Method for extracting raw data from an image resulting from a camera shot

• US 7,606,439 B2
• Filed: 03/23/2005
• Issued: 10/20/2009
• Est. Priority Date: 03/23/2004
• Status: Expired due to Fees

• Current Assignee: Realeyes3d
• Original Assignee: Realeyes 3D
• Inventors: Lefebure, Martin, Benabid, Anas
• Primary Examiner(s): COUSO, YON JUNG
• Application Number:
  US11/086,985
• Publication Number:
  US 20050212925A1
• Time in Patent Office: 1,672 Days
• Field of Search: 382/254, 382/274, 382/275, 348/207.1, 348/533, 348/607, 348/222.1, 725/105, 725/116
• US Class Current: 382/274
• CPC Class Codes: G06T 3/00 Geometric image transformations in the plane of the image

Abstract

The method according the invention allows the extracting raw data from an image resulting from a camera shot.

It comprises determining, for each point of the image of a combination V₀[C,L] of color components of the image, calculating, for each point of the image, of a value V_N+1[C,L], iterating said calculating a predetermined number of times then taking into account the values of the final image V_Nfinal [C, L] in each point of the image, calculating for each point of the image of the difference D [C, L]=V_Nfinal [C, L]−V₀ [C, L], calculating of a noise contextual datum V_S, correcting the extracted raw data D[C, L], with the contextual datum V_S, calculating of a corrected value I*[C, L] taking into D*[C, L] and presenting the extracted data under a desired angle.

Claims

1. Method for extracting raw data from a digital image taken by a photographic or cinematographic camera for correcting the extracted raw data and for generating an improved digital image containing the corrected raw data, said method comprising:

determining, for each point of said image located by column C and line L of the image, a value V₀[C, L] consisting of a combination of colour components of the image, expressed as;

V₀[C, L]=α

Red [C, L]+β

green [C, L]+γ

blue [C, L]

wherein α

, β

, γ

are coefficients satisfying the following relationship

α

+β

+γ

=1

α

+β

+γ

≧

0

calculating, for each point of the image, a threshold value V_Back.(C, L),

calculating for each point of the image, raw data consisting in the difference D[C, L] by means of the relations D[C, L]=V_Back.−

V₀[C, L] in the case where the image comprises dark data on a bright background and V₀[C, L]−

V_Back. in the case where the image comprises bright data on a dark background, and

calculating a threshold value V_S consisting of a noise contextual datum from at least one contrast histogram and/or from the probability q that a regional maximum of the raw data contains noise,

correcting said raw data by using the threshold value V_S for;

either calculating projective deformation and corrections to be made depending on said projective deformations

and/or by comparing for each point of the image the value D[C, L] with said threshold value V_S to determine a corrected raw data value D*[C, L] to be extracted in the following way;

if D[C, L]<

V_S then D*[C, L]=0

if D[C, L]≧

V_S then D*[C, L]=D[C, L]

else

D*[C, L]=D[C, L]−

V_S

calculating, for each point of the image, a corrected value I*[C, L] from the corrected raw data D*[C, L] by the following relation

I*[C, L]=I_max−

f.D*[C, L]

with I_max=value of a bright background or

I*[C, L]=I_min+f.D*[C, L]

with I_min=value of a dark background

storing the corrected value in a memory so as to obtain said improved image.

2. The method according to claim 1, wherein the background value V_Back. is determined by an operating sequence comprising the following steps:

calculating for each point of the image, a value V_N+1[C,L] which is the maximum value (when the image comprises dark data on a bright background) and the minimum value (when the image comprises bright data on a dark background) between value V_N[C, L] and different averages of values of V_N on symmetrical structuring items centred on [C, L],

iterating said calculation a predetermined number of times (N_final) and then taking into account the values of the final image V<sub>N—

final</sub> as the values of the background image V_Back..

3. The method according to claim 2, wherein the calculation of value V_N+1[C,L] is achieved by a relationship of the type:

$V_{N+1}<br><br>\left[C,L\right]=\begin{array}{c}\max <br><br>\left(\mathrm{dark}<br><br>\text{/}<br><br>\mathrm{bright}<br><br><br><br>\mathrm{background}\right)\\ \left(\mathrm{or}<br><br><br><br>\min <br><br><br><br>\left(\mathrm{bright}<br><br>\text{/}<br><br>\mathrm{dark}<br><br><br><br>\mathrm{background}\right)\right)\end{array}$ $\left\{V_N<br><br>\left[C,L\right]<br><br>\frac{V_N<br><br>\left[C+1,L+1\right]+V_N<br><br>\left[C-1,L-1\right]}{2}<br><br>\frac{V_N<br><br>\left[C+1,L-1\right]+V_N<br><br>\left[C-1,L+1\right]}{2}<br><br>\frac{V_N<br><br>\left[C,L+1\right]+V_N<br><br>\left[C,L-1\right]}{2}<br><br>\frac{V_N<br><br>\left[C+1,L\right]+V_N<br><br>\left[C-1,L\right]}{2}\right\}.$

4. The method according to claim 1, wherein the background image V_Back. is determined by an operating sequence comprising the following steps:

generating an image V_N+1 four times smaller than V_N comprising the calculation for each point of the image, of a value V_N+1[C,L] which is the maximum (when the image comprises dark data on bright background) and the minimum (when the image comprises bright data on a dark background) between a local average of V_N centred on the point [2C+½

, 2L+½

] and at least a local average including a larger number of pixels;

image V_N+1 being four times smaller than image V_N,

iterating said calculation a predetermined number of times N_final, and

interpolating values of the image V<sub>N—

Final</sub> in order to obtain the values of image V_Back. which has the same size as the initial image V₀.

5. The method according to claim 4, wherein the value V_N+1 [C, L] is determined by an operating sequence comprising:

$V_{N+1}<br><br>\left[C,L\right]=\begin{array}{c}\max <br><br>\left(\mathrm{dark}<br><br>\text{/}<br><br>\mathrm{bright}<br><br><br><br>\mathrm{background}\right)\\ \left(\mathrm{or}<br><br><br><br>\min <br><br><br><br>\left(\mathrm{bright}<br><br>\text{/}<br><br>\mathrm{dark}<br><br><br><br>\mathrm{background}\right)\right)\end{array}$ $\left\{\frac{V_N<br><br>\left[2<br><br>C,2<br><br>L\right]+V_N\left[2<br><br>C+1,2<br><br>L\right]+V_N<br><br>\left[2<br><br>C,2<br><br>L+1\right]+V_N<br><br>\left[2<br><br>C+1,2<br><br>L+1\right]}{4},<br><br>\left(V_N<br><br>\left[2<br><br>C-1,2<br><br>L-1\right]+V_N<br><br>\left[2<br><br>C-1,2<br><br>L\right]+V_N<br><br>\left[2<br><br>C-1,2<br><br>L+1\right]+V_N<br><br>\left[2<br><br>C-1,2<br><br>L+2\right]+V_N<br><br>\left[2<br><br>C,2<br><br>L-1\right]+V_N<br><br>\left[2<br><br>C,2<br><br>L\right]+V_N<br><br>\left[2<br><br>C,2<br><br>L+1\right]+V_N<br><br>\left[2<br><br>C,2<br><br>L+2\right]+V_N<br><br>\left[2<br><br>C+1,2<br><br>L-1\right]+V_N<br><br>\left[2<br><br>C+1,2<br><br>L\right]+V_N<br><br>\left[2<br><br>C+1,2<br><br>L+1\right]+V_N<br><br>\left[2<br><br>C+1,2<br><br>L+2\right]+V_N<br><br>\left[2<br><br>C+2,2<br><br>L-1\right]+V_N<br><br>\left[2<br><br>C+2,2<br><br>L\right]+V_N<br><br>\left[2<br><br>C+2,2<br><br>L+1\right]+V_N<br><br>\left[2<br><br>C+2,2<br><br>L+2\right]\right)/16\right\}.$

6. The method according to claim 1, which comprises a phase for removing luminous and/or electronic noise present in the raw datum D[C,L], this noise removal phase consists of calculating the noise contextual datum V_S and of comparing for each point of the image, the value D[C,L] with the threshold value V_S so as to determine the value D*[C, L] to be extracted in the following way:

if D[C, L]<

V_S then D*[C, L]=0,

if D[C, L]≧

V_S value D[C, L] is retained, i.e. D*[C, L]=D[C, L] or is replaced with D[C, L]−

V_S i.e. D*[C, L]=D[C, L]−

V_S,

the generation of image I*(p) containing the extracted data according to the subtractive principle resulting from calculating;

when the image comprises dark data on a bright background, I*(p)=I_max−

f.D*(p),

where I_max is the value of the bright background which may be equal to 255, and

when the image comprises dark data on a bright background, I*(p)=I_min+f.D*(p),

with I_min which may be equal to 0.

7. The method according to claim 1, wherein the calculation of V_S comprises the following operating phases:

calculating the raw data to be extracted D(p)

calculating a value S_max from D(p), and

calculating the contrast histogram using D(p) and inferring the value V_S from said histogram and probability q.

8. The method according to claim 7, wherein D(p) is replaced with an estimation S(p).

9. The method according to claim 8, wherein the calculation of S(p) comprises the following operating phases:

for each direction d with 0<

|d|<

D;

if the following condition

convexity of I on [p−

d, p+d] comprising

I(p+(1−

2λ

)d)≦

λ

I(p−

d)+(1−

λ

)I(p+d) for any 0≦

λ

≦

1 in case of dark data on bright background, or

concavity of I on [p−

d, p+d], comprising

I(p+(1−

2λ

)d)≧

λ

I(p−

d)+(1−

λ

)I(p+d) for any 0≦

λ

≦

1 in case of bright data on dark background is satisfied,

then G(p,d)=(I(p+d)+I(p−

d))/2 is calculated,

or else G(p,d)=0, and

a value S(p) is calculated which is equal to the maximum value of G(p,d) for all directions d with 0<

|d|<

D.

10. The method according to claim 7, wherein said calculation of the contrast histogram and value V_S comprises:

a first step wherein for each pixel p in the image D(p), if D(p) is a regional maximum, H(D(p)) is incremented according to the relationship H(D(p))←

H(D(p))+1,

a second step wherein the identities S=S_max and N=1/q are determined and as long as H(S) is less than N, S is replaced with S−

1, the final value of S is called S_min, N being the minimum number of regional maximum pixels such that the mathematical expected value of the number of pixels containing noise is larger than or equal to 1, and

a third step wherein value V_S is calculated according to formula

V_S=r.S_min+(1−

r).S_max, with ½

≦

r≦

1.

11. The method according to claim 1, wherein the calculation of V_S comprises the following operating phases:

when the image comprises dark data on a bright background, calculating raw data to be extracted Dpits(p) and

(when the image comprises dark data on a bright background, calculating raw data to be extracted Dbumps(p),

calculating values Spits_max, wherein Spits_max comprises maximum values of the pits of Dpits(p) and Sbumps_max comprises minimum values of bumps of Dbumps(p)),

calculating contrast histogram H_pits and H_bumps from Dpits(p) and Dbumps(p), and

inferring the value of V_S from the Hpits and Hbumps histograms.

12. The method according to claim 1, wherein the calculation of V_S comprises the following operating phases:

when the image comprises dark data on a bright background, calculating raw data to be extracted Spits(p) and

(when the image comprises dark data on a bright background, calculating raw data to be extracted Sbumps(p),

calculating values Spits_max, wherein Spits_max comprises maximum values of the pits of Spits(p) and Sbumps_max comprises minimum values of bumps of Sbumps(p)),

calculating contrast histogram H_pits and H_bumps from Spits(p) and Sbumps(p)

inferring the value of V_S from the Hpits and Hbumps histograms.

13. The method according to claim 11, wherein Spits(p) and Sbumps(p) have values which are determined according to the following operating sequences:

determination of the value of Spits(p),

for each pixel p of image I, the following is performed;

for each direction d, with 0<

|d|<

D,

if condition

convexity of I on [p−

d, p+d] comprising

I(p+(1−

2λ

)d)≦

λ

I(p−

d)+(1−

λ

)I(p+d) for any 0≦

λ

≦

1, representing a dark data/bright background is satisfied,

then G(p,d)=(I(p+d)+I(p−

d))/2 is calculated,

or else G(p,d)=0,

Spits(p)=maximum value of G(p,d) is calculated for all directions d with 0<

|d|<

D

determination of the value of Sbumps(p);

for each pixel p of image I, the following is performed;

for each direction d, with 0<

|d|<

D.

if condition

concavity of I on [p−

d, p+d] comprising

I(p+(1−

2λ

)d)≧

λ

I(p−

d)+(1−

λ

)I(p+d) for any 0≦

λ

≦

1, is satisfied,

then G(p,d)=(I(p+d)+I(p−

d))/2 is calculated, or else G(p,d)=0,

Sbumps(p)=maximum value of G(p,d) is calculated for all directions d with 0<

|d|<

D.

14. The method according to claim 11, wherein the calculation of the Hpits and Hbumps histograms and the calculation of V_S include the following phases:

determination of the value of Hpits;

the pit histogram H_pits is reset to 0 for each value of s between 0 and the maximum value of the pits, S_pit_max,

for each pixel p of image D(p), the following calculations are performed;

i. if D(p) is a regional maximum,

H_pits(D(p)) is incremented in the following way;

H_pits(D(p))←

H_pits(D(p))+1,

determination of the value of Hbumps;

the bump histogram H_bump(s) is reset to 0 for each s between 0 and the maximum value of the bumps, S_bumps_max,

for each pixel p of image D(p), the following calculations are performed;

if D(p) is a regional maximum,

H_bumps(D(p))is incremented in the following way;

H_bumps(D(p))←

H_bumps(D(p))+1,

the calculating V_S comprising the following step;

a step for superimposing pit H_pits and bump H_bumps histograms, including the following phases;

S_max is calculated according to the expression;

S_max=Max, where maximum value of the pits S_pits_max, maximum value of the bumps S_bumps_max,

H_max is calculated according to the expression;

H_max=maximum value of the pits H_pits(S) and the bumps H_bumps(S) for all values of S,

s0 is calculated according to the expression;

s0=maximum value of S such that

H_pits(S)=H_max,

or H_bumps(s)=H_max,

s=s0+1 is calculated and α

is selected such that 0<

α

<

½ and

as long as;

|ln(1+H_pits(s)−

ln(1+H_bump(s))|<

α

.ln(1+H_max)

s←

s+1 is performed, wherein ln is Napier'"'"'s logarithm function, and

the value S_min is determined by the final value of s incremented by 1, and

a step for calculating the extraction threshold V_S according to the relationship;

V_S=r.S_min+(1−

r)·

S_max where ½

<

r≦

1.

15. The method according to claim 1, in the case when extracted data from an image or of an image containing them, are shown according to a desired view angle, from an image taken by a camera under any incidence, which comprises the steps:

searching for at least four identifiable characteristic points of a pattern present in the image taken by the camera defining contextual data, these characteristic points may consist of corners of the image,

optionally extracting data according to predetermined criteria,

calculating geometrical deformations to be made to the raw image or to the extracted data or to the image containing them, from the relative position of the four points, with respect to relative reference positions,

determining corrections to be made to the raw image or to the extracted data or to the image containing them, depending on the geometrical deformations, and

generating a corrected image taking into account the thereby determined corrections.

16. The method according to claim 15, for obtaining a corrected image having the same proportions as the object, said method comprises the determination of the real height/width ratio of the quadrilateral defined by the points and the taking into account of this ratio r in the generation of the corrected image.

17. The method according to claim 16, wherein said quadrilateral is the projection of a rectangle, and the determination of the proportions of the rectangle is performed in accordance with a process comprising the following steps:

determining vanishing points from contours of the pattern and determining a horizon line connecting the vanishing points,

determining the coordinates of the projection point F of the optical centre O of the camera on the horizon line,

calculating the camera base point as orthogonal projection of the optical centre of the camera on the plane of the pattern, from distances between the vanishing points and the projection point F and from the distance between this projection point F and the optical centre O,

calculating the focal length from the distances between the optical centre, the projection point F and the camera base point,

calculating the coordinates of the intersection points between the vanishing lines and the lines connecting the camera base point and the vanishing points as well as points O₁, O₂, P₁, P₂, located on the vanishing lines, at a conventional distance from the camera base point, and

calculating the ratio of the sides of the initial pattern from the coordinates calculated beforehand by considering that the rectangle O₁, O₂, P₁, P₂, is the projection of a square extending in the plane of the pattern.

18. The method according to claim 17, which comprises the following steps in the case when both vanishing points exist,

calculating the coordinates of the point F by projecting the centre of the image O on the horizon line (F₁, F₂),

calculating the position of the base of the camera by its distance to point F, dist(X,F), by means of the relationship:

$\mathrm{dist}<br><br>\left(X,F\right)=\frac{\mathrm{dist}<br><br>\left(F,F_1\right)·<br><br>\mathrm{dist}<br><br>\left(F,F_2\right)}{\mathrm{dist}<br><br>\left(O,F\right)}$

calculating the focal length f with the relationship;

f=√

{square root over (dist(O, X)·

dist(O, F))}{square root over (dist(O, X)·

dist(O, F))}

determining the coordinates of points M₁, N₁, O₁ and P₁ from the values calculated beforehand,

determining the coordinates of points M₂, N₂, O₂ and P₂ from the values calculated earlier, and

calculating the physical aspect ratio r by using the crossed ratios and the fact that the rectangle O₁, O₂, P₁, P₂ is the projection of a square extending in the plane of the pattern according to the relationship

$r=\frac{\mathrm{dist}<br><br>\left(M_1,N_1\right)}{\mathrm{dist}<br><br>\left(M_2,N_2\right)}·<br><br>\frac{\mathrm{dist}<br><br>\left(F_1,O_1\right)}{\mathrm{dist}<br><br>\left(F_1,M_1\right)}·<br><br>\frac{\mathrm{dist}<br><br>\left(F_1,P_1\right)}{\mathrm{dist}<br><br>\left(F_1,N_1\right)}·<br><br>\frac{\mathrm{dist}<br><br>\left(F_2,M_2\right)}{\mathrm{dist}<br><br>\left(F_2,O_2\right)}·<br><br>\frac{\mathrm{dist}<br><br>\left(F_2,N_2\right)}{\mathrm{dist}<br><br>\left(F_2,P_2\right)}.$

19. The method according to claim 16, wherein, in the case when only one pair of vanishing lines intersect at a vanishing point whereas the other two vanishing lines are parallel, vanishing point projected to infinity, the calculation of the ratio r will be performed from a pre-established focal length f of the camera.

20. The method according to claim 19, wherein said ratio r is obtained according to the following relationship:

wherein f is

$r=\frac{\mathrm{dist}<br><br>\left(A,B\right)·<br><br>\mathrm{dist}<br><br>\left(C,D\right)}{\left(\mathrm{dist}<br><br>\left(C,D\right)-\mathrm{dist}<br><br>\left(A,B\right)\right)·<br><br>\sqrt{f^2+{\mathrm{dist}<br><br>\left(O,F_2\right)}^2}}$

 

the focal length of the camera, wherein the focal length f is calculated beforehand.

21. The method according to claim 16, wherein, if there is no vanishing point, the ratio r is equal to the ratio

$r=\frac{\mathrm{dist}}{}$

( A , B ) dist ⁡

( A , D ) .

22. The method according to claim 15, wherein, the generation of a corrected image comprises an operating sequence including the following phases:

creating an initial, deformed binary mask of the area to be corrected by isolating the useful portion of the initial image containing the extracted data and by assigning the same binary value to the pixels of this useful portion,

calculating an ideal binary mask by a direct homographic transformation of the initial mask, based on the transformation of any polygonal shape into a reference polygonal shape, and

for each pixel (u, v) of the ideal binary mask, calculating by inverse homography, the position (x, y) in the initial image, determining the value of the final image at pixel (u, v) by an (x, y) interpolated value in the initial image.

23. The method according to claim 22, wherein said step for creating a binary mask comprises assigning a zero value to all the pixels which are outside a quadrilateral surrounding the useful portion of the image as well as to the pixels which do not correspond to optionally extracted data.

24. The method according to claim 23, wherein the pixels are considered to be within the quadrilateral if they are always on the same side as a point G inside the quadrilateral relatively to the limits of the quadrilateral, point G may consist of the centre of gravity or the point of intersection of the diagonals.

25. The method according to the claim 1, which comprises a precalculation of the images of intermediate lines and intermediate columns in order to obtain the images of subpixels by intersection of precalculated images of intermediate lines and intermediate columns.

26. The method according to the claim 22, wherein, in the calculation of the final image, to each pixel of the ideal binary mask is assigned an intensity value which is calculated by finding the position of this pixel in the initial image or the image of the extracted data.

27. The method according to the claim 22, wherein, in the phase for calculating the final image, the images are precalculated by inverse homography of the lines and columns of the ideal mask, and in that the position of a given pixel in the initial image is then inferred by calculating the intersection of two lines.

28. The method according to claim 15, wherein the step for creating the final image comprises the calculation of the position in the deformed image of a pixel u,v of the ideal mask, by the intersection of the precalculated inverse images of line v and column u, an intersection which defines an x,y point of the initial image, and in that an intensity value is assigned to the pixel (u,v) which is then interpolated at the x,y point of the initial image or image of data extracted from the luminance image or from each colour channel.

29. The method according to claim 28, wherein the interpolation is bilinear.

30. The method according to the claim 1, which comprises determination of the colour of a pixel in the final image is performed according to a subtractive principle.

31. The method according to the claim 1, which comprises the determination of a threshold value V_S in order to select values to be extracted for the extraction according to a subtractive principle of handwritten or printed information contained in the image delivered by the camera.

32. The method according to claim 31, wherein the threshold value V_S corresponds to the threshold value of the gradient for removing a grid of lines and/or to a noise removal threshold.

33. The method according to claim 1, according to which extraction of information from a colour image is performed with red, green and blue colour channels, the aforementioned steps for extracting data being followed for each colour channel by the determination of a threshold for each channel, the extraction of colour information from said RGB channels and their recombination into a final colour image being made by extracting the red, green, blue values in each pixel for which it is seen that the threshold has been exceeded.

34. The method according to claim 33, wherein, for each of the pixels, one successively proceeds with extracting the luminance, extracting the raw data D(p), calculating the noise contextual datum V_S, extracting the noise corrected raw data D*(p) by means of the noise contextual datum, and then generating the luminance image corrected by the following calculation:

$I^{*}<br><br>\left(p\right)=\{\begin{array}{c}{I}_{\max }-f·<br><br>D^{*}<br><br>\left(p\right)<br><br>\left(\mathrm{dark}<br><br><br><br>\mathrm{data}/\mathrm{bright}\right)<br><br>\left(I_{\max <br><br>}<br><br>\mathrm{may}<br><br><br><br>\mathrm{be}<br><br><br><br>\mathrm{equal}<br><br><br><br>\mathrm{to}<br><br><br><br>255\right)\\ I_{\min }+f·<br><br>D^{*}<br><br>\left(p\right)<br><br>\left(\mathrm{bright}<br><br><br><br>\mathrm{data}/\mathrm{dark}\right)<br><br>\left(I_{\min <br><br>}<br><br>\mathrm{may}<br><br><br><br>\mathrm{be}<br><br><br><br>\mathrm{equal}<br><br><br><br>\mathrm{to}<br><br><br><br>\mathrm{zero}\right)\end{array}.$

35. The method according to claim 33, wherein the colour sought for the final image is obtained according to a subtractive principle comprising two steps:

the background colour is estimated or set arbitrarily and then determined chrominance contrasts are removed from the background colour, as with a filter.

36. The method according to claim 35, which comprises the extraction of the noise contextual datum V_S, and then the extraction of the noise corrected raw data (D*_R,D*_G,D*_B) of the colour channels by calculating the raw data of the channels D_R, D_G, D_B expressing the contrast between the observed chrominance RGB and that of the background (V_R^Back., V_G^Back., V_B^Back.), by thresholding with the datum and generating the corrected chrominance image by means of the relationship

$R^{*}$

G * ⁢

B * = { ( V R Back . , V G Back . , V B Back . ) - f ·

( D R * , D G * , D B * ) ⁢

( dark ⁢

⁢

data / bright ) ( ( V R Back . , V G Back . , V B Back . ) ⁢

⁢

may ⁢

⁢

be ⁢

⁢

equal ⁢

⁢

to ⁢

⁢

( 255 , 255 , 255 ) ) ( V R Back . , V G Back . , V B Back . ) + f ·

( D R * , D G * , D B * ) / ( bright ⁢

⁢

data / dark ) ( ( V R Back . , V G Back . , V B Back . ) ) ⁢

⁢

may ⁢

⁢

be ⁢

⁢

equal ⁢

⁢

to ⁢

⁢

( 0 , 0 , 0 ) ) } .

37. The method according to claim 36, wherein, if in the original image, the chrominance of a pixel representing a white area of the supporting medium is (V_R^Back.,V_G^Back.,V_B^Back.)=(160,140,110), and/or of a pixel representing a blue writing area of the supporting medium, has a chrominance (V_R⁰,V_G⁰,V_B⁰)=(120,115,105) and where the optical noise corrected white/blue contrast is (D*_R,D*_G,D*_B)=(160−

120,140−

115,110−

105)=(40,25,5) and where, moreover, the chrominance of the pixels of the final image representing the white areas of the supporting medium is set to (R_B,G_B,B_B)=(255, 255,

     255), the chrominance of the pixel representing a blue writing area of the supporting medium in the final image is then determined by subtracting the contrast weighted earlier by an f factor from that of the white, so that the corrected chrominance (R*,G*,B*) of the final image at this pixel will be, if f=1,

(R*,G*,B*)=(R_B−

D*_R,G_B−

D*_G,B_B−

D*_B)=(255−

40, 255−

25, 255−

5)=(215, 230,

     250).

38. The method according to claim 33, wherein a grey level image I(p) may either be a combination of the three colour channels of the image (red, blue, green) or one of these channels, and said method further comprises the following steps:

a first step for calculating a histogram of pits, H_pits,

a second step for calculating a histogram of bumps, H_bumps,

a third step for superimposing histograms of the pits and bumps,

a step for comparing the H_pits and H_bumps histograms,

a step for calculating the extraction threshold V_S,

a step for extracting the raw data by means of threshold V_S,

a step for extracting luminance information L(p),

wherein the step for comparing the H_pits and H_bumps histograms includes the following operating phases, for β

>

0;

a) calculating a value N_pits from the relationship;

N_pits=sum of H_pits(s)^β

for s=S_min to s=S_pits_max,

b) calculating a value N_bumps from the relationship;

N_bumps=sum of H_bumps(s)^β

for s=S_min to s=S_bumps_max, and

c) if N_pits is less that N_bumps, then the dark-on-bright-background information should be extracted or else the bright-on-dark-background information should be extracted.

39. The method according to claim 38, wherein the calculation of the extraction threshold is performed according to the relationship

V_S=r.S_min+(1−

r)·

S_max, with ½

≦

r≦

1.

40. The method according to claims 38, wherein the step for extracting luminance information L(p) includes the following operating phases:

if the information is dark-on-bright-background information, calculating the difference image D from I,

or else calculating D from I_max−

I,

for each pixel p in the difference image D(p), if D(p)<

V_S, then D(p) is considered relevant and is extracted,

if the information is black-on-bright-background information, calculating a value L(p)=I_max−

f.D(p), I_max may be equal to 255,

or else, the value L(p)=I_min+f.D(p) is calculated, I_min may be equal to 0,

if D(p) is not considered relevant,

if the information is dark-on-bright-background information, the value L(p) is equal to I_max (bright background), I_max may be equal to 255,

or else, the value L(p) is equal to I_min representing a dark background, I_min may be equal to 0.

41. The method according to the claim 1, comprising simulation of an image, projected from data contained in a rectangular object with apices (A,B,C,D) having a prescribed physical aspect ratio r=CD/AB, a prescribed projected point D in the image and a known projected distance (CD), with a camera having a prescribed focal length (f), a tilt angle ((π

/2)−

i) where i is the angle of incidence, an angle of rotation α

around the axis of the camera and if i≠

0, a prescribed skew angle (β

) relatively to one of the existing vanishing points F₁, said method comprising the following steps;

a first step for calculating the position of the three unknown points (A,B,C) consistently with the physical aspect ratio r,

a second step for calculating homographic relationships in order to project the information contained in the original rectangular object onto the simulated image, and

a third step for determining luminance and chrominance of the simulated image with the homographic relationships determined earlier.