This specification describes techniques for verifying authenticity of an image. One example method includes identifying a baseline image depicting a baseline background; identifying a comparison image depicting a card; identifying a comparison background in the comparison image, wherein the comparison background is an area of the comparison image other than an area occupied by the card; determining a probability that the baseline background matches the comparison background; determining that the probability satisfies a verification threshold; and in response to determining that the probability satisfies a verification threshold, determining that the comparison image was acquired by capturing an image of a physical card corresponding to the card depicted in the comparison image.

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 method of detecting background pixels in a video, the video including a sequence of frames, each frame having a pitch and people. The method includes segmenting the pitch and determining a height of one of the people. An inclusion mask for the sequence of frames is then created using the pitch segmentation and the people's height. Each of the sequence of frames is segmented into foreground and background pixels. The method then creates a foreground appearance model from the foreground pixels inside said inclusion mask over the sequence of frames, and a background appearance model from the background pixels inside said inclusion mask over the sequence of frames. The method then uses the created foreground and background appearance models to segment one of the sequence of frames into definite foreground, definite background and uncertain pixels.

In a method of converting an input image patch to a text output, a convolutional neural network (CNN) is applied to the input image patch to estimate an n-gram frequency profile of the input image patch. A computer-readable database containing a lexicon of textual entries and associated n-gram frequency profiles is accessed and searched for an entry matching the estimated frequency profile. A text output is generated responsively to the matched entries.

This invention provides a system and method for determining the level of clutter in an image in a manner that is rapid, and that allows a scoring process to quickly determine whether an image is above or below an acceptable level of clutterfor example to determine if the underlying imaged runtime object surface is defective without need to perform a more in-depth analysis of the features of the image. The system and method employs clutter test points that are associated with regions on the image that should contain a low gradient magnitude, indicative of emptiness. This enables the runtime image to be analyzed quickly by mapping trained clutter test points at locations in the coordinate space in which lack of emptiness indicates clutter, and if detected, can rapidly indicate differences and/or defects that allow for the subject of the image to be accepted or rejected without further image analysis.

Cell culture device includes incubator unit that cultures cells in culture container, transport unit that transports culture container to incubator unit, task setting unit that sets a task relating to a culture of the cells, imaging unit serving as an example of a management information acquisition unit that acquires management information attached to culture container, and storage unit that stores the management information acquired by imaging unit. From storage unit, task setting unit reads the management information of plurality of culture containers serving as targets for carrying out the task, and causes display serving as an example of a display unit to display the management information.

A sensing method for a counterfeit bill detector includes (i) using multiple image-sensing modules to scan multiple pieces of image information of a bill and converting the multiple pieces of image information into multiple values of digital image, (ii) comparing each value of digital image with a pre-stored image threshold value to generate a reference value, (iii) adding the multiple reference values to generate an image validation value, (iv) comparing the image validation value with a pre-stored validation threshold value to acquire a comparison result, and (v) determining if the multiple pieces of image information are valid according to the comparison result. The sensing method tackles the issue of unsynchronized actions in bill sensing and bill scanning and simultaneously eliminates the problem of distorted scanned bill image arising from entry of misaligned bill to improve accuracy in bill validation.

The present invention discloses a method, a system, an electronic device, and a medium for classifying license plates based on deep learning that are applied to an electronic device. The method includes: acquiring at least one photograph sent by a terminal device; preprocessing the acquired photograph such that the preprocessed photograph matches a plurality of input parameters of a pre-trained recognition model; and inputting the preprocessed photograph to the pre-trained recognition model to recognize corresponding vehicle use information of the photograph, and sending the corresponding vehicle use information of the photograph to the terminal device. Thus, with this disclosure, the use of a vehicle in a photograph can be automatically and accurately recognized and further the photographs can be accurately classified, thereby improving the accuracy as well as the efficiency.

An information processing device (10) acquires a plurality of ledger sheets having the same layout, compares the contents of each column at the same position each of the acquired plurality ledger sheets having the same layout, discriminates the type of each column according to the comparison result, and stores the information on the type of each column in a storage unit (14). Moreover, the information processing device (10) acquires position information of a processing target ledger sheet, compares information on the type of a column and the content of each column using information on a registered style with respect to the acquired ledger sheet, discriminates the type of each column of the processing target ledger sheet according to the comparison result, and specifies style candidates of the processing target ledger sheet on the basis of the discrimination result.

To quickly specify a region of a character group included in an image, image obtaining means of an image processing device obtains the image data. Expansion means expands and unites a plurality of regions respectively indicating objects included in an image indicated by the image data obtained by the image obtaining means. Character region determining means determines whether all or some of the regions, which are expanded and united by the expansion means, are character regions. Processing performing means performs predetermined processing on a region that is determined by the character region determining means as a character region.