The present disclosure relates generally to the processing of machine-readable visual encodings in view of contextual information. One embodiment of aspects of the present disclosure comprises obtaining image data descriptive of a scene that includes a machine-readable visual encoding; processing the image data with a first recognition system configured to recognize the machine-readable visual encoding; processing the image data with a second, different recognition system configured to recognize a surrounding portion of the scene that surrounds the machine-readable visual encoding; identifying a stored reference associated with the machine-readable visual encoding based at least in part on one or more first outputs generated by the first recognition system based on the image data and based at least in part on one or more second outputs generated by the second recognition system based on the image data; and performing one or more actions responsive to identification of the stored reference.

An image correction method and a processor are disclosed. The method includes performing a feature point search on a quick response (QR) code image to determine multiple feature points, dividing a coded area of the QR code image into multiple sub-regions according to the multiple feature points, determining a compensation vector for each sub-region according to the feature points corresponding to each sub-region, and compensating and correcting each sub-region according to the compensation vector of each sub-region to obtain a corrected image. Thus, the solution provided by the present application can avoid interference between different sub-regions by means of correcting the QR code image in a regional manner using the compensation vectors, thereby more accurately correcting the distortion of the QR code image.

Machine vision techniques for determining a region of interest (ROI) are disclosed herein. An example implementation includes a computing device for executing an application, the application operable to: (1) receive a first image; (2) set an first ROI of the first image to a field of view (FOV) of the first image; (3) determine a barcode within the first ROI; (4) determine a bounding box of the barcode; (5) form a second ROI based on the bounding box; (6) receive a second image; and (7) set an ROI of the second image to be the second ROI of the first image.

A system includes an authentication server and a media authentication device. The authentication server stores authenticated media files. The authenticated media files have an associated string value. Generally, the authentication device is used to scan a barcode on a media sample, determine a string value from the scanned barcode, generate a string value based on fragments of the media sample, and determine a string value from an authenticated media file on the authentication server. The authentication device then concatenates the barcode string value with the generated string value, and it concatenates the authentication string value with the generated string value. A hash function is applied to the concatenated string values, and the resulting hash values are compared. The authentication device causes to be displayed an indication that the media sample is authentic when the hash values match.

Embodiments of the disclosure relate generally to imaging devices and indicia reading devices. An imaging apparatus comprises an image sensor, an optical window positioned in front of the image sensor and a light source enclosing a perimeter of the optical window such that an illumination cone of the light source overlaps a portion of a near field of view cone of the imaging apparatus. The portion of the near field of view cone extends from a surface of the optical window to a threshold distance from the optical window. The light source is configured to produce a first illumination along a first direction extending towards a scene to be imaged.

A method of reading machine-readable marks on a movable support and object of a sample instrument. The method includes capturing a first image of the moveable support as the moveable support moves from a first position to a second position using an image capture device; determining whether a first fiducial machine-readable mark on the moveable support is in the first image; determining, when the first fiducial machine-readable mark is in the first image, whether a first machine-readable mark on a first object coupled to the moveable support is in the first image at a predetermined position relative to the first fiducial machine-readable mark; and associating information decoded from the first machine-readable mark on the first object with a first location on the moveable support associated with the first fiducial machine-readable mark.

Provided is an optical code that retains compatibility with existing optical codes and is used for recording information different from the information recorded under prescribed conditions. Also provided are a method and a device for reading the optical code. The optical code includes a plurality of light color modules of a light color and a plurality of dark color modules of a dark color to record first data. At least the light color modules or the dark color modules are made up of a first module and a second module having different reflectance for light of a predetermined wavelength. Second data is recorded in a pattern of the first and second modules.

A method of image analysis is provided for recognition of a pattern in an image. The method includes receiving a plurality of images acquired by a camera, where the plurality of images include a plurality of optical patterns in an arrangement. The method also includes matching the arrangement to a pattern template, wherein the pattern template is a predefined arrangement of optical patterns. The method also includes identifying an optical pattern of the plurality of optical patterns as a selected optical pattern based on a position of the selected optical pattern in the arrangement. The method also includes decoding the selected optical pattern to generate an object identifier and storing the object identifier in a memory device.

Techniques for optimizing one or more imaging settings for a machine vision job are provided. An example method includes configuring a machine vision job by setting a plurality of banks of imaging parameters, with each of the plurality of banks of imaging parameters being different from each other; transmitting the machine vision job to an imaging device; and executing the machine vision job on the imaging device to: (a) capture an image with the imaging device operating pursuant to one of the plurality of banks of imaging parameters; (b) attempt to decode a barcode within the image; (c) responsive to successfully decoding the barcode within the image, successfully ending the barcode reader tool; and (d) responsive to unsuccessfully decoding the barcode within the image, repeating (a)-(d) with another one of the one of the plurality of banks of imaging parameters.

The present application relates to a code acquisition system and method. The code acquisition system comprises a transfer track; an image capture device and an image processing device. The image capture device is configured to capture memory stick images. The code area definition module in the image processing device is configured to divide into code areas according to the types, sizes and positions of pre-loaded memory stick codes. The code identification module identifies the memory stick codes according to the memory stick images and the code areas.