Qualcomm Incorporated

Emilio Maggio

Qi Pan

Romain Tallonneau

Gerhard Reitmayr

Zsolt Szalavari

Michael Gervautz

Alessandro Boccalatte

Wonwoo Lee

Michal Havlena

Jiri Hörner

Stefan Walk

A system and method can support image based determination of mobile device location through recognition of surface features for a previously scanned physical environment. The system and method can also support authoring and positioning of augmented reality features in an authoring interface using the same images and positions of surface features that are to be used for subsequent mobile device localization. As a result, mobile devices leveraging those same images and positions of surface features for localization will be more likely to obtain a localization that is consistent with the positioning displayed in the authoring interface. Augmented reality features authored using the same scan of the environment can be reliably displayed to an end user of an augmented reality application in a position consistent with their authoring in a common coordinate system, even though the authoring may have been performed remotely, away from the actual situs of the physical environment.

Machine vision determination of location based on recognized surface features and use thereof to support augmented reality

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.

Dynamic extension of map data for object detection and tracking

Disclosed are a system, apparatus, and method for detecting objects. Input image frames may be received and distinct regions created within each frame. Descriptors may be extracted from the regions according to their associated probability. Extracted descriptors may be matched to reference descriptors. Votes or confidence is cast for particular regions according to region properties. The region properties may be determined from center voting methods based on vector intersection to other vectors or intersections with a region. The probability of selecting particular regions can increase with each vote or increase in confidence for a region. In response to updating probabilities, additional regions may be selected and additional descriptors may be extracted. Additional voting iterations can update the probability of selecting a next region. An object pose may be estimated in response to meeting one or more thresholds.

Optimized object detection

Methods, systems, computer-readable media, and apparatuses for assigning a color class of a defined finite set of colors to at least one sub-region within a test image are presented. A plurality of sub-regions are identified within a test image. A first sub-region color value is determined for a selected first sub-region of the test image. Using the first sub-region color value and a plurality of zero-order probability distributions, a first color class of the defined finite set of colors is determined as a hypothesis color for the first sub-region. A second sub-region color value is determined for a selected second sub-region of the test image. Using the second sub-region color value and a conditional probability distribution conditioned on the hypothesis color for the first sub-region, a second color class of the defined set of colors is determined. The second color class is assigned to the second sub-region.

Probabilistic color classification

Growing community of inventors

Vienna, Austria

Emilio Maggio