Honeywell International Inc.

Hand Held Products, Inc.

Scott McCloskey

Ryan Andrew Lloyd

Sharath Venkatesha

Jingquan Li

Brian L Jovanovski

Kwong Wing Au

Ben Miller

H Sprague Ackley

Jan Jelinek

Ynjiun P Wang

William H Havens

Robert M Hussey

Timothy P Meier

Sean Philip Kearney

Erik Van Horn

Robert J Hennick

Donald Anderson

Franck Laffargue

Kelly P Muldoon

Nianyi Li

Yunqian Ma

Valerie Guralnik

Yuanyuan Ding

Pedro Davalos

Matthew Edward Lewis Jungwirth

Kirk A Schloegel

Keigo Hirakawa

Jingchen Liu

Michael Albright

Xinqing Guo

Can Chen

A mobile dimensioning device, i.e. a mobile dimensioner, is described that uses a dynamic accuracy while still being compatible with the NIST standard. Even if the accuracy division is dynamic and not predetermined, a mobile dimensioning device of the present invention reports the actual dimensioning prior to measurement capture and can therefore be certified and used in commercial transactions.

Method for a mobile dimensioning device to use a dynamic accuracy compatible with NIST standard

A system and process to determine whether a digital image has been manipulated involves determining expected locations of non-standard pixels in the digital image, and determining a feature for evaluating the non-standard pixels. The feature in pixels of the digital image that are located at the expected locations of non-standard pixels is then measured, and a statistical measure of the feature of pixels in the digital image that are located at the expected locations of non-standard pixels is evaluated. The digital image is assessed to determine a probability that the digital image includes a manipulated portion, based on the statistical measure. The system and process can also determine a make and model of an image sensing device via an examination of the non-standard pixels.

Image forensics using non-standard pixels

Various embodiments described herein relate to techniques for computing dimensions of an object. In this regard, a dimensioning system converts points cloud data associated with an object into a density image for a scene associated with the object. The dimensioning system also segments the density image to determine a void region in the density image that corresponds to the object. Furthermore, the dimensioning system determines, based on the void region for the density image, dimension data indicative of one or more dimensions of the object.

Dark parcel dimensioning

Various embodiments described herein relate to techniques for computing dimensions of an object using multiple range images. In this aspect, the multiple range images are captured from selective locations and satisfy a pre-defined criterion. In accordance with various embodiments, at least a pair of 3D points are identified from the multiple range images, which correspond to at least one geometric feature on a surface of the object. In this regard, a correspondence score is estimated for the identified at least one pair of 3D points. The correspondence score is then utilized for registering the at least one pair of 3D points. Based in part on the registration of the 3D points and 3D point clouds retrieved from the captured images, the dimensions of the object are computed.

Methods, systems, and apparatuses for computing dimensions of an object using range images

A terminal for measuring at least one dimension of an object includes a range camera, a visible camera, and a display that are fixed in position and orientation relative to each other. The range camera is configured to produce a range image of an area in which the object is located. The visible camera is configured to produce a visible image of an area in which the object is located. The display is configured to present information associated with the range camera's field of view and the visible camera's field of view.

Dimensioning system

Methods to improve the accuracy of non-contact measurements of an object's dimensions using a dimensioning system are disclosed. The methods include a method for creating a mathematical model (i.e., error model) based on an observed correlation between errors in an estimated dimension and the characteristics of the measurement used to obtain the estimated dimension. These error models may be created for various dimensions and stored for future use. The methods also include a method for using the stored error models to reduce the error associated with a particular dimensioning-system measurement. Here an error model is used to create an estimated error. The estimated error is then removed from the estimate of the dimension to produce a final estimate of the dimension that is more accurate.

Methods for improving the accuracy of dimensioning-system measurements

Various embodiments described herein relate to techniques for computing dimensions of a non-cuboid shaped object using angular estimates associated with 3D points captured in an image of a scene including the object. In this regard, a dimensioning system may capture at least one image of the scene including the object and identify one or more planes on the object from the captured at least one image. Further, a surface plane from amongst the one or more identified planes that contains a maximum number of three-dimensional (3D) surface points on the object is determined. Upon determination of the surface plane, angular estimation is determined representing angular values between a normal to the surface plane and a normal to a reference plane. Accordingly, the angular estimation is further used along with an estimated height of the object for computing the dimensions of the object.

Methods, systems, and apparatuses for computing dimensions of an object using angular estimates

An image forensics system estimates a camera response function (CRF) associated with a digital image, and compares the estimated CRF to a set of rules and compares the estimated CRF to a known CRF. The known CRF is associated with a make and a model of an image sensing device. The system applies a fusion analysis to results obtained from comparing the estimated CRF to a set of rules and from comparing the estimated CRF to the known CRF, and assesses the integrity of the digital image as a function of the fusion analysis.

Determining image forensics using an estimated camera response function

A system assesses the integrity of a digital image by detecting an edge in the digital image and defining a patch of pixels encompassing the edge. The system then generates data relating to intensity and gradient magnitude for pixels in the patch, analyzes the data relating to intensity and gradient magnitude, and determines that the digital image has been forged or the digital image has not been forged based on the analysis of the data relating to intensity and gradient magnitude.

Determining image forensics using gradient statistics at edges

Obtaining a good image is important for reading indicia. Often, however, objects are not well aligned with an indicia reader's digital imager. This misalignment may lead to images in which an indicium may appear distorted due to perspective. This perspective distortion makes indicia reading difficult. In general, perspective distortion of an imaged object may be corrected using knowledge of the true size, shape, and/or orientation of the physical object. The present invention embraces the use of a dimensioning system to obtain the 3D information necessary to provide this knowledge. Then, using this knowledge, to transform a distorted image of an indicium into a corrected image of the indicium without distortion. This corrected image may then be processed to read the undistorted indicium.

Image transformation for indicia reading

A dimensioning system including a computing device running an alignment software program is disclosed. The alignment software uses range information from a range sensor in order to generate alignment messages. The alignment messages may help a user define a frame of reference and align the dimensioning system's range sensor for improved dimensioning performance.

Dimensioning system with guided alignment

The present disclosure relates to devices, methods, and systems for cargo sensing. One cargo presence detection system includes one or more sensors positioned in an interior space of a container, and arranged to provide spatial data about at least a portion of the interior space of the container and a detection component that receives the spatial data from the one or more sensors and identifies if one or more cargo items are present in the interior space of the container based on analysis of the spatial data.

Cargo sensing detection system using spatial data

Systems and methods for calibrating a volume dimensioner are provided. In one embodiment, a calibrating system comprises a dimensioner and a reference object. The dimensioner is configured to remotely sense characteristics of an object and calculate physical dimensions of the object from the sensed characteristics. The reference object has predefined physical dimensions and an outside surface that exhibits a pattern of reference markings. The dimensioner is configured to be calibrated using the reference object as a basis for comparison.

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Minneapolis, MN, United States of America

Scott McCloskey