Sharp Laboratories of America, Inc

Amazon Technologies, Inc.

University of Southern California

Chang Yuan

Xinyu Xu

Geoffrey Heller

Scott J Daly

Petrus J L Van Beek

Daniel Bibireata

Dilip Kumar

Louis LeRoi LeGrand, Iii

Sridhar Boyapati

Xiaofeng Ren

Oleg Rybakov

Alexander Clark Prater

Elie Micah Kornfield

Masakazu Yanase

Leo Benedict Baldwin

Hao Pan

Xiao-Fan Feng

Jim Oommen Thomas

Christopher John Lish

Isaac D Cohen

Francislav Petrov Penov

Sharadh Ramaswamy

Wei Li

Gerard Medioni

Hae-Jong Seo

Avnish Sikka

David Paul Ramos

Dean Messing

Rakesh Madhavan Nambiar

Christopher S A MacGregor

Junxiong Jia

Matias Omar Gregorio Benitez

Richard Howard Suplee, Iii

Xiaofan Feng

Honghong Peng

Neil David Cooper

Laura Varnum Finney

Charles P Vaughn

Qiang Zhang

Jinman Kang

Keith Harrison Goodman

Fan Wang

Xiaofeng Fan

Robert Thomas Held

Saurabh Verma

This disclosure describes a system for tracking removal or placement of items at inventory locations with a materials handling facility. In some instances, a user may remove an item from an inventory location and the inventory management system may detect that removal and update a user item list associated with the user to include an item identifier representative of the removed item. Likewise, if the user places an item at an inventory location, the inventory management system may detect that placement and update the user item list to remove an item identifier representative of the placed item.

Detecting inventory changes

A mobile device for projecting images on a surface and for maintaining a position of the image on the surface. The device including an image rendering module to scan the surface and to modify the image based on data obtained about a three dimensional nature of the surface. The device further including an optical image stabilization to adjust an alignment of the projector based on orientation data of the device, an electronic image stabilization module to shift the image within a projection area based on the orientation data and an image-tracking module to shift the image within the projection area based on changes in a relative positions of the image and a target.

Self stabilizing projector

Detecting item interaction and movement

A user can capture an image of an object, using a computing device, to obtain information about that object. If a specular highlight (or other saturated region) is detected, the device can attempt to determine a location of a light source associated with the highlight. The device can then provide instructions as to a direction to move in order to reduce the presence of the specular highlight in subsequent images. Multiple images of the object can be captured and analyzed to generate a three-dimensional reconstruction of the environment, whereby a position of the light source can be determined. In other embodiments, movement of the specular reflections in response to movement of the device is used to determine a direction of the light source. In other embodiments, an image of the user is captured to determine the position of the light source based on shadows or reflections on the user.

Image capture guidance to reduce specular reflection effects

Various embodiments utilize two-dimensional ('2D') and three-dimensional ('3D') object features for purposes such as object recognition and/or image matching. For example, a user can capture an image (e.g., still images or video) of an object and can receive information about items that are determined to match the object. For example, the image can be analyzed to detect visual features (e.g., corners, edges, etc.) of the object and the detected visual features can be combined to generate a combined visual feature vector which can be used for object recognition, image matching, or other such purposes. Other approaches utilize the image to generate a 3D model of the object represented in the image, which can be used to determine at least one object or types of objects that match the object represented in the image.

Object recognition for three-dimensional bodies

The accuracy of a text recognition process can be improved using a set of semantic boosting rules, as may be contained in a sequence or other such arrangement. When text is output from a text recognition process, that text can have alternatives and confidence values for different characters or portions of the string. In order to improve the accuracy, this data can be processed using the organized rules, where rules are applied as long as any preconditions for that rule are satisfied, and each rule has the ability to modify the confidence values or modify one or more of the alternatives. When a result it produced with a minimum confidence level, or all applicable rules have been applied, the result can be provided as a refined text output of the recognition process.

Semantic boosting rules for improving text recognition

Various embodiments provide an image pre-processing method to accelerate image computing tasks. For example, an image is obtained from a camera of a portable computing device. Salient features of the image are identified by a number of techniques, such as identifying feature points, identifying gradients within the image, and other techniques and methods discussed herein. Based at least in part on the identified salient features, a saliency map of the image is computed. The saliency map includes a saliency value for each of at least a portion of the plurality of pixels. In this example pixels above a threshold saliency value are processed with an optical character recognition engine.

Image preprocessing for character recognition

Various embodiments utilize geometric hashing to automatically recognize and track and an object. For example, a user can capture an image of a product, or other object, and a point detection algorithm can identify particular features of the product and designate feature points of the product within the captured image. Hash values are then determined for each feature point by determining a basis for the image and determining the location of each feature point relative to that basis. A hash table including the identified hash values is then created and compared to hash values for either a stored product when performing object recognition or from a reference image when performing object tracking.

Object recognition and tracking

Approaches enable the projection of one or more visual elements, such as one or more dynamically changing graphical elements, that can substantially bound, or otherwise at least partially surround or identify, an object recognized by a computing device. The computing device can project the graphical elements to collectively appear as a bounding element for the recognized/actionable object or object portion. As such, the graphical elements can appear as a bounding element that adorns, decorates, highlights, and/or emphasizes, etc., the recognized/actionable object or object portion. The graphical elements to be dynamic. For example, the graphical elements can be projected to move around individually over time, while still appearing to at least partially surround the recognized/actionable object or object portion. Further, the graphical elements can be used to improve various object recognition approaches.

Using projection for visual recognition

A system for identifying a repair cut location for a defect in a liquid crystal device includes receiving an input image, a defect mask image, and a landmark structure image. The system determines a repair cut location, based upon the input image, the defect mask image, and the landmark structure image, for a liquid crystal device proximate the defect. The determination may be based upon a type of said defect, a cause of said defect, a position of said defect, and a spatial relationship of the defect and a structure of the landmark image.

System for defect detection and repair

Various embodiments enable a computing device to capture multiple images (or video) of text and provide at least a portion of the same to a recognizer to separately recognize text from each image. Each of the recognized outputs will typically include one or more text strings for each image. Substrings common to each of the one or more text strings are computed and compared to each text string within each image to determine an alignment consensus for each substring within the text. A template string is generated that includes each common substring in a position corresponding to a determined alignment for a respective substring. A character frequency vote is then applied to unresolved portions and the final text string is determined by filling the unresolved spaces with the character having the highest occurrence rate for a respective space.

Image-based character recognition

Various embodiments enable regions of text to be identified in an image captured by a camera of a computing device for preprocessing before being analyzed by a visual recognition engine. For example, each of the identified regions can be analyzed or tested to determine whether a respective region contains a quality associated with poor text recognition results, such as poor contrast, blur, noise, and the like, which can be measured by one or more algorithms. Upon identifying a region with such a quality, an image quality enhancement can be automatically applied to the respective region without user instruction or intervention. Accordingly, once each region has been cleared of the quality associated with poor recognition, the regions of text can be processed with a visual recognition algorithm or engine.

Local image enhancement for text recognition

A method for classification of samples comprising providing a trained statistical model based upon a set of initial samples. Receiving a set of first samples and training a first statistical model base upon the first set of samples, where the first statistical model is of the same class as the trained statistical model. Receiving a set of second samples and training a second statistical model base upon the second set of samples, where the second statistical model is of the same class as the trained statistical model. The trained statistical model, the first statistical model, and the second statistical model, being independent of each other and collectively used to classify another sample.

Supervised and semi-supervised online boosting algorithm in machine learning framework

Growing community of inventors

Seattle, WA, United States of America

Chang Yuan