A decoding device includes a processor configured to: analyze a captured image having a 2D captured image coordinate system to identify an instance of a type of anchor marking therein; derive an orientation thereof relative to the captured image coordinate system; correlate the type of anchor marking to a relative location of a corresponding type of target marking within a 2D normalized coordinate system; employ the location and orientation of the instance of the type of anchor marking within the captured image coordinate system, and the relative location of the type of target marking within the normalized coordinate system to derive a location of an instance of the type of target marking within the captured image coordinate system; attempt interpretation of the instance of the type of target marking at the derived location to decode data thereat; and in response to a successful decode, transmit the data to another device.

A system and method for processing a machine-readable code associated with an object moving relative to an imaging device may include imaging the machine readable code. A determination may be made as to whether image data of the machine-readable code is clipped along a leading edge of a first image frame. If the machine-readable code is determined to be clipped, (i) image data of the machine-readable code in the first image frame may be skipped, and (ii) image data contained in a subsequent image frame may be processed. Otherwise, if the code is not determined to be clipped, processing image data in the subsequent image frame may be skipped.

A user instrument for engaging in a transaction includes a graphically encoded icon having a static portion, and an intrinsic portion comprising an area of stimuli-responsive material defining a first machine-readable indicia. At least a portion of the stimuli-responsive material transforms from a first state to a second state in response to a trigger. The transformation from the first state to the second state of the portion of the stimuli-response material results in a second machine-readable indicia. The transformation of the stimuli-responsive material from the first state to the second state is semi-irreversible. The second machine-readable indicia comprises information to permit or deny the user to engage in a second transaction via the user instrument.

The present disclosures relates to finding or localizing machine readable indicia (e.g., a barcode or digital watermark) in imagery. One claim recites an apparatus comprising: memory for buffering blocks of image data, the image data having been captured with a camera and depicting a printed object; one or more processors programmed for: generating an edge orientation sensitive feature set from the image data; using a first trained classifier to determine whether the feature set includes data representing a barcode; and using N additional trained classifiers to determine an orientation angle associated with the barcode, wherein N comprises an integer greater than 3, and wherein the orientation angle is selected based on a probability metric. Of course, other claims and combinations are provided too.

An image processing method including a detection step for detecting an end of a code element constituting a code image included in an input image, a transfer step for transferring first data constituting one end in a width direction of the code element to, as second data, a position, in the code element, at an inner side from the one end in the width direction, and a gray-scale value conversion step for converting a gray-scale value of the first data to shorten a length in the width direction of the code element.

A barcode detection method includes obtaining a gradient of each pixel in an image, generating a gradient phase and a gradient magnitude of each pixel according to the gradient, and binarizing the gradient magnitude of each pixel to generate a binary image, generating a sliding window on the image, sampling the binary image vertically and horizontally within the sliding window to generate the numbers of grayscale value variations in the vertical and horizontal directions, locating the most intensive flip region according to the grayscale variations in the vertical and horizontal directions, locating a core barcode region according to the most intensive flip region, capturing the gradient phase of the pixels in the core barcode region to generate a gradient phase distribution, generating a barcode format detection result according to the gradient phase distribution, and locating the barcode region according to the barcode format detection result.

An image output device includes an acquisition unit that acquires original image data indicating an original image, a storage unit that certifies, for each of blocks, whether the original image is a barcode area and that stores output image data for outputting the original image, and an output unit that performs output based on the output image data using methods that are different between an area indicated to be the barcode area and an area indicated not to be the barcode area, wherein the output image data is JPEG data compressed for the each block.

Method and apparatus are disclosed for mobile device tethering for a remote parking assist system of a vehicle. An example vehicle includes first and second wireless modules and a processor. The processor calculates trajectories of the vehicle and a mobile device and estimates a location of the mobile device. When the mobile device is within a threshold distance of the vehicle, the processor polls a key fob at an interval based on a comparison of the trajectories and estimate a location of the key fob. When the key fob is within the threshold distance, the processor enables autonomous parking.

The techniques described herein relate to methods, apparatus, and computer readable media configured to decode a symbol in a digital image. A digital image of a portion of a symbol is received, which includes a grid of pixels and the symbol includes a grid of modules. A spatial mapping is determined between a contiguous subset of modules in the grid of modules to the grid of pixels. Causal relationships are determined, using the spatial mapping, between each module and the grid of pixels. A set of valid combinations of values of neighboring modules in the contiguous subset of modules are tested against the grid of pixels using the causal relationships. A value of at least one module of the two or more neighboring modules is determined based on the tested set of valid combinations. The symbol is decoded based on the determined value of the at least one module.

The proposed method and apparatus localizes one or more one-dimensional barcodes present in a digital image. The method uses a line segment detection algorithm detecting line segments in the digital image. Further, the line segments detected are clustered together as belonging to probable barcodes based on the orientation and distance between each line segment. The line segments in a cluster are joined by growing a ray in the direction perpendicular to the line segments. Further, the various clusters are subjected to connected component analysis in order to identify valid barcodes. Further, the location of the barcode and the bounding box details of the barcode are returned. Based on the location and bounding box details, the barcode may be read using barcode readers.