WO2020164812A1 - Procédé de reconstruction d'une représentation numérique de caractéristiques d'objet d'un objet d'inspection dans l'espace local d'un système à rayons x - Google Patents

Procédé de reconstruction d'une représentation numérique de caractéristiques d'objet d'un objet d'inspection dans l'espace local d'un système à rayons x Download PDF

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Publication number
WO2020164812A1
WO2020164812A1 PCT/EP2020/050271 EP2020050271W WO2020164812A1 WO 2020164812 A1 WO2020164812 A1 WO 2020164812A1 EP 2020050271 W EP2020050271 W EP 2020050271W WO 2020164812 A1 WO2020164812 A1 WO 2020164812A1
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Prior art keywords
ray
feature
coordinates
pattern
feature pattern
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PCT/EP2020/050271
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German (de)
English (en)
Inventor
Maximilian Wattenberg
Philipp Klein
Thorsten Buzug
Maik STILLE
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Yxlon International Gmbh
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Publication of WO2020164812A1 publication Critical patent/WO2020164812A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]

Definitions

  • the present invention relates to a method for reconstructing a digital representation of object features of an examination object in the spatial area of an X-ray system, which has at least one X-ray tube, at least one X-ray detector and an examination object arranged in between, in which the examination object is irradiated by means of X-rays.
  • the area of application for the invention is X-ray-based material testing.
  • Industrial companies such as the car industry or electronics manufacturers use X-ray systems as part of X-ray-based material testing to test components for properties. These properties include dimensional accuracy, i.e. the conformity of a workpiece with the computer model or the specifications, the integrity of the component or the porosity of the processed materials.
  • the use of X-rays for imaging offers the possibility of examining hidden structures. Such methods are of interest to companies that want to check the spatial position, orientation and extent of one or more object features of the objects to be examined with the aid of imaging methods. This measuring task should be carried out with as few projections as possible, so that such a test needs as little time as possible in order to be able to carry out an inline test of all test objects.
  • the previously used methods for recognizing and reconstructing object features in an examination subject are computed tomography-based examinations in which several hundred projection images are recorded, from which volumetric slice images (tomograms) are then reconstructed.
  • the gray values of the volumetric pixels (voxels) represent approximately the different materials of the examination object.
  • Object features are shown in the tomographic representative tion of the examination object is determined and inspected.
  • the recording of the projection images, the reconstruction of the tomogram and the measurement task take a long time to complete.
  • the object is achieved by a method which reconstructs a digital representation of object features of an examination object in the local area of an X-ray system, the X-ray system having at least one X-ray tube, at least one X-ray detector and an examination object arranged in between, and with the method radiating through the examination object using X-rays , the method comprising the following steps:
  • Certain object features are clearly represented by individual patterns in projection images of an X-ray system. These samples may not be sufficient for inspection, since the three-dimensional spatial position, orientation and extent of the object features are of decisive importance. According to the invention, such features can be clearly reconstructed in the local area without a tomogram from a few projection images. Since the method requires fewer projection images in comparison to the prior art, this means a great saving in time. In addition, no complex computation of a tomogram is required for the reconstruction method according to the invention, which leads to further acceleration. With the help of the invention, a significantly higher throughput in object analysis is achieved in processes of quality control or inspection.
  • the invention reconstructs an object feature of an examination subject.
  • An examination object is defined as a physical object from which projection images are created with the aid of an X-ray system or were recorded in advance.
  • An examination subject is typically positioned on a manipulator or attached to it.
  • An object feature describes a property or structure of an examination object or a partial area of an examination object, which produces a recognizable feature pattern in the projection image.
  • the object feature can occur in the examination object at several points, the feature positions. Examples of object features are the surface of an object to be examined, interfaces between two different materials, cracks in the material of the object to be examined, porosity of the material, inclusions in the material or even entire partial structures of the sub- search object.
  • Substructures can, for example, be wires, plates, balls and / or rods located on the examination object.
  • the feature reconstruction describes the process of calculating a digital representation of a higher-dimensional object feature on the basis of lower-dimensional projections. This can mean both that a three-dimensional object to be examined is reconstructed from two-dimensional projection images and that a two-dimensional sectional plane of the object to be examined is reconstructed from one-dimensional projection images.
  • the x-ray system consists of at least one x-ray tube, at least one x-ray detector and an examination subject - if necessary, several examination objects can also belong to the x-ray system.
  • the examination object is actually not part of the X-ray system, but rather a manipulator to which the examination object is attached - since this does not make any difference in methodology, the examination object is defined as part of the X-ray system.
  • the X-ray system is combined with an evaluation / reconstruction computer and a control computer in an X-ray system. Examples of X-ray systems are computer tomographs and laminography systems.
  • the system geometry is described by the position of the components of the X-ray system with respect to one another, that is to say the relative positions and orientations of the X-ray tube (s), the examination object (s) and the X-ray detector (s).
  • the system geometry can be made available by the X-ray system, calculated or estimated from the projection images, or calculated or approximated in some other way.
  • X-ray systems are used to record data in order to record and save projection images of the examination subject.
  • the X-ray tube generates X-rays, the intensity of which is recorded in various detector elements of the X-ray detector.
  • the intensity is on the way from Focus of the X-ray tube to the detector element based on the path length through the respective material, the absorption properties of the materials of the examination object and other effects weakened and changed.
  • the X-ray system supplies a projection image.
  • a projection image is the graphic representation of the intensities of the X-rays measured by the X-ray detector. For this purpose, numerical values are assigned to each pixel that reflect the measured values.
  • the projection image is divided into pixels, each pixel being assigned to a detector element. Different structures in the examination object lead to different numerical values in different pixels, which can be displayed as gray value images.
  • the numerical values of a detector element depend on its distance from the focus of the X-ray tube and the physical interactions of the X-ray radiation with the medium passed between them.
  • the invention covers the cases when there is no 1: 1 assignment between pixels in the projection image and detector elements, but rather the projection image is interpolated or extrapolated from detector elements.
  • a blob is an alternative for a pixel, with each blob having a coordinate and an intensity similar to a pixel.
  • a blob has in addition to the coordinate a Gaussian description for the course of the intensity in the radial direction of this coordinate.
  • the intensity profiles of neighboring blobs are superimposed in such a projection image, so that a closed image impression is created.
  • the method of the invention receives a list of Giionsbil countries and the descriptions of the respective system geometries under which the projection images were created.
  • the input data can also be added in a different form, such as serial.
  • the methodology of the invention consists of repeating reconstruction steps. The methodology described below is identical for each reconstruction step. In each reconstruction step, two different projection images I (GA) and I (GB) and the associated system geometries GA and G B are used.
  • This component of the invention has the task of recognizing the feature pattern of an object feature in the projection image of an examination subject.
  • the feature pattern denotes a characteristic pattern in the gray values of the projection image, which describe the position of the projection of the object feature in the projection image.
  • Feature patterns can be image sections with gray value gradients, strong contrasts, or other structures. Since examination objects can have an object feature at several feature positions, the pattern recognition must recognize all areas in the projection image in which the feature pattern occurs and identify them separately.
  • the feature recognition is not limited to gray-scale-based projection images.
  • the invention also encompasses those pattern recognitions which gain feature patterns from this information.
  • the pattern recognition according to the invention includes any desired image processing algorithms which fulfill the task of extracting a feature pattern from a projection image.
  • the pattern conversion has the task of converting image areas in which a feature pattern was detected by the pattern recognition into a mathematical description.
  • Some pattern recognition algorithms return coordinates directly. For example, marching squares algorithms return the contour of an image section as a list of coordinates, with each of these coordinates being able to be interpreted as a separate feature pattern coordinate. For example, the positions of the corners of a projected cube in the projections can be determined by the Harris corner detector. Each of these coordinates is converted into a feature pattern coordinate.
  • the feature pattern coordinate indicates the position in detector coordinates.
  • Other algorithms such as the Canny edge detector, return a binary gray value image in which the set pixels represent the edge, so that the pixels have to be converted into detector coordinates during the pattern conversion. Examples of additional dimensions of the feature pattern coordinates become clear when spheres represent the object feature to be reconstructed.
  • the spherical centers and the spherical radii can be determined from the projection image and summarized in a feature pattern coordinate.
  • the reconstruction has the task of clearly defining the object features in the local area.
  • the direction and position of the rays that point from the focus of the X-ray tube to the feature coordinates in the projection image are determined and summarized in a list of ray paths.
  • this is represented by the systems with line and without line - from both projection images I (GA) and I. (GB) according to the associated system geometries G A and G B into the object coordinate system.
  • the positions of the X-ray tube foci for both system geometries G A and G B are converted into object coordinates.
  • the examination object is thus virtually fixed and the x-ray tube and the x-ray detector are positioned around the examination object relative to this.
  • the position of the rays relative to the object under examination can also take place on a different path, for example in a separate coordinate system or in homogeneous coordinates .
  • the ray intersection coordinates represent positions at which the object feature could be located in the local area.
  • the beam intersection point coordinates can also be determined, for example, by spanning an area between the focus of the X-ray tube and two adjacent feature pattern coordinates. The coordinate can then be determined as a beam intersection point coordinate at which a beam from a second X-ray tube to a feature pattern coordinate in the second projection image penetrates this area.
  • the determination of the ray intersection coordinates is not limited to the use of two projection images and two system geometries, but can also be made from several such data pairs. In these cases, the ray intersection coordinates can no longer be determined by the simple calculation of ray intersections, but must be determined using suitable approaches, such as the method of least squares.
  • New system geometries and projection images can be used to determine further ray intersection coordinates in the spatial area. It is also covered by the invention if only one reconstruction step is carried out. This can be the case when a feature position can be determined unambiguously and with sufficient accuracy after a reconstruction step.
  • ray intersection coordinates are calculated using the methods described above. Ray intersection coordinates arise predominantly where a feature position is located in the examination object. For two main reasons, however, ray intersection coordinates can also arise in the local space where no object feature is positioned in the physical examination object: On the one hand, all structures of the examination object are superimposed along the X-rays in the projection image. This makes the task of pattern recognition quite difficult, so that image structures can be incorrectly interpreted as feature patterns. On the other hand, for examination objects, in which the same type of object features occurs at several points in the examination subject, several feature coordinates are also determined in the projection image.
  • the clusters determined from the ray intersection coordinates can either be correctly positioned in the vicinity of feature coordinates or at positions in the object under investigation where the feature cannot be found (false-positive reason). Correctly positioned clusters have a higher number of elements
  • these dimensions are also transferred to the ray intersection coordinates.
  • these dimensions can be used for the cluster analysis and, on the other hand, the combination of the dimensions can be used to clearly describe the object feature in the local area. This unambiguous ok
  • An advantageous development of the invention provides that the steps according to the first five indents are repeated several times before the step of the last indent follows.
  • the methods for feature recognition since they are based on discretized projection images, are not as precise as desired, so that the intersection point coordinates could not exactly match the feature position in the examination object and thus have a small error.
  • Repeating the reconstruction steps and using additional system geometries mean that several data points are available for a position of an object feature. These data points are averaged by the clustering, which minimizes the reconstruction error.
  • an examination object in which an object feature occurs in several places can lead to false-positive ray intersection coordinates during the reconstruction.
  • Another advantageous development of the invention provides that the system geometry of the X-ray system is known for each projection image, with the particular X-ray tube position being reduced to the position of its respective focus point. If the movement of the focus is known, this information can be incorporated into the reconstruction. Since the physical process, i.e. the weakening of the X-rays as they travel through the examination object and the generation of the projection image, is inverted with mathematical methods during the reconstruction, the reduction of the position of the X-ray tube to its focus leads to a higher accuracy of the reconstruction result.
  • Another advantageous development of the invention provides that the feature pattern coordinates are defined in such a way that the feature pattern in the projection image is clearly described using mathematical methods and the position of the feature pattern is clearly specified in detector coordinates.
  • the conversion of the feature pattern into a mathematical description offers the possibility of achieving a higher accuracy by interpolating between pixels after the image processing in order to obtain a higher accuracy and a better estimation of the position of the feature pattern.
  • An indication of the position of the feature pattern in detector coordinates offers the possibility of taking into account the detector geometry of the X-ray detector and allowing this to flow into the reconstruction. This allows the accuracy of the reconstruction result to be increased.
  • the feature pattern coordinates also contain dimensions that describe the shape, size and orientation of the feature pattern. These additional dimensions represent, for example, the radius of a detected sphere. This additional information can be used during the calculation of the ray intersection coordinates. In the case of two intersecting beams, it is possible to check whether the radii of the associated detected projected sphere match. If these match, a ray intersection coordinate is generated. If the radii do not match, no ray intersection coordinate is generated. In this way, the number of false-positive ray intersection coordinates can already be reduced in the reconstruction steps.
  • the step of calculating the ray intersection coordinates includes that the rays intersection coordinates of two beam paths that do not intersect but have a smallest distance from each other, which is below a predeterminable threshold value, as the center of these smallest Distance is defined.
  • This beam center point, specified as the beam intersection coordinate is only if the distance between the two beams is below a certain threshold value.
  • clusters are defined as groups of ray intersection coordinates which are all within a predeterminable acceptance radius.
  • the grouping of the ray intersection point coordinates in clusters offers the advantage that feature positions do not consist of a single data point, but can be averaged from several data in a cluster.
  • the clusters also offer the possibility of making a statement about the reliability of the feature using the number of ray intersection coordinates contained.
  • individual ray intersections may represent an incomplete description of a feature.
  • the alignment of a feature can be stored as a vector in ray intersection coordinates. Individual reconstruction steps show an approximate alignment of the feature and save this in the ray intersection coordinates. A clear description of the alignment could only come about when the individual vectors of the ray intersection coordinates are superimposed by the clustering.
  • a further advantageous development of the invention provides that a limit value or threshold value method is then carried out and only those clusters are retained that have a predeterminable minimum number of beam intersection point coordinates, this minimum number being dependent on the feature to be reconstructed, the number of those used Projections and the X-ray system used. This ensures that only real feature positions are displayed and the false positives are deleted.
  • the choice of a minimum number offers the possibility of influencing the reconstruction result. rivers. If necessary, a high minimum number will also delete clusters that are located at positions of an actual feature expression, but were not detected in some projections. A low minimum number will leave a higher number of clusters for evaluation, but carries the risk that false-positive clusters are included in the solution set.
  • the possibility of making settings here allows the user to weigh up the two options - high number of data with lower security and high security with lower number of data - and to match them to the examination object and the measuring task.
  • a further advantageous development of the invention provides that the center of the ray intersection coordinates within a cluster is defined as the reconstructed position of the feature.
  • a measuring system has a limited accuracy and its measured values therefore have inherent errors. Correct beam intersection coordinates from different reconstruction steps can thus be positioned in the vicinity of an actual feature position, but who are always subject to a certain error. By recording several data points, an average data point can be calculated, the error of which is smaller than that of outliers in the individual measurements.
  • Another advantageous development of the invention provides that the determination of the beam paths from the respective X-ray tube to the feature pattern is based on the focus of the respective X-ray tube.
  • the projection image shows all the structures of the examination subject superimposed in a plane as the X-rays from the focus of the X-ray tube to the detector elements of the X-ray detector specify.
  • a reduction of the X-ray tube position to the position of its focus can thus lead to a higher accuracy of the reconstruction result with precise knowledge of the focus position.
  • the alternative, when the X-ray tube is not reduced to the position of the focal point in system geometries, is also covered by the invention.
  • Another advantageous development of the invention provides that not only the intensity values of the attenuated X-ray beam are used in the projection images, but also additional information from an X-ray detector that counts photons or an energy-resolving X-ray detector.
  • the number of photons or the energy spectrum provide further structural information about the examination subject. It is precisely in this information that some object features can be particularly well delimited from image areas without these features. Image recognition methods that take this information into account can lead to feature recognition that is more reliable than a method based purely on gray values.
  • the task is also through an X-ray system with at least one
  • X-ray tube at least one X-ray detector and an examination object arranged therebetween, in which the X-ray system is configured so that it can carry out a method according to the invention.
  • FIG 5 shows schematic representations of the clustering method for the second examination object.
  • FIG. 1 an examination object 5 is shown, which in the local area of a
  • the X-ray system has two X-ray tubes 1, 1 ', each of which has an X-ray detector 2, 2' opposite.
  • X-ray system is around a perpendicular to the plane of the sheet, through the
  • Rotatable axis of rotation running through the center of the circle.
  • the X-ray tubes 1, 1 generate fan beams whose fans run in the plane of the sheet.
  • X-ray detectors 2, 2 ’ line detectors are used.
  • Examination object 5 is a two-dimensional section of a 3D object, which in the cutting plane is essentially a triangle of uniform
  • Material corresponds to which lies in the plane of the sheet.
  • the system geometry is defined by the relative position of X-ray tube 1, 1 'and X-ray detector 2, 2' in connection with the position of the examination object 5 between these two components.
  • the system geometry is defined by the relative position of X-ray tube 1, 1 'and X-ray detector 2, 2' in connection with the position of the examination object 5 between these two components.
  • Image processing methods detect the projected object feature 4 as three in each projection image Feature pattern coordinates 3, 3 '- except in the special case that two corners of the triangle lie on a ray.
  • the beam ratios of different system geometries are set in relation to characterize the object feature 4 in the spatial area.
  • Object feature 4 could be located, these ray intersection coordinates 6 represent potential feature positions.
  • the determination of the object feature 4 is thus determined for several projection geometries.
  • FIG. 1 b the X-ray system has been rotated around the axis of rotation while the examination object 5 is held.
  • Object features 4 of a three-dimensional examination object 5 are to be reconstructed, an x-ray tube 1, 1 ', which has a cone beam, and a two-dimensional x-ray detector 2, 2' are used. Such a method is explained in more detail below with reference to FIGS. 3 to 5.
  • 2 shows a two-dimensional illustration of the clustering method. This is used to combine ray intersection coordinates 6 in clusters and to distinguish clusters in the vicinity of real object features from false-positive clusters.
  • beam intersection coordinates 6 are localized outside the cluster from other projections, they generate a new cluster, as is shown in FIG. 2b. If ray intersection coordinates 6 are calculated in other projections, which are located within the acceptance radius 8, these are added to the cluster, as shown in FIG. 2c. Clusters that are located in the vicinity of an object feature 4 (see FIG. 2e) represent correctly reconstructed feature positions 7 and differ from false-positive clusters in that they have a higher number of elements and can thus be filtered, as in FIG. 2d is shown.
  • Fig. 2e the cluster of Fig. 2d is shown after the reconstructed feature position 7 (as a cross represents Darge) was calculated from the rays intersection point coordinates 6 there.
  • a part of the two-dimensional section of the examination object 5 (the triangle in FIG. 1) with its object feature 4 (a corner) is inserted. Because of measurement inaccuracies, the reconstructed feature position 7 and the real location of the object feature 4 do not exactly coincide.
  • FIGS. 3 to 5 an exemplary embodiment of a method according to the invention is described with reference to FIGS. 3 to 5, in which the examination subject 5 is a cube made of a single material.
  • the object features to be reconstructed are the corners of the cube.
  • X-ray detector 2, 2 ' shown. Taking into account the system geometry and the coordinates of the projected corners in the projection image
  • the cube is scanned by an X-ray system under different system geometries, a projection image (see FIG. 3a) being recorded for each system geometry.
  • the list of projection images of the cube can have been included under system geometries in which the X-ray tube 1 was positioned in front of the cube and the X-ray detector 2 behind it, or the X-ray tube 1 'to the left and the X-ray detector 2' to the right of the cube.
  • the list of system geometries then describes these positions using coordinates and solid angles for the X-ray tube 1, 1 ', the cube and the X-ray detector 2, 2' (see Fig. 4). Between X-ray detector 2, 2 'and
  • the positions of the corners of the projected cube can be determined in the projections by the Harris corner detector, for example. It is
  • Object coordinates describe the relative positional relationship of the feature pattern coordinates 3, 3 'to the examination object 4. The result is shown in FIG. 3c (see also FIG. 4).
  • the mean coordinate of all the ray intersection coordinates 6 contained in the cluster is calculated for each remaining cluster.
  • the middle position of all feature positions is called a point shown.
  • Feature positions 7 as the measurement result of the feature reconstruction for the object feature corners of the cube.
  • the ideal edges of the cube are shown in dashed lines.
  • the deviation of the positions of the points of the real feature positions, the corners of the four edges shown in broken lines, from the measured feature positions 7 results from the fact that a certain
  • a representation of the feature reconstruction can be found in FIG. 4.
  • X-ray detectors 2, 2 determined in two different projection geometries.
  • object features 4 in the local area, which can be summarized as follows.
  • object features 4 are projected onto an X-ray detector 2, 2 ', whereby feature patterns are reflected in the gray values of a
  • Pattern recognition determines these feature patterns. The recognized
  • Feature patterns are characterized by a mathematical description, the so-called feature pattern coordinates 3, 3 '.
  • the feature pattern coordinate 3, 3 ' contains information about the position and optionally other feature dimensions, such as the orientation or the size of the feature pattern in the
  • Projection images parameterized in the same way under changed system geometries Taking into account the different system geometries, the feature pattern coordinates 3, 3 'become the object coordinate system converted. Beam paths from the focus of the X-ray tube 1, 1 ', also in object coordinates, to the feature pattern coordinates 3, 3' are determined for all system geometries. From neighborhood relationships, intersections, relative positional relationships, or related methods between
  • Ray intersection coordinates 6 determined. Ray intersection coordinates 6 represent possible locations for feature positions and contain dimensions, such as the position, orientation and shape of the object feature 4 in the spatial space. These ray intersection coordinates 6 are in several

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Abstract

La présente invention concerne un procédé de reconstruction d'une représentation numérique de caractéristiques d'objet (4) d'un objet d'inspection (5) dans l'espace local d'un système à rayons X qui comporte au moins un tube à rayons X (1, 1'), au moins un détecteur à rayons X (2, 2') et un objet d'inspection (5) agencé entre eux, selon lequel l'objet d'inspection (5) est irradié par des rayons X, dont les étapes consistent : en l'établissement d'au moins deux images de projection de l'objet d'inspection au moyen du système à rayons X; en la mise en œuvre d'une reconnaissance de motifs pour la localisation des zones d'image dans les images de projection qui contiennent un motif caractéristique prédéfinissable; en la mise en œuvre d'une conversion de motifs destinée à convertir le motif caractéristique en coordonnées de motif caractéristique (3, 3'); en la détermination des chemins de rayonnement de la position relative respective du tube à rayons X aux coordonnées de motif caractéristique (3, 3') en prenant en compte les géométries de système du système à rayons X lors de l'établissement des images de projection; en le calcul des coordonnées de points d'intersection de rayonnement (6), qui indiquent des positions caractéristiques potentielles, à partir des chemins de rayonnement de deux géométries de système; en la mise en œuvre d'une analyse de groupes avec extraction de la position ou des positions caractéristiques (7) reconstruites à partir de toutes les coordonnées d'intersection de rayonnement (6) calculées. L'invention concerne en outre un système à rayons X qui est conçu de telle sorte qu'il peut réaliser un procédé selon l'invention.
PCT/EP2020/050271 2019-02-12 2020-01-08 Procédé de reconstruction d'une représentation numérique de caractéristiques d'objet d'un objet d'inspection dans l'espace local d'un système à rayons x WO2020164812A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115511831A (zh) * 2022-09-27 2022-12-23 佳木斯大学 一种组织胚胎病理切片的数据分析处理系统及方法
CN116196022A (zh) * 2023-04-28 2023-06-02 之江实验室 扇形x光束穿过介质时的通量分布计算方法和系统

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004114221A1 (fr) * 2003-06-24 2004-12-29 Philips Intellectual Property & Standards Gmbh Dispositif permettant de generer une image tridimensionnelle d'un objet deplace
EP1898208A1 (fr) * 2006-09-06 2008-03-12 YXLON International X-Ray GmbH Installation et procédé CT pour la vérification d'objets

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011011684A2 (fr) * 2009-07-24 2011-01-27 The Research Foundation Of State University Of New York Sélection de vues optimales pour reconstruction par tomodensitométrie

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004114221A1 (fr) * 2003-06-24 2004-12-29 Philips Intellectual Property & Standards Gmbh Dispositif permettant de generer une image tridimensionnelle d'un objet deplace
EP1898208A1 (fr) * 2006-09-06 2008-03-12 YXLON International X-Ray GmbH Installation et procédé CT pour la vérification d'objets

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
IZUMI M ET AL: "Back projection voting considering the error of camera orientation", EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, 1996. EFTA '96. PROCEEDI NGS., 1996 IEEE CONFERENCE ON KAUAI, HI, USA 18-21 NOV. 1996, NEW YORK, NY, USA,IEEE, US, vol. 2, 18 November 1996 (1996-11-18), pages 712 - 716, XP010204189, ISBN: 978-0-7803-3685-8, DOI: 10.1109/ETFA.1996.573992 *
KAWATO S: "STRUCTURE RECOVERY FROM MULTIPLE IMAGES BY DIRECTLY ESTIMATING THE INTERSECTIONS IN 3-D SPACE", IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, INFORMATION & SYSTEMS SOCIETY, TOKYO, JP, vol. E77-D, no. 9, September 1994 (1994-09-01), pages 966 - 972, XP000477272, ISSN: 0916-8532 *
XUE LIN ET AL: "A method for improving measurement accuracy of cylinders in dimensional CT metrology", COMPUTER AIDED DESIGN, ELSEVIER PUBLISHERS BV., BARKING, GB, vol. 69, 4 August 2015 (2015-08-04), pages 25 - 34, XP029280416, ISSN: 0010-4485, DOI: 10.1016/J.CAD.2015.07.015 *

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CN116196022A (zh) * 2023-04-28 2023-06-02 之江实验室 扇形x光束穿过介质时的通量分布计算方法和系统
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