Abstract:
System for providing a mobile user, object related information related to an object visible thereto, the system including a camera directable toward the object, a local interest points and semi global geometry (LIPSGG) extraction processor, and a remote LIPSGG identifier, the camera acquiring an image of at least a portion of the object, the LIPSGG extraction processor being coupled with the camera, the LIPSGG extraction processor extracting an LIPSGG model of the object from the image, remote LIPSGG identifier being coupled with the LIPSGG extraction processor via a network, the remote LIPSGG identifier receiving the LIPSGG model from the LIPSGG extraction processor, via the network, the remote LIPSGG identifier identifying the object according to the LIPSGG model, the remote LIPSGG identifier retrieving the object related information, the remote LIPSGG identifier providing the object related information to the mobile user operating the camera.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is the national phase of International (PCT) Patent Application Serial No. PCT/IL2008/000276, filed on Mar. 4, 2008, published under PCT Article 21(2) in English, which claims priority to and the benefit of U.S. Provisional Patent Application No. 60/893,034, filed on Mar. 5, 2007, the disclosure of each of which is incorporated herein by reference. 
    
    
     FIELD OF THE DISCLOSED TECHNIQUE 
     The disclosed technique relates to wireless communication systems in general, and to methods and systems for providing a mobile user, information related to an object, in particular. 
     BACKGROUND OF THE DISCLOSED TECHNIQUE 
     A user is often interested to obtain information related to an object, while she is physically located in front of the object. For example, the user is interested to obtain information related to a consumer product (e.g., technical specification, comparable prices, user manual concerning to a refrigerator), while she is examining the refrigerator in a department store. 
     Systems and methods for providing the user information related to an object, according to an image of the object are known in the art. Such a system includes a mobile station, such as a cellular phone, a personal digital assistant (PDA), or a digital camera, and a server which are connected together via a wireless network. The mobile station acquires the image of the object and sends the image to the server. The server retrieves the relevant information from a database connected thereto, according to the image, and sends the relevant information to the mobile station. 
     Object identification methods (i.e., the identification of an object, by a processor, according to an image of the object) are also known in the art. Generally, the user captures a digital image of an object. A processor is determining values for a set of predetermined parameters of the image, such as contrast, color and the like. The processor compares the values of the parameters to a plurality of sets of values, relating to a plurality of objects, stored in a database and determines the best match. 
     Reference is now made to  FIG. 1 , which is a schematic illustration of a system generally referenced  50 , for providing information related to an object, according to an image of the object, constructed and operative as known in the art. System  50  includes a cellular phone  52 , a communication interface  54 , an image processor  56  and a database  58 . Cellular phone  52  is coupled with communication interface  54  via a network  60 . Image processor  56  is coupled with communication interface  54  and with database  58 . Network  60  is a cellular communication network. Database  58  includes information (e.g., technical specification, user manual) related to each of a plurality of objects (not shown), such as an object  64 . Object  64  is a consumer product, such as an MP3 player (i.e., motion picture expert group layer 3 player). 
     Cellular phone  52  includes a camera (not shown). Cellular phone  52  acquires an image  62  of object  64 , via the camera. Cellular phone  52  sends image  62  to image processor  56 , via network  60 . Image processor  56  identifies object  64  according to image  62 , and according to the data stored in database  58 . Image processor  56  retrieves the information from database  58 , and sends this information to cellular phone  52  via communication interface  54 . 
     U.S. Pat. No. 7,016,532 B2 issued to Boncyk et al., and entitled “Image Capture and Identification System and Process”, is directed to an image identification system for receiving a digital image, identifying the object in the image and transmitting a response associated with the identified object. The system includes a digital camera and a server. 
     A user captures an image of an object by employing the digital camera. The user transmits the captured image from the digital camera to the server via a cable or via a wireless communication network. The server identifies the object of the captured image by employing an identification algorithm. The identification algorithm includes two modules. 
     A first module identifies recognizable symbols such as letters, numerals, and barcodes. After identifying the recognizable symbols the server searches a database to find a match for the identified symbols. A second module decomposes the captured image into quantifiable parameters and then the server searches the database for a match for the quantifiable parameters. The server compares a match value respective of each of the first module and the second module, and chooses the best match as the identified object. The server transmits a URL associated with the identified object to the digital camera. 
     US Patent Application Publication No. 2005/0185060 A1 to Neven, SR., and entitled “Image Based Inquiry System for Search Engines for Mobile Telephones with Integrated Camera”, is directed to an image based inquiry system for receiving information regarding an image of an object captured by a cellular phone camera. The system includes the cellular phone equipped with a digital camera and a server. A user captures the image of the object (e.g., a street sign, an automobile, an electric product). The cellular phone transmits the captured image to the server. The server identifies the object according to the captured image. The server converts the image information into symbolic information, for example plain text. The server operates a search engine by employing the symbolic information. The server transmits to the cellular phone a list of links to database entries containing information about the object. 
     US Patent Application Publication No. 2005/0261990 A1 to Gocht et al., and entitled “Mobile Query System and Method Based on Visual Cues”, is directed to a query system for mobile query based on visual cues. The system includes a mobile phone equipped with a camera and a mobile query service. The camera of the mobile phone captures an image of a visual cue (e.g., text, graphics). The mobile phone sends the captured image to the mobile query service via a mobile network. The mobile query service identifies the user, according to the phone number of the mobile phone, and identifies the visual cue. The mobile query service formats a query according to the identified object and the user information. The mobile query service finds the user preferences, if they exist (i.e., if the user is registered with the mobile query service), and adds them to the query. The mobile query service sends a response related to the query, to the mobile phone. A plurality of affiliates provides a response to the mobile query service (e.g., media outlets, advertisers, retailers). The mobile query service sends the response to the mobile phone, to an Email address of the mobile phone or to both. 
     A report entitled “A Picture is Worth a Thousand Keywords: Image-Based Object Search on a Mobile Platform”, presented in conference on human factors in computing systems, Portland, Or, April 2005, is directed to a system for mobile web search using object appearance. The system includes a mobile phone, a remote database and a remote processor. The remote processor is coupled with the remote database. The cellular phone is coupled with the remote processor via a cellular network. The remote database includes images of a plurality of objects, and information related to the objects. A user captures a first image of an object which he intends to query. 
     The user captures a second image. The second image is taken from the same perspective of the first image. The second image lacks the object of interest (i.e., the second image is different from the first image by the lack of the object of interest). The user transmits the first and the second images to the remote processor via the cellular network. The remote processor compares the first image with the second image, in order to segment the object of interest (i.e., by comparing the first image with the second image, the processor finds the boundaries of the object of interest, since the object of interest is the difference between the first image and the second image). The remote processor compares the segmented object of interest with a plurality of images, of the objects stored on the remote database, and identifies the object of interest. The remote processor retrieves information related to the object of interest, from the remote database and transmits this information to the cellular phone. 
     U.S. Pat. No. 6,512,919 B2 issued to Ogasawara, and entitled “Electronic Shopping System Utilizing a Program Downloadable Wireless Videophone”, is directed to an electronic system which facilitates purchase transactions via a wireless videophone. The electronic shopping system includes a server and a mobile videophone. The videophone is integrally equipped with a camera. When a user, equipped with a videophone, enters a store, the user dials a number of the shopping service of that store. A shopping system application is automatically downloaded into the videophone of the user. 
     The user acquires an image of the barcode of a desired product. The shopping system application transmits the image of the barcode to the shopping system server. The shopping system server identifies the barcode, in the image of the barcode, employing pattern recognition. The shopping system server identifies the desired product. The shopping system server transmits to the videophone of the user, the price and the item description relating to the desired product. The shopping system enables the user to purchase the desired product through the videophone. 
     U.S. Pat. No. 6,595,417 B2 issued to O&#39;Hagan et al., and entitled “Electronic Shopping System”, is directed to an electronic system for use by customers in a retail shopping environment. The electronic system includes a host computer and a portable terminal. The portable terminal is located within a retail environment and interacts with the host computer to access product information. The host computer includes a product database, a host computer communication interface and a host computer processor. The portable terminal includes a barcode scanner, a display, a terminal processor and a terminal communication interface. The portable terminal can be attached to a shopping cart. 
     A user enters a retail environment (e.g., a large store) and takes a shopping cart, equipped with a portable terminal. The user finds a desired product and employs the barcode scanner of the portable terminal to scan the barcode, of the desired product. The portable terminal transmits identification data related to the desired product, to the host computer, according to the barcode of the desired product. The host computer retrieves information related to the desired product from a product database (e.g., the price of the product, the availability of the product in the inventory of the store, consumer information related to the desired product, and the like). The portable terminal displays to the user, the information related to the desired product. 
     SUMMARY OF THE DISCLOSED TECHNIQUE 
     It is an object of the disclosed technique to provide a novel method and system for providing a mobile user, object related information related to an object visible thereto, which overcomes the disadvantages of the prior art. 
     In accordance with the disclosed technique, there is thus provided a system for providing a mobile user, object related information related to an object visible thereto. The system includes a camera, a local interest points and semi global geometry (LIPSGG) extraction processor, and a remote LIPSGG identifier. The camera is coupled with the LIPSGG extraction processor. The LIPSGG extraction processor is coupled with the remote LIPSGG identifier, via a network. 
     The camera is directable toward the object. The camera acquires an image of at least a portion of the object. The LIPSGG extraction processor extracts an LIPSGG model of the object from the image. The remote LIPSGG identifier receives the LIPSGG model from the LIPSGG extraction processor, via the network. The remote LIPSGG identifier identifies the object, according to the LIPSGG model, and retrieves the object related information. The remote LIPSGG identifier provides the object related information to the mobile user operating the camera. 
     In accordance with another aspect of the disclosed technique, there is thus provided a system for providing a mobile user, object related information related to an object visible thereto. The system includes a camera, a local interest points and semi global geometry (LIPSGG) extraction processor, and a mobile communication interface. The camera is coupled with the LIPSGG extraction processor. The LIPSGG extraction processor is coupled with the mobile communication interface. 
     The camera is directable toward the object. The camera acquires an image of at least a portion of the object. The LIPSGG extraction processor extracts an LIPSGG model of the object from the image. The mobile communication interface transmits the LIPSGG model to a remote LIPSGG identifier via a network. The mobile communication interface receives the object related information from the remote LIPSGG identifier. 
     In accordance with a further aspect of the disclosed technique, there is thus provided a system for providing a mobile user, object related information related to an object visible thereto. The system includes a remote local interest points and semi global geometry (LIPSGG) identifier. The remote LIPSGG identifier includes a remote LIPSGG identifier communication interface, and an LIPSGG extraction processor. 
     The remote LIPSGG identifier communication interface receives an image of at least a portion of the object. The remote LIPSGG identifier receives an image of at least a portion of the object. The LIPSGG extraction processor extracts an LIPSGG model from the image. The remote LIPSGG identifier identifies the object, according to the LIPSGG model, and retrieves the object related information. The remote LIPSGG identifier communication interface transmits the object related information to the mobile user via a network. 
     In accordance with another aspect of the disclosed technique, there is thus provided a method for providing a mobile user, object related information related to an object visible thereto. The method includes the procedures of extracting an LIPSGG model of the object, from an image of at least a portion of the object, and transmitting LIPSGG model data respective of the LIPSGG model to an LIPSGG identifier, via a network. The method further includes the procedures of identifying the object according to the LIPSGG model data, retrieving the object related information respective of the object from a database, and transmitting the object related information to the mobile user, via the network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The disclosed technique will be understood and appreciated more fully from the following detailed description taken in conjunction with the drawings in which: 
         FIG. 1 , is a schematic illustration of a system, for providing information related to an object, according to an image of the object, constructed and operative as known in the art; 
         FIG. 2 , is a schematic illustration of a system, for providing a mobile user, general information related to an object, constructed and operative in accordance with an embodiment of the disclosed technique; 
         FIG. 3 , is a schematic illustration of a system, for providing a mobile user, general information related to an object, constructed and operative in accordance with another embodiment of the disclosed technique; and, 
         FIG. 4 , is a schematic illustration of a method for operating the system of  FIG. 2 , operative in accordance with a further embodiment of the disclosed technique. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     The disclosed technique overcomes the disadvantages of the prior art by transmitting general information related to an object, to a mobile station, according to features in an image of the object, acquired by the mobile station. The mobile station acquires the image of the object, identifies points in the image, which correspond to sharp variations in values of pixels and identifies the relative position of such points in the image. The mobile station transmits the data respective of these points and their relative position in the image to a remote processor. The remote processor identifies the object according to these points and their relative position, retrieves the general information from a database, and transmits the data respective of the general information, to the mobile station. Additionally, the remote processor can retrieve the general information according to the location of the object, determined by a global positioning system (GPS) receiver, incorporated with the mobile station. 
     The term “camera” herein below refers to an image acquisition device. The camera can record the image digitally (e.g., by employing a charge-coupled device—CCD, complementary metal oxide semiconductor—CMOS), light-sensitive material (e.g., black and white film, color film), and the like. The camera can either be a still camera (i.e., a camera acquiring still images of an object) or a video camera. The term “object” herein below refers to a solid object, such as a consumer product, street sign, electric appliance, automobile, art piece, wine bottle, and the like. 
     It is noted that different elements within the solid object can be arranged in more than one configuration (i.e., the object is a multiple configuration object). For example, a car has a first configuration with closed doors, and a second configuration with open doors. Another example of a solid object with more than one configuration is a folding cellular phone, with a first configuration of a folded folding cellular phone, and a second configuration of an open folding cellular phone. However, the elements of the solid object maintain their form, in each of the configurations. 
     The term “network” herein below refers to either a wireless communication network, or a wired communication network. The network can include links such as Wi-Fi, WiMax, Zigbee, Bluetooth, cellular, free air optic, satellite, wired internet, wireless Internet, and the like. It is noted, that different types of data can be transferred via the network, such as Multimedia Messaging Service (MMS), Short Messaging Service (SMS), audio, video, HyperText Markup Language (HTML), Extended Markup Language (XML), Really Simple Syndication (RSS), and the like. 
     The term “local interest point” herein below, refers to a point in an image of an object, where there are sharp variations in pixel values, due to physical features of the object, such as an intersection of physical elements of the object, a hole, and the like. A feature vector represents a region surrounding a local interest point, based on variation in the pixel values. The feature vector describes the appearance of the object at the region surrounding the local interest point. 
     The term “semi-global geometry” herein below, refers to the relative distances and relative angles between the local interest points of the object. The semi-global geometry of the object defines the relative position of each of the local interest points in relation to other local interest points of the object. In case only a portion of the object is visible in the image of the object, the semi-global geometry of the object defines the relative positions of each of the local interest points in the image of the object, in relation to other interest points in the image of the object. In case of a multiple configuration object, the semi-global geometry defines the relative positions of each of the local interest points, in each element of the object, in relation to other local interest points in the same element. 
     It is noted, that the relative angles and distances of the semi-global geometry are independent of scale and rotation, thereby allowing identification of the object, according to images of the object acquired from different view points, or at different scales. According to the disclosed technique, the identification process is independent of rotations in two dimensions, as well as in three dimensions (i.e., up to a predetermined angle of rotation). 
     It is further noted, that the relative angles and distances of the semi-global geometry are substantially constant, even in case only a portion of the object is visible in the image of the object, thereby allowing identification of the object, according to partially occluded images of the object. Relative angles and distances of the semi-global geometry of the elements of the object are independent of the configuration of the object. 
     Reference is now made to  FIG. 2 , which is a schematic illustration of a system generally referenced  100 , for providing a mobile user, general information related to an object, constructed and operative in accordance with an embodiment of the disclosed technique. System  100  includes a camera  102 , a Local Interest Points and Semi-Global Geometry (LIPSGG) extraction processor  104 , a mobile processor  106 , a user interface  108 , a mobile communication interface  110  and an LIPSGG identifier  112 . LIPSGG identifier  112  includes an LIPSGG identifier communication interface  114 , an image processor  116  and a database  118 . 
     Database  118  includes data respective of identification information related to each of a plurality of objects, and data respective of general information, related to each of the objects. The data, respective of identification information related to each of the objects, stored on database  118 , is separated into models (not shown). 
     Each of the models corresponds to a different image of the respective object. Thus, the same object relates to several different models, corresponding to different images of the same object (e.g., a first model is derived from an image of the object from a top side of the object, and a second model is derived from an image of the object from a side view thereof). In case the object relates to more than one model, image processor  116  can reconstruct a 3D model according to the models of the object (e.g., in case the object relates to two models corresponding to two different images of the object, acquired from two different viewpoints, image processor  116  can reconstruct a stereoscopic model of the object). 
     Each of the models includes a plurality of feature vectors (not shown) and the semi-global geometry of the object. The feature vectors describe the variation in the pixel values in a local region surrounding each of the local interest points. 
     The general information, related to each of the objects, is information, which interests a mobile user, located next to one of the objects. The general information can be for example, a review about an electric appliance, a manual of an electric appliance, tourist information related to a tourist attraction, information related to an art piece in a museum, and the like. 
     It is noted, that database  118  can be located either on a single server, or consist of a plurality of databases (not shown) distributed in a network of servers such as the Internet. User interface  108  can be an aural device (i.e., speaker), visual device (i.e., display), tactile device (e.g., pointing device, keypad), and the like, or a combination thereof. 
     LIPSGG extraction processor  104  is coupled with camera  102 . Mobile processor  106  is coupled with LIPSGG extraction processor  104 , user interface  108  and with mobile communication interface  110 . Mobile communication interface  110  is coupled with LIPSGG identifier communication interface  114  via a network  120 . Image processor  116  is coupled with LIPSGG identifier communication interface  114  and with database  118 . In the example set forth in  FIG. 2 , network  120  is the Internet. Mobile communication interface  110  operates as a web browser. LIPSGG identifier communication interface  114  operates as a web server. 
     A mobile user (not shown) directs camera  102  towards an object  122  (e.g., an MP3 player). Alternatively, the mobile user directs camera  102  towards a two-dimensional image of the object (e.g., an image of the MP3 player at a catalog). Camera  102  acquires one or more images (not shown) of one or more portions of object  122 . 
     Alternatively, camera  102  is replaced with an image source  102 . The image of the object is obtained from any image source known in the art, such as an image database, a memory unit, a scanner, a web site, a camera, and the like. For example, when the user browses through a web site (e.g., an online catalog) and views an object of interest (e.g., an electric appliance), the user sends the image of the object to LIPSGG extraction processor  104  for the identification process to begin. 
     LIPSGG extraction processor  104  identifies a plurality of local interest points (not shown), in an image (not shown) of object  122 , according to variation in the values of the pixels around the local interest points. LIPSGG extraction processor  104  defines a plurality of feature vectors, describing the variation in the pixel values in the local region surrounding each of the local interest points. LIPSGG extraction processor  104  defines the semi-global geometry of object  122 , as the relative position of each of the local interest points, within the image of object  122 . 
     LIPSGG extraction processor  104  extracts an LIPSGG model  124  from the image of object  122 . LIPSGG extraction processor  104  transmits data respective of LIPSGG model  124  to LIPSGG identifier  112  via network  120 . In the example set forth in  FIG. 2 , the data which LIPSGG extraction processor  104  transmits to LIPSGG identifier  112 , respective of object  122 , is acquired by camera  102 , while the user is present in front of object  122 . 
     Alternatively, LIPSGG extraction processor  104  can extract an LIPSGG model  124  from the image of object  122 , even though the image of object  122  was acquired by a camera (not shown), other than camera  102 , and at a previous point in time (e.g., the image of object  122  was acquired by scanning a consumer product catalog, and by transmitting the scanned image to LIPSGG extraction processor,  104  via mobile communication interface  110 ). 
     Thus, the identification process according to the disclosed technique, is independent of the source of the image of object  122 . For example, a user employing a laptop computer for retrieving an image of the object from an image database, via a wireless network, thereby employing the system of the disclosed technique, in order to retrieve information related to the object. 
     Further alternatively, LIPSGG extraction processor  104  can be located within LIPSGG identifier  112 . In this case, camera  102  is coupled with mobile processor  106 . Camera  102  transmits the image of object  122  to LIPSGG identifier  112 , via network  120 , instead of data respective of LIPSGG model  124 . 
     Image processor  116  compares the feature vectors of LIPSGG model  124 , with the feature vectors of the models stored in database  118 . Image processor  116  retrieves data respective of a plurality of models from database  118 , according to a predetermined number of matches between the feature vectors of LIPSGG model  124 , and the feature vectors of each of the models. 
     Image processor  116  compares the semi-global geometry of LIPSGG model  124  with the semi-global geometry of each of the retrieved models. Image processor  116  identifies object  122 , according to a confidence level of the match between the local interest points and the semi-global geometry of the acquired image of object  122  (i.e., of LIPSGG model  124 ), and those stored in database  118  (i.e., the interest points and semi-global geometry of the retrieved models). Image processor  116  identifies object  122  as one of the objects, having their respective data stored on database  118 . 
     It is noted, that in case image processor  116  reconstructs a 3D model, according to one of the retrieved models, image processor  116  compares the semi-global geometry of LIPSGG model  124 , with the semi-global geometry of the reconstructed 3D model, as well as with the semi-global geometry of the retrieved models. This feature enables processor  116  to identify object  122  at a higher confidence level. 
     It is noted, that it is imperative that the match values of the local interest points, and the semi-global geometry exceed a predetermined minimal value, in order to identify object  122  correctly. It is further noted, that the identification process of object  122  is two-dimensional rotation-invariant (i.e., independent of rotation), since the local interest points and their relative positions remain the same under one or more rotations. 
     It is noted, that the identification process according to the disclosed technique, is scale invariant (i.e., independent of the scale of the image of object  122 , and not affected by the absolute distances between different interest points), and is a function of relative distances between interest points. Thus, LIPSGG identifier  112  can identify object  122  whether camera  102  acquires the image of object  122  from a position relatively close to object  122 , or relatively far there from. 
     The identification process, according to the disclosed technique, is occlusion invariant (i.e., LIPSGG identifier can identify object  122  in case only a portion of object  122  is visible in the image of object  122 , since the relative distances and angles between the visible interest points are substantially invariant). The identification process is also configuration invariant (i.e., in case object  122  is a multiple configuration object, LIPSGG identifier can identify object  122  in each configuration of object  122 ), since the relative distances and angles between the local interest points in each element, are substantially invariant. 
     It is further noted, that the identification process according to the disclosed technique, is color invariant, and is a function of variations in the pixel values. Thus, LIPSGG identifier  112  can identify object  122  whether camera  102  acquires a black and white image of object  122 , a color image of object  122 , or a color filtered image of object  122  (i.e., an image acquired by a camera with an assembled filter, blocking a partial range of light wavelengths). Alternatively, the identification process is color dependant (i.e., the pixel values corresponding to colors are defined in the feature vectors, and are employed during the identification process). For example, LIPSGG identifier  112  will identify a model of a red object, only in case the model of the red object is stored on database  118 . 
     The identification process according to the disclosed technique, is three-dimensional substantially-small-angle rotation-invariant (i.e., affine invariance for angles small enough to negligibly affect the semi-global geometry). The three-dimensional substantially-small-angle rotations invariance is a function of the negligible affect of three-dimensional substantially-small-angle rotations on the planar angles and distances between interest points in the image of object  122 . 
     According to another aspect of the disclosed technique, in case image processor  116  identifies object  122 , as an object having its respective data stored on database  118 , with a predetermined high match value, image processor  116  ads LIPSGG model  124  to database  118  as a new model, related to the identified object. 
     Image processor  116  retrieves general information related to object  122  from database  118 . Image processor  116  transmits data respective of the general information related to object  122  to mobile processor  106  via LIPSGG identifier communication interface  114 . Mobile processor  106  presents the data, respective of the general information, related to object  122 , to the mobile user via user interface  108 . 
     Reference is now made to  FIG. 3 , which is a schematic illustration of a system generally referenced  150 , for providing a mobile user, general information related to an object, constructed and operative in accordance with another embodiment of the disclosed technique. System  150  includes a camera  152 , a LIPSGG extraction processor  154 , a GPS receiver  156 , a mobile processor  158 , a user interface  160 , a mobile communication interface  162  and a LIPSGG identifier  164 . LIPSGG identifier  164  includes an LIPSGG identifier communication interface  166 , an image processor  168  and a database  170 . 
     LIPSGG extraction processor  154 , user interface  160 , and database  170  are similar to LIPSGG extraction processor  104  ( FIG. 2 ), user interface  108 , and database  118 , respectively, as described herein above. LIPSGG extraction processor  154  is coupled with camera  152 . Mobile processor  158  is coupled with LIPSGG extraction processor  154 , user interface  160 , GPS receiver  156  and with mobile communication interface  162 . Mobile communication interface  162  is coupled with LIPSGG identifier communication interface  166  via a network  172 . Image processor  168  is coupled with LIPSGG identifier communication interface  166  and with database  170 . The mobile user directs camera  152  towards an object  174  (e.g., the Eiffel tower in Paris). Camera  152  acquires one or more images (not shown) of one or more portions of object  174 . LIPSGG extraction processor  154  extracts an LIPSGG model  176  of the image of object  174 . GPS receiver  156  obtains a set of GPS coordinates of object  174 . 
     Mobile processor  158  transmits data respective of LIPSGG model  176  and of the set of GPS coordinates of object  174  to LIPSGG identifier  164  via network  172 . Image processor  168  matches object  174  with an object from a plurality of objects of database  170 , according to LIPSGG model  176  and according to the set of GPS coordinates of object  174 . Image processor  168  retrieves general information related to object  174  from database  170 . Image processor  168  transmits data respective of the general information related to object  174 , to mobile processor  158 , via LIPSGG identifier communication interface  166 . Mobile processor  158  presents the general information, related to object  174 , to the mobile user via user interface  160 . 
     Reference is now made to  FIG. 4 , which is a schematic illustration of a method for operating the system of  FIG. 2 , operative in accordance with a further embodiment of the disclosed technique. In procedure  200 , at least one image of at least a portion of an object, is acquired by an image source. With reference to  FIG. 2 , camera  102  acquires an image of object  122 . Alternatively, an image source may include a terminal for retrieving an image of an object from a web site. 
     In procedure  202 , an LIPSGG model of the object is extracted from the image. With reference to  FIG. 2 , LIPSGG extraction processor  104  extracts LIPSGG model  124  of object  122 , from the image acquired in procedure  200 . 
     In procedure  204 , LIPSGG model data respective of the LIPSGG model, is transmitted to an LIPSGG identifier, via a network. With reference to  FIG. 2 , processor  106  transmits data respective of the LIPSGG model which extraction processor  104  extracted in procedure  202 , to LIPSGG identifier  112 , via communication interfaces  110  and  114 , and network  120 . 
     In procedure  206 , the identity of the object is identified according to the LIPSGG model data. With reference to  FIG. 2 , image processor  116  identifies the identity of object  122 , according to the data received from processor  106 , respective of the LIPSGG model of object  122 , in procedure  204 , by employing database  118 . 
     In procedure  208 , object related information respective of the identified object, is retrieved. With reference to  FIG. 2 , image processor  116  retrieves information related to object  122 , from database  118 . 
     In procedure  210 , the object related information is transmitted to a user operating the camera, via the network. With reference to  FIG. 2 , image processor  116  transmits the object related information, which was retrieved from database  118  in procedure  208 , to processor  106 , via communication interfaces  114  and  110 , and network  120 . Processor  106  provides the object related information to user interface  108  (e.g., a display of camera  102 ), for the display to display the object related information. Alternatively, image processor  116  transmits the object related information, which was retrieved from database  118  in procedure  208 , to a computer station, the user is currently employing. 
     It will be appreciated by persons skilled in the art that the disclosed technique is not limited to what has been particularly shown and described hereinabove. Rather the scope of the disclosed technique is defined only by the claims, which follow.