Provided are a method and apparatus for generating and providing a data video for generating training data of an artificial intelligence model and a recording medium on which a program for the same is recorded. According to various embodiments of the present disclosure, a method includes generating light detection and ranging (LiDAR) images using LiDAR point cloud data for a predetermined area, generating a LiDAR video using each of the LiDAR images as a unit frame, and providing a user interface (UI) that outputs the generated LiDAR video, when a unit frame change request is acquired from a user, comparing a first unit frame currently being output with a second unit frame after the first unit frame to detect at least one pixel whose attribute changes, and updating only the one or more detected pixels in the first unit frame using the second unit frame.

A system and a method are disclosed for a structured-light system to estimate depth in an image. An image is received in which the image is of a scene onto which a reference light pattern has been projected. The projection of the reference light pattern includes a predetermined number of particular sub-patterns. A patch of the received image and a sub-pattern of the reference light pattern are matched based on either a hardcode template matching technique or a probability that the patch corresponds to the sub-pattern. If a lookup table is used, the table may be a probability matrix, may contain precomputed correlations scores or may contain precomputed class IDs. An estimate of depth of the patch is determined based on a disparity between the patch and the sub-pattern.

An image processing method generates a training dataset for training a machine learning model to predict illumination conditions for different positions relative to a scene, the training dataset including training images and reference data. The method includes: obtaining a training image of a training scene acquired by a first camera having an associated first coordinate system; determining local illumination maps associated to a respective position in the training scene in a respective second coordinate system and representing illumination received from different directions around the position; transforming the position of each local illumination map from the second to the first coordinate system; responsive to determining that the transformed position of a local illumination map is visible: transforming the local illumination map from the second to the first coordinate system and including the transformed local illumination map and its transformed position in the reference data associated to the training image.

A system and method are capable of ensuring that one or more text strings will be able to be fully rendered in a target area of a user interface or a target area of a graphics file. The system and method determine the number of pixels of first and second reference text that fit in the target area in the horizontal direction and the vertical direction, respectively, determine the number of pixels of string text in the horizontal direction and the vertical direction, and compare the number of pixels in the horizontal direction of the first reference text and the vertical direction of the second reference text respectively to the number of pixels in the horizontal direction and the vertical direction of the text string that is desired to be rendered in the target area to determine whether the text string will fit in the target area.

A method for detecting defects in appearance of a product from images thereof, applied in an electronic device, obtains positive sample images, negative sample images, and product sample images, divides the product sample images into input image blocks, and inputs the input image blocks into a pre-trained autoencoder to obtain reconstructed image blocks. The electronic device determines corresponding pixel points in the input image blocks, and corresponding pixel difference values, and generates feature connection regions of each input image block according to the positive sample images and the pixel difference values. The electronic device generates a first threshold, selects target regions from the feature connection regions and the first threshold, and generates a second threshold. The electronic device further determines a detection result of a product sample in the product sample image according to an area of the target area and the second threshold.

There is provided a method of re-classifying a clinically significant feature of a medical image as an artifact, comprising: feeding a target medical image captured by a specific medical imaging sensor at a specific setup into a machine learning model, obtaining a target feature map as an outcome of the machine learning model, wherein the target feature map includes target features classified as clinically significant, analyzing the target feature map with respect to sample feature map(s) obtained as an outcome of the machine learning model fed a sample medical image captured by at least one of: the same specific medical imaging sensor and the same specific setup, wherein the sample feature map(s) includes sample features classified as clinically significant, identifying target feature(s) depicted in the target feature map having attributes matching sample feature(s) depicted in the sample feature map(s), and re-classifying the identified target feature(s) as an artifact.

One embodiment of the present invention sets forth a technique for performing inference operations associated with a trained machine learning model. The technique includes comparing a first input image with a plurality of image representations that are associated with a plurality of output classes predicted by the trained machine learning model. The technique also includes determining that the first input image does not match any image representation included in the plurality of image representations and subsequently determining that the first input image does match a first alternative representation that is associated with a first output class included in the plurality of output classes. The technique further includes generating a first prediction that indicates that the first input image is a member of the first output class.

A skin texture control system, a skin texture control method, a skin texture sampling device, and a computer-readable medium storing a code of the skin texture control method are provided. The skin texture control system includes a skin texture feature generation module for generating a skin texture feature of a skin texture, which includes a sampling unit for sampling the skin texture and generating skin texture sampling data in a first period of a working period; and a feature generating unit for generating skin texture stripe data according to the skin texture sampling data and generating the skin texture feature of the skin texture by the skin texture stripe data in a second period; and a control module connected to the skin texture feature generating module for receiving the skin texture feature and outputting a control command by the skin texture feature of the skin texture in a third period.

The present invention relates to a system for determining the number of small products in the bag package from the x-ray image that enables detecting the missing quantities of small products or packages in a disorganized and random position in the bag package.

An image processing system or electronic device may implement processing circuitry. The processing circuitry may receive an image, such as financial document image. The processing circuitry may determine a character count for the financial document image or particular portions of the financial document image without recognizing any particular character in the financial document image. In that regard, the processing circuitry may determine a top left score for pixels in the financial document, the top left score indicating or representing a likelihood that a particular pixel corresponds to a top left corner of a text character. The processing circuitry may also determine top right score for image pixels. Then, the processing circuitry may identify one or more text chunks using the top left and top rights scores for pixels in the financial document image. The processing circuitry may determine a character count for the identified text chunks.