A9.com, Inc.

Daqri, LLC

Nalin Senthamil

Bryan E Feldman

Gurumurthy D Ramkumar

Arnab S Dhua

Arnab Sanat Kumar Dhua

Brian Mullins

William F Stasior

Eric Douglas Lundquist

An image localization system is described. A server stores a localization model that associates location and orientation data with each picture among a plurality of pictures. The server receives a query for a location and an orientation of a device. The query includes a first picture. A second picture that matches the first picture is identified. The server determines the location and the orientation of the device based on the location and orientation data corresponding to the second picture. The location and orientation data indicates position, orientation, three-dimensional geometry, gyroscope measurement, and accelerometer measurement.

Deep image localization

A device for multi-spectrum segmentation for computer vision is described. A first optical sensor operates within a first spectrum range and generates first image data corresponding to a first image captured by the first optical sensor. A second optical sensor operates within a second spectrum range different from the first spectrum range and generates second image data corresponding to a second image captured by the second optical sensor. The device identifies a first region in the first image, maps a first portion of the first image to a second portion of the second image data, provides the second portion of the second image data to a server that generates augmented reality content based on the second portion of the second image data. The device displays the augmented reality content.

Multi-spectrum segmentation for computer vision

Systems and methods track one or more points between images. A point for tracking may be selected, at least in part, on a determination of how discriminable the point is relative to other points in a region containing the point. A point of an image being tracked may be located in another image by matching a patch containing the point with another patch of the other image. A search for a matching patch may be focused in a region that is determined based at least in part on an estimate of movement of the point between images. Points may be tracked across multiple images. If an ability to track one or more points is lost, information about the points being tracked may be used to relocate the points in another image.

Tracking objects between images

An image localization system is described. A receiver receives visual inertial navigation (VIN) data and corresponding image data from devices, the VIN data indicating VIN states and corresponding poses of the devices. A training module generates a localization model based on the VIN data and corresponding image data from the plurality of devices. The image data includes images, the localization model correlating the VIN states and poses with each image among the plurality of images. An image localizer receives a query for a geographic location and a pose of a device. The query includes a picture. The image localizer compares the picture with images from the localization model, identifies an image based on the localization model, the image matching the picture in the query, and determines the geographic location and the pose of the device based on the VIN state and pose corresponding to the identified image.

A system and method for multi-spectrum segmentation for computer vision is described. A first sensor captures an image within a first spectrum range and generates first sensor data. A second sensor captures an image within a second spectrum range different than the first spectrum range and generates second sensor data. A multi-spectrum segmentation module identifies a segmented portion of the image within the second spectrum range based on: the second sensor data, a subset of the first sensor data corresponding to the segmented portion of the image within the second spectrum range, and a segmented portion of the image at the second spectrum range corresponding to the subset of the first sensor data. The multi-spectrum segmentation module identifies a physical object in the segmented portion of the image within the second spectrum range, and a device generates augmented reality content based on the identified physical object.

Techniques for augmenting an image of an object captured and displayed in real time with associated content are disclosed. In one embodiment, the method for augmenting the image includes receiving information defining a sampled frame of a video being captured by an electronic device in substantially real time, determining information representative of an object captured in the sampled frame based on the received information, causing the determined information to match stored information defining a plurality of items to locate an item matched to the captured object, retrieving content associated with the matched item, and providing the retrieved content for display with the captured image on the electronic device. The retrieved content may be rendered in an overlay element that overlays the captured image displayed on the electronic device. The rendered content is configured to enable a user to interact with the content.

Augmenting a live view

The present technology relates to methods, systems and computer program products for detecting objects in images captured by a camera of a mobile device. The detection of objects (such as barcodes, QR codes, and text) in images can be based at least in part on image gradients of the image. The image can be divided into a plurality of regions, each having a dominant gradient direction. Based at least in part on the dominant gradient directions of the regions satisfying orientation thresholds, the regions can be identified as candidate or non-candidate regions for corresponding to a predetermined object. The candidate regions can be merged if they satisfy a connecting criterion. The merged regions can then be analyzed to determine if the merged regions satisfy a geometric property of the predetermined object, such as having a rectangular shape substantially similar to that of a barcode.

Detecting objects in images using image gradients

Processes such as image matching can be improved by utilizing a Polar Accumulated Signed Gradient (PASG), a feature descriptor vector that utilizes polar coordinates. Such an approach is efficient because it avoids the costly image rotation and resampling of conventional processes. Further, the gradients can be sampled over different regions in order to achieve effective weighting without analyzing all gradients and applying a weighting factor. Such efficiencies enable image matching to be performed on devices with limited computing capacity, such as smart phones and tablet computers.

Growing community of inventors

Santa Clara, CA, United States of America

Nalin Senthamil