Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: A plurality of binary support vector machines (SVM) are trained to vote between two of a plurality of classes. Each of the binary SVMs vote between a different pair of the plurality of classes. A set of validated samples is input to each of the binary SVMs. The set of validated samples includes samples of each of the classes. Likelihood estimates are determined based on votes of the binary SVMs. A prior probability distribution (prior) is assigned for each of the classes. A posterior probability is calculated for each of the classes based on the determined likelihood estimates and the assigned priors.

Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: Techniques associated with three-dimensional object modeling are described in various implementations. In one example implementation, a method may include receiving a plurality of two-dimensional images depicting views of an object to be modeled in three dimensions. The method may also include, processing the plurality of two-dimensional images to generate a three-dimensional representation of the object, and analyzing the three-dimensional representation of the object to determine whether sufficient visual information exists in the plurality of two-dimensional images to generate a three-dimensional model of the object. The method may also include, in response to determining that sufficient visual information does not exist for a portion of the object, identifying the portion of the object to a user.

Abstract: A pair of video frames may be used for simultaneous localization and mapping (SLAM) initialization. The pair of frames may be determined according to a translation threshold. Whether the translation threshold is met may be determined by obtaining the pair of video frames and estimating the translation between the frames.

Abstract: Techniques for occluding augmented reality objects are described in various implementations. In one example implementation, a method may include receiving an image captured by an image capture device and identifying a foreground object depicted in the image. The foreground object may be free of fiducial markers and may be positioned between the image capture device and a background depicted in the image. The method may also include generating an augmented reality image depicting an augmented reality object at least partially occluded by the foreground object such that, when the augmented reality image is displayed, the augmented reality object appears to be positioned behind the foreground object.

Abstract: A system may include query circuitry. The query circuitry determine a set of structured data terms relevant to a specific data type by performing a preconfigured query for the specific data type on a structured dataset. The preconfigured query may be generated according to a predefined business rule for the specific data type. The query circuitry may further generate an unstructured search query from the set of structured data terms and execute the unstructured search query on an unstructured dataset to obtain unstructured search results.

Abstract: A method for object recognition performed by a computing device of an augmented reality system. The method includes receiving an image from a user, determining channels that a user is subscribed to, and determining a list of servers that host the channels that the user is subscribed to by using groups of channels that are distributed among a cluster of servers. The method further includes selecting, using the computing device, which servers from the list of servers are to be used to query the channels that the user is subscribed to. In addition, the method includes querying, using the computing device, the selected servers for the channels that the user is subscribed to with the image to determine at least one object that matches the image from object databases for the channels that the user is subscribed to. The method also includes retrieving and sending content associated with the at least one object.

Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: A plurality of binary support vector machines (SVM) are trained to vote between two of a plurality of classes. Each of the binary SVMs vote between a different pair of the plurality of classes. A set of validated samples is input to each of the binary SVMs. The set of validated samples includes samples of each of the classes. Likelihood estimates are determined based on votes of the binary SVMs. A prior probability distribution (prior) is assigned for each of the classes. A posterior probability is calculated for each of the classes based on the determined likelihood estimates and the assigned priors.

Abstract: Techniques for matching a feature of captured visual data are described in various implementations. In one example implementation, a server from among plural servers matches a feature of captured visual data of a physical target received from an electronic device with features of one of a plurality of partitions. Based on the matching, an object is identified that corresponds to the captured visual data.

Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.

Abstract: A pair of video frames may be used for simultaneous localization and mapping (SLAM) initialization. The pair of frames may be determined according to a translation threshold. Whether the translation threshold is met may be determined by obtaining the pair of video frames and estimating the translation between the frames.

Abstract: Techniques for occluding augmented reality objects are described in various implementations. In one example implementation, a method may include receiving an image captured by an image capture device and identifying a foreground object depicted in the image. The foreground object may be free of fiducial markers and may be positioned between the image capture device and a background depicted in the image. The method may also include generating an augmented reality image depicting an augmented reality object at least partially occluded by the foreground object such that, when the augmented reality image is displayed, the augmented reality object appears to be positioned behind the foreground object.