Patent application number | Description | Published |
20120287035 | Presence Sensing - One embodiment may take the form of a method of operating a computing device in a reduced power state and collecting a first set of data from at least one sensor. Based on the first set of data, the computing device determines a probability that an object is within a threshold distance of the computing device and, if so, the device activates at least one secondary sensor to collect a second set of data. Based on the second set of data, the device determines if the object is a person. If it is a person, a position of the person relative to the computing device is determined and the computing device changes its state based on the position of the person. If the object is not a person, the computing device remains in a reduced power state. | 11-15-2012 |
20120314962 | AUTO-RECOGNITION FOR NOTEWORTHY OBJECTS - Techniques for automatically identifying famous people and/or iconic images are provided. Object descriptors (or “faceprints”) of the famous people and iconic images are generated and “shipped” with a digital image management application used by an end-user. The digital image management application analyzes a digital image (generated, for example, by a digital camera operated by the end-user) to detect an object, such as a face, and generates a faceprint. The digital image management application compares the faceprint to the faceprints of the famous people and/or iconic images. If a match is found, then data that identifies the corresponding person or object is displayed to the end-user. | 12-13-2012 |
20130155063 | Face Feature Vector Construction - Systems, methods, and computer readable media for determining and applying face recognition parameter sets are described. In general, techniques are disclosed for identifying and constructing a unique combination of facial recognition discriminators into a “face feature vector” that has been found to be more robust (e.g., stable to image noise, a person's pose, and scene illumination) and accurate (e.g., provide high recognition rates) than prior art techniques. More particularly, a face feature vector may be generated by the combination of shape descriptors (e.g., as generated by two-dimensional and three-dimensional shape models) and texture descriptors (e.g., as generated by global and local texture models). | 06-20-2013 |
20130300830 | Automatic Detection of Noteworthy Locations - By providing 3D representations of noteworthy locations for comparison with images, the 3D location of the imaging device, as well as the orientation of the device may be determined. The 3D location and orientation of the imaging device then allows for enhanced navigation in a collection of images, as well as enhanced visualization and editing capabilities. The 3D representations of noteworthy locations may be provided in a database that may be stored local or remote to the imaging device or a programmable device processing images obtained from the imaging device. | 11-14-2013 |
20130321392 | Identifying and Parameterizing Roof Types in Map Data - Methods and apparatus for a roof analysis tool for constructing a parameter set, where the parameter set is derived from mapping data for a map region, and where the parameter set describes the roofs for the buildings within the map region. In some cases, the parameter set includes a list of roof type identification values and the respective buildings in the map region for which a given roof type identification value corresponds. The roof analysis tool may operate on a server and work in conjunction with a mobile device, where the mobile device may display map views of a map region such that the map view is based on a three-dimensional model of the map region, and where a portion of the three-dimensional model is based on data generated on the mobile device and a portion of the three-dimensional model is based on data generated on the server. | 12-05-2013 |
20140050404 | Combining Multiple Image Detectors - A technique for combining multiple individual feature detectors to identify a combined feature in a digital image is disclosed. A combined feature detection rule may specify multiple individual feature detectors with which an image is to be analyzed. The multiple individual feature detectors may identify constituent parts of the combined feature and/or may identify features based on different image properties. An analysis of the image with the specified feature detectors may result in the identification of multiple candidate regions (i.e., regions within which the detectors identify their respective features). The combined feature detection rule may operate directly on the multiple candidate regions to adjust the spatial properties of the candidate regions and group the adjusted candidate regions into candidate region groups, it may then be determined if one or more of the candidate region groups is representative of a presence of the combined feature in the image. | 02-20-2014 |
20140071308 | Automatic Image Orientation and Straightening through Image Analysis - Systems, methods, and computer readable media for adjusting the orientation of an image frame and a scene depicted in the image frame are described. In general, techniques are disclosed for analyzing an image with one or more feature detectors to identify features in the image. An alignment or position associated with one or more features identified in the image may be used to determine a proper orientation for the image frame. The image can then be rotated to the proper orientation. It may also be determined if a scene depicted in the image is properly aligned in the rotated image orientation. If not, alignment information associated with the identified features may be utilized to straighten the depicted scene. | 03-13-2014 |
20140355821 | Object Landmark Detection in Images - Techniques are provided to improve the performance and accuracy of landmark point detection using a Constrained Local Model. The accuracy of feature filters used by the model may be improved by supplying positive and negative sets of image data from training image regions of varying shapes and sizes to a linear support vector machine training algorithm. The size and shape of regions within which a feature filter is to be applied may be determined based on a variance in training image data for a landmark point with which the feature filter is associated. A sample image may be normalized and a confidence map generated for each landmark point by applying the feature filters as a convolution on the normalized image. A vector flow map may be pre-computed to improve the efficiency with which a mean landmark point is adjusted toward a corresponding landmark point in a sample image. | 12-04-2014 |