Patent application number | Description | Published |
20110222774 | IMAGE FEATURE DETECTION BASED ON APPLICATION OF MULTIPLE FEATURE DETECTORS - In a particular embodiment, a method includes applying a first feature detector to a portion of an image to detect a first set of features. The first set of features is used to locate a region of interest, and a boundary corresponding to the region of interest is determined. The method also includes displaying the boundary at a display. In response to receiving user input to accept the displayed boundary, a second feature detector is applied to an area of the image encapsulated by the boundary. | 09-15-2011 |
20110255781 | EFFICIENT DESCRIPTOR EXTRACTION OVER MULTIPLE LEVELS OF AN IMAGE SCALE SPACE - A local feature descriptor for a point in an image is generated over multiple levels of an image scale space. The image is gradually smoothened to obtain a plurality of scale spaces. A point may be identified as the point of interest within a first scale space from the plurality of scale spaces. A plurality of image derivatives is obtained for each of the plurality of scale spaces. A plurality of orientation maps is obtained (from the plurality of image derivatives) for each scale space in the plurality of scale spaces. Each of the plurality of orientation maps is then smoothened (e.g., convolved) to obtain a corresponding plurality of smoothed orientation maps. Therefore, a local feature descriptor for the point may be generated by sparsely sampling a plurality of smoothed orientation maps corresponding to two or more scale spaces from the plurality of scale spaces. | 10-20-2011 |
20110299770 | PERFORMANCE OF IMAGE RECOGNITION ALGORITHMS BY PRUNING FEATURES, IMAGE SCALING, AND SPATIALLY CONSTRAINED FEATURE MATCHING - A method for feature matching in image recognition is provided. First, image scaling may be based on a feature distribution across scale spaces for an image to estimate image size/resolution, where peak(s) in the keypoint distribution at different scales is used to track a dominant image scale and roughly track object sizes. Second, instead of using all detected features in an image for feature matching, keypoints may be pruned based on cluster density and/or the scale level in which the keypoints are detected. Keypoints falling within high-density clusters may be preferred over features falling within lower density clusters for purposes of feature matching. Third, inlier-to-outlier keypoint ratios are increased by spatially constraining keypoints into clusters in order to reduce or avoid geometric consistency checking for the image. | 12-08-2011 |
20110299782 | FAST SUBSPACE PROJECTION OF DESCRIPTOR PATCHES FOR IMAGE RECOGNITION - A method for generating a feature descriptor is provided. A set of pre-generated sparse projection vectors is obtained. A scale space for an image is also obtained, where the scale space having a plurality scale levels. A descriptor for a keypoint in the scale space is then generated based on a combination of the sparse projection vectors and sparsely sampled pixel information for a plurality of pixels across the plurality of scale levels. | 12-08-2011 |
20120027290 | OBJECT RECOGNITION USING INCREMENTAL FEATURE EXTRACTION - In one example, an apparatus includes a processor configured to extract a first set of one or more keypoints from a first set of blurred images of a first octave of a received image, calculate a first set of one or more descriptors for the first set of keypoints, receive a confidence value for a result produced by querying a feature descriptor database with the first set of descriptors, wherein the result comprises information describing an identity of an object in the received image, and extract a second set of one or more keypoints from a second set of blurred images of a second octave of the received image when the confidence value does not exceed a confidence threshold. In this manner, the processor may perform incremental feature descriptor extraction, which may improve computational efficiency of object recognition in digital images. | 02-02-2012 |
20130293532 | SEGMENTATION OF 3D POINT CLOUDS FOR DENSE 3D MODELING - Techniques for segmentation of three-dimensional (3D) point clouds are described herein. An example of a method for user-assisted segmentation of a 3D point cloud described herein includes obtaining a 3D point cloud of a scene containing a target object; receiving a seed input indicative of a location of the target object within the scene; and generating a segmented point cloud corresponding to the target object by pruning the 3D point cloud based on the seed input. | 11-07-2013 |
20150049942 | PERFORMING VOCABULARY-BASED VISUAL SEARCH USING MULTI-RESOLUTION FEATURE DESCRIPTORS - 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 processors may generate a hierarchically arranged data structure to be used when classifying objects included within a query image based on multi-resolution query feature descriptor extracted from the query image at a first scale space resolution and a second scale space resolution. The hierarchically arranged data structure may represent a first query feature descriptor of the multi-resolution feature descriptor extracted at the first scale space resolution and a second corresponding query feature descriptor of the multi-resolution feature descriptor extracted at the second scale space resolution hierarchically arranged according to the first scale space resolution and the second scale space resolution. The processors may then perform a visual search based on the generated data structure. | 02-19-2015 |
20150049943 | PERFORMING VOCABULARY-BASED VISUAL SEARCH USING MULTI-RESOLUTION FEATURE DESCRIPTORS - 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. | 02-19-2015 |