The invention relates to a vehicle (1) comprising: a chassis (2) which has a longitudinal axis (A2) and which is supported by wheels; a cab (5) mounted on the chassis (2); a trailer (50) pivotally connected to the chassis (2), the trailer (50) having a longitudinal axis (A50); a camera monitoring system (40) which includes a camera arranged on a supporting arm mounted on the cab (5), for providing a captured image of an area located rearward of the cab (5); at least one lighting assembly (30) which is mounted on the cab (5) and which includes at least one light source (38) configured to project a picture rearward of the cab (5), the picture forming a mark on a functional face (54) of the trailer (50), said mark being detectable by the camera, and being representative of an angle (α) between the trailer longitudinal axis (A50) and the chassis longitudinal axis (A2).

A method of identifying data items by wave blocks, each wave block comprising a set of unique features distinguishable from the unique features of other wave blocks. The unique features of the wave blocks are extracted and stored. A plurality of wave tags are defined, each comprising a set wave blocks. A mapping of the set of wave blocks to each wave tag is stored. A request for a wave tag to identify a data item is received and a wave tag is assigned to the data item. The wave tag is broadcasted and is captured by a capturing device, which extracts the unique features of the wave blocks. The wave tag is identified by comparing the extracted features of the wave blocks with the stored features of the plurality of wave blocks. The data item is identified from the mapping of the data item to the wave tag.

Systems and methods for determining shrinkage of a commodity sold by a store are provided which may include: identifying data indicative of a volume of the commodity, associating a label with a container that holds the commodity, collecting the label information with a reader and transmitting collected label information to at least one server, automatically determining data characterizing the material in the container, associating the label information and the data characterizing the material in the container, and determining performance data related to the commodity by comparing the data indicative of the volume of the commodity supplied to the store, the data indicative of the volume of the commodity sold by the store, and the data characterizing the material in the container.

There is provided a method including generating an initial token being associated with a given context, and sending the initial token to a destination device. The method also includes receiving a scanned token generated by a mobile device scanning an output of the destination device. The output may be generated based on the initial token. In addition, the method includes authenticating the scanned token by comparing the scanned token to the initial token. Furthermore, the method includes generating a productivity indicator based on the scanned token, and outputting the productivity indicator.

Various embodiments disclosed herein describe a method comprising receiving indicia data from an indicia scanner. The indicia data comprises at least decoded data obtained based on decoding an indicium in an image, an image tile comprising a portion of the indicium, and/or location of one or more corners of the portion of the indicium in the image. Further, the method includes generating an image of an ideal indicium based on at least the decoded data. Thereafter, the image of the ideal indicium is modified to generate a modified image of the ideal indicium. Further, the portion of the indicium is retrieved from modified image. A clean image tile comprises a portion of the ideal indicium. Furthermore, the method includes generating training data, wherein the training data includes the portion of the indicium and the portion of the ideal indicium.

A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization may be used for Hemolysis, Icterus, and/or Lipemia, or Normal detection. The method captures one or more images of a labeled specimen container including a serum or plasma portion, processes the one or more images to provide segmentation data and identification of a label-containing region, and classifying the label-containing region with a convolutional neural network (CNN) to provide a pixel-by-pixel (or patch-by-patch) characterization of the label thickness count, which may be used to adjust intensities of regions of a serum or plasma portion having label occlusion. Optionally, the CNN can characterize the label-containing region as one of multiple pre-defined label configurations. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.

Various embodiments disclosed herein describe a method comprising receiving indicia data from an indicia scanner. The indicia data comprises at least decoded data obtained based on decoding an indicium in an image, an image tile comprising a portion of the indicium, and/or location of one or more corners of the portion of the indicium in the image. Further, the method includes generating an image of an ideal indicium based on at least the decoded data. Thereafter, the image of the ideal indicium is modified to generate a modified image of the ideal indicium. Further, the portion of the indicium is retrieved from modified image. A clean image tile comprises a portion of the ideal indicium. Furthermore, the method includes generating training data, wherein the training data includes the portion of the indicium and the portion of the ideal indicium.

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.

A food-recognition engine can be used with a mobile device to identify, in real-time, foods present in a video stream. To capture the video stream, a user points a camera of the mobile device at foods they are about to consume. The video stream is displayed, in real-time, on a screen of the mobile device. The food-recognition engine uses several neural networks to recognize, in the video stream, food features, text printed on packaging, bar codes, logos, and “Nutrition Facts” panels. The neural-network outputs are combined to identify foods with high probabilities. The foods may be packaged or unpackaged, branded or unbranded, and labeled or unlabeled, and may appear simultaneously within the view of the mobile device. Information about recognized foods is displayed on the screen while the video stream is captured. The user may log identified foods with a gesture and without typing.

A method in a mobile computing device includes: obtaining order collection data containing (i) item identifiers corresponding to a set of target items and a set of non-target items, (ii) a reference layout indicating, within a region of a facility, respective positions of the target items and the non-target items, and (iii) item recognition data for the target items and the non-target items; controlling an image sensor of the mobile computing device to acquire an image of a portion of the region; based on the item recognition data, detecting an item from the image; when the detected item is a target item, controlling an output assembly of the mobile computing device to present a prompt to collect the detected item; and when the detected item is a non-target item, controlling the output assembly to present a directional guide towards a selected target item based on the reference layout.