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BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a process for the automatic recognition of
an image on the basis of a corresponding reference image.
The present invention applies to all fields where it is necessary to
recognize an image or an object, e.g. by comparing the image or the object
with a reference image or object. It is particularly applicable to the
field of robotics, in which research is presently directed towards
so-called "intelligent" robots able to interpret information from a
external medium.
2. Description of the Prior Art
A process for the automatic recognition of an image on the basis of a
corresponding reference image is known, and consists in each case of
obtaining, by optoelectronic detection means, analog signals, whose
amplitudes are respectively dependent on the intensity levels of the light
rays reflected from the image. This process then consists of digitizing by
means of an analog-digital converter, the values of the amplitudes of the
analog signals, the digitized values being recorded in a memory. By means
of a processing unit receiving the stored digital values corresponding to
the amplitudes, digital values corresponding to a contour line
characterizing the image are then determined. For example, this contour
line can be determined by detecting intensity transitions in the light
rays from the image, wherein the transitions are defined relative to a
predetermined intensity threshold.
The digital values corresponding to the coordinates of the contour line
points are stored. This known process also consists of coding, by means of
the processing unit receiving the digital values of the coordinates of the
contour line points, segments whose ends are located on the lines. These
segments make it possible to represent it at the very best in an
approximate manner. This is followed by an individual comparison of the
segments corresponding to the contour line of the image to be recognized
with the segments corresponding to the contour line of the reference
image.
Various methods make it possible to code successive segments defining a
contour line and there are also various methods for comparing the segments
defining the contour line.
One of the methods used for coding segments defining a contour line
consists of defining each segment by the cartesian coordinates of its
ends, the segments representing very varied orientations relative to a
reference direction. This method consists of obtaining, by optoelectronic
detection means, analog signals, whose amplitudes respectively depend on
the intensity levels of the light rays from the image or the contour to be
recognized. The values of the amplitudes of the analog signals are
digitized and the digital values obtained are recorded in a memory. By
means of a processing unit receiving the stored digital values of the
amplitudes, digital values corresponding to the coordinates to the points
of at least one contour line characterizing the image in a reference line
X, Y are determined, the values of these coordinates then being stored.
Using the processing unit receiving the digital values of the coordinates
of the contour line points, successive segments whose ends are located on
this line are coded, these codes then are stored at the same time as the
digital values of the coordinates of the ends of the corresponding
segments. For each segment of the line to be recognized and the reference
contour and on the basis of coded values of the segments and the
coordinates of their ends, a pair of characteristic values .rho.i,
.theta.i are obtained and, in curvilinear coordinates, correspond
respectively to the length .rho.i of each segment and to the angle
.theta.i formed by the segment with respect to a reference direction. The
pairs of characteristic values obtained respectively for the segments of
the contour to be recognised and the reference contour are compared.
This method is particularly ineffective, when the image has continuous
contours for which it is then necessary to record a large amount of
information for adjacent points. The main disadvantage of this method is
that it unnecessarily fills up the memory of the processing unit making it
possible to analyze the contour on the basis of these segments.
One of the methods making it possible to carry out a comparison between the
segments corresponding to the contour line of the image to be recognized
and the segments corresponding to the contour line of the reference image
consists of using "developed codes" of the segments defining the contour
to be recognized and the segments defining the reference contour. A
developed code is the curve obtained by carrying on the abscissa the
curvilinear path of the segments and on the ordinate the angle formed
between the considered segment and the horizontal. This curve is called a
"signature". The aforementioned known method then consists of
investigating whether the distance between the signature of the reference
contour and the signature of the contour to be recognized is of a minimum
nature. Thus, when this distance is at a minimum, it is possible to affirm
that the contour to be recognized corresponds to the reference contour.
Thus, the measurement of the distance between the two signatures consists
of measuring the area separating them. The main disadvantage of this
method is that it requires a long processing time.
SUMMARY OF THE INVENTION
The object of the invention is to obviate the above described disadvantages
and more particularly to make it possible to automatically recognize an
image on the basis of a corresponding reference image, without it being
necessary to take up a large amount of space in the memory of a processing
unit and without it being necessary to occupy the processing unit for a
long period of time.
The present invention is therefore directed at a process for the automatic
recognition of an image (I) on the basis of a corresponding reference
image, wherein for both the reference image and the image to be
recognized, the following steps are performed:
obtaining by optoelectronic detection means (C), analog signals whose
amplitudes respectively depend on the intensity levels of the light rays
reflected from the image,
digitizing the values of the amplitudes of the analog signals, the digital
values obtained being recorded in a memory (M),
determining, by means of a processing unit (UT) receiving the stored
digital values of the amplitudes, digital values corresponding to the
coordinates of the points of at least one contour line (L) characterizing
the image in a reference plane (XY), the values of these coordinates then
being stored,
coding, by means of the reference unit (UT) receiving the digital values of
the coordinates of the points of the contour line (L), successive segments
(Si), whose ends are located on the line, the coded segments then being
stored at the sametime as the digital values of the coordinates of the
ends of the corresponding segments,
obtaining, for each segment of the contour to be recognized and the
reference contour, and on the basis of the coded segments (Si) and
coordinates of their ends, a pair of characteristic values .rho.i,
.theta.i, which respectively correspond in curvilinear coordinates to the
length .rho.i of each segment and to the angle .theta.i formed by the
segment with a reference direction,
comparing the pairs of characteristic values obtained respectively for the
segments of the contour to be recognized and the reference contour,
wherein the comparison includes investigating, on the basis of these
pairs, coincidences between large corresponding characteristic segments of
the reference contour and the contour to be recognized, a large segment
resulting from a contour being a segment which can be inscribed in a
rectangle of limiting dimensions .DELTA..rho..sub.s,.DELTA..theta..sub.s,
a large segment for which .DELTA..rho..sub.s is larger than
.DELTA..theta..sub.s said to be a rectilinear characteristic of the
contour (.DELTA..rho..sub.s is locally the greatest length of a segment
which is compatible with an angular variation .DELTA..theta..sub.s), a
large segment for which .DELTA..theta..sub.s is larger than
.DELTA..rho..sub.s being called the angle variation characteristic of the
contour (.DELTA..theta..sub.s being locally the greatest angular variation
compatible with a segment length .DELTA..rho..sub.s), a large segment A of
the reference contour coinciding with a large segment B of the contour to
be recognized for a displacement (.rho..sub.o,.theta..sub. o), if by
making coincide the origin of the reference defining A and the point
(.rho..sub.o,.theta..sub.o) in the reference defining B, the following
properties are proved in curvilinear coordinates
.rho..sub.A -.rho..sub.B -.rho..sub.o <.DELTA..rho..sub.1
.theta..sub.A -.theta..sub.B -.theta..sub.o <.DELTA..theta..sub.1
wherein .DELTA..rho..sub.1 and .DELTA..theta..sub.1 are fixed.
According to another feature, the investigation of the coincidences also
consists in the developed images (DL1, DL2) on the basis of the respective
segments of the two contour lines of the reference image and the image to
be recognized in a reference (.rho., .theta.) of bringing the unknown
developed image DL.sub.2 to a length equal to the length of the developed
reference image DL.sub.1 by a homothetic transformation of its evolute of
ratio L.sub.1 /L.sub.2 and then, for these two images centered around the
mean value (.theta..sub.o) and both having the same length (L.sub.1)
determining the area (A) separating said two images, the comparison of
this area with a threshold making it possible to affirm, when it is below
the threshold, that the image to be recognised corresponds to the
reference image.
BRIEF DESCRIPTION OF THE DRAWINGS
A more complete appreciation of the invention and many of the attendant
advantages thereof will be readily obtained as the same becomes better
understood by reference to the following detailed description when
considered in connection with the accompanying drawings, wherein:
FIG. 1 is a schematic block diagram of an apparatus making it possible to
perform the process according to the invention;
FIG. 2 is a representation of, a contour line of an object or image
obtained during the performance of the process according to the invention,
the contour line then being segmented.
FIG. 3 is a graph illustrating segmentation of a contour line in accordance
with the so-called Freeman code;
FIG. 4 is a diagramatic representation of Freeman vectors associated with
the Freeman code;
FIG. 5 is a graph illustrating, diagrammatically, a segmented contour line
in a cartesian coordinate X, Y giving a better understanding of the
representation of the contour line in curvilinear polar coordinates;
FIG. 6 is a diagrammatic representation of, a contour line in a cartesian
coordinate X, Y and the developed image of the contour line in curvilinear
polar coordinates in a reference (.rho., .theta.).
FIG. 7 is a diagrammatic example of a hexagonal contour line in a cartesian
coordinate X, Y and the developed image of the contour line in polar
curvilinear coordinates in a reference (.rho., .theta.).
FIG. 8 is a diagrammatic representation of a developed image of a contour
line permitting a better understanding of one of the performance modes of
the process according to the invention, in which use is made of the
so-called "large segment" principle;
FIG. 9 is a diagrammatic representation of a contour line in a cartesian
coordinate X, Y permitting a better understanding of the "large segment"
method;
FIGS. 10, 11 and 12 are diagrammatic representations of a developed image
in polar curvilinear coordinates designed to provide a better
understanding of certain definitions regarding the developed image of a
segmented contour line.
FIG. 13 is a diagrammatic representation of a developed image in polar
curvilinear coordinates of another embodiment of the process according to
the invention, in which use is made of the area separating the developed
images corresponding respectively to the lines of the reference contour
and the contour to be recognized.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
Referring now to the drawings, wherein like reference numerals designate
identical or corresponding parts throughout the several views
FIG. 1 diagrammatically shows an apparatus making it possible to perform
the process according to the invention. This apparatus includes
optoelectronic detection means C e.g. constituted by a video camera. These
detection means supply analog signals, whose amplitudes respectively
depend on the intensity levels of the light rays from an image or object I
to be recognized and a reference image or object. The apparatus according
to the invention makes it possible to compare these two images. The
optoelectronic means C are connected to a processing unit UT, e.g. by
means of an analog-digital converter CAN.
This processing unit is itself connected to a memory, as well as to a
display means V, making it possible e.g. to monitor the operation of the
apparatus. An information or data input console CE is connected to the
processing unit UT. The analog-digital converter CAN makes it possible to
convert the values of the amplitudes of the signal supplied by the
optoelectronic means C into digital values, which are then recorded in a
memory M.
Following this digitization of the signal supplied by the optoelectronic
means, the process of the invention, both for the object or image to be
recognized and for the reference image or object, determines by means of
the processing unit (UT), digital values corresponding to coordinates of
points of a contour line characteristic of the reference image and
corresponding to coordinates of the points of this contour line for the
image to be recognized. These digital values are recorded in the
processing unit memory M.
Thus, for example, if the image to be recognized is a surface I having a
dark colour on a light background and the reference image is itself a
surface of the same shape and size and of dark color on a light
background, processing unit UT makes it possible to record in memory M
digital values corresponding respectively to coordinates of the points of
a contour line L for the reference image and to the coordinates of the
points of a contour line of the same shape and size for the image to be
recognized. The contour line L is shown in FIG. 2.
Preferably, according to the process of the invention, segments of the
contour line are coded in accordance with the Freeman vector coding
method. This coding method can be better understood by referring to FIGS.
3 and 4. It is assumed that the contour L of the image to be recognized or
the reference image shown in FIG. 3 is spatially digitized in a matrix
representing M.times.N points in a reference plane X, Y. The Freeman
coding method consists of coding elementary displacements performed along
a segmented contour line L. In the selected matrix representation, in
which the matrix is square (M=N), there are only 8 orientation
possibilities for passing from one point to another contour. These 8
possibilities are represented by the 8 Freeman vectors numbered 0 to 7 in
FIG. 4. These 8 vectors are in fact displaced by .pi./4 in the considered
matrix representation example. Thus, the Freeman vectors make it possible
to define successive coded oriented segments, whose ends are located in
the vicinity of each contour line. The coordinates of these ends are
obviously known on the basis of coordinates of the points of each contour
line.
In the segmented contour example L shown in FIG. 3, if the starting point
for the course of this contour is point A of coordinates 2 and 3 in the
chosen matrix, the Freeman chain associated with contour L can be
expressed, as a function of the vector numbers of FIG. 4, in the following
way:
7, 0, 1, 6, 6, 6, 5, 4, 3, 2, 2, 2.
This coding is particularly interesting because it shows the orientation of
the segments of the contour line relative to the reference direction X.
The following operation consists of defining code segments Si, whose ends
are located on each contour line, so as to bring about an optimum approach
of each contour line by a segmented contour line.
This is followed by a comparison of the coded segments corresponding to the
contour line of the image to be recognized and the coded segments
corresponding to the contour lines of the reference image. For each of the
contour lines of the reference image and the image to be recognized, this
comparison firstly consists of obtaining for each segment, on the basis of
digital values corresponding to the ends of these segments, a pair of
characteristic values corresponding respectively to the angle
.theta..sub.1 formed by the segment with respect to the reference
direction X, as well as the length .rho..sub.i of the segment. This
amounts to carrying out a curvilinear polar coding of each segment. The
curvilinear abscissa of each segment end is recorded, together with
corresponding orientation of the segment. In FIG. 5 in cartesian
coordinates, the coordinates of points A, B, C . . . F are respectively
(X.sub.0, Y.sub.0), (X.sub.1, Y.sub.1), (X.sub.2, Y.sub.2) . . . (X.sub.i,
Y.sub.i). For example, segment AB is defined by the coordinates of its
ends. In curvilinear polar coordinates, each segment is defined by the
curvilinear abscissa of its end and by its orientation: .rho..sub.1,
.theta..sub.1 e.g. for segment AB, if A is the starting point of the
contour. Segment BC is defined by its curvilinear abscissa .rho..sub.1,
its length .rho..sub.2 and its orientation .theta..sub.2. The curvilinear
polar coding described hereinbefore leads to the translation of the
segmented contour line being invariable and to a rotation being translated
by a "continuous component", on the complete coded segments, the
orientation of each segment being increased by the rotation angle value.
The comparison of the segments then consists of investigating whether there
is a group of coincidences, respectively between the pairs of
characteristic values (.rho..sub.i, .theta..sub.i) corresponding to the
contour line of the image to be recognized and the pairs of characteristic
values corresponding to the contour line of the reference image.
This coincidence group investigation will now be described in greater
detail with the aid of FIG. 6. It consists of detecting a group of
coincidences between the pairs of characteristic values corresponding to
the contour line of the image to be recognized and the pairs of
characteristic values corresponding to the contour line of the reference
image on the basis of developed images of these two lines in a cartesian
coordinate .rho., .theta.. In the latter, on the abscissa is plotted
curvilinear abscissas .rho..sub.i of segments Si of the reference contour
or the contour to be recognized. On the ordinate is plotted the angles
.theta..sub.i formed respectively by the segments of the contour to be
recognized or the reference contour with the hereinbefore defined
reference direction X. The developed image of a contour line L in
coordinate X, Y is represented by DL in coordinate .rho., .theta..
FIG. 7 permits a better understanding of the obtaining of a developed image
in curvilinear polar coordinates. FIG. 7 shows in a cartesian coordinate
X, Y, a hexagon whose apices are shown at A, B, C . . . . These apices
have as their respective coordinates (X.sub.1,Y.sub.1), (X.sub.2,Y.sub.2),
(X.sub.3,Y.sub.3). The segments AB, BC have the respective lengths
.rho..sub.1, .rho..sub.2, . . . . The angles formed by these segments with
the reference direction X are respectively .theta..sub.1, .theta..sub.2, .
. . . It is assumed in FIG. 7 that in the representation in curvilinear
polar coordinates .rho., .theta., the hexagon L is traversed in direction
AB from point A. In coordinate .rho., .theta., the developed image of the
hexagon is a succession of "steps", the first step is parallel to axis
.rho. and has as ordinates .theta..sub.1. Its length is that of segment AB
and consequently corresponds to the curvilinear abscissa of end B of the
segment, i.e. .rho..sub.1 .multidot.A vertical segment parallel to axis
.theta. is then found, whose ordinate is .theta..sub.2. In the same way,
the second step corresponds to segment BC and it is parallel to axis .rho.
and has a length .rho..sub.2, its curvilinear abscissa being .rho..sub.1
+.rho..sub.2.
In order to investigate the coincidences between the contour line of the
reference image and the contour line of the image to be recognized, the
developed images of these lines are represented in a coordinate .rho.,
.theta. in such a way as to investigate these coincidences on the basis of
the superimposing of these two developed images.
An attempt will now be made to establish whether it is possible to simplify
the developed images of the reference contour line and the contour line of
the image to be recognized.
It will be investigated whether there is an elementary group of developed
images corresponding to a group of segments, which can be reduced to a
single resultant developed image corresponding to a single resultant
segment, the latter continuing to define a portion of the considered
contour line. As will be shown hereinafter, this resultant segment must
remain within a predetermined angular tolerance relative to the angle
formed by the first segment of the considered group and the reference
direction. The investigation of the coincidences will then be carried out
on developed images in which the resultant developed images have been
investigated.
FIGS. 8 and 9 provide a better understanding of this performance of the
process according to the invention. FIG. 8 shows a developed image of a
contour in curvilinear polar coordinates in a reference .rho., .theta.. If
the developed image DL shown in FIG. 8 corresponds e.g. to the development
of the contour line of the reference image, there is obviously a not shown
developed image for the contour line of the image to be recognized, which
must be compared with the reference developed image shown in the drawing.
The term large segment or resultant segment of a contour line will be used
for any succession of segments, whose developed coding is written in a
rectangle of size .DELTA..rho.,.DELTA..theta., as shown in FIG. 8.
If .DELTA..rho. is larger than .DELTA..theta., the resultant segment in a
cartesian coordinate X, Y can be called "rectilinear characteristic of
length .DELTA..rho.". If .DELTA..rho. is smaller than .DELTA..theta., the
resultant segment in cartesian coordinate X, Y can be called
"characteristic of angle .DELTA..theta.".
As shown in FIG. 9, a rectilinear characteristic means on contour line L in
a cartesian coordinate X, Y that the group of segments AB, BC, CD, DE, FG
can be approached or approximated by a single segment AG, if all the
segments are included in an angle .theta..sub.1 relative to the angle
.DELTA..theta. formed by the first segment AB of this group and the
reference direction X. In the developed image of FIG. 8, this group of
segments is contained in the rectangle of dimensions .DELTA..rho.,
.DELTA..theta. (point G having for its curvilinear abscissa the value
.rho..sub.1 +.DELTA..rho.).
If all the segments of the group have directions remaining in a
predetermined angular tolerance .DELTA..theta. relative to the direction
.theta..sub.1 of the first segment, these segments can be approached or
approximated by a single segment AG, which is translated into the
developed image of FIG. 8 by the replacement of the developed images of
these segments contained in rectangle .DELTA..rho., .DELTA..theta. by a
single developed image represented by a single step of length
.DELTA..rho.. An investigation will now be made as to whether it is
possible to carry out the same simplification for other portions of the
considered contour line.
The characteristic extraction algorithm consists of calculating for each
segment origin the greatest length .DELTA..rho..sub.s compatible with an
imposed angular error .DELTA..theta..sub.o and the largest angular error
.DELTA..theta..sub.s compatible with the imposed length
.DELTA..rho..sub.o.
The algorithm then classifies the large characteristic segments into two
tables with their curvilinear coordinates:
rectilinear characteristics: .DELTA..rho..sub.i, .rho..sub.i, .theta..sub.i
by decreasing order of the values .DELTA..rho..sub.i,
angle characteristics: .DELTA..theta..sub.j, .rho..sub.j, .theta..sub.j by
decreasing order of the values .DELTA..theta..sub.j.
The classification stops as soon as the characteristic becomes
non-significant (as soon as it drops below the largest characteristic
divided by a predetermined integer, e.g. 3).
According to this first embodiment of the process according to the
invention, the coincidences between the extracted characteristics for the
reference contour line and for the contour line to be recognized will be
investigated by superimposing these two extracted characteristics.
Similarities are made to appear in the overall characteristics extracted:
the large segments. Two large or resultant segments are said to be
compatible to within (.rho..sub.l, .theta..sub.l) of their lengths are
equal to within .rho..sub.l and their widths are equal to within
.theta..sub.l.
A large segment A (.rho..sub.A, .theta..sub.A, .DELTA..rho..sub.A,
.DELTA..theta..sub.A) is said to coincide with a large segment B
(.rho..sub.B, .theta..sub.B, .DELTA..rho..sub.B, .DELTA..theta..sub.B) for
a displacement (.rho..sub.o, .theta..sub.o) if on bringing about the
coincidence between the origin of the developed reference defining A with
the point (.rho..sub.o, .theta..sub.o) in the developed reference defining
B, the following properties are proved:
the curvilinear abscissas of the large segments A and B have an error or
variation given within a threshold .DELTA..rho..sub.l :
.rho..sub.A -.rho..sub.B -.rho..sub.o <.DELTA..rho..sub.l
the angles corresponding to the coordinates of the large segments A and B
have a variation or error below a given threshold .DELTA..theta..sub.l :
.theta..sub.A -.theta..sub.B -.theta..sub.o <.DELTA..theta..sub.l,
the dimensions of the large segments being compatible to within
(.rho..sub.l, .theta..sub.l).
Thus, the coincidence of these two large characteristic segments means that
two corresponding images contain two characteristic lines which are
"alike", located on the same curvilinear abscissa and having an identical
orientation (to within the tolerance variations). The recognition
algorithm based on the principle of coincidences consequently brings about
coincidence between a characteristic large segment of the image to be
recognized and a characteristic large segment of a reference image. If
these large segments satisfy the likeness criteria, the displacement
vector (.rho..sub.o, .theta..sub.o) is calculated, followed by an
investigation as to whether the other large segments of the image to be
recognized coincide with the large segments of the reference image.
The number of marked coincidences constitutes the recognition criterion. It
is considered that the image to be recognized corresponds to the reference
image as a function of the value of the number of detected coincidences.
In practice, this algorithm is very fast, because the number of large
segments is generally limited.
According to a second embodiment, the process according to the invention
includes combining the first mode with a known distance measuring process.
This known process consists in the developed images of two contour lines
of the reference image and the image to be recognized, in the coordinate
.rho., .theta., of calculating the mean angular values .theta..sub.m1 and
.theta..sub.m2 respectively corresponding to the angular mean values of
the segments of these lines. The mean values in question are then centered
on a common value .theta..sub.o in the two developed images of the two
lines. Homothetic transformation with a constant ratio is used for
obtaining in developed images of two contour lines, equal sums of segment
lengths. Following the treating of these two images, the area separating
them are determined and the determined area represents the likeness
between said two developed images. The comparison of the determined area
with a limiting area of threshold makes it possible to affirm when the
representative area is below the threshold that the image to be
consequently recognized corresponds to the reference image.
FIGS. 10 and 11 will permit a better understanding of this coincidence
investigation procedure. FIG. 10 shows the developed image DL of the
reference contour line or the contour line to be recognized. This
developed image in coordinate .rho., .theta. can be qualified as the
"signature" of the contour line. The signature of a closed, segmented
contour is in fact its developed image. In this developed image, the
curvilinear abscissa is limited between 0 and the perimeter P of the
contour. This perimeter can be called the "signature length", or
alternatively the developed image length. This length extends between 0
and P in the drawing. The representation of a segmented contour by its
developed image is biunivocal. In the represented example, the initially
considered segment on the contour line forms an angle .theta..sub.1 with
the reference direction X in a cartesian coordinate X, Y. The considered
contour being assumed as closed, the signature extends over 2.pi. on axis
.theta..
The signature or "standardized developed image" in the coordinate .rho.,
.theta. is that representing a mean zero angle value in the coordinate. A
developed image or signature is standardized by calculating the mean value
.theta..sub.m of its angle over the totality of its length or perimeter
(mean value of the angle .theta. formed by segments Si with the reference
direction X in coordinate X, Y).
The standardized developed image or standardized signature is shown in FIG.
11. The standardized developed image or signature can be now considered in
reference (.rho.', .theta.). Thus, this standardization consists of
calculating the mean values .theta..sub.m of the angles .theta..sub.i
formed in the segmental contour line by the different segments with the
reference direction. The reference (.rho., .theta.) is in fact a
translated reference of reference (.rho.', .theta.) of a value
.theta..sub.m. In this other embodiment of the process according to the
invention, mean values .theta..sub.m are sought for the developed images
or signatures of the reference contour of the contour to be recognized.
In this embodiment of the process according to the invention, there is an
evaluation of the likeness between the developed images or signatures of
the reference contour line and the line of the contour to be recognized.
The first operation performed in this embodiment is to investigate the
mean value .theta..sub.m of the angles for the segments for each of these
contours, so as to be able to center the signatures on the same value
.theta..sub.o, which can have a value .theta..sub.o =0. The likeness can
only be evaluated if the two developed images or signatures are
standardized (.theta..sub.m centered on .theta..sub.o for the two
developed images) and if the two developed images or signatures have the
same length or perimeter P.
FIG. 13 shows in coordinates .rho., .theta., developed images or signatures
DL.sub.1, DL.sub.2 corresponding respectively to the contour lines of a
reference image and an image to be recognized and not shown in FIG. 13.
These two signatures are standardized (centered on the mean value
.theta..sub.m =0) and have the same length or perimeter. As the lengths or
perimeters are equal in the two developed images, this in fact corresponds
in the two segmented contour lines to obtaining equal segment length sums
for these two lines.
The distance between the two developed images or segmented signatures
DL.sub.1 and DL.sub.2 in standardized form and having the same length is
equivalent to the sum, in absolute values, of the areas defined by the
segments representing said signatures. This sum is in fact the hatched
area A in the drawing. This area represents the likeness between the two
developed images or signatures. Therefore, it represents the likeness
between the segmented lines of the contours of the reference image and the
image to be recognized. Thus, it is possible to fix a limit or threshold,
such that when area A separating the two developed images or signatures is
below this threshold, it is possible to conclude that the image to be
recognised corresponds to the reference image.
In the second embodiment of the process according to the invention, it is
possible to combine the known signature comparison process described
hereinbefore with the first embodiment, in which are used the rectilinear
characteristics and the large segment angle characteristics. Thus, the
advantages of the first embodiment of the process according to the
invention are combined with those of the known process, i.e. rapidity of
sorting the rectilinear characteristics and angle characteristics and the
high accuracy of the correlation method on the signatures. In this second
embodiment of the invention, the algorithm is as follows. The number of
coincidences between the large segments of the image or the contour to be
recognised and the image or the reference contour is calculated. This
leads to the aforementioned coincidence table. Each column contains the
number of coincidences obtained, called coincidence notes.
At the end of this processing, the likeness of the signatures defined
hereinbefore is measured for the reference image which had the greatest
number of coincidences with the image to be recognized. The abscissa taken
as the origin on the image or contour to be recognised is the abscissa of
the first large segment. The abscissa taken as the origin on the reference
contour or image is that of the corresponding large segment. This likeness
measurement is only carried out for a small number of displacements (e.g.
approximately 1/20 of the perimeter) of the image or contour.
If, as a result of this calculation, the minimum distance found is too
great, the immediately lower "note" is taken from the characteristics
table provided that it remains acceptable (above a minimum value) and the
likeness measurement is recommenced with the new reference image.
Obviously, numerous modifications and variations of the present invention
are possible in light of the above teachings. It is therefore to be
understood that within the scope of the appended claims, the invention may
be practiced otherwise than as specifically described herein.
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