DE10245900A1 - Image based query system for search engines or databases of mobile telephone, portable computer uses image recognition to access more information about objects in image - Google Patents

Image based query system for search engines or databases of mobile telephone, portable computer uses image recognition to access more information about objects in image Download PDF

Info

Publication number
DE10245900A1
DE10245900A1 DE10245900A DE10245900A DE10245900A1 DE 10245900 A1 DE10245900 A1 DE 10245900A1 DE 10245900 A DE10245900 A DE 10245900A DE 10245900 A DE10245900 A DE 10245900A DE 10245900 A1 DE10245900 A1 DE 10245900A1
Authority
DE
Germany
Prior art keywords
image
information
under
recognition
camera
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
DE10245900A
Other languages
German (de)
Inventor
Hartmut Senior Neven
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Google LLC
Original Assignee
NEVEN JUN
Neven Jun Hartmut Profdr
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by NEVEN JUN, Neven Jun Hartmut Profdr filed Critical NEVEN JUN
Priority to DE10245900A priority Critical patent/DE10245900A1/en
Publication of DE10245900A1 publication Critical patent/DE10245900A1/en
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G06F16/5854—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using shape and object relationship
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/94—Hardware or software architectures specially adapted for image or video understanding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/14—Image acquisition
    • G06V30/142—Image acquisition using hand-held instruments; Constructional details of the instruments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions

Abstract

End unit e.g. mobile telephone, has integral camera connected to data transfer network (13). Server computer (17) runs recognition program to analyze input image (12) from telephone and applies symbolic indicia to image. Search engine (16) uses indicia to find information about the image to send back to end unit. Independent claims included for: 1) Town or museum guide using additional position information for image recognition, 2) Product information system providing information for items photographed by mobile camera,

Description

AbstractAbstract

Mobiltelefone und Computer werden zunehmend mit einer Kamera ausgestattet. Dies ermöglicht es, statt blosser Texteingaben auch Bilder an Suchmaschinen oder Datenbanken als Eingabe zu senden. Fortschritte bei Bilderkennungsverfahren wiederum ermöglichen zunehmend das automatische Erkennen von Objekten, Buchstabenfolgen oder Symbolen in digitalen Bildern. Dies erlaubt es, die Bildinformation in ein symbolisches Format, z.B. Klartext, umzuwandeln, um damit Informationen zu dem gezeigten Objekt abzurufen.Cell phones and computers will be increasingly equipped with a camera. This enables Instead of just entering text, images on search engines or databases to send as input. Advances in image recognition processes again enable increasingly the automatic recognition of objects, sequences of letters or symbols in digital images. This allows the image information in a symbolic format, e.g. Plaintext to convert to use it Get information about the object shown.

Beschreibungdescription

Ein Mensch sieht einen Gegenstand, und sofort stellt sein Gedächtnis Informationen bereit, die mit dem Gegenstand in Zusammenhang stehen. Extrem nützlich wäre ein System, das diese Leistung nachbildet oder sogar erweitert.A human sees an object and immediately restores his memory Information related to the subject. Extremely useful would be a System that simulates or even extends this service.

Moderne Verfahren der Bilderkennung erlauben es, zunehmend besser Objekte, Landschaften, Gesichter, Symbole, Buchstabenfolgen etc. in Bildern zu erkennen. Mehr und mehr Kameras sind an Geräte angeschlossen, die an Datenfernübertragungsnetzwerke angebunden sind. Solch eine Konfiguration unterstützt die folgende Anwendung. Mit der Kamera in einem Endgerät (1), z.B. in einem Mobiltelefon, wird ein Bild oder eine kurze Bildsequenz aufgenommen. Dieses Bild (2) oder diese Bilder werden dann per Datenfernübertragung (3) an einen Serverrechner (7) geschickt. Dort läuft ein Bilderkennungsverfahren (4), das die Bildinformation in symbolische Information (5), z.B. Klartext, umwandelt. Z.B. erkennt das Bilderkennungsverfahren, dass auf dem Bild der Eiffelturm zu erkennen ist. Alles weitere funktioniert nun ähnlich wie bei einer traditionellen Suchmaschine (6) im Internet. Der Serverrechner schickt dem Nutzer eine Liste zurück mit „Links" auf Datenbankeingaben oder Webseiten, die Informationen über das gezeigte Objekt (8) enthalten.Modern methods of image recognition allow objects, landscapes, faces, symbols, sequences of letters, etc. to be recognized increasingly better in images. More and more cameras are connected to devices that are connected to remote data transmission networks. The following application supports such a configuration. With the camera in a device ( 1 ), for example in a mobile phone, a picture or a short picture sequence is taken. This picture ( 2 ) or these pictures are then transmitted via remote data transmission ( 3 ) to a server computer ( 7 ) cleverly. An image recognition process is running there ( 4 ) which converts the image information into symbolic information ( 5 ), e.g. plain text. For example, the image recognition process recognizes that the Eiffel Tower can be seen on the image. Everything else now works similarly to a traditional search engine ( 6 ) on the Internet. The server computer sends the user a list with "links" to database entries or websites that contain information about the displayed object ( 8th ) contain.

1. Bilderkennung1. Image recognition

Dieser Abschnitt gibt einen groben Überblick über eine mögliche Methode zur Objekterkennung. Eine genauere Beschreibung zu Verfahren für die Objekterkennung ist in den folgenden Publikationen beschrieben: J. Buhmann, M. Lades and C.v.d.Malsburg, „Size and Distortion Invariant Object Recognition by Hierarchical Graph Matching", in Proceedings of the IJCNN International Joint Conference on Neural Networks, San Diego 1990, pp. II-411-416 und „High-Level Vision: Object Recognition and Visual Cognition", Shimon Ullman, MIT Press; ISBN: 0262710072; July 31, 2000. Verfahren zur automatischen Schriftzeichenerkennung sind beschrieben in: „Optical Character Recognition: An Illustrated Guide to the Frontier" Kluwer International Series in Engineering and Computer Science, 502, by Stephen V. Rice, George Nagy, Thomas A. Nartker, 1999.This section gives a rough overview of one possible Object detection method. A more detailed description of procedures for object recognition is described in the following publications: J. Buhmann, M. Lades and C.v.d.Malsburg, "Size and Distortion Invariant Object Recognition by Hierarchical Graph Matching ", in Proceedings of the IJCNN International Joint Conference on Neural Networks, San Diego 1990, pp. II-411-416 and "High-Level Vision: Object Recognition and Visual Cognition ", Shimon Ullman, MIT Press; ISBN: 0262710072; July 31, 2000. Procedure for automatic character recognition are described in: "Optical Character Recognition: An Illustrated Guide to the Frontier "Kluwer International Series in Engineering and Computer Science, 502, by Stephen V. Rice, George Nagy, Thomas A. Nartker, 1999.

1.1 Aufbau einer Objektrepräsentation1.1 Structure of an object representation

Die meisten Objekterkennungsverfahren, die heute verwendet werden, benutzen eine Anzahl von Beispielbildern (21), um dem Objekt angepasste Merkmalsdetektoren (22) zu trainieren.Most object recognition techniques used today use a number of sample images ( 21 ) to feature detectors adapted to the object ( 22 ) to train.

1.2 Erkennung1.2 detection

Bei der Erkennung werden die trainierten Merkmalsdetektoren (32) verwendet, um die von ihnen repräsentierten Merkmale in einem Eingabebild (31) aufzufinden. Dieses geschieht durch einen Suchprozess. Jeder Merkmalsdetektor gibt einen Konfidenzwert aus, der angibt, wie gut er das von ihm repräsentierte Merkmal in dem Bild erkennt. Wenn die akkumulierten Konfidenzwerte (33) aller Merkmalsdetektoren einen vorgegebenen Schwellenwert überschreiten, nimmt man an, dass das Objekt erkannt wurde.The trained feature detectors ( 32 ) used to represent the characteristics they represent in an input image ( 31 ) to find. This is done through a search process. Each feature detector outputs a confidence value that indicates how well it recognizes the feature it represents in the image. If the accumulated confidence values ( 33 ) of all feature detectors exceed a predetermined threshold value, it is assumed that the object has been recognized.

2. Anwendungsbeispiele2. Examples of use

Natürlich ist die automatische Bilderkennung noch weit davon entfernt, die Leistungen des menschlichen Sehsystems zu erreichen. Daher wird man sich zunächst auf Situationen beschränken, die von existierenden Bildverarbeitungssystemen gut behandelt werden können. Im folgenden beschreibe ich eine Reihe von Anwendungsfeldern und beschreibe ihre spezifischen Schwierigkeiten.Of course, the automatic one Image recognition still far from the performance of human Vision system. Therefore, you will initially limit yourself to situations that are affected by existing image processing systems can be treated well. in the In the following I describe a number of fields of application and describe their specific difficulties.

Stadt- und MuseumsführerCity and museum guide

Visuell Gebäude zu erkennen, ist mit heutigen Methoden gut realisierbar. Es hilft natürlich, wenn der Nutzer das Gebäude frontal und senkrecht fotografiert und nicht aus einem schrägen Winkel. Des weiteren kann man die Bilderkennung unterstützen, indem man Positionsinformationen mitverwendet. Viele Telefone werden mit GPS (Global Positioning System) ausgestattet, so dass man jederzeit bis auf wenige Meter weiss, wo sich das Telefon befindet. Diese Information kann man nutzen, um bei der Bildverarbeitung nur solche Gebäude oder Gebäudedetails in Betracht zu ziehen, die in Nähe sind. Da das Gebäude zu verschiedenen Tageszeiten erkennbar sein soll, muss man beim Ausbauen der visuellen Repräsentation darauf achten, dass entsprechendes Bildmaterial mit aufgenommen werden muss. Für die meisten Bilderkennungsverfahren bedeutet das, dass man einfach mehrere Bilder unter verschiedenen Beleuchtungssituationen aufnimmt und diese bei der Modellkonstruktion verwendet.Visually recognizing buildings is with today's Methods easy to implement. Of course, it helps if the user does that building Photographed frontally and vertically and not from an oblique angle. Of further one can support the image recognition by providing position information concomitantly. Many phones come with GPS (Global Positioning System), so that you can go down to a few meters at any time knows where the phone is. You can get this information use to only such buildings or building details to consider that in the vicinity are. Because the building to be recognizable at different times of the day, Develop the visual representation make sure that appropriate images are included must become. For Most image recognition processes mean that you can easily takes several pictures under different lighting situations and used this in the model construction.

Sehr einfach wäre es auch, einen universellen Kunstführer zu bauen, der einem Informationen, z.B. zu einem Gemälde, gibt. Da Bilder zweidimensional sind, ist die Erkennung wesentlich vereinfacht.It would also be very easy to create a universal to build an art guide that gives you information such as a painting. Because images are two-dimensional, recognition is much easier.

ProduktinformationenProduct Information

Eine andere Kategorie von Objekten sind Produkte wie Autos, Bücher oder Spielzeuge. Sieht der Nutzer ein Automodell, das ihn interessiert, kann er einfach davon ein Bild aufnehmen, und er wird z.B. zu einer entsprechenden Webseite mit weiteren Produktinformationen geleitet. Wiederum wird es in den frühen Phasen eines solchen Services nützlich sein, wenn der Nutzer Fotos von exakten Frontal- oder Seitenansichten aufnimmt und zum Serverrechner schickt. In späteren Versionen, wenn die Poseninvarianz verbessert worden ist, braucht sich der Nutzer weniger einzuschränken. Es ist wichtig, den bildbasierten Suchservice so zu gestalten, dass es ähnlich wie beim jetzigen World Wide Web jedem Anbieter von Informationen ermöglicht wird, für seine Webseite eine bildbasierte Suchfunktion anzubieten. Auf diese Weise kann leicht sichergestellt werden, dass für viele Produkte eine bildbasierte Suchfunktion zur Verfügung steht, da z.B. Autohersteller ein grosses Interesse daran haben werden, dass ihre neuesten Modelle per Bildaufnahme erkannt werden können.Another category of objects are products like cars, books or toys. If the user sees a car model that interests them, he can just take a picture of it and he will e.g. to a corresponding website with further product information. Again, it gets in the early Phases of such a service are useful if the user has photos of exact frontal or side views records and sends to the server computer. In later versions, when the pose invariance has been improved, the user needs to restrict himself less. It it is important to design the image-based search service in such a way that it similar to With the current World Wide Web, every provider of information is enabled for his Website to offer an image-based search function. In this way it can easily be ensured that for many products an image-based Search function available stands because e.g. Car manufacturers have a great interest in it that their latest models are recognized by image acquisition can.

TexterkennungOCR

Ein weiterer nützlicher Service besteht darin, dass man Texterkennung anbietet. Für den Reisenden nach Tokio oder Paris, der der Landessprache nicht mächtig ist, wäre es von grossem Wert, wenn er seine Kamera auf ein Schild richten kann und er dann eine Übersetzung und weitere Informationen zu dem erkannten Text erhält. Steht man beispielsweise in Tokio vor einer Sushibar, wäre es doch von grossem Wert, wenn man sofort und mühelos den entsprechenden Eintrag in einem Restaurantführer lesen könnte. Gerade für Besucher, die japanische Schriftzeichen nicht lesen können, ist dies eine sehr bequeme Lösung, um an weitere Informationen zu kommen.Another useful service is that to offer text recognition. For the traveler to Tokyo or Paris who does not speak the national language powerful is, would be it is of great value when he points his camera at a sign can and then he translates and get more information about the recognized text. Stands for example, in Tokyo in front of a sushi bar, it would be of great value if you immediately and effortlessly make the appropriate entry in a restaurant guide could read. Especially for Visitors who cannot read Japanese characters this is a very convenient solution to get more information.

Gesichtserkennungface recognition

Gesichtserkennung ist ein weiterer Spezialfall. Menschen, die aus irgendwelchen Gründen möchten, dass man schnell mehr über sie erfahren kann, können Aufnahmen von ihrem Gesicht verfügbar machen, die dann von der Bilderkennung genutzt werden können.Face recognition is another Special case. People who for some reason want you to know more about them quickly can experience Images of her face available make, which can then be used by image recognition.

Das vollausgebaute SystemThe fully equipped system

Die Zahl der Anwendungsbereiche liesse sich noch lange fortsetzen. Kataloge für Antiquitäten, Pflanzen- und Tierbestimmungsbücher können mit dem beschriebenen System wesentlich effizienter gestaltet werden. Oder man stelle sich ein Teil einer Apparatur vor, für das man Ersatz oder weitere Erklärungen braucht. Man nimmt einfach ein Bild auf, und schnell wird man auf Kennung und Hersteller oder einen entsprechenden Abschnitt in einem Handbuch verwiesen. Ein System, das einem Zusatzinformationen zu Reklametafeln gibt, ist eine weitere Anwendung. In jedem dieser Fälle nimmt der Nutzer einfach ein Bild des Gegenstandes von Interesse auf und schickt es zum Rechner, auf dem die Bilderkennung läuft. Die Bilderkennung sendet entsprechende symbolische Informationen, die das Objekt beschreibt, an die Suchmaschine, die letztlich die Information, die zum Nutzer geschickt wird, auswählt.The number of application areas can be continue for a long time. Catalogs for antiques, plant and animal identification books can be used with the described system can be designed much more efficiently. Or imagine a piece of equipment for which one needs replacement or more Explanations needs. You just take a picture and you quickly become one Identifier and manufacturer or a corresponding section in one Reference manual. A system that provides additional information Billboards there is another application. In each of these Cases takes the user simply sends and sends an image of the object of interest it to the computer on which the image recognition runs. The image recognition sends corresponding symbolic information that describes the object, to the search engine, which ultimately provides the information to the user is sent.

In der vollen Ausbaustufe hat man ein System, das man mit einem externen visuellen Gedächtnis vergleichen könnte. Jeder Gegenstand, jeder Text, jedes Symbol, jedes Gesicht, letztlich eine grosse Anzahl von Ansichten der Erdoberfläche ist in dem System gespeichert und wird kontinuierlich durch die Nutzer auf dem neuesten Stand gehalten. Letztlich hat man ein globales System, das unser Wissen über die Dinge auf unserem Planeten speichert und jederzeit zur Verfügung stellt.In the full expansion stage you have a system that you can compare with an external visual memory could. Every object, every text, every symbol, every face, ultimately a large number of views of the earth's surface are stored in the system and is continuously updated by users held. Ultimately, you have a global system that shares our knowledge of the Saving things on our planet and making them available at all times.

Claims (10)

Ein System zur bildbasierten Anfrage an Suchmaschinen oder Datenbanken, gekennzeichnet durch a) Ein Endgerät mit eingebauter Kamera, welches an ein Datenfernübertragungsnetz angeschlossen ist. b) Ein Serverrechner, auf dem ein Programm zur Objekterkennung läuft, welches eingesandte Bilder analysiert und mit einer symbolischen Indizierung versieht. c) Eine Suchmaschine, welche die Bildindizes nutzt, um Informationen zu dem Bild zu finden und zu dem Endgerät zurückzuschicken.A system for image-based search engine queries or databases, characterized by a) A terminal with built-in Camera connected to a long-distance data transmission network connected. b) A server computer on which a program for Object recognition is running, which analyzes submitted images and with a symbolic Indexing provides. c) A search engine that contains the image indexes uses to find information about the image and send it back to the terminal. Ein System, wie beschrieben unter 1), das für Mobiltelefone oder mobile Computer ausgelegt ist, die eine eingebaute Kamera haben.A system as described under 1) that for mobile phones or mobile computers that have a built-in camera. Ein Stadt- oder Museumsführer, der das unter 2) beschriebene System verwendet, um einem Nutzer Informationen zu geben zu Objekten, von denen er zuvor ein Bild aufgenommen hat.A city or museum guide, who described the under 2) System used to give a user information about objects, of which he has previously taken a picture. Ein System wie unter 3), bei dem zusätzlich Positionsinformation verwendet wird, um die Bilderkennung geeignet einzuschränken.A system as in 3), in which additional position information is used to appropriately restrict image recognition. Ein System wie unter 2), das Produktinformationen bereitstellt zu Produkten, die zuvor mit der mobilen Kamera fotografiert wurden.A system as under 2), the product information provides products that were previously photographed with the mobile camera were. Ein System wie unter 2), bei der die Objekterkennung auch in der Lage ist, Textzeichen oder Symbole zu erkennen.A system as in 2), in which the object detection is also able to recognize text characters or symbols. Ein System wie unter 2), bei der das System insbesondere in der Lage ist, Gesichter zu erkennen.A system like 2) where the system is particularly able to recognize faces. Ein System wie unter 2), das genutzt wird, dem Nutzer zusätzliche Information zu Reklametafeln zu geben.A system as under 2) that is used, the user additional To give information about billboards. Ein elektronisches Bedienungshandbuch, welches ein System wie unter 2) benutzt, um schnell Zugang zu entsprechenden Abschnitten im Handbuch zu navigieren.An electronic operating manual, which a System as under 2) used to quickly access appropriate Navigate sections in the manual. Ein System wie unter 2), das es den Anbietern von Informationen ermöglicht, selbständig neue Einträge in das Bildverarbeitungssystem vorzunehmen, um so die Abfrage ihrer Daten per Bildeingabe zu ermöglichen.A system as under 2) that the providers of Information enables independent New entries in the image processing system so as to query their To enable data by image input.
DE10245900A 2002-09-30 2002-09-30 Image based query system for search engines or databases of mobile telephone, portable computer uses image recognition to access more information about objects in image Ceased DE10245900A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
DE10245900A DE10245900A1 (en) 2002-09-30 2002-09-30 Image based query system for search engines or databases of mobile telephone, portable computer uses image recognition to access more information about objects in image

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE10245900A DE10245900A1 (en) 2002-09-30 2002-09-30 Image based query system for search engines or databases of mobile telephone, portable computer uses image recognition to access more information about objects in image

Publications (1)

Publication Number Publication Date
DE10245900A1 true DE10245900A1 (en) 2004-04-08

Family

ID=31984348

Family Applications (1)

Application Number Title Priority Date Filing Date
DE10245900A Ceased DE10245900A1 (en) 2002-09-30 2002-09-30 Image based query system for search engines or databases of mobile telephone, portable computer uses image recognition to access more information about objects in image

Country Status (1)

Country Link
DE (1) DE10245900A1 (en)

Cited By (44)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2006025797A1 (en) * 2004-09-01 2006-03-09 Creative Technology Ltd A search system
EP1650678A1 (en) * 2004-10-25 2006-04-26 Alcatel Method for Data Exchange Between a Mobile Terminal and a Server
DE102005008035A1 (en) * 2005-02-22 2006-08-31 Man Roland Druckmaschinen Ag Dynamic additional data visualization method, involves visualizing data based on static data received by reading device, where static data contain text and image data providing visual observation and/or printed side information of reader
WO2006120293A1 (en) * 2005-04-18 2006-11-16 Sture Udd Method and apparatus for handling of information
EP1738316A2 (en) * 2004-04-16 2007-01-03 Mobot, Inc. Mobile query system and method based on visual cues
WO2007080219A1 (en) * 2006-01-13 2007-07-19 Teknillinen Korkeakoulu Metadata associated with a printed image
EP1839193A1 (en) * 2004-12-31 2007-10-03 Nokia Corporation Provision of target specific information
EP1973315A1 (en) * 2007-03-21 2008-09-24 Lucent Technologies Inc. Image recognition for placing a call
WO2008134901A1 (en) * 2007-05-08 2008-11-13 Eidgenössische Technische Zürich Method and system for image-based information retrieval
EP2071800A2 (en) 2007-12-14 2009-06-17 Vodafone Holding GmbH Method for creating local connections between electronic end devices
US7565139B2 (en) 2004-02-20 2009-07-21 Google Inc. Image-based search engine for mobile phones with camera
DE102008007646A1 (en) * 2008-02-06 2009-08-13 Zumtobel Lighting Gmbh Method for releasing and transmitting product-specific information by object, particularly by light to communication device, involves capturing and determining object identifying information through communication device
EP2101284A2 (en) 2008-03-11 2009-09-16 Vodafone Holding GmbH Method and device for analysing digital images
EP2105845A1 (en) * 2008-03-28 2009-09-30 Neoperl GmbH Identification method
EP2172875A1 (en) 2008-08-26 2010-04-07 Vodafone Holding GmbH Method for feature selection of images in an image data base
US7697735B2 (en) 2004-06-21 2010-04-13 Google Inc. Image based multi-biometric system and method
US20100122283A1 (en) * 2008-11-12 2010-05-13 Alcatel-Lucent Usa Inc. Targeted advertising via mobile enhanced reality
US7751805B2 (en) 2004-02-20 2010-07-06 Google Inc. Mobile image-based information retrieval system
DE102009007715A1 (en) 2009-02-05 2010-08-19 Vodafone Holding Gmbh Method for automatically finding electronic images in Internet, involves storing electronic images of data sets in result file, where correlation degree of electronic images with electronic search image exceeds preset threshold
WO2011017557A1 (en) * 2009-08-07 2011-02-10 Google Inc. Architecture for responding to a visual query
DE102009043641A1 (en) * 2009-09-09 2011-03-10 Sureinstinct Gmbh I.G. Method for displaying information concerning an object
US7925676B2 (en) 2006-01-27 2011-04-12 Google Inc. Data object visualization using maps
US7953720B1 (en) 2005-03-31 2011-05-31 Google Inc. Selecting the best answer to a fact query from among a set of potential answers
US20110137895A1 (en) * 2009-12-03 2011-06-09 David Petrou Hybrid Use of Location Sensor Data and Visual Query to Return Local Listings for Visual Query
US8055674B2 (en) 2006-02-17 2011-11-08 Google Inc. Annotation framework
US8065290B2 (en) 2005-03-31 2011-11-22 Google Inc. User interface for facts query engine with snippets from information sources that include query terms and answer terms
DE102011075372A1 (en) * 2011-05-05 2012-11-08 BSH Bosch und Siemens Hausgeräte GmbH System for the extended provision of information to customers in a sales room for home appliances and associated method and computer program product
DE102011076074A1 (en) * 2011-05-18 2012-11-22 BSH Bosch und Siemens Hausgeräte GmbH System for the extended provision of information on a product and the associated method and computer program product
DE102005048205B4 (en) * 2005-10-07 2012-12-27 Vodafone Holding Gmbh Method and system for electronic research on content within a presentation
US8670597B2 (en) 2009-08-07 2014-03-11 Google Inc. Facial recognition with social network aiding
US8805079B2 (en) 2009-12-02 2014-08-12 Google Inc. Identifying matching canonical documents in response to a visual query and in accordance with geographic information
US8811742B2 (en) 2009-12-02 2014-08-19 Google Inc. Identifying matching canonical documents consistent with visual query structural information
US8935246B2 (en) 2012-08-08 2015-01-13 Google Inc. Identifying textual terms in response to a visual query
US8954426B2 (en) 2006-02-17 2015-02-10 Google Inc. Query language
US8977639B2 (en) 2009-12-02 2015-03-10 Google Inc. Actionable search results for visual queries
US9087059B2 (en) 2009-08-07 2015-07-21 Google Inc. User interface for presenting search results for multiple regions of a visual query
US9183224B2 (en) 2009-12-02 2015-11-10 Google Inc. Identifying matching canonical documents in response to a visual query
US9219840B2 (en) 2005-02-11 2015-12-22 Mobile Acuitv Limited Storing information for access using a captured image
US9405772B2 (en) 2009-12-02 2016-08-02 Google Inc. Actionable search results for street view visual queries
DE202016004430U1 (en) 2016-07-20 2016-08-04 Christian Schlemmer System for automatic detection of plants
DE102015007434A1 (en) 2015-06-15 2016-12-15 Mediabridge Technology GmbH information device
US9530229B2 (en) 2006-01-27 2016-12-27 Google Inc. Data object visualization using graphs
US9892132B2 (en) 2007-03-14 2018-02-13 Google Llc Determining geographic locations for place names in a fact repository
DE202018104016U1 (en) * 2018-07-12 2019-10-15 Zumtobel Lighting Gmbh Assignment of a luminaire ID using physical properties introduced in production

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10110979A1 (en) * 2001-03-07 2002-09-26 Siemens Ag Optical pattern and information association device for universal remote-control device for audio-visual apparatus

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10110979A1 (en) * 2001-03-07 2002-09-26 Siemens Ag Optical pattern and information association device for universal remote-control device for audio-visual apparatus

Cited By (79)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7751805B2 (en) 2004-02-20 2010-07-06 Google Inc. Mobile image-based information retrieval system
US7565139B2 (en) 2004-02-20 2009-07-21 Google Inc. Image-based search engine for mobile phones with camera
EP1738316A2 (en) * 2004-04-16 2007-01-03 Mobot, Inc. Mobile query system and method based on visual cues
EP1738316A4 (en) * 2004-04-16 2009-03-04 Mobot Inc Mobile query system and method based on visual cues
US7697735B2 (en) 2004-06-21 2010-04-13 Google Inc. Image based multi-biometric system and method
WO2006025797A1 (en) * 2004-09-01 2006-03-09 Creative Technology Ltd A search system
EP1650678A1 (en) * 2004-10-25 2006-04-26 Alcatel Method for Data Exchange Between a Mobile Terminal and a Server
WO2006045978A2 (en) * 2004-10-25 2006-05-04 Alcatel Lucent Method for exchanging information between a mobile terminal and a server
FR2878392A1 (en) * 2004-10-25 2006-05-26 Cit Alcatel METHOD OF EXCHANGING INFORMATION BETWEEN A MOBILE TERMINAL AND A SERVER
WO2006045978A3 (en) * 2004-10-25 2006-06-22 Cit Alcatel Method for exchanging information between a mobile terminal and a server
EP2264621A3 (en) * 2004-12-31 2011-11-23 Nokia Corp. Provision of target specific information
US9451219B2 (en) 2004-12-31 2016-09-20 Nokia Technologies Oy Provision of target specific information
EP1839193A1 (en) * 2004-12-31 2007-10-03 Nokia Corporation Provision of target specific information
US9596414B2 (en) 2004-12-31 2017-03-14 Nokie Technologies Oy Provision of target specific information
US9715629B2 (en) 2005-02-11 2017-07-25 Mobile Acuity Limited Storing information for access using a captured image
US9219840B2 (en) 2005-02-11 2015-12-22 Mobile Acuitv Limited Storing information for access using a captured image
US9418294B2 (en) 2005-02-11 2016-08-16 Mobile Acuity Limited Storing information for access using a captured image
US10445618B2 (en) 2005-02-11 2019-10-15 Mobile Acuity Limited Storing information for access using a captured image
US10776658B2 (en) 2005-02-11 2020-09-15 Mobile Acuity Limited Storing information for access using a captured image
DE102005008035A1 (en) * 2005-02-22 2006-08-31 Man Roland Druckmaschinen Ag Dynamic additional data visualization method, involves visualizing data based on static data received by reading device, where static data contain text and image data providing visual observation and/or printed side information of reader
US8065290B2 (en) 2005-03-31 2011-11-22 Google Inc. User interface for facts query engine with snippets from information sources that include query terms and answer terms
US8224802B2 (en) 2005-03-31 2012-07-17 Google Inc. User interface for facts query engine with snippets from information sources that include query terms and answer terms
US8650175B2 (en) 2005-03-31 2014-02-11 Google Inc. User interface for facts query engine with snippets from information sources that include query terms and answer terms
US7953720B1 (en) 2005-03-31 2011-05-31 Google Inc. Selecting the best answer to a fact query from among a set of potential answers
WO2006120293A1 (en) * 2005-04-18 2006-11-16 Sture Udd Method and apparatus for handling of information
DE102005048205B4 (en) * 2005-10-07 2012-12-27 Vodafone Holding Gmbh Method and system for electronic research on content within a presentation
WO2007080219A1 (en) * 2006-01-13 2007-07-19 Teknillinen Korkeakoulu Metadata associated with a printed image
US7925676B2 (en) 2006-01-27 2011-04-12 Google Inc. Data object visualization using maps
US9530229B2 (en) 2006-01-27 2016-12-27 Google Inc. Data object visualization using graphs
US8055674B2 (en) 2006-02-17 2011-11-08 Google Inc. Annotation framework
US8954426B2 (en) 2006-02-17 2015-02-10 Google Inc. Query language
US9892132B2 (en) 2007-03-14 2018-02-13 Google Llc Determining geographic locations for place names in a fact repository
WO2008115474A1 (en) * 2007-03-21 2008-09-25 Lucent Technologies Inc. Image recognition for placing a call
EP1973315A1 (en) * 2007-03-21 2008-09-24 Lucent Technologies Inc. Image recognition for placing a call
WO2008134901A1 (en) * 2007-05-08 2008-11-13 Eidgenössische Technische Zürich Method and system for image-based information retrieval
DE102007060095A1 (en) 2007-12-14 2009-06-18 Vodafone Holding Gmbh Method for establishing local connections between electronic terminals
EP2071800A2 (en) 2007-12-14 2009-06-17 Vodafone Holding GmbH Method for creating local connections between electronic end devices
DE102008007646A1 (en) * 2008-02-06 2009-08-13 Zumtobel Lighting Gmbh Method for releasing and transmitting product-specific information by object, particularly by light to communication device, involves capturing and determining object identifying information through communication device
DE102008013608A1 (en) 2008-03-11 2009-10-29 Vodafone Holding Gmbh Method and device for analyzing digital images
EP2101284A2 (en) 2008-03-11 2009-09-16 Vodafone Holding GmbH Method and device for analysing digital images
WO2009118081A1 (en) * 2008-03-28 2009-10-01 Neoperl Gmbh Identification method
EP2105845A1 (en) * 2008-03-28 2009-09-30 Neoperl GmbH Identification method
EP2172875A1 (en) 2008-08-26 2010-04-07 Vodafone Holding GmbH Method for feature selection of images in an image data base
US20100122283A1 (en) * 2008-11-12 2010-05-13 Alcatel-Lucent Usa Inc. Targeted advertising via mobile enhanced reality
DE102009007715A1 (en) 2009-02-05 2010-08-19 Vodafone Holding Gmbh Method for automatically finding electronic images in Internet, involves storing electronic images of data sets in result file, where correlation degree of electronic images with electronic search image exceeds preset threshold
US10515114B2 (en) 2009-08-07 2019-12-24 Google Llc Facial recognition with social network aiding
US10534808B2 (en) 2009-08-07 2020-01-14 Google Llc Architecture for responding to visual query
WO2011017557A1 (en) * 2009-08-07 2011-02-10 Google Inc. Architecture for responding to a visual query
US8670597B2 (en) 2009-08-07 2014-03-11 Google Inc. Facial recognition with social network aiding
US10031927B2 (en) 2009-08-07 2018-07-24 Google Llc Facial recognition with social network aiding
US9208177B2 (en) 2009-08-07 2015-12-08 Google Inc. Facial recognition with social network aiding
US9087059B2 (en) 2009-08-07 2015-07-21 Google Inc. User interface for presenting search results for multiple regions of a visual query
US9135277B2 (en) 2009-08-07 2015-09-15 Google Inc. Architecture for responding to a visual query
DE102009043641A1 (en) * 2009-09-09 2011-03-10 Sureinstinct Gmbh I.G. Method for displaying information concerning an object
US8977639B2 (en) 2009-12-02 2015-03-10 Google Inc. Actionable search results for visual queries
US8811742B2 (en) 2009-12-02 2014-08-19 Google Inc. Identifying matching canonical documents consistent with visual query structural information
US9405772B2 (en) 2009-12-02 2016-08-02 Google Inc. Actionable search results for street view visual queries
US9183224B2 (en) 2009-12-02 2015-11-10 Google Inc. Identifying matching canonical documents in response to a visual query
US9087235B2 (en) 2009-12-02 2015-07-21 Google Inc. Identifying matching canonical documents consistent with visual query structural information
US8805079B2 (en) 2009-12-02 2014-08-12 Google Inc. Identifying matching canonical documents in response to a visual query and in accordance with geographic information
AU2010326655B2 (en) * 2009-12-03 2013-11-21 Google Llc Hybrid use of location sensor data and visual query to return local listings for visual query
US10346463B2 (en) 2009-12-03 2019-07-09 Google Llc Hybrid use of location sensor data and visual query to return local listings for visual query
CN102770862A (en) * 2009-12-03 2012-11-07 谷歌公司 Hybrid use of location sensor data and visual query to return local listings for visual query
WO2011068574A3 (en) * 2009-12-03 2011-11-10 Google Inc. Hybrid use of location sensor data and visual query to return local listings for visual query
CN102770862B (en) * 2009-12-03 2016-10-19 谷歌公司 It is used in mixed way position sensor data and virtual query to include to this locality returning virtual query
US20110137895A1 (en) * 2009-12-03 2011-06-09 David Petrou Hybrid Use of Location Sensor Data and Visual Query to Return Local Listings for Visual Query
US9852156B2 (en) * 2009-12-03 2017-12-26 Google Inc. Hybrid use of location sensor data and visual query to return local listings for visual query
DE102011075372A1 (en) * 2011-05-05 2012-11-08 BSH Bosch und Siemens Hausgeräte GmbH System for the extended provision of information to customers in a sales room for home appliances and associated method and computer program product
WO2012156245A1 (en) 2011-05-18 2012-11-22 BSH Bosch und Siemens Hausgeräte GmbH System for providing information on a domestic appliance in an enhanced manner, corresponding method, and computer program product
DE102011076074A1 (en) * 2011-05-18 2012-11-22 BSH Bosch und Siemens Hausgeräte GmbH System for the extended provision of information on a product and the associated method and computer program product
US9372920B2 (en) 2012-08-08 2016-06-21 Google Inc. Identifying textual terms in response to a visual query
US8935246B2 (en) 2012-08-08 2015-01-13 Google Inc. Identifying textual terms in response to a visual query
DE102015007434A1 (en) 2015-06-15 2016-12-15 Mediabridge Technology GmbH information device
DE102017103486A1 (en) 2016-07-20 2018-01-25 M-Farms Gmbh System for automatic detection of plants
WO2018015030A1 (en) 2016-07-20 2018-01-25 M-Farms Gmbh System for automatically recognizing plants
DE202016004430U1 (en) 2016-07-20 2016-08-04 Christian Schlemmer System for automatic detection of plants
US10997416B2 (en) 2016-07-20 2021-05-04 M-Farms Gmbh System for automatically recognizing plants
US11798275B2 (en) 2016-07-20 2023-10-24 M-Farms Gmbh System for automatically recognizing plants
DE202018104016U1 (en) * 2018-07-12 2019-10-15 Zumtobel Lighting Gmbh Assignment of a luminaire ID using physical properties introduced in production

Similar Documents

Publication Publication Date Title
DE10245900A1 (en) Image based query system for search engines or databases of mobile telephone, portable computer uses image recognition to access more information about objects in image
Rasti et al. Convolutional neural network super resolution for face recognition in surveillance monitoring
DE102017011262A1 (en) Theme linking and marking for dense images
US8024359B2 (en) System and method for accessing electronic data via an image search engine
DE102017216000A1 (en) Gesture control for communication with an autonomous vehicle based on a simple 2D camera
CN109492122B (en) Method and device for acquiring merchant information, terminal and computer-readable storage medium
CN105095831A (en) Face recognition method, device and system
DE202014010966U1 (en) Geo-photo search based on the expected conditions at a location
Chandran et al. Missing child identification system using deep learning and multiclass SVM
DE10040899A1 (en) Method and device for decoding optical codes
CN115294150A (en) Image processing method and terminal equipment
Stylianou et al. Traffickcam: Crowdsourced and computer vision based approaches to fighting sex trafficking
CN106295514A (en) A kind of method and device of image recognition exercise question display answer
CN106708963B (en) Website editor article entry method and system in artificial intelligence mode
DE102015008874A1 (en) Data of complement documents for web services on the Internet.
DE102004001595A1 (en) Method for informative description of picture objects
Kazaryan et al. The automated space-monitoring system of waste disposal sites
US11210830B2 (en) System and method for associating images and text
Cvitkovic Some requests for machine learning research from the East African tech scene
Snoussi et al. Arabic document segmentation on a smartphone towards Big Data HAJJ rules extraction
CN113743251A (en) Target searching method and device based on weak supervision scene
CN106776838A (en) A kind of massive video analysis and quick retrieval system based on cloud computing
Vadivukarassi et al. A framework of keyword based image retrieval using proposed Hog_Sift feature extraction method from Twitter Dataset
Ramesh Babu et al. A Novel Framework design for Semantic based Image retrieval as a Cyber Forensic Tool
Bouhlel et al. Tir-gan: Thermal images restoration using generative adversarial network

Legal Events

Date Code Title Description
OP8 Request for examination as to paragraph 44 patent law
8127 New person/name/address of the applicant

Owner name: GOOGLE, INC., MOUNTAIN VIEW, CALIF., US

8128 New person/name/address of the agent

Representative=s name: PATENTANWAELTE VON KREISLER, SELTING, WERNER ET CO

8131 Rejection