Abstract: Methods, systems, and apparatus for recognizing objects and providing content related to the recognized objects are described. In one aspect, a method includes detecting presence of one or more objects depicted in a viewfinder of a camera of the mobile device. In response to detecting the presence of the one or more objects, image data representing the one or more objects is sent to a content distribution system that selects content related to objects depicted in images. A location of each of the one or more objects in the viewfinder of the camera is tracked while waiting to receive content from the content distribution system. Content related to the one or more objects is received from the content distribution system. A current location of each object in the viewfinder is determined and the content related to the object is presented within the viewfinder at the current location of the object.

Abstract: Methods, systems, and apparatus for recognizing objects and providing content related to the recognized objects are described. In one aspect, a method includes detecting presence of one or more objects depicted in a viewfinder of a camera of the mobile device. In response to detecting the presence of the one or more objects, image data representing the one or more objects is sent to a content distribution system that selects content related to objects depicted in images. A location of each of the one or more objects in the viewfinder of the camera is tracked while waiting to receive content from the content distribution system. Content related to the one or more objects is received from the content distribution system. A current location of each object in the viewfinder is determined and the content related to the object is presented within the viewfinder at the current location of the object.

Abstract: The present disclosure relates to techniques for reconstructing an object in three dimensions that is captured in a set of two-dimensional images. The object is reconstructed in three dimensions by computing depth values for edges of the object in the set of two-dimensional images. The set of two-dimensional images may be samples of a light field surrounding the object. The depth values may be computed by exploiting local gradient information in the set of two-dimensional images. After computing the depth values for the edges, depth values between the edges may be determined by identifying types of the edges (e.g., a texture edge, a silhouette edge, or other type of edge). Then, the depth values from the set of two-dimensional images may be aggregated in a three-dimensional space using a voting scheme, allowing the reconstruction of the object in three dimensions.

Abstract: Methods, systems, and apparatus for recognizing objects and providing content related to the recognized objects are described. In one aspect, a method includes detecting presence of one or more objects depicted in a viewfinder of a camera of the mobile device. In response to detecting the presence of the one or more objects, image data representing the one or more objects is sent to a content distribution system that selects content related to objects depicted in images. A location of each of the one or more objects in the viewfinder of the camera is tracked while waiting to receive content from the content distribution system. Content related to the one or more objects is received from the content distribution system. A current location of each object in the viewfinder is determined and the content related to the object is presented within the viewfinder at the current location of the object.

Abstract: The present disclosure relates to techniques for reconstructing an object in three dimensions that is captured in a set of two-dimensional images. The object is reconstructed in three dimensions by computing depth values for edges of the object in the set of two-dimensional images. The set of two-dimensional images may be samples of a light field surrounding the object. The depth values may be computed by exploiting local gradient information in the set of two-dimensional images. After computing the depth values for the edges, depth values between the edges may be determined by identifying types of the edges (e.g., a texture edge, a silhouette edge, or other type of edge). Then, the depth values from the set of two-dimensional images may be aggregated in a three-dimensional space using a voting scheme, allowing the reconstruction of the object in three dimensions.

Abstract: The present disclosure relates to techniques for reconstructing an object in three dimensions that is captured in a set of two-dimensional images. The object is reconstructed in three dimensions by computing depth values for edges of the object in the set of two-dimensional images. The set of two-dimensional images may be samples of a light field surrounding the object. The depth values may be computed by exploiting local gradient information in the set of two-dimensional images. After computing the depth values for the edges, depth values between the edges may be determined by identifying types of the edges (e.g., a texture edge, a silhouette edge, or other type of edge). Then, the depth values from the set of two-dimensional images may be aggregated in a three-dimensional space using a voting scheme, allowing the reconstruction of the object in three dimensions.

Abstract: The present disclosure relates to techniques for reconstructing an object in three dimensions that is captured in a set of two-dimensional images. The object is reconstructed in three dimensions by computing depth values for edges of the object in the set of two-dimensional images. The set of two-dimensional images may be samples of a light field surrounding the object. The depth values may be computed by exploiting local gradient information in the set of two-dimensional images. After computing the depth values for the edges, depth values between the edges may be determined by identifying types of the edges (e.g., a texture edge, a silhouette edge, or other type of edge). Then, the depth values from the set of two-dimensional images may be aggregated in a three-dimensional space using a voting scheme, allowing the reconstruction of the object in three dimensions.

Abstract: Systems and method for the reconstruction of an articulated object are disclosed herein, The articulated object can be reconstructed from image data collected by a moving camera over a period of time. A plurality of 2D feature points can be identified within the image data. These 2D feature points can be converted into three-dimensional space, which converted points are identified as 3D feature points. These 3D feature points can be used to identify one or several rigidity constrains and/or kinematic constraints. These rigidity and/or kinematic constraints can be applied to a model of the reconstructed articulated object.

Abstract: Systems and method for the reconstruction of an articulated object are disclosed herein, The articulated object can be reconstructed from image data collected by a moving camera over a period of time. A plurality of 2D feature points can be identified within the image data. These 2D feature points can be converted into three-dimensional space, which converted points are identified as 3D feature points. These 3D feature points can be used to identify one or several rigidity constrains and/or kinematic constraints. These rigidity and/or kinematic constraints can be applied to a model of the reconstructed articulated object.

Abstract: The disclosure provides an approach for transferring image edits from a source image to target images. In one embodiment, a warp application receives a user-selected region of interest in a source image and determines for the region of interest content-aware bounded weight functions and seed locations for the same. For each of the target images, the warping application initializes a linear blend skinning subspace warp to a projection onto a feature space of a piecewise affine map from scale invariant feature transform features of the source image to the target image. After initializing the warps, the warping application iteratively optimizes the warps by applying the inverse compositional Lucas-Kanade procedure and using the content-aware weight functions in said procedure. Edits made to the source image may automatically be transferred to target images by warping those edits via the optimized warp function for the respective target images.

Abstract: The disclosure provides an approach for transferring image edits from a source image to target images. In one embodiment, a warp application receives a user-selected region of interest in a source image and determines for the region of interest content-aware bounded weight functions and seed locations for the same. For each of the target images, the warping application initializes a linear blend skinning subspace warp to a projection onto a feature space of a piecewise affine map from scale invariant feature transform features of the source image to the target image. After initializing the warps, the warping application iteratively optimizes the warps by applying the inverse compositional Lucas-Kanade procedure and using the content-aware weight functions in said procedure. Edits made to the source image may automatically be transferred to target images by warping those edits via the optimized warp function for the respective target images.