Zebra Technologies Corporation

Symbol Technologies, LLC

David S Koch

Dongqing Chen

Hao Zheng

Zhiheng Jia

Alan J Epshteyn

Raghavendra Tenkasi Shankar

Zhijia Yuan

Yusuf Dalal

Yegor D Sinelnikov

Joseph Boriotti

Thomas F Boehm

Shan Chu

Neeta R Joshi

Wen-Yung Chang

Ruth Zhang

Neeharika Nelaturu

Kui Liu

Lindsay M Fahmi

Jing Lu

Alan J Livingston

A method includes: capturing an image; partitioning the image into sub-images; for each sub-image: providing the sub-image to a detection model, receiving, from the detection model, one or more sub-image regions of interest (SROIs), each SROI defined by (i) a position of the SROI, and (ii) one of a set of symbology categories, each symbology category encompassing a plurality of barcode symbologies; generating one or more regions of interest (ROI) from the SROIs, each ROI defined by a position of the ROI in the image, and a symbology category; and providing the ROIs to a decoder.

Image-based barcode detection

A computing device includes: a housing having a plurality of sides defining a housing interior; a first outer lens disposed on a first side of the housing; a second outer lens disposed on a second side of the housing; an image sensor in the housing interior, the image sensor having first and second capture regions; an optical divider in the housing interior, the optical divider configured to (i) direct light from the first outer lens along a first optical path, and (ii) direct light from the second outer lens along a second optical path; and a lens assembly between the optical divider and the image sensor, the lens assembly configured to (i) focus light received along the first optical path onto the first capture region of the image sensor, and (ii) focus light received along the second optical path onto the second capture region of the image sensor.

Optical divider for multi-field of view camera

A computing device for extracting target data from a source document includes: a memory storing target data extraction rules; a processor connected with the memory, the processor configured to: obtain text recognition data extracted from an image of the source document, the text recognition data indicating locations of text structures in the source document; define text lines based on the text recognition data; identify a reference string from the text recognition data; select a subset of the text lines based on a location of the reference string and the target data extraction rules; and output the subset of the text lines as the target data.

System and method for extracting target data from labels

A method for classifying depth scan data at a computing device includes: obtaining, at the computing device, a set of depth measurements and a graphical representation of the depth measurements; automatically selecting, at the computing device, a subset of the depth measurements indicating a region of interest; rendering, on a display of the computing device, an image including (i) the graphical representation of the depth measurements and (ii) a graphical indication of the region of interest overlaid on the graphical representation of the depth measurements; receiving, via an input device, a selection associated with the image; and generating a region of interest indicator based on the subset of the depth measurements and the selection.

Method and apparatus for classifying depth scans

A computing device includes: a memory storing an audio sequence and a reference attribute associated with playback of the audio sequence; a speaker; a microphone; an audio enclosure supporting the speaker and the microphone; a processor configured to: simultaneously (i) control the speaker to play the audio sequence, and (ii) control the microphone to capture a test recording corresponding to playback of the audio sequence; based on a comparison of the reference attribute to a test attribute associated with the test recording, detect occlusion of the audio enclosure; and in response to detecting occlusion of the audio enclosure, generate a notification message.

Automated audio assembly performance assessment

A method of detecting sensor obstructions in a computing device includes: at an emitter, emitting a beam of light through a scan window toward a treaded surface; at an image sensor, for a sequence of positions of the computing device along the treaded surface: capturing a sequence of images corresponding to the sequence of positions, each image in the sequence having a first region and a second region; wherein the first regions depict a first subset of reflections of the beam of light originating from a first depth range; and wherein the second regions depict a second subset of the reflections originating from a second depth range; at a controller: receiving the sequence of images; determining, based on the second regions, whether an intensity of the second subset of the reflections exceeds a occlusion threshold; and when the determination is affirmative, generating an alert indicating obstruction of the scan window.

Computing device and method of detecting sensor obstructions

A device for measuring tread depth includes: an image sensor; first and second depth sensing modules spaced apart along a separation axis, each including (i) an emitter configured to emit a beam of light, and (ii) an optical assembly configured to direct reflections of the beam of light onto a respective region of the image sensor; a controller connected to the image sensor and configured, responsive to the device traversing a treaded surface, to: receive, from the image sensor, a sequence of images corresponding to sequential positions of the device traversing the treaded surface in a travel direction substantially perpendicular to the separation axis, the sequence of images depicting successive reflections of the beams from the treaded surface; determine, for each image in the sequence of images, a first depth measurement and a second depth measurement; and store, in a memory, the first and second depth measurements.

Apparatus and method for determining tread depth

A method of decoding spatially related indicia includes: at an imaging controller, controlling an image sensor to capture an image containing a plurality of indicia; at the imaging controller, detecting image positions of each of the indicia; at the imaging controller, for each of a plurality of indicia pairs: determining whether the image positions of the indicia in the pair have a predefined spatial relationship; and responsive to determining that the indicia in the pair have the predefined spatial relationship, presenting (i) values decoded from the indicia in the pair, and (ii) an indicator that the decoded values are related.

Method and apparatus for decoding spatially related indicia

A method and apparatus with predictive triggering includes a housing including electronics operable to perform a process that can be activated by a user depressing a trigger with a finger. A trigger is operable to activate the electronics to perform the process. A proximity sensor is operable to detect a user's finger in proximity to the trigger. A processor coupled to the electronics, trigger, and proximity sensor, wherein when the proximity sensor detects a user's finger in proximity to the trigger, before the user's finger actually depresses the trigger, the processor initiates activation of the electronics to perform a portion of the process, and when the user's finger depresses the trigger, the processor directs the electronics to complete the process.

Predictive triggering in an electronic device

Implementations relate to a device and method for barcode scanning and dimensioning. In some implementations, the method includes acquiring a two-dimensional (2D) preview image of an object, and processing the 2D preview image to determine one or more dark areas and to determine a location of a code on the object. The method also includes acquiring a three-dimensional (3D) image of the object based on the one or more dark areas, and processing the 3D image to determine depth data and to determine dimensions of the object. The method also includes acquiring a 2D data capture image of the object based on the depth data in the processed 3D image, where the 2D data capture image captures the code. The method also includes reading the code based on the 2D data capture image.

Barcode scanning and dimensioning

Disclosed herein are methods and systems for highlighting box surfaces and edges in mobile box dimensioning. An embodiment takes the form of a method that includes obtaining a three-dimensional (3D) point cloud from a depth sensor when the depth sensor is positioned such that an aiming indicator appears on a first surface of an object; processing the 3D point cloud to identify an extent of the first surface; further processing the 3D point cloud to identify a second surface that is adjacent and normal to the first surface, and to identify an extent of the second surface; and displaying at least part of the 3D point cloud via a user interface, including displaying the identified first surface in a first color and the identified second surface in a second color different from the first color.

Methods and systems for highlighting box surfaces and edges in mobile box dimensioning

An imaging device includes an illumination element; an auto-exposure element; and a processor disabling the auto-exposure element and subsequently disabling the illumination element.

Method for optimizing auto-exposure performance of an imaging device

A system and method configures a scanner. The method comprises generating an object using an output arrangement of a host device. The object includes configuration data which corresponds to a peripheral device. The method comprises scanning the object using a scanner of the peripheral device to obtain the configuration data. The method comprises configuring the peripheral device as a function of the configuration data. The method comprises coupling the peripheral device to the host device.

System and method for configuring a scanner

A system is provided for selecting a particular barcode on an item that includes multiple barcodes. The system (1) acquires, via a barcode imager, an image of the item within the field of view of the barcode imager, (2) analyzes the acquired image to determine a set of potential barcode fields, (3) determines, using a predetermined criterion (e.g., distance from center of imaged area, type of bar code, etc.), a candidate barcode field from among the set of potential barcode fields, (4) displays a modified image derived from the acquired image, wherein the candidate barcode field is identified within the modified image, and (5) allows the user to initiate a decode session when the candidate barcode field includes the desired barcode. The acquired may be modified to highlight the candidate barcode field in any number of ways, including, for example, superimposing an artificial reticule around the candidate field, superimposing an artificial scan line over the field, altering the contrast of the field, and/or altering the brightness of the field.

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East Islip, NY, United States of America

David S Koch