A9.com, Inc.

Fuji Xerox Co., Ltd.

Michael Patrick Cutter

Mark Scott Waldo

Shaun Michael Post

Sean Niu

Colin Jon Taylor

Rupa Chaturvedi

Stefan Vant

Andrea Zehr

Peiqi Tang

Jared Corso

Jason Canada

Patrick Chiu

Arnab Sanat Kumar Dhua

Yu Lou

Chelhwon Kim

Surendar Chandra

Ming Du

Nicholas Corso

Rahul Bhotika

Ismet Zeki Yalniz

Chun-Kai Wang

Daniya Zamalieva

Song Cao

Users can view images or renderings of items placed (virtually) within a physical space. For example, a rendering of an item can be placed within a live camera view of the physical space. A snapshot of the physical space can be captured and the snapshot can be customized, shared, etc. The renderings can be represented as two-dimensional images, e.g., virtual stickers or three-dimensional models of the items. Users can have the ability to view different renderings, move those items around, and develop views of the physical space that may be desirable. The renderings can link to products offered through an electronic marketplace and those products can be consumed. Further, collaborative design is enabled through modeling the physical space and enabling users to view and move around the renderings in a virtual view of the physical space.

Object preview in a mixed reality environment

Systems and methods for generating high resolution dewarped images for an image of a document captured by a 3D stereo digital camera system, or a mobile phone camera capturing a sequence of images, which may improve OCR performance. Example embodiments include a compact stereo camera with two sensors mounted at fixed locations, and a multi-resolution pipeline to process and to dewarp the images using a three dimensional surface model based on curve profiles of the computed depth map. Example embodiments also include a mobile phone including a camera which captures a sequence of images, and a processor which computes a disparity map using the captured sequence of image frames, computes a model of the at least one document page by generating a cylindrical three dimensional geometric surface using the computed disparity map, and renders a dewarped image from the computed model.

Generating hi-res dewarped book images

Systems and methods for a markerless approach to displaying an image of a virtual object in an environment are described. A computing device is used to capture an image of a real-world environment; for example including a feature-rich planar surface. One or more virtual objects which do not exist in the real-world environment are displayed in the image, such as by being positioned in a manner that they appear to be resting on the planar surface, based at least on a sensor bias value and scale information obtained by capturing multiple image views of the real-world environment.

Markerless image analysis for augmented reality

An auxiliary device can be used to display a fiducial that contains information useful in determining the physical size of the fiducial as displayed on the auxiliary device. A primary device can capture image data including a representation of the fiducial. The scale and orientation of the fiducial can be determined, such that a graphical overlay can be generated of an item of interest that corresponds to that scale and orientation. The overlay can then be displayed along with the captured image data, in order to provide an augmented reality experience wherein the image displayed on the primary device represents a scale-appropriate view of the item in a location of interest corresponding to the location of the auxiliary device. As the primary device is moved and the viewpoint of the camera changes, changes in relative scale and orientation to the fiducial are determined and the overlay is updated accordingly.

Auxiliary device as augmented reality platform

The accuracy of an image matching process can be improved by determining relevant swatch regions of the images, where those regions contain representative patterns of the items of interest represented in those images. Various processes examine a set of visual cues to determine at least one candidate object region, and then collate these regions to determine one or more representative swatch images. For apparel items, this can include locating regions such as an upper body region, torso region, clothing region, foreground region, and the like. Processes such as regression analysis or probability mapping can be used on the collated region data (along with confidence and/or probability values) to determine the appropriate swatch regions.

Region selection for image match

Growing community of inventors

Mountain View, CA, United States of America

Michael Patrick Cutter