A method for generating training data for object detection is provided. The method includes dividing an image into a plurality of sliced images. The image includes meta information for an object in the image, the meta information including bounding box information associated with a bounding box surrounding the object, and marker information for markers associated with the object, the markers being located within the bounding box. The method includes designating at least one sliced object image from among the plurality of sliced images, at least a portion of the bounding box overlapping with each of the respective sliced object images. The method also includes designating at least one training data image from among the designated sliced object images based on the marker information. The method also includes generating the training data from the designated training data images.

A image processing device which can reduce information such as a pattern, a scar and the like on a surface of a drug which are other than an engraved mark, to accurately extract the engraved mark. The image processing device includes: an obtaining unit configured to obtain a plurality of images of a drug having an engraved mark on a surface of the drug, with emitting directions of light to the surface different from each other; an edge image generating unit configured to apply respectively to the plurality of images, edge extracting filters in directions in conformity with the emitting directions, the edge extracting filters having a size in conformity with a width of a groove of the engraved mark, and generate a plurality of edge images; and an image composing unit configured to compose the plurality of edge images and generate a composite image.

An image processing device includes: a delimiting line detection unit configured to detect a delimiting line candidate based on image data obtained by capturing a surrounding of a vehicle, the delimiting line candidate being a candidate of a delimiting line that delimits a parking space; and an exclusion determination unit configured to determine whether or not to exclude the delimiting line candidate detected by the delimiting line detection unit from the candidate of the delimiting line. In a case where a plurality of the delimiting line candidates is detected within a predetermined range in the image data, the exclusion determination unit determines whether or not to exclude the delimiting line candidate from the candidate of the delimiting line by comparing edge strength of the plurality of delimiting line candidates.

A computer-implemented method of using augmented reality (AR) to detect a plane and a spatial configuration of an object, the method comprising the steps of detecting one or more edges of the object; identifying one or more lines of the object; filtering the one or more lines of the object; iteratively approximating one or more distributions of the one or more lines; and determining boundaries of the object.

Processing a dithered image comprising a grid of pixels including defining an array of pixels corresponding to a sub-region of the image; performing edge detection along the rows and the columns of the array; counting the number of edges detected along the rows of the array to determine the number of horizontal edges in the array; counting the number of edges detected along the columns of the array to determine the number of vertical edges in the array; identifying whether the sub-region is dithered based on the number of horizontal and vertical edges in the array; and selectively processing the corresponding sub-region of the image based on whether or not the sub-region is identified to be dithered. The identification step may also be based on the lengths of segments of similar pixels in the lines of the array.

A platform, device and process for capturing images of the surface of a gaming table and determining the quantity, identity, and arrangement of chips bet at a gaming table. Image data is captured corresponding to the one or more chips positioned in at least one betting area on a gaming surface of the respective gaming table and the data is processed to filter out the background, establish a two dimensional grid of points of interests and corresponding histograms for classifying the one or more chips through identifying a dominant classification of each row in the grid of points of interests.

A system for capturing a product tag image, extracting product information, and validating information. The extracted information is then utilized to populate a universal (brand independent) product registration database. To incentivize end users to register their new and in service products, entry is simplified by the use of an application on a smartphone, tablet or personal computer. A user downloads an application and enters their information then takes at least one picture/scan/image of the product label(s) that contain: 1) manufacturer name (and/or brand name), 2) model number, 3) serial number; and 4) date of production. Said information is extracted by Optical Character Recognition (OCR) and then validated before entering said database.

The present invention provides a method in which visual and non-visual semantic attributes are associated with a visual comprising preferably an input step, a preliminary visual processing step, a semantic concept processing step, a semantic context processing step, a semantic marker processing step, a semantic inheritance processing step, a semantic instance processing step, and a lexical functions step, as well as a computing device which is capable of performing said method.

Image annotation includes: accessing initial object prediction information associated with an image, wherein the initial object prediction information includes a plurality of initial predictions associated with a plurality of objects in the image, including bounding box information associated with the plurality of objects; presenting the image and at least a portion of the initial object prediction information to be displayed; receiving adjusted object prediction information pertaining to at least some of the plurality of objects, wherein the adjusted object prediction information is obtained from a user input made via a user interface configured for a user to make annotation adjustments to at least some of the initial object prediction information; and outputting updated object prediction information, wherein the updated object prediction information is based at least in part on the adjusted object prediction information.

A method for generating training examples for a product recognition model is disclosed. The method includes capturing images of a product using an array of cameras. A product identifier for the product is associated with each of the images. A bounding box for the product is identified in each of the images. The bounding boxes are smoothed temporally. A segmentation mask for the product is identified in each bounding box. The segmentation masks are optimized to generate an optimized set of segmentation masks. A machine learning model is trained using the optimized set of segmentation masks to recognize an outline of the product. The machine learning model is run to generate a set of further-optimized segmentation masks. The bounding box and further-optimized segmentation masks from each image are stored in a master training set with its product identifier as a training example to be used to train a product recognition model.