Provided is a system and method for authentication of collectable objects. A hi-resolution digital camera in communication with a nonvolatile data storage device having a data partition capable of being made immutable is provided. The nonvolatile data storage device is compatible with a computerized device, and the hi-resolution digital camera is operated to record at least one hi-resolution digital image of at least one unique appearance characteristic of a collectable object at an image resolution of at least 300 pixel dots per inch at 1:1 image scale. The at least one hi-resolution digital image is stored in the data partition of the nonvolatile data storage device, together with additional image data. A tamper-resistant marking associated with the collectable object is placed on the nonvolatile data storage device.

The presented invention relates to a computer-implemented recognition method (100) for unambiguously recognizing an object. The recognition method (100) comprises a first determining step (101) for determining, by means of a first optical sensor (201) at a first point in time, reference information by capturing a number of symbols applied to a reference object, a training step (103) for training a machine learner on the basis of the reference information and a provided ground truth which assigns respective reference information to a first class or a further class, a second determining step (105) for determining, by means of a second optical sensor (205) at a second point in time, sample information by capturing a number of symbols applied to a sample object, an assigning step (107) for the assigning of the sample information to the first class or the further class by the machine learner, and an outputting step (109) for outputting a validation signal in case the machine learner assigns the sample information to the first class.

Furthermore, the presented invention relates to a recognition system (200).

Disclosed is an approach of on-device partial recognition that includes performing partial recognition on an image of a document captured by a mobile device to detect and/or recognize a specific area (e.g., barcodes, non-relevant text, etc.) and filling the recognized area with a solid color. Because the solid color area has a maximum compression ratio, this approach can lead to image size reduction and increased network throughput for client-server based data recognition where further processing such as advanced data extraction is performed at the server side. The approach can be enforced with neural network algorithms to exclude non-relevant information (e.g., logos, phrases, words, etc.).

For scanning optical patterns, such as two-dimensional QR codes, with a mobile device at increased distances, a first image is acquired. A region of interest likely containing the optical pattern in the first image is identified. The mobile device then zooms in on the region of interest and a second image is acquired. The optical pattern is then decoded using the second image.

A system and method are provided for presenting self-diagnostic test instructions in the form of audiovisual messages. The system and method include collecting by a user of a testing device a biologic sample for use with a testing device, assigning correlative values as test results, and receiving the test results at a server disposed on a network. Some aspects of the system and method present test instructions to the user in the form of audiovisual messages. The audiovisual messages are provided to the user as a response to an interaction with a retail diagnostic product. In some aspects, the complete audiovisual message is presented before the user may complete a self-diagnostic test.

An inspection method is provided for checking the integrity of a combination of a security marking and an identification label, the security marking including at least one contrast field having a comparatively high reflectivity in a first and a second wavelength range, and a security field, having different reflection properties in the first wavelength range compared to the second wavelength range, and the identification label having at least one light background around mark components printed with dark color. The inspection method may include capturing possibly averaged gray values of the contrast field and the identification label background, comparing the gray values, and determining whether the gray value of the contrast field of the security marking deviates from the gray value of the background of the identification label by less than a predefined maximum amount.

Methods and apparatus are described that use machine vision and machine learning to eliminate many of the steps of a typical retail transaction. For example, implementations are contemplated in which a user simply removes an item from a retail location to effect the transaction.

In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views—further minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and used—or ignored—in product identification. A great variety of other features and arrangements are also detailed.

Multiple field of view (FOV) systems are disclosed herein. An example system includes a bioptic barcode reader having a target imaging region. The bioptic barcode reader includes at least one imager having a first FOV and a second FOV and is configured to capture an image of a target object from each FOV. The example system includes one or more processors configured to receive the images and a trained object recognition model stored in memory communicatively coupled to the one or more processors. The memory includes instructions that, when executed, cause the one or more processors to analyze the images to identify at least a portion of a barcode and one or more features associated with the target object. The instructions further cause the one or more processors to determine a target object identification probability and to determine whether a predicted product identifies the target object.

The present disclosure is related to an optical encoder which is configured to provide precise coding reference data by feature recognition technology. To apply the present disclosure, it is not necessary to provide particular dense patterns on a working surface. The precise coding reference data can be generated by detecting surface features of the working surface.