Abstract:
Embodiments disclosed pertain to mobile device based text detection and tracking. In some embodiments, a first reference frame is obtained by performing Optical Character Recognition (OCR) on an image frame captured by a camera to locate and recognize a first text block. A subsequent image frame may be selected from a set of subsequent image frames based on parameters associated with the selected subsequent image and a second reference frame may be obtained by performing OCR on the selected subsequent image frame to recognize a second text block. A geometric relationship between the first and second text blocks is determined based on a position of the first text block in the second reference frame and a pose associated with the second reference frame.
Abstract:
Disclosed is a method and apparatus for exemplars-based color classification. In one embodiment, the functions implemented include: processing an image captured by a camera to identify a first color profile that most closely matches colors of the image, wherein the first color profile is selected from a plurality of color profiles each color profile encoding data related to how two or more component colors appear under a different lighting condition.
Abstract:
Disclosed is a method and apparatus for selecting a part of SLAM map information of a first device for transmission to a second device in a collaborative SLAM environment. In one embodiment, the functions implemented include: determining whether a 3D registration transformation between a map of the first device and a map of the second device is available; and transmitting a part of map information of the first device to the second device according to one of a first strategy or a second strategy based on whether or not a 3D registration transformation between the map of the first device and the map of the second device is available.
Abstract:
A computer-implemented method of tracking a target object in an object recognition system includes acquiring a plurality of images with a camera and simultaneously tracking the target object and dynamically building online map data from the plurality of images. Tracking of the target object is based on the online map data and the offline map data. In one aspect, tracking the target object includes enabling only one of the online map data and offline map data for tracking based on whether tracking is successful. In another aspect, tracking the target object includes fusing the online map data with the offline map data to generate a fused online model.
Abstract:
A computer-implemented method of tracking a target object in an object recognition system includes acquiring a plurality of images with a camera. The method further includes simultaneously tracking the target object and dynamically building environment map data from the plurality of images. The tracking of the target object includes attempting to estimate a target pose of the target object with respect to the camera based on at least one of the plurality of images and based on target map data. Next, the method determines whether the tracking of the target object with respect to the camera is successful. If not, then the method includes inferring the target pose with respect to the camera based on the dynamically built environment map data. In one aspect the method includes fusing the inferred target pose with the actual target pose even if tracking is successful to improve robustness.
Abstract:
A method of building a database for an object recognition system includes acquiring several multi-view images of a target object and then extracting a first set of features from the images. One of these extracted features is then selected and a second set of features is determined based on which of the first set of features include both, descriptors that match and keypoint locations that are proximate to the selected feature. If a repeatability of the selected feature is greater than a repeatability threshold and if a discriminability is greater than a discriminability threshold, then at least one derived feature is stored to the database, where the derived single feature is representative of the second set of features.