EP2283937B1 - Procédé et dispositif de transport d'objets sur des points cible dépendant de motifs - Google Patents

Procédé et dispositif de transport d'objets sur des points cible dépendant de motifs Download PDF

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Publication number
EP2283937B1
EP2283937B1 EP10172124.9A EP10172124A EP2283937B1 EP 2283937 B1 EP2283937 B1 EP 2283937B1 EP 10172124 A EP10172124 A EP 10172124A EP 2283937 B1 EP2283937 B1 EP 2283937B1
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Prior art keywords
image
image pattern
abb
sorting system
transported
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EP10172124.9A
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German (de)
English (en)
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EP2283937A1 (fr
Inventor
Zhe Li
Martin Neschen
Matthias Schulte-Austum
Wolf-Stephan Wilke
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Siemens AG
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Siemens AG
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C3/00—Sorting according to destination
    • B07C3/10—Apparatus characterised by the means used for detection ofthe destination
    • B07C3/14—Apparatus characterised by the means used for detection ofthe destination using light-responsive detecting means

Definitions

  • the invention relates to a method and a device for transporting objects, in particular postal items, to target points which depend on image patterns.
  • a delivery agent determines that a catalog transported as a mail item can not be delivered to the recipient specified on the mailpiece and provides the mailpiece with a return note.
  • This mail again goes through a sorting process.
  • An image of each mail item to be returned is generated. These images contain areas of images of sender addresses. Similarities of those areas in the images showing sender addresses are determined. Those images that are sufficiently similar are treated as images of postal items, all of which are to be returned to the same sender.
  • an editor reads the sender information in one of these images and enters it into a data acquisition device. All mail with similar sender images, a recognized sender is sent back to them.
  • DE 102007038186 B4 describes a method in which a sender sends a lot of many similar mailings an image of a surface of these similar mailings to a transporter. These mail items are sorted together with other mail items in a sorting system. Using the image, it is decided whether or not a mail item to be sorted belongs to this quantity.
  • US 20080008376 A1 For example, a method is described for identifying a postal indicia on the envelope of a mailpiece. A "region of interest" is identified and at least one candidate is found in this "region of interest”. Each candidate calculates a set of feature values for which an image of the envelope is generated and evaluated. Each candidate is classified using these characteristic values.
  • an apparatus which objects according to their respective Surface characteristics classified.
  • Each object (“ceramic tile 31”) is transported by a conveyor belt 30 under a video camera 14, cf. Fig. 2 ,
  • the video camera 14 has an IR filter 36 and a zoom lens 35 and is controlled by a microcontroller 17.
  • An image evaluation unit 18 evaluates images from the video camera 14.
  • the test apparatus 11 is trained to learn which features the images are to be examined to classify the objects ("ceramic tiles").
  • a sample of objects is transported past the video camera 14.
  • the device uses many feature determining algorithms and statistical analysis routines and selects some of them.
  • a classification phase an object is evaluated on the basis of an image in real time.
  • Fig. 3 describes the training phase closer.
  • a step 74 an image of an object having multiple feature extraction algorithms is examined to determine the degree to which an image of an object has certain features.
  • methods are selected.
  • a "feature ranking algorithm” identifies those features that best divide the objects into different gradings. There are several quality levels. By way of example, the methods are called “n-means clustering" and “k-nearest neighbors”, and also “membership-based classification” similar to "fuzzy logic”.
  • a first separation complex 1 comprises an endless belt conveyor 4 with a conveyor belt 7.
  • a color camera 12, an NIR spectrometer 13 with an NIR sensor 14 and an optical scanning head 15 are mounted above the belt conveyor 4.
  • the color camera 12 detects a detection area 17 on the conveyor belt 4, the scanning head 15 a detection area 18, also on the conveyor belt 4.
  • the color camera 12 detects the shape and the surface condition of objects in the detection area 17, the scanning head 15 the material properties of objects in the detection area 18.
  • a computer 16 evaluates measured data from the detection areas 17, 18, and applies an image analysis method to match objects in terms of shape, size, chroma, texture, and the like.
  • a postage stamp detection section 15 uses colors to detect a postage stamp on a mailpiece, cf. Fig. 1 .
  • a "postage stamp area extraction section 17" prepares an image of a mail item in order to deliver a computer-accessible image of the postage stamp on this mail item. This is done sequentially for multiple mailings.
  • a "postage stamp clustering section 18" stores postage stamp images generated in this way until a maximum number of stored images has been reached. Then, a postage stamp image is randomly selected from the data store. Images similar to this image are determined. The similar images form a first group of postage stamps.
  • a "postage stamp template registering candidate section 19" selects groups with many members and then weights these groups.
  • a device and a method are described which automatically recognize an address block on a mailpiece and mark a recognized address block. In this case, a difference between images of two successive postal items is determined.
  • the invention is based on the object, a method and an apparatus for transporting objects to target points in which image patterns on objects are evaluated during transport, avoiding the need to manually enter computer-readable descriptions of image patterns.
  • the classifying apparatus includes an image pickup device and a grouper.
  • the sorting system also has an image pickup device and a picture pattern recognizer.
  • a training phase and then a sorting phase are carried out.
  • the sorting system is used at least in the sorting phase.
  • the items to be transported in the sample pass through the classification device.
  • the image capture device generates at least one image of each subject of the sample.
  • the grouper automatically determines which different image patterns are displayed on at least one subject of the sample. In doing so, the grouper applies a clustering method to the images of the sample.
  • the thus determined image patterns are stored in the image pattern data memory.
  • the invention eliminates the need for a processor to make computer-available descriptions of image patterns available in a training phase so that the sorting system can use those image patterns for the sorting phase. This is particularly advantageous if new patterns are added to objects on an ongoing basis, so that a training phase with manual input would always be too late.
  • the invention further eliminates the need for a mailer of items to communicate a computer-accessible description of a picture pattern to the sorting system. It is enough that the sender - or a third party - provides items with the image pattern.
  • the sorting system is also used for the training phase.
  • the imaging device use in both phases.
  • the clustering method is performed to form groups of images, each group comprising at least two images that show the same image pattern. It is also possible to set a minimum number of images per cluster that is significantly larger than 2.
  • image features are specified.
  • By evaluating an image it is determined which value each feature assumes for this image. This creates a feature value vector for the image.
  • Characteristic feature value vectors for the image patterns are stored in the image data memory.
  • a feature value vector is generated for each object to be transported and compared with the stored feature value vectors. This makes it particularly easy to decide whether and, if so, which image pattern is displayed on the object.
  • the solution according to the method and the device according to the solution can be z. B. for the transport of flat mail, packages, packages, pieces of luggage or workpieces in a manufacturing use.
  • the method is used to recognize image patterns on mailpieces.
  • the mailpieces are copies of an issue of a journal or a catalog that will be sent by post.
  • the mailpieces can be flat or even packages or freight items.
  • a postal service provider is to send many identical copies together with other mailpieces to their respective recipients.
  • the picture patterns are z. As logos or emblems, with which sender provide their mail.
  • the image pattern identifies the sender and is z. B. a protected figurative mark. Of course, the pattern may contain letters of a natural language or other characters.
  • the image pattern on a mailpiece should be recognized, in particular, if the mailpiece can not be delivered to the recipient whose postal address is specified on the mailpiece.
  • the receiver is unknown warped.
  • the recipient has moved and has communicated his new address to the postal service provider.
  • the mailpiece should not be forwarded to the new address due to a sender's "endorsement". Rather, either the mailpiece should be destroyed, and a notification about the non-delivery of the mailpiece should be sent to the sender ("sender notification").
  • the mailing provided with information about the new address should be returned to the sender. It is possible that the item of mail is provided with an "endorsement", which determines what should happen to the item of mail if the item of mail can not be sent to the address specified on the item of mail.
  • the sender address it is necessary to determine the sender address. If an image pattern of the sender can be recognized on the mail piece, it is easier to sort the mailpieces to be returned according to the respective sender.
  • the addresses and image patterns of registered senders are stored in an image address database of the postal service provider. It is also possible to perform a procedure like this in DE 19836767 C1 is described. Recognized are those mail items that are each provided with a representation of the same postal item.
  • the delivery address of the sender who has provided mailpieces with this image mark is determined, for which purpose an image of a mailpiece with this image mark is evaluated.
  • the delivery address of the sender is z. B. automatically by character recognition ("Optical Character Recognition", OCR) or manually determined by video encoding.
  • the recognized image patterns as well as further details of the sender on the mailpiece are compared with this database. After a successful comparison, it is possible to group the mail items by sender to be able to deliver all the mailpieces to the same sender. In the simplest case, it suffices to sort the mailpieces to be returned according to sender image patterns without necessarily automatically deciphering the sender address during sorting.
  • a sender delivers a lot of similar mailings, e.g. As the magazine copies of an issue, without delivery address and provided with its own pattern to the postal service provider.
  • the sender sends an address list with delivery addresses to the postal service provider, preferably in computer-available form. At least one copy of the quantity of similar mailpieces is to be delivered to each delivery address in this address list. It is possible that the address list for each delivery address contains the number of copies to be delivered to this delivery address.
  • the postal service provider evaluates the address list and assigns each copy with an address from the address list. In this case, the similar mailpieces are to be distinguished from other mailpieces due to the same image pattern.
  • a random sample with image pattern images is generated and evaluated.
  • Fig. 1 illustrates the first process step.
  • image patterns are recognized by clustering.
  • a camera 1 of the sorting system generates from each mail item Ps-a, Ps-b, ... at least one image Abb-a, Abb-b, .... It is possible that several cameras produce multiple images of the same mail item from different directions to also capture sender information on the back of a flat mail piece or because the mail piece is a package and six surfaces are eligible to show sender details. It is also possible for an image Abb-a, Abb-b,... To be generated and evaluated first and only if this image does not show the recipient address or the sender address, the item of mail is turned over and another image is generated ,
  • the n images Abb-a, Abb-b, ... are evaluated.
  • the n images are oriented and scaled before the actual evaluation. Orientation ensures that in all images with the same image pattern, the image pattern appears in the same orientation, ie not even standing upright and once standing upside down.
  • the images are oriented on the basis of the respective recipient address in the image. Scaling normalizes all images to the same size, even if the images come from different sizes of mail. It is also possible, the actual evaluation without first orienting and scaling the images.
  • each image is first searched for the area containing information about the sender of the mailpiece.
  • This area could contain an image of a sender's image pattern. It is of course possible that individual images do not contain any sender information.
  • the image is displayed on a video display device of a video coding station.
  • An engineer marks the area containing the sender details in the displayed image, or specifies that the image does not contain any sender information.
  • the sender uses a mouse to position a rectangle around the sender specified area.
  • the processor enters the sender address himself. However, he can not enter an image pattern, at least if it consists of more than one text or shows a text in a specific typography.
  • Predetermined m characteristics that can be calculated by evaluating a mailpiece image. For each one postal item of the random sample, a feature value vector with m feature values is generated in each case by the respective image Abb-a, Abb-b,... Of the mail item being automatically evaluated. This feature value vector indicates which m values assume the m features for this mail item.
  • color gamut area For each color range, parameters of that area in the image Abb-a, Abb-b, ... are calculated, which includes pixels with hues from this color range ("color gamut area"). These parameters include, for. For example, the size of the color area area itself, the edge length of a square circumscribing the area, the diagonal of a circumscribing ellipse, the centroid, and the perimeter length of the color area area. It is possible to divide the image of the mail item into areas, eg. In four quadrants, and calculate the respective parameters of the gamut area in each quadrant.
  • a cluster educator 3 applies a clustering method.
  • Cluster analysis or “clustering” is generally understood to mean a method in which a set of objects, which are each described by a feature value vector, are divided into groups, the objects of a group being similar to one another.
  • Clustering is a method of "unsupervised learning” and is z. In the fields of machine learning, data mining, pattern recognition, image analysis, and bioinformatics Examples of clustering methods are k-means clustering, fuzzy c-means clustering, "QT clustering algorithm”, "locality-sensitive hashing” and graph theory models.
  • a distance measure between two feature value vectors is specified.
  • the distances between two characteristic value vectors of the sample are calculated.
  • n images Abb-a, Abb-b, ... of the sample these are n * (n-1) / 2 distance calculations. All images whose distances according to this distance measure are smaller than a predetermined limit are treated as images of a group (a "cluster") showing the same image pattern.
  • the groups are calculated so that at least two images of the sample fall into each group.
  • the cluster-forming device 3 automatically recognizes which different image patterns are contained in the images Abb-a, Abb-b,.... The same picture pattern, z. As the same logo of a magazine publisher, can occur on different mailings of the same sender in different sizes and - due to variations in the printing process and environmental conditions when recording the images - in different configurations. Therefore, when applying the clustering method, the clustering device 3 forms groups of approximately similar image patterns.
  • the identified groups of similar image patterns are stored in an image database 4 or other suitable data storage.
  • an image data base 4 with reference image patterns is automatically generated.
  • each group is stored by storing in the image pattern database 4 a computer-accessible description of that area in the space of the possible feature value vectors in which all the feature value vectors of this group are located. It is also possible to store a characteristic feature value vector or a characteristic image from this group. It is possible, but not necessary, to additionally extract one image pattern from the images and to store a description of this image pattern.
  • a sorting phase that is to say a second method step of the exemplary embodiment, mailpieces again pass through the sorting system.
  • Fig. 2 illustrates the second pass of mail through the sorting system of FIG Fig. 1 , wherein the recognition results from the training phase, ie the first pass are applied
  • An image pattern recognizer 5 identifies an image pattern on a mailpiece and compares this image pattern with those reference image patterns which were automatically determined in the first method step and stored in the image pattern database 4.
  • all mail items are spent from the same sorting end in a transport container, z. B. a postal parcel.
  • This transport container is provided with the postal address of the sender and transported to this postal address.
  • the common sender information is not necessarily determined during sorting, but only when the transport container is labeled.
  • the common sender address of all mail items that are to be returned to the same sender is additionally determined during the sorting, that is, in the sorting phase. For this purpose, one of these mailings selected.
  • the sender address applied to the mailpiece is determined. First, an attempt is made to automatically determine and decipher the sender address on the selected mail piece. If this is not successful, an image of the surface of the mail item is displayed on a screen of a video coding station. An engineer marks the area in the image that shows the sender details. It is attempted to automatically read the address in this area. If this fails, the processor enters the sender details.
  • the mailings of a particular sender do not contain sender information, at least not on an externally visible page.
  • an issue of a journal may show a picture pattern of the issuing publisher in the cover page, but no sender address.
  • an image pattern address database 6 is therefore additionally specified in the second pass.
  • This image pattern address database 6 contains several data records. Each record comprises a reference image pattern of a sender as well as a delivery address of this sender. The record may contain additional information, e.g. B. Billing information for transportation services provided to this sender.
  • the image pattern of the selected mailpiece is determined. This image pattern is compared with reference image patterns of data sets of the image pattern address database 6. The record with the most similar reference image pattern is automatically selected. If this automatic selection does not succeed, an editor selects the correct image pattern under automatically selected similar reference image patterns. The delivery address that is included in the record with the most similar reference image pattern is used as the delivery address for all mailpieces with this image pattern. The mailpieces are preferably transported together to this delivery address of the sender.

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Claims (10)

  1. Procédé de transport d'objets à des points de destination, dans lequel on effectue les stades suivants dans une phase d'apprentissage
    - on fait passer un échantillon témoin ayant des objets (Ps-a, Ps-b, ... ) à transporter dans un dispositif de classification,
    - le dispositif de classification a un appareil ( 1 ) de prise de vue et un groupeur ( 3 ),
    - l'appareil ( 1 ) de prise de vue produit de chaque objet ( Ps-a, Ps-b, ... ) de l'échantillon témoin respectivement au moins une reproduction ( Abb-a, Abb-b, ... ),
    - le groupeur ( 3 ) constate automatiquement, par utilisation d'un procédé clustering sur les reproductions ( Abb-a, Abb-b, ... ), les motifs d'image différents qui sont représentés sur respectivement au moins un objet ( Ps-a, Ps-b, ... ) de l'échantillon témoin, et
    - on mémorise les motifs d'image ainsi déterminés dans une mémoire ( 4 ) de données de motifs d'image,
    et dans lequel, dans une phase de tri, on effectue, pour chaque objet ( Ps-a, Ps-b, ... ) à transporter, les stades dans lesquels
    - on fait passer l'objet ( Ps-a, Ps-b, ... ) à transporter dans une installation de tri,
    - l'installation de tri a un appareil ( 1 ) de prise de vue et un détecteur ( 5 ) de motifs d'image,
    - l'appareil ( 1 ) de prise de vue produit de l'objet ( Ps-a, Ps-b, ... ) à transporter au moins une reproduction ( Abb-a, Abb-b, ... ),
    - le détecteur ( 5 ) de motifs d'image constate automatiquement en exploitant la reproduction ( Abb-a, Abb-b, ... ) s'il est représenté sur l'objet un motif d'image mémorisé dans la mémoire ( 4 ) de données de motifs d'image et, si oui, lequel
    - l'installation de tri détermine, s'il est représenté sur l'objet ( Ps-a, Ps-b, ... ) à transporter un motif d'image de ce genre, en utilisant le motif d'image, un point de destination où cet objet doit être transporté, et
    - l'installation de tri déclenche un transport de l'objet ( Ps-a, Ps-b, ... ) à ce point de destination.
  2. Procédé suivant la revendication 1,
    caractérisé en ce que
    l'installation de tri
    - comprend aussi le groupeur ( 3 ) et
    - est utilisée aussi pour les stades du procédé de la phase d'apprentissage.
  3. Procédé suivant la revendication 1 ou la revendication 2,
    caractérisé en ce que
    dans la phase de tri, on effectue, pour chaque modèle d'image mémorisé dans la mémoire ( 4 ) de données de motifs d'image, les stades dans lesquels
    - on détermine sur lesquels des objets ( Ps-a, Ps-b, ... ) à transporter ce motif d'image est représenté,
    - on détermine un point de destination commun où tous les objets ( Ps-a, Ps-b, ... ) ayant ce motif d'image doivent être transportés, et
    - l'installation de tri déclenche le transport de tous les objets ( Ps-a, Ps-b, ... ) ayant ce motif d'image au point de destination déterminé.
  4. Procédé suivant la revendication 3,
    caractérisé en ce que
    pour au moins un motif d'image, la détermination du point de destination comprend les stades dans lesquels
    - on choisit l'un des objets ayant ce motif d'image,
    - en exploitant la au moins une reproduction de cet objet choisi, on détermine le point de destination.
  5. Procédé suivant la revendication 3,
    caractérisé en ce que
    pour au moins un motif d'image, la détermination du point de destination comprend les stades, dans lesquels on se donne une mémoire ( 6 ) de données de point de destination motifs d'image,
    dans lequel sont mémorisés plusieurs motifs d'image de référence et, pour chaque motif d'image de référence respectivement, une information de point de destination, on compare le motif d'image à des motifs d'image de référence de la mémoire ( 6 ) de données de point de destination motifs d'image,
    comme résultat de la comparaison, on choisit un motif d'image de référence et
    on détermine l'information de point de destination du motif d'image de référence choisi et on l'utilise comme point de destination commun.
  6. Procédé suivant l'une des revendications 1 à 5,
    caractérisé en ce que
    on se donne plusieurs caractéristiques de reproduction, le groupeur ( 3 ) dans la phase d'apprentissage
    - détermine, pour chaque reproduction d'un objet de l'échantillon témoin, la valeur prise par chaque caractéristique prescrite pour cette reproduction,
    - calcule ainsi pour chaque reproduction respectivement un vecteur de valeur de caractéristique,
    - utilise les vecteurs de valeur de caractéristique pour constater les motifs d'image qui différents sont représentés sur les objets.
  7. Procédé suivant la revendication 6,
    caractérisé en ce que
    le groupeur ( 3 ) dans la phase d'apprentissage
    - rassemble en un groupe toutes les reproductions dans les vecteurs de valeur de caractéristique diffèrents les uns des autres au plus d'une limite prescrite ou calculée dans la phase d'apprentissage et
    - décide automatiquement que, dans deux reproductions qui appartiennent à deux groupes différents, des motifs d'image différents sont représentés.
  8. Procédé suivant la revendication 6 ou la revendication 7,
    caractérisé en ce que
    le stade dans lequel les motifs d'image déterminés dans la phase d'apprentissage sont mémorisés dans la mémoire ( 4 ) de données de motifs d'image,
    comprend le stade de mémorisation d'une description disponible par calcul d'une région dans l'espace des vecteurs de valeur de caractéristique possible, et
    le stade dans lequel le détecteur ( 5 ) de motifs d'image constate automatiquement dans la phase de tri si un motif d'image est représenté sur une reproduction et, si oui, lequel,
    comprend le stade suivant lequel le détecteur ( 5 ) de motifs d'image
    - calcule un vecteur de valeur de caractéristique pour la reproduction et
    - compare le vecteur de valeur de caractéristique aux descriptions de vecteur de valeur de caractéristique mémorisées dans la mémoire de données de motifs d'image.
  9. Dispositif de transport d'objets à des points de destination,
    dans lequel le dispositif comprend
    - un dispositif de classification,
    - une installation de tri et
    - une mémoire ( 4 ) de données de motifs d'image,
    le dispositif de classification a un appareil ( 1 ) de prise de vue et un groupeur ( 3 ),
    l'installation de tri a un appareil ( 1 ) de prise de vue et un détecteur ( 5 ) de motifs d'image,
    le dispositif est conformé de manière à effectuer dans une phase d'apprentissage les stades suivants lesquels
    - on fait passer un échantillon témoin ayant des objets ( Ps-a, Ps-b, ... ) à transporter dans un dispositif de classification,
    - l'appareil ( 1 ) de prise de vue produit de chaque objet ( Ps-a, Ps-b, ... ) de l'échantillon témoin respectivement au moins une reproduction ( Abb-a, Abb-b, ... ),
    - le groupeur ( 3 ) constate automatiquement par utilisation d'un procédé clustering sur les reproductions ( Abb-a, Abb-b, ... ) les motifs d'image différents qui sont représentés sur respectivement au moins un objet ( Ps-a, Ps-b, ... ) de l'échantillon témoin, et
    - le dispositif mémorise les motifs d'image ainsi déterminés dans une mémoire ( 4 ) de données de motifs d'image, et
    le dispositif est conformé, en outre, pour effectuer, dans une phase de tri pour chaque objet ( Ps-a, Ps-b, ... ) à transporter, les stades suivant lesquels
    - on fait passer l'objet ( Ps-a, Ps-b, ... ) à transporter dans une installation de tri,
    - l'appareil ( 1 ) de prise de vue produit de l'objet ( Ps-a, Ps-b, ... ) à transporter au moins une reproduction ( Abb-a, Abb-b, ... ),
    - le détecteur ( 5 ) de motifs d'image constate automatiquement en exploitant la reproduction ( Abb-a, Abb-b, ... ) s'il est représenté sur l'objet un motif d'image mémorisé dans la mémoire ( 4 ) de données de motifs d'image et, si oui, lequel
    - l'installation de tri détermine, s'il est représenté sur l'objet ( Ps-a, Ps-b, ... ) à transporter un motif d'image de ce genre, en utilisant le motif d'image, un point de destination où cet objet doit être transporté, et
    - l'installation de tri déclenche un transport de l'objet ( Ps-a, Ps-b, ... ) à ce point de destination.
  10. Dispositif suivant la revendication 9,
    caractérisé en ce que
    l'installation de tri
    - comprend aussi un groupeur ( 3 ) et
    - est conformée aussi pour l'exécution des stades du procédé de la phase d'apprentissage.
EP10172124.9A 2009-08-07 2010-08-06 Procédé et dispositif de transport d'objets sur des points cible dépendant de motifs Active EP2283937B1 (fr)

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Application Number Priority Date Filing Date Title
DE200910036626 DE102009036626A1 (de) 2009-08-07 2009-08-07 Verfahren und Vorrichtung zum Transportieren von Gegenständen an von Bildmustern abhängende Zielpunkte

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EP2283937A1 EP2283937A1 (fr) 2011-02-16
EP2283937B1 true EP2283937B1 (fr) 2013-12-18

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