Abstract:
A system, article, and method of curved object recognition using image matching for image processing, comprising: using paired 2D-3D point(s) to form a perspective projection function to determine a geometric correspondence between target object and reference object(s) and that converts the 2D points into 3D points at the target object.
Abstract:
An apparatus is disclosed. The apparatus comprises an optical sensing system that comprises at least one camera, the at least one camera being configured to acquire an image of a physical environment. The apparatus further comprises a processing system. The processing system comprises an orientation and position determination module configured to detect salient features from the image, and determine a change in orientation and/or position of the apparatus with respect to the physical environment based on the detected salient features. The processing system also comprises a rendering module configured to determine a rendering of the physical environment based on the image and on the determined change in orientation and/or position of the apparatus, and provide data related to the rendering of the physical environment to a display system.
Abstract:
A system and method are provided for analyzing a video. The method comprises: sampling the video to generate a plurality of spatio-temporal video volumes; clustering similar ones of the plurality of spatio-temporal video volumes to generate a low-level codebook of video volumes; analyzing the low-level codebook of video volumes to generate a plurality of ensembles of volumes surrounding pixels in the video; and clustering the plurality of ensembles of volumes by determining similarities between the ensembles of volumes, to generate at least one high-level codebook. Multiple high-level codebooks can be generated by repeating steps of the method. The method can further include performing visual event retrieval by using the at least one high- level codebook to make an inference from the video, for example comparing the video to a dataset and retrieving at least one similar video, activity and event labeling, and performing abnormal and normal event detection.
Abstract:
In general, techniques are described for performing a vocabulary-based visual search using multi-resolution feature descriptors. A device may comprise one or more processors configured to perform the techniques. The one or more processors may to apply a partitioning algorithm to a first subset of target feature descriptors to determine a first classifying data structure to be used when performing a visual search with respect to a query feature descriptor. The one or more processors may then apply the partitioning algorithm to a second subset of the target feature descriptors to determine a second classifying data structure to be used when performing the visual search with respect to the same query feature descriptor.
Abstract:
Recognition process (1) of an object (An) in a query image (2), performing a training step(6) that comprises: -providing (20) a set of training images (Ti), each training image (Ti) comprising an object tag (LOGOi); -determining (21) for each training image (Ti) of said set a plurality of first descriptors (11), each first descriptor (11) being a vector that represents pixel properties in a subregion (Sri) of the associated training image (Ti); -determining (23) a group of exemplar descriptors (111) describing the set of training images (Ti) and resulting from a selection of the first descriptors (11)based on the position of said subregion (Sri) in the associated training image (Ti) and on the pixel properties of said first descriptors (11); performing a query step (7) comprising: -receiving (30) the query image (2) and defining (31) a plurality of vectors (V') of second descriptors (3) describing the properties of said query image (2); -determining (35) a visual similarity coefficient based on a comparison between each one of said second descriptor (3) of said query image (2) and each one of said exemplar descriptors (111) of said group in order to automatically recognize the object (An) in said query image (2) with respect to the object tag (LOGOi) coupled to one of said exemplar descriptor (111).
Abstract:
Method and systems for generating reference features sets for slices of a reference image. The reference features sets generated from slices enables better object recognition and/or tracking when a camera image only shows a portion of the reference image. Metadata is used to link the reference features set of the original image and of the slices together as belonging to the same object, providing hierarchical relationship information and/or spatial relationship information. An image processing function may be dynamically configured on the basis of whether an object has been successfully detected and the metadata associated with the object.