A display device includes: pixels arranged in a display area; a timing controller which generates image data of each frame based on an input image signal of the each frame, the timing controller including a logo controller which detects a logo image and a logo area including the logo image from the input image signal of the each frame to control luminance of the logo image; and a data driver which generates a data signal based on the image data and supplies the data signal to the pixels. The logo controller generates a first logo map based on an input image signal of a previous frame, generates a second logo map based on an input image signal of a current frame, and determines a similarity between the first logo map and the second logo map to selectively change luminance of a logo image of a next frame.

A non-transitory computer-readable medium includes instructions that when executed by a processor cause the processor to perform a method for identifying products from on-shelf sensors and image data. The method may include receiving data captured using a plurality of sensors positioned between at least part of a retail shelf and one or more products placed on the at least part of the retail shelf. The method may also include receiving an image of the at least part of the retail shelf and at least one of the one or more products. The method may also include analyzing the captured data and the image to determine a product type of the one or more products.

Systems, devices, media, and methods are presented for graphical icon identification within an image or video stream. The systems and methods receive an image including a graphical icon. The systems and methods identify a set of proposed regions of the image, at least one proposed region of the set of proposed regions containing the graphical icon and extract a set of semantic features for each proposed region of the set of proposed regions. Based on the set of semantic features of the set of proposed regions, the systems and methods identify a set of proposed icons corresponding to the graphical icon included in the image and determine a match between the graphical icon and at least one proposed icon of the set of proposed icons.

Disclosed are an apparatus for sensing a moving golf ball and a method for sensing the moving golf ball using the apparatus. The apparatus includes an image acquisition unit for acquiring consecutive images of a ball, an image processing unit for extracting a feature portion of the ball from the acquired images, and a spin calculation unit for performing any one of forward operation analysis and inverse operation analysis to calculate spin of the ball.

Provided is machine learning apparatus comprising: a dataset for input to a training procedure of a machine learning model; data capture logic operable to capture from an object at least one datum for inclusion in the dataset; association logic operable to derive an additional characteristic of the object; annotator logic operable in response to the data capture logic and the association logic to create an annotation linking the additional characteristic with the at least one datum; storage logic operable to store the or each datum with an associated annotation in the dataset; and input logic to supply the dataset as machine learning input.

A system including one or more processors and one or more non-transitory computer-readable storage media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: training a neural network detection model with a training dataset comprising synthetic training images by: using a transformation algorithm to create the synthetic training images by appending edge case training images to one or more compliant images; receiving, at the neural network detection model, as trained, at least one image; and determining, using the neural network detection model, as trained, whether the at least one image comprises non-compliant content. Other embodiments are disclosed herein.

A computer-implemented tool for automated classification and interpretation of documents, such as life science documents supporting clinical trials, is configured to perform a combination of raw text, document construct, and image analyses to enhance classification accuracy by enabling a more comprehensive machine-based understanding of document content. The combination of analyses provides context for classification by leveraging relative spatial relationships among text and image elements, identifying characteristics and formatting of elements, and extracting additional metadata from the documents as compared to conventional automated classification tools, wherein natural language processing (NLP) is applied to associate text with tokens, and relevant differences and similarities between protocols are identified.

A wearable apparatus and method are provided for capturing image data. In one implementation, a wearable apparatus for capturing image data is provided. The wearable apparatus includes at least one image sensor for capturing image data of an environment of a user, wherein a field of view of the image sensor includes a chin of the user. The wearable apparatus includes two or more microphones, and an attachment mechanism configured to enable the image sensor and microphones to be worn by the user. The wearable apparatus includes a processing device programmed to capture at least one image, identify the chin of the user to obtain a location of the chin, select a microphone from the two or more microphones based on the location, process input from the selected microphone using a first processing scheme, and process input from a microphone that is not selected using a second processing scheme.

A purpose of the invention is to provide a method and a system for adding or superimposing predetermined pictures, especially advertisement pictures, on predetermined location(s) of moving object(s), especially the clothing of sports players during a match, in a video stream. More generally, the invention aims at superimposing, in a video stream, at least one predetermined image portion at a predetermined location of at least one moving object image, to simulate that the object carries the predetermined image portion on. The disclosure also provides a system for implementing a bidding process to determine which symbols, logos, or messages are overlaid for various simultaneous broadcasts.

A cascade auto-review system for automated classification and annotation of input is provided. An example system is structure adaptive and task oriented and includes a communication module configured to receive the input including images, videos, and metadata. The system further includes a plurality of subsystems. Each subsystem has a series of successive classifier stages configured to detect tags in the input and approve or reject the tags based on the images, the videos, and the metadata. The system further includes a database to store results of the classification and annotation. The results are used to train computer vision and machine learning algorithms.