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WO1999003065A1 - Method for detecting and/or determining characteristics related to remarkable points of an image - Google Patents

Method for detecting and/or determining characteristics related to remarkable points of an image Download PDF

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Publication number
WO1999003065A1
WO1999003065A1 PCT/FR1998/001418 FR9801418W WO9903065A1 WO 1999003065 A1 WO1999003065 A1 WO 1999003065A1 FR 9801418 W FR9801418 W FR 9801418W WO 9903065 A1 WO9903065 A1 WO 9903065A1
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image
points
point
anisotropy
terms
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PCT/FR1998/001418
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French (fr)
Inventor
Naamen Keskes
Pierre Baylou
Sébastien Guillon
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Elf Exploration Production
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Priority to EP98935085A priority Critical patent/EP0923764A1/en
Priority to CA002264903A priority patent/CA2264903A1/en
Publication of WO1999003065A1 publication Critical patent/WO1999003065A1/en
Priority to NO990883A priority patent/NO990883L/en

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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30Analysis
    • G01V1/301Analysis for determining seismic cross-sections or geostructures
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/10Segmentation; Edge detection
    • G06T7/12Edge-based segmentation
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • G06T2207/20164Salient point detection; Corner detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • G06T2207/30101Blood vessel; Artery; Vein; Vascular
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30181Earth observation
    • G06T2207/30184Infrastructure

Definitions

  • the present invention relates to a method for detecting and / or determining characteristics linked to remarkable points of a given multidimensional image and capable of being implemented, in particular for the detection of virtual contours which may be present in said image.
  • the virtual contours result from the proximity of remarkable points grouped or not grouped in elongated supports, said remarkable points being in particular junction points, angular points or even termination points.
  • a human observer when studying or interpreting an image, whatever the nature of said image, attempts to determine the contours of what could be considered to be a characteristic event of the image.
  • the transposition of this faculty of observation of the human eye into an automatic interpretation system proves to be very delicate and it is, in general, difficult to develop and apply operators giving an account of a subjective impression.
  • a virtual contour sometimes designated by the expressions of subjective or perceptual contour, is identified by the eye in two stages, one detecting the endings and the other trying to establish the relations of connectedness between said endings.
  • VS ⁇ are pairs of neighboring pixels (in the sense of 4v),
  • G j and G are the modules of the gradients at positions i and j of two neighboring pixels, and cq; is the angle formed by the gradient vectors of pixels i and j.
  • the object of the present invention is to propose a method which makes it possible to transform a given initial image into another image or representation in which specific characteristics of the initial image are highlighted. This method considers the directional coherence of the gradient vectors according to cliques of order 2.
  • Another object of the present invention is to propose a method which is applicable whatever the nature of the initial image, such as for example a seismic image, a medical image in which one wishes to highlight in particular the branching or the division blood vessels, an aerial image to highlight in particular a crossing of roads, a place or a characteristic object.
  • a seismic image in which we wish to highlight seismic characteristics relating in particular to seismic horizons, to zone boundaries (contours), to chaotic or channeling textures, without considering this enumeration as exhaustive.
  • An object of the present invention is a method of detecting and determining characteristics of a multidimensional image linked to remarkable points of said image, which is characterized in that it consists in evaluating the variability of a local dip of a first point of the image with respect to at least one other point located in the vicinity of said first point, by calculating the local anisotropy on the field of gradients of said point, said anisotropy being dependent on terms related to a dispersion of the orientations and to the modulus of the gradient vectors, and in that at least one of said terms is weighted. According to another characteristic, each of said terms is weighted.
  • the weighting is carried out by raising at least one of said terms to a power.
  • each of the terms is raised to a power whose power factor is different from one term to another.
  • the weighting is carried out according to the nature of the remarkable point to be detected.
  • the method is applied to a seismic image.
  • the points to be detected are points selected from the junction points, the angular points or the end points.
  • An advantage of the present invention resides in the fact that only the remarkable points which have a discontinuous character are detected, and this, by means of an operator for measuring the local anisotropy of the field of gradients around said remarkable points because there is noted that locally, along a contour, the gradients have a strongly anisotropic distribution with a dominant direction orthogonal to said contour, while at the remarkable points the distribution of the gradients is more isotropic.
  • Another advantage of the present invention is that the operator is configurable, which makes it possible to adapt it as a function of the type of remarkable point to be detected and / or to be highlighted as well as the signal / noise ratio. In other words, we can adjust the weighting of each operator terms depending on the nature of the remarkable point to be detected.
  • FIG. 1 is an image extracted from a seismic section having a fault and a channel
  • - Figure 2 is a representation of the anisotropy of the image of Figure 1
  • - Figure 3 is an image extracted from a seismic section having a channel
  • Figures 4 and 5 are representations of the anisotropy of the image of Figure 3.
  • the present invention uses an operator which makes it possible to analyze the local dispersion of the orientations of the gradient vector of the pixels of an image.
  • the gradients of the points (pixels) have a strongly anisotropic distribution, that is to say that the dominant direction is orthogonal to the contour, except at break points, angular points or triple points on which the distribution of the gradients is more isotropic.
  • the degree of anisotropy of the distribution of the gradients is measured.
  • the anisotropy is determined locally for each pixel of an image rather than globally over the entire image.
  • the determination which is carried out consists in calculating the orientation differences for pairs of neighboring points, for example for pairs of neighboring pixels, said differences being weighted by the modules of the gradients of the pixels considered.
  • Another aspect of the invention is to change the normalization factor which is represented by the denominator of formulas (1) or (2), which leads to favor the pixels of strong gradient.
  • the determination of the anisotropy to be carried out is then calculated by the formula:
  • the points to be detected having different properties depending on the type of image to be processed, it is proposed according to another aspect of the invention to weight the angular dispersion factor of the gradients represented by the numerator of the formula (3) and / or the normalization factor.
  • Gi and G are the modules of the gradient vectors G ⁇ and Gj with the neighboring pixels i and j considered and which belong to the set of cliques of order 2, in the sense of the neighborhood 4v, in the plane the gradient used being in particular the gradient of DERICHE as explained for example in the book by JP COCQUEREZ and S.
  • n is a parameter which manages the influence of the amplitude of the gradients
  • - p is a parameter which manages the influence of the angular variation of the pairs of vector gradients on the anisotropy
  • q is a parameter which also manages the influence of the amplitude of the gradients and which must be combined with the parameter n, because when one increases the parameter q, one limits the effect of the parameter n and one attenuates the contribution of strong gradients.
  • the parameter n is increased and the parameter q is decreased. This makes it possible to limit false detections in noisy regions in which the points have an isotropic gradient field but small amplitudes.
  • the sinP function becomes very selective around ⁇ / 2 and we only detect at the limit the points of the contours located on a right angle.
  • the large angles are favored and the anisotropy is then sensitive only to strong angular variations.
  • FIG. 1 is an image extracted from a seismic section presenting a fault F and a channel C.
  • n greater than q (n> q) makes it possible to take more account of the contrasting regions.
  • p greater than 1 makes it possible to be more selective on the dip variations.
  • the anisotropy is calculated locally on an observation window, by summing the terms on the pairs of points which are neighbors in the 4v sense.
  • a 5x5 or 7x7 size window is enough to give good results. If we want to weight the contribution of each pair of points as a function of their difference in the center of the observation window, we can use a low-pass filter of the DERICHE type whose impulse response is:
  • Two images are then calculated, one being representative of the numerator and the other being representative of the denominator of formula (5), considering only the pairs of neighboring points including the current point.
  • Each component is then filtered recursively before calculating the isotropy as the ratio of two filtered images.
  • transition from a two-dimensional (2D) image to a three-dimensional (3D) image is a simple extension by taking for each point the 3D gradient and considering the neighborhood in the sense of 6v.

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  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
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  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geophysics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
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Abstract

The invention concerns a method for detecting and/or determining characteristics related to remarkable points in an image, characterised in that it consists in evaluating the local dip variability of a first point in the image relative to at least another point located in the proximity of said first point, by computing the local anisotropy on said point gradient field, said anisotropy being dependent on terms related to a dispersion of orientations and to the module of gradient vectors, and at least one of said terms is weighted.

Description

Méthode de détection et/ou de détermination de caractéristiques liées à des points remarquables d'une image Method for detecting and / or determining characteristics linked to remarkable points in an image
La présente invention concerne une méthode de détection et/ou de détermination de caractéristiques liées à des points remarquables d'une image multidimensionnelle donnée et susceptible d'être mise en oeuvre pour notamment la détection de contours virtuels susceptibles d'être présents dans ladite image.The present invention relates to a method for detecting and / or determining characteristics linked to remarkable points of a given multidimensional image and capable of being implemented, in particular for the detection of virtual contours which may be present in said image.
Les contours virtuels résultent de la proximité de points remarquables regroupés ou non dans des supports longiformes, lesdits points remarquables étant notamment des points de jonction, des points anguleux ou encore des points de terminaison. Un observateur humain, lorsqu'il étudie ou interprète une image, quelle que soit la nature de ladite image, tente de déterminer les contours de ce qui pourrait être considéré comme étant un événement caractéristique de l'image. La transposition de cette faculté d'observation de l'oeil humain dans un système d'interprétation automatique s'avère très délicate et il est, en général, difficile d'élaborer et d'appliquer des opérateurs rendant compte d'une impression subjective. En général, un contour virtuel, désigné parfois par les expressions de contour subjectif ou perceptuel, est repéré par l'oeil en deux étapes, l'une détectant les terminaisons et l'autre s'efforçant d'établir les relations de connexité entre lesdites terminaisons. II existe des méthodes susceptibles de détecter des points remarquables d'une image. Une des méthodes est due aux travaux de J.P. COCQUEREZ et S. PHILIPP décrits dans un livre intitulé "Analyse d'images : filtrage et segmentation" (MASSON, 1995). Une autre méthode, due aux travaux de P. BAYLOU et al. , est décrite dans un article intitulé "Evaluation de l'anisotropie des textures. Comparaison des méthodes appliquées à la caractérisation de matériaux" GRETSI 95, JUAN LES PINS, pages 1245-1248, 1995.The virtual contours result from the proximity of remarkable points grouped or not grouped in elongated supports, said remarkable points being in particular junction points, angular points or even termination points. A human observer, when studying or interpreting an image, whatever the nature of said image, attempts to determine the contours of what could be considered to be a characteristic event of the image. The transposition of this faculty of observation of the human eye into an automatic interpretation system proves to be very delicate and it is, in general, difficult to develop and apply operators giving an account of a subjective impression. In general, a virtual contour, sometimes designated by the expressions of subjective or perceptual contour, is identified by the eye in two stages, one detecting the endings and the other trying to establish the relations of connectedness between said endings. There are methods capable of detecting remarkable points of an image. One of the methods is due to the work of J.P. COCQUEREZ and S. PHILIPP described in a book entitled "Image analysis: filtering and segmentation" (MASSON, 1995). Another method, due to the work of P. BAYLOU et al. , is described in an article entitled "Evaluation of the anisotropy of textures. Comparison of the methods applied to the characterization of materials" GRETSI 95, JUAN LES PINS, pages 1245-1248, 1995.
Toutefois, ces méthodes proposent de calculer l'anisotropie globale d'une image qui est donnée par la formule : G, G, i '..) However, these methods propose to calculate the global anisotropy of an image which is given by the formula: G, G, i '.. )
Iso = klIso = kl
G, G, d) ι.JG, G, d) ι.J
dans laquellein which
C\ ; sont des couples de pixels voisins (au sens de 4v),VS\ ; are pairs of neighboring pixels (in the sense of 4v),
Gj et G; sont les modules des gradients aux positions i et j de deux pixels voisins, et cq ; est l'angle formé par les vecteurs gradients des pixels i et j .G j and G; are the modules of the gradients at positions i and j of two neighboring pixels, and cq; is the angle formed by the gradient vectors of pixels i and j.
De telles déterminations de l'anisotropie globale ne font aucune différence entre les forts et faibles gradients et présentent, en conséquence, un faible pouvoir séparateur ou un pouvoir discriminateur faible puisque les vecteurs gradients de tous les pixels sont pris en considération. De plus, elles nécessitent de calculer tous les angles formés entre les vecteurs gradients ainsi que leurs arguments car cq j = arg(Cq)-arg(Gi) . Enfin, ces méthodes sont très sensibles aux bruits et ne permettent pas de procéder à une distinction entre les vecteurs gradients attachés à un point remarquable et ceux attachés à un bruit. Ainsi, la sélectivité obtenue par ces méthodes est vraiment très réduite.Such determinations of the overall anisotropy make no difference between the strong and weak gradients and therefore have a weak separating power or a weak discriminating power since the gradient vectors of all the pixels are taken into account. In addition, they require to compute all the angles formed between the gradient vectors as well as their arguments because cq j = arg (Cq) -arg (Gi). Finally, these methods are very sensitive to noise and do not allow a distinction to be made between gradient vectors attached to a remarkable point and those attached to noise. Thus, the selectivity obtained by these methods is really very reduced.
La présente invention a pour but de proposer une méthode qui permette de transformer une image initiale donnée en une autre image ou représentation dans laquelle sont mises en valeur des caractéristiques spécifiques de l'image initiale. Cette méthode considère la cohérence directionnelle des vecteurs gradients selon des cliques d' ordre 2.The object of the present invention is to propose a method which makes it possible to transform a given initial image into another image or representation in which specific characteristics of the initial image are highlighted. This method considers the directional coherence of the gradient vectors according to cliques of order 2.
Un autre but de la présente invention est de proposer une méthode qui soit applicable quelle que soit la nature de l'image initiale, comme par exemple une image sismique, une image médicale dans laquelle on veut mettre en évidence notamment l'embranchement ou la division des vaisseaux sanguins, une image aérienne pour mettre en évidence notamment un croisement de routes, un lieu ou un objet caractéristique. Dans ce qui suit on donne l'exemple d'une image sismique dans laquelle on souhaite mettre en valeur des caractéristiques sismiques relatives notamment à des horizons sismiques, à des limites de zones (contours), à des textures chaotiques ou chenalisantes, sans considérer cette énumération comme exhaustive. Un objet de la présente invention est une méthode de détection et de détermination de caractéristiques d' une image multidimensionnelle liées à des points remarquables de ladite image, qui est caractérisée en ce qu'elle consiste à évaluer la variabilité d'un pendage local d'un premier point de l'image par rapport à au moins un autre point situé au voisinage dudit premier point, en calculant l'anisotropie locale sur le champ de gradients dudit point, ladite anisotropie étant dépendante de termes liés à une dispersion des orientations et au module des vecteurs gradients, et en ce qu'au moins un desdits termes est pondéré. Selon une autre caractéristique, chacun desdits termes est pondéré.Another object of the present invention is to propose a method which is applicable whatever the nature of the initial image, such as for example a seismic image, a medical image in which one wishes to highlight in particular the branching or the division blood vessels, an aerial image to highlight in particular a crossing of roads, a place or a characteristic object. In what follows we give the example of a seismic image in which we wish to highlight seismic characteristics relating in particular to seismic horizons, to zone boundaries (contours), to chaotic or channeling textures, without considering this enumeration as exhaustive. An object of the present invention is a method of detecting and determining characteristics of a multidimensional image linked to remarkable points of said image, which is characterized in that it consists in evaluating the variability of a local dip of a first point of the image with respect to at least one other point located in the vicinity of said first point, by calculating the local anisotropy on the field of gradients of said point, said anisotropy being dependent on terms related to a dispersion of the orientations and to the modulus of the gradient vectors, and in that at least one of said terms is weighted. According to another characteristic, each of said terms is weighted.
Selon une autre caractéristique, la pondération est réalisée en élevant au moins un desdits termes à une puissance.According to another characteristic, the weighting is carried out by raising at least one of said terms to a power.
Selon une autre caractéristique, chacun des termes est élevé à une puissance dont le facteur de puissance est différent d' un terme à l'autre.According to another characteristic, each of the terms is raised to a power whose power factor is different from one term to another.
Selon une autre caractéristique, la pondération est effectuée en fonction de la nature du point remarquable à détecter.According to another characteristic, the weighting is carried out according to the nature of the remarkable point to be detected.
Selon une autre caractéristique, la méthode est appliquée à une image sismique. Selon une autre caractéristique, les points à détecter sont des points sélectionnés parmi les points de jonction, les points anguleux ou les points terminaisons.According to another characteristic, the method is applied to a seismic image. According to another characteristic, the points to be detected are points selected from the junction points, the angular points or the end points.
Un avantage de la présente invention réside dans le fait qu'on ne détecte que les points remarquables qui présentent un caractère discontinu et ce, au moyen d'un opérateur de mesure d'anisotropie locale du champ de gradients autour desdits points remarquables car on a constaté que localement, le long d'un contour, les gradients présentent une distribution fortement anisotrope avec une direction dominante orthogonale audit contour, alors qu'aux points remarquables la distribution des gradients est plus isotrope.An advantage of the present invention resides in the fact that only the remarkable points which have a discontinuous character are detected, and this, by means of an operator for measuring the local anisotropy of the field of gradients around said remarkable points because there is noted that locally, along a contour, the gradients have a strongly anisotropic distribution with a dominant direction orthogonal to said contour, while at the remarkable points the distribution of the gradients is more isotropic.
Un autre avantage de la présente invention est que l'opérateur est paramétrable, ce qui permet de l'adapter en fonction du type de point remarquable à détecter et/ou à mettre en valeur ainsi que du rapport signal/bruit. En d'autres termes, on peut ajuster la pondération de chacun des termes de l'opérateur en fonction de la nature du point remarquable à détecter.Another advantage of the present invention is that the operator is configurable, which makes it possible to adapt it as a function of the type of remarkable point to be detected and / or to be highlighted as well as the signal / noise ratio. In other words, we can adjust the weighting of each operator terms depending on the nature of the remarkable point to be detected.
D'autres avantages et caractéristiques ressortiront à la lecture de la description de la méthode selon l'invention, ainsi que des dessins annexés sur lesquels :Other advantages and characteristics will emerge on reading the description of the method according to the invention, as well as the appended drawings in which:
- la figure 1 est une image extraite d'une section sismique présentant une faille et un chenal,FIG. 1 is an image extracted from a seismic section having a fault and a channel,
- la figure 2 est une représentation de l'anisotropie de l'image de la figure 1 , - la figure 3 est une image extraite d' une section sismique présentant un chenal,- Figure 2 is a representation of the anisotropy of the image of Figure 1, - Figure 3 is an image extracted from a seismic section having a channel,
- les figures 4 et 5 sont des représentations de l'anisotropie de l'image de la figure 3.- Figures 4 and 5 are representations of the anisotropy of the image of Figure 3.
La présente invention utilise un opérateur qui permet d'analyser la dispersion locale des orientations du vecteur gradient des pixels d'une image. En effet, localement et le long de contours délimitant, par exemple une faille ou un chenal, comme ceux représentés sur les figures 1 et 3, les gradients des points (pixels) ont une distribution fortement anisotrope c'est-à-dire que la direction dominante est orthogonale au contour, sauf aux points de ruptures, aux points anguleux ou aux points triples sur lesquels la distribution des gradients est plus isotrope. Selon l'invention, on mesure le degré d'anisotropie de la distribution des gradients.The present invention uses an operator which makes it possible to analyze the local dispersion of the orientations of the gradient vector of the pixels of an image. Indeed, locally and along delimiting contours, for example a fault or a channel, like those represented in FIGS. 1 and 3, the gradients of the points (pixels) have a strongly anisotropic distribution, that is to say that the dominant direction is orthogonal to the contour, except at break points, angular points or triple points on which the distribution of the gradients is more isotropic. According to the invention, the degree of anisotropy of the distribution of the gradients is measured.
Afin d'évaluer la variabilité de l'orientation des gradients, on détermine localement l'anisotropie pour chaque pixel d'une image plutôt que globalement sur toute l' image. La détermination qu'on effectue consiste à calculer les différences d'orientation pour des couples de points voisins, par exemple pour des couples de pixels voisins, lesdites différences étant pondérées par les modules des gradients des pixels considérés.In order to evaluate the variability of the orientation of the gradients, the anisotropy is determined locally for each pixel of an image rather than globally over the entire image. The determination which is carried out consists in calculating the orientation differences for pairs of neighboring points, for example for pairs of neighboring pixels, said differences being weighted by the modules of the gradients of the pixels considered.
Si on utilisait la formule générale (1), il faudrait mesurer les arguments des gradients puisque l'angle oq entre les deux directions est égal à arg(G*)-arg(G^.If we used the general formula (1), we would have to measure the arguments of the gradients since the angle oq between the two directions is equal to arg (G *) - arg (G ^.
Pour éviter la mesure des angles α er le numérateur de la formule (1) par ∑G, G y, s ~m
Figure imgf000006_0002
Figure imgf000006_0001
donne la formule suivante :
Figure imgf000007_0001
To avoid measuring angles α er the numerator of formula (1) by ∑G, G y, s ~ m
Figure imgf000006_0002
Figure imgf000006_0001
gives the following formula:
Figure imgf000007_0001
Le calcul qui résulte de l'application de la formule (2) est moins coûteux car seules des multiplications sont à effectuer.The calculation which results from the application of formula (2) is less costly since only multiplications are to be carried out.
La détermination de l'anisotropie effectuée à l'aide de ce calcul s'est révélée peu sensible aux amplitudes des gradients, les zones de faibles gradients pouvant donner des résultats similaires à ceux obtenus pour des zones de forts gradients.The determination of the anisotropy carried out using this calculation proved to be insensitive to the amplitudes of the gradients, the zones of weak gradients being able to give results similar to those obtained for zones of strong gradients.
Un autre aspect de l'invention est de changer le facteur de normalisation qui est représenté par le dénominateur des formules (1) ou (2), ce qui conduit privilégier les pixels de fort gradient. La détermination de l'anisotropie à effectuer est alors calculée par la formule :Another aspect of the invention is to change the normalization factor which is represented by the denominator of formulas (1) or (2), which leads to favor the pixels of strong gradient. The determination of the anisotropy to be carried out is then calculated by the formula:
Figure imgf000007_0002
Figure imgf000007_0002
L'anisotropie locale calculée avec la formule (3) permet alors de détecter uniquement les points caractéristiques de fort gradient alors qu'avec la formule (2), les points sont détectés indépendamment de la norme de leur gradient, seule intervient la dispersion angulaire des gradients.The local anisotropy calculated with formula (3) then makes it possible to detect only the characteristic points of strong gradient whereas with formula (2), the points are detected independently of the norm of their gradient, only the angular dispersion of the gradients.
Les points à détecter ayant des propriétés différentes suivant le type d'image à traiter, il est proposé selon un autre aspect de l'invention de pondérer le facteur de dispersion angulaire des gradients représenté par le numérateur de la formule (3) et/ou le facteur de normalisation.The points to be detected having different properties depending on the type of image to be processed, it is proposed according to another aspect of the invention to weight the angular dispersion factor of the gradients represented by the numerator of the formula (3) and / or the normalization factor.
De la sorte, l'anisotropie est mesurée à l'aide de la formule :In this way, the anisotropy is measured using the formula:
Figure imgf000007_0003
Figure imgf000007_0003
dans laquellein which
Gi et G; sont les modules des vecteurs gradients G\ et Gj aux pixels voisins i et j considérés et qui appartiennent à l'ensemble des cliques d'ordre 2, au sens du voisinage 4v, dans le plan le gradient utilisé étant notamment le gradient de DERICHE tel qu'explicité par exemple dans le livre de J.P. COCQUEREZ et S. PHILIPP "Analyse d'images : filtrage et segmentation" , MASSON, 1995, n est un paramètre qui gère l'influence de l'amplitude des gradients, - p est un paramètre qui gère l'influence de la variation angulaire des couples de vecteurs gradients sur l' anisotropie, q est un paramètre qui gère également l'influence de l'amplitude des gradients et qui doit être combiné avec le paramètre n, car lorsqu'on augmente le paramètre q, on limite l'effet du paramètre n et on atténue la contribution des forts gradients.Gi and G; are the modules of the gradient vectors G \ and Gj with the neighboring pixels i and j considered and which belong to the set of cliques of order 2, in the sense of the neighborhood 4v, in the plane the gradient used being in particular the gradient of DERICHE as explained for example in the book by JP COCQUEREZ and S. PHILIPP "Image analysis: filtering and segmentation", MASSON, 1995, n is a parameter which manages the influence of the amplitude of the gradients, - p is a parameter which manages the influence of the angular variation of the pairs of vector gradients on the anisotropy, q is a parameter which also manages the influence of the amplitude of the gradients and which must be combined with the parameter n, because when one increases the parameter q, one limits the effect of the parameter n and one attenuates the contribution of strong gradients.
Lorsqu'on souhaite détecter les points dont le gradient est de forte amplitude, constitués par exemple par les points de contours bien marqués, alors on augmente le paramètre n et on diminue le paramètre q. Cela permet de limiter les fausses détections dans les régions bruitées dans lesquelles les points présentent un champ de gradient isotrope mais de faibles amplitudes. Lorsqu'on augmente le paramètre p, la fonction sinP devient très sélective autour de π/2 et on ne détecte plus à la limite que les points des contours situés sur un angle droit. Ainsi, lorsqu' on augmente le paramètre p, les angles importants sont privilégiés et l'anisotropie n'est alors sensible qu'aux fortes variations angulaires.When it is desired to detect the points whose gradient is of high amplitude, constituted for example by well-marked contour points, then the parameter n is increased and the parameter q is decreased. This makes it possible to limit false detections in noisy regions in which the points have an isotropic gradient field but small amplitudes. When we increase the parameter p, the sinP function becomes very selective around π / 2 and we only detect at the limit the points of the contours located on a right angle. Thus, when the parameter p is increased, the large angles are favored and the anisotropy is then sensitive only to strong angular variations.
De ce qui précède, on constate que suivant la nature des points qu'on désire détecter, on peut donner à chacun des trois paramètres n, p et q les valeurs les plus appropriées à ces points.From the foregoing, it can be seen that, depending on the nature of the points which it is desired to detect, it is possible to give each of the three parameters n, p and q the values most suitable for these points.
Des exemples d'application sont donnés à propos des figures 1 à 5.Examples of application are given with reference to FIGS. 1 to 5.
La figure 1 est une image extraite d' une section sismique présentant une faille F et un chenal C. Afin de détecter les zones de ruptures qui comprennent des terminaisons ou des points triples et qui sont associées à des régions de forts gradients, on prend n = l , p = 1 ,5 et q = 0,5. Le choix de n plus grand que q(n > q) permet de tenir compte plus fortement des régions contrastées. Le choix de p plus grand que 1 permet d'être plus sélectif sur les variations de pendage.FIG. 1 is an image extracted from a seismic section presenting a fault F and a channel C. In order to detect the zones of ruptures which include terminations or triple points and which are associated with regions of strong gradients, we take n = l, p = 1, 5 and q = 0.5. The choice of n greater than q (n> q) makes it possible to take more account of the contrasting regions. The choice of p greater than 1 makes it possible to be more selective on the dip variations.
Le calcul de l'anisotropie selon la méthode de la présente invention pour l'image conduit à une représentation de ladite anisotropie telle que représentée sur la figure 2. On constate sur la figure 2 que la faille F et le chenal C sont bien détectés.The calculation of the anisotropy according to the method of the present invention for the image leads to a representation of said anisotropy as as shown in Figure 2. We see in Figure 2 that the fault F and the channel C are well detected.
Pour l'image de la figure 3 qui est extraite d'une section sismique, on veut mettre en évidence la présence apparente d'un chenal. Pour cette image de la figure 3, on a effectué deux déterminations de l'anisotropie dont les résultats sont représentés sur les figures 4 et 5.For the image of Figure 3 which is extracted from a seismic section, we want to highlight the apparent presence of a channel. For this image of FIG. 3, two determinations of the anisotropy were carried out, the results of which are shown in FIGS. 4 and 5.
La première détermination est effectuée avec : n = l ,2 ; p=0,5 ; q=0,5 et donne l'enveloppe du chenal qui est bien détecté comme cela apparaît sur la figure 4. La deuxième détermination est effectuée avec : n = 2 ; p=2 ; q=0,5, ce qui permet d'obtenir des points remarquables à l'intérieur du chenal, ainsi que cela apparaît sur la figure 5.The first determination is made with: n = 1.2; p = 0.5; q = 0.5 and gives the envelope of the channel which is well detected as it appears on figure 4. The second determination is carried out with: n = 2; p = 2; q = 0.5, which allows remarkable points to be obtained inside the channel, as shown in Figure 5.
Selon un autre aspect de la présente invention, il est possible de procéder également à un filtrage. Pour cela, on effectue le calcul de l'anisotropie localement sur une fenêtre d' observation, en sommant les termes sur les couples de points qui sont voisins au sens 4v. Une fenêtre de taille 5x5 ou 7x7 suffit à donner de bons résultats. Si l'on désire pondérer la contribution de chaque couple de points en fonction de leur écart au centre de la fenêtre d'observation, on peut utiliser un filtre passe-bas du type DERICHE dont la réponse impulsionnelle est :According to another aspect of the present invention, it is also possible to carry out a filtering. For this, the anisotropy is calculated locally on an observation window, by summing the terms on the pairs of points which are neighbors in the 4v sense. A 5x5 or 7x7 size window is enough to give good results. If we want to weight the contribution of each pair of points as a function of their difference in the center of the observation window, we can use a low-pass filter of the DERICHE type whose impulse response is:
Figure imgf000009_0001
Figure imgf000009_0001
Le choix de permet de gérer la largeur de la fenêtre de calcul ; en pratique on choisit α ≈ 1.The choice of allows you to manage the width of the calculation window; in practice we choose α ≈ 1.
On calcule alors deux images, l' une étant représentative du numérateur et l'autre étant représentative du dénominateur de la formule (5), en ne considérant que les couples de points voisins incluant le point courant.Two images are then calculated, one being representative of the numerator and the other being representative of the denominator of formula (5), considering only the pairs of neighboring points including the current point.
Chaque composante est ensuite filtrée recursivement avant de calculer l'isotropie comme le rapport de deux images filtrées.Each component is then filtered recursively before calculating the isotropy as the ratio of two filtered images.
Le passage d'une image bidimensionnelle (2D) à une image tridimensionnelle (3D) est une simple extension en prenant pour chaque point le gradient 3D et en considérant le voisinage au sens de 6v. The transition from a two-dimensional (2D) image to a three-dimensional (3D) image is a simple extension by taking for each point the 3D gradient and considering the neighborhood in the sense of 6v.

Claims

REVENDICATIONS
1. Méthode de détection et de détermination de caractéristiques d'une image multidimensionnelle liées à des points remarquables de ladite image, caractérisée en ce qu'elle consiste à évaluer la variabilité d'un pendage local d'un premier point de l'image par rapport à au moins un autre point situé au voisinage dudit premier point, en calculant l'anisotropie locale sur le champ de gradients dudit point, ladite anisotropie étant dépendante de termes liés à une dispersion des orientations et au module des vecteurs gradients, et en ce qu'au moins un desdits termes est pondéré.1. Method for detecting and determining the characteristics of a multidimensional image linked to remarkable points of said image, characterized in that it consists in evaluating the variability of a local dip of a first point of the image by relative to at least one other point located in the vicinity of said first point, by calculating the local anisotropy on the field of gradients of said point, said anisotropy being dependent on terms related to a dispersion of the orientations and to the modulus of the gradient vectors, and in this that at least one of said terms is weighted.
2. Méthode selon la revendication 1 , caractérisée en ce que chacun desdits termes est pondéré. 2. Method according to claim 1, characterized in that each of said terms is weighted.
3. Méthode selon la revendication 1 ou 2, caractérisée en ce que la pondération est réalisée en élevant au moins un desdits termes à une puissance.3. Method according to claim 1 or 2, characterized in that the weighting is carried out by raising at least one of said terms to a power.
4. Méthode selon la revendication 1 ou 2, caractérisée en ce que chacun des termes est élevé à une puissance dont le facteur de puissance est différent d'un terme à l'autre.4. Method according to claim 1 or 2, characterized in that each of the terms is raised to a power whose power factor is different from one term to another.
5. Méthode selon l'une des revendications 1 à 4, caractérisée en ce que la pondération est effectuée en fonction de la nature du point remarquable à détecter.5. Method according to one of claims 1 to 4, characterized in that the weighting is carried out according to the nature of the remarkable point to be detected.
6. Méthode selon l'une des revendications 1 à 4, caractérisée en ce qu'elle est appliquée à une image sismique.6. Method according to one of claims 1 to 4, characterized in that it is applied to a seismic image.
7. Méthode selon la revendication 6, caractérisée en ce que les points caractéristiques à détecter sont des points sélectionnés parmi les points de jonction, les points anguleux ou les points terminaisons. 7. Method according to claim 6, characterized in that the characteristic points to be detected are points selected from the junction points, the angular points or the termination points.
PCT/FR1998/001418 1997-07-07 1998-07-02 Method for detecting and/or determining characteristics related to remarkable points of an image WO1999003065A1 (en)

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