible camera 14 which therefore records the three channels during an acquisition of an image of the object 12.

[0041] We will denote (IR, IG, IB) the 3 channels of an image I. Each of these channels can be represented by a matrix whose dimensions are those of the image in pixels, say mxn (m and n being two non-null integers), so a representation of the entire image is a three-dimensional array, that is to say an array of dimension mxnx 3.

[0042] Colours can be coded with more or less precision.

[0043] Image files generally encode each color on 8 or 16 bits (so 24 bits per pixel or 48 bits per pixel), which means that each pixel of the image has a choice of 256 colors (28) or 65536 colors (216).

[0044] It is assumed that all the images considered in the following have an identical coding, even if it means carrying out preprocessing before implementing the process which will be described in the following.

[0045] According to one example, the camera 14 operates in autofocus, which means that the focal length varies very slightly from one image capture to another.

[0046] Alternatively, the focal length is maintained from one image capture to the next. This can be useful for obtaining a depth perception since the focal area can include elements slightly above or below the targeted object 12.

[0047] The calculator 16 is an electronic circuit designed to manipulate and / or transform data represented by electronic or physical quantities in registers of the calculator 16 and / or memories into other similar data corresponding to physical data in register memories or other types of display devices, transmission devices or storage devices.

[0048] As specific examples, the calculator 16 is implemented as a programmable logic component, such as an FPGA (Field Programmable Gate Array), or as an integrated circuit, such as an ASIC (Application-Specific Integrated Circuit).

[0049] Alternatively, when the method is implemented in the form of one or more software programs, i.e., in the form of a computer program, also called a computer program product, it is further capable of being stored on a computer-readable medium, not shown. The computer-readable medium is, for example, a medium capable of storing electronic instructions and being connected to a bus of a computer system. By way of example, the readable medium is an optical disc, a magneto-optical disc, ROM, RAM, any type of non-volatile memory (e.g., FLASH or NVRAM), or a magnetic card. A computer program comprising software instructions is then stored on the readable medium.

[0050] The operation of the calculator 16 of the determination system 10 is now illustrated with reference to [Fig.2] which illustrates an example of the implementation of a method for determining the texture of an object 12.

[0051] The determination process comprises a obtaining step E20, a selection step E22, a combination step E24, a subtraction step E26 and a determination step E28.

[0052] During the E20 acquisition step, the computer 16 obtains a set of images.

[0053] The images were previously acquired by camera 14.

[0054] In the example described, each sensor acquires an image of the object 12, preferably taken under the same shooting conditions.

[0055] The computer 16 thus obtains images of the same object 12 taken on several spectral bands. Each spectral band has an overlap with at least one other spectral band.

[0056] At the end of the E20 acquisition step, the computer 16 therefore has four images: a red image, a green image, a blue image and a white image.

[0057] An example of these images can be seen in [Fig.3], with the white image at the top left, the red image at the top right, the green image at the bottom left and the blue image at the bottom right.

[0058] During the selection step E22, the calculator 16 selects a reference image, which here is the white image.

[0059] More generally, the computer 16 selects as a reference image the image corresponding to the widest spectral band.

[0060] The E24 combination step aims to create reconstructed images.

[0061] The terms “reconstituted” or “recombined” are also used to refer to reconstructed images.

[0062] In the example corresponding to [Fig.4], the combination step E24 comprises a conversion substep and a combination substep E24.

[0063] During the conversion substep, the red, green, and blue images are converted into greyscale images. These latter images correspond to a representation of luminance.

[0064] The calculator 16 combines the three channels to produce three images, denoted GrR, GrG and GrB, each with a size mxn.

[0065] The combination is achieved by a linear combination of the channels according to the following formulas:

[0066] GrR = aRRR +^RG + y^RB GrG = a&GR + [iGGG + yGnB] GrR - aBJfR + PRBG + V^b

[0067] Where: • aR, aG and are coefficients between 0 and 1, * P & fi g ct fis are coefficients between 0 and 1, and • YR, and b are coefficients between 0 and 1.

[0068] Preferably, the coefficients previously grouped into three groups are each equal.

[0069] According to a particular embodiment, the following values ​​are chosen: • aR ~ aG ~ aB ~ 0.2989 or aR = aG = aR = 0.299, ^ = / ^ = / ^ = 0.582.61

[0070] The different images GrR, GrG and GrB are visible on [Fig.4].

[0071] During the combination substep, the calculator 16 combines the images GrR, GrG and GrB thus obtained to obtain a new color image having a dimension of mxnx 3.

[0072] This can be written mathematically as follows:

[0073] C: (Cg, C& CB) - (Rr RG, RB)

[0074] Where: • C designates the new color image, • CR is the first channel of the new C color image, • CG is the second channel of the new C-color image, and • CB is the third channel of the new C color image

[0075] The new image thus obtained is the image of [Fig.5].

[0076] During the subtraction step E26, the calculator 16 subtracts the combined image from the reference image (the white image in this example).

[0077] Such an operation can be written mathematically as follows:

[0078] TR=WR-CR tg=wg-cg tb = wb^cb

[0079] Where: • ? R, are the components along each channel of the difference vector r, and • WG and are the corresponding components of the white image.

[0080] With this expression, it is possible to have negative values.

[0081] To resolve this issue, the calculator 16 implements an additional operation on negative values.

[0082] For example, a simple additional operation is to set the set of negative values ​​to 0.

[0083] Another example of an additional operation is to modify the value according to a modulo.

[0084] Typically, for a set of values ​​between 0 and 255 (i.e., a modulo of 256), if = 10 and CR = 20, then TR = -10, which with the modulo 256 becomes TR = 246 (indeed, 255 corresponds here to -1).

[0085] The image thus obtained corresponding to the difference vector is visible on [Fig.6].

[0086]

[0087]

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[0101] During the determination step E28, the calculator 16 determines the texture image from the subtracted image. A texture image here is an image that represents the surface roughness of object 12. To be more precise, this is an image where the focus surface is selected, and only small variations in altitude around this surface are examined. Indeed, for excessively large variations in roughness, these are out of focus, resulting in a lack of visible detail. According to the example described, the determination step E28 is a scaling of the texture image. The images obtained by this operation are visible in [Fig.7]. Depending on the embodiments, scaling is implemented globally (top image of [Fig.7]) or channel by channel (bottom image of [Fig.7]). In each case, this allows us to obtain a scaled texture image Æ of which each component (on a respective channel) is noted ( DG, DB ). It comes like this: ^R “ ^R^R + Wr Db = SbTb + vb Or: • ôR and ^r are two scaling coefficients for the red channel (respectively the first coefficient and the second coefficient), • and ^g are the two scaling coefficients for the green channel, and • 0B and 7g are the two scaling coefficients for the blue channel. In the first case corresponding to global scaling, each first scaling coefficient is the same from one channel to another and, similarly, each second scaling coefficient is the same from one channel to another. Calculator 16 then determines the scaling coefficients using the following formulas: <5 1 — max^VyminiP) And miM)) Or: • The index i designates the channel, i being able to be R, G or B in the example described, • max(D) denotes the largest value of the matrix U. • min(D) denotes the smallest value of the matrix D

[0102] According to another embodiment, the correction applied depends from one channel to another.

[0103] Thus, calculator 16 obtains the scaling coefficients using the following formulas:

[0104] x =______!------ max(Pù-min(Pù

[0105] and

[0106] _

[0107] Where: • The index i designates the channel, i being able to be R, G or B in the example described, • max(DJ) denotes the maximum value of the matrix corresponding to and • minÇD,) denotes the minimum value of the matrix corresponding to D,.

[0108] The difference between the two techniques just described becomes clear when expressing the minima and maxima since:

[0109] max(D) =max(max(DR), max{DG),max{DB) ).

[0110] and [YES] min(D) = min(min(DR), min(DG), min(DB) ).

[0112] To clearly highlight the advantage of the process, it is helpful to compare figures 8 and 9.

[0113] Fig. 8 shows the image of object 12 seen in white light, while the two figures 9 show the texture images obtained after implementation of the process (top image global scaling technique and bottom image channel-by-channel scaling technique).

[0114] In [Fig.8], black rocks in visible light are represented with dots, red rocks with crosses and white rocks with nothing.

[0115] The images in Figures 9 clearly distinguish these three types of rocks, insofar as black rocks appear flat with steep edges, red rocks appear flat but with smoother edges, and white rocks appear with relief and smooth edges.

[0116] The process thus makes it possible to distinguish the nature of the rocks by texture.

[0117] Furthermore, the method is easy to implement, the computational load associated with the the process being reduced.

[0118] This process could advantageously be used to differentiate two rocks of the same color but different compositions and thus annotate images of this type to serve as a training dataset for an artificial intelligence algorithm used for contour recognition, or automatic rock recognition.

[0119] Other embodiments of the determination process offering the same advantages are also conceivable.

[0120] A first example is described with reference to figures 10 to 14.

[0121] In this case, the combination step E24 is implemented differently.

[0122] In this case, the combination step E24 comprises an extraction substep and a E24 combination substep.

[0123] The extraction substep consists of extracting each channel corresponding to an object 12 illuminated by the corresponding spectral band. This means that a red image Rr is extracted from the red channel of the red image, a green image Gg is extracted from the +green channel of the green image, and a blue image Bb is extracted from the blue channel of the blue image.

[0124] Formally, this amounts to the conversion sub-operation in which the coefficients are fixed as follows:

[0125] aR = / 3G = yB=l ^g~ ^b~ PR~ PB~TR~TG~^-

[0126] The different images Rr, Gg and Bb are visible on [Fig. 10].

[0127] The combination substep is similar to the previous substep, the new mathematical expression becoming:

[0128] C.^CG,CBÏ = (R^

[0129] The new image thus obtained according to the second example is the image of [Fig.11].

[0130] The image obtained after implementation of the subtraction step E26 is shown in [Fig. 12] while the images obtained after implementation of the determination step E28 are shown in [Fig. 13] (top figure, case of correction on all channels and, bottom figure, case of channel-by-channel correction).

[0131] Figures 14 are equivalent to Figures 9 and therefore correspond to the texture images obtained at the end of this determination process. These images should therefore be compared to the image in [Fig. 8].

[0132] Here again, it is clear that texture images make it possible to identify the differences in texture between the different rocks.

[0133] It is also possible to improve certain steps of the process by adding additional operations.

[0134] For example, according to a more elaborate embodiment, given that the values ​​of the previous components Tr, Tg, Tb, Wr, Wg and WB can be negative, the subtraction step E26 of the process includes a preliminary operation of converting each of the components into a format allowing each of the values ​​to be expressed as a signed integer.

[0135] According to another example, an additional multiplicative coefficient can be applied to each of the scaling coefficients.

[0136] Such a multiplicative coefficient is, for example, chosen to ensure that the scaling coefficients have values ​​that extend over a larger value range.

[0137] This example is compatible with both scaling techniques (global or channel by channel).

Claims

Demands

1. A method for determining the texture of an object (12), the method being implemented by a computer (16) of a system for determining (10) the texture of an object (12) and comprising a step of: - obtaining a set of images of the object (12), each image of the set being an image of the object (12) on a respective spectral acquisition band, each spectral acquisition band having at least one overlap with another spectral acquisition band, - selecting a reference image from among the images of the set obtained, - subtracting the reference image from an image obtained from the other image(s) of the image set, and - determining the texture image from the subtracted image.

2. A determination method according to claim 1, wherein the image set comprises at least three images, the method further comprising a step of combining images other than the reference image to obtain a combined image in the spectral band of the reference image, the combined image being the image that is subtracted from the reference image during the subtraction step.

3. Method of determination according to claim 2, wherein each image corresponds to an observation channel of a camera (14) used to obtain the image, the combination step comprising the application of a square matrix comprising 9 elements to images derived from the images corresponding to an observation channel.

4. Method of determination according to claim 3, wherein the matrix comprises a column in which the value of each element is equal to 1, the other elements being null.

5. A method of determination according to any one of claims 1 to 4, wherein the determination step is a scaling of the subtracted image.

6. A method of determination according to claim 5, wherein the scaling involves applying a different scaling function from one observation channel to another.

7. A determination method according to claim 5, wherein the scaling involves the application of a scaling function common to all observation channels.

8. A method of determination according to any one of claims 1 to 7, wherein the frequency bands are selected from a visible channel, a red channel, a blue channel and a green channel of a camera (14).

9. A method of determination according to any one of claims 1 to 7, wherein the frequency bands are selected from the ultraviolet and visible.

10. Calculator (16) of a system for determining (10) the texture of an object (12), the calculator (16) being capable of: - obtaining a set of images of the object (12), each image in the set being an image of the object (12) on a respective spectral acquisition band, each spectral acquisition band having at least one overlap with another spectral acquisition band, - selecting a reference image from among the images in the set obtained, - subtracting the reference image from an image obtained from the other image(s) in the image set, and - determining the texture image from the subtracted image.

11. A system for determining the texture of an object (12) (10), the determination system (10) comprising: - a camera (14) suitable for acquiring a set of images of the object (12), each image in the set being an image of the object (12) on a respective spectral acquisition band, each spectral acquisition band having at least one overlap with another spectral acquisition band, and - a computer (16) according to claim 10.

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