A coating analyzer is configured to receive electronic image data of a physical coating and to generate information regarding the pigments of the physical coating. The coating analyzer applies a computer vision model trained on baseline image data to the electronic image data. The coating analyzer assigns color values to the pigments forming the electronic image data and generates pigment groups based on the assigned color values. The pigment groups provide color palette data regarding the pigments forming the coating.

A method for analyzing a specification parameter of an electronic component includes inputting a package type and at least one engineering drawing image of an electronic component; acquiring a probability value that in each view of the different viewing directions each of the plurality of specification parameter of the electronic component is labeled; taking the view of each of the plurality of specification parameters in the view direction with a highest probability value as a recommended view; performing a box selection on the plurality of specification parameters for at least one engineering drawing image with the same viewing direction as that of the recommended view by an object detection model; and identifying box-selected specification parameters to acquire a size value of identified specification parameters from the at least one engineering drawing image, and converting the size value into a corresponding editable text for output.

A method of inspecting images on printed products by a computer in a printing machine. Printed products are recorded and digitized by an image sensor of an image inspection system in the course of the image inspection process, and the computer compares them to a digital reference image. If deviations are found, the defective printed products are removed. The computer analyzes the deviations found in the course of the image inspection process together with further data from other system parts and from the machine, determines specific defect classes and the causes thereof based on the defects by machine learning processes, assigns the defects found in the image inspection process to the defect classes in a corresponding way, and displays the classified detected defects with their defect classes and causes to an operator of the machine so that the operator can initiate specific measures to eliminate the defect causes.

A control device controls sectors for the assembly of turbine stators of a turbine. Each turbine stator is formed of an assembly of sectors juxtaposed to one another, and each sector has a reference. The control device includes an automated system for identifying the sector with means for reading the sector reference, a database of the references of the sectors that form the turbine stators of the turbine, and means for associating the read reference of the sector with a determined turbine stator of the turbine.

Embodiments described herein provide a system for assessing the surface of an object for detecting contamination or other defects. During operation, the system obtains an input image indicating the contamination on the object and generates a synthetic image using an artificial intelligence (AI) model based on the input image. The synthetic image can indicate the object without the contamination. The system then determines a difference between the input image and the synthetic image to identify an image area corresponding to the contamination. Subsequently, the system generates a contamination map of the contamination by highlighting the image area based on one or more image enhancement operations.

Embodiments relate to a method and system for determining a situation of a facility by imaging a sensing data of the facility including receiving sensing data through a plurality of sensors at a query time, generating a situation image at the query time, showing the situation of the facility at the query time based on the sensing data, and determining if an abnormal situation occurred at the query time by applying the situation image to a pre-learned situation determination model.

A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Dynamic thin film interferometry is a technique used to non-invasively characterize the thickness of thin liquid films that are evolving in both space and time. Recovering the underlying thickness from the captured interferograms, unconditionally and automatically is still an open problem. A compact setup is provided employing a snapshot hyperspectral camera and the related algorithms for the automated determination of thickness profiles of dynamic thin liquid films. The technique is shown to recover film thickness profiles to within 100 nm of accuracy as compared to those profiles reconstructed through the manual color matching process. Characteristics and advantages of hyperspectral interferometry are discussed including the increased robustness against imaging noise as well as the ability to perform thickness reconstruction without considering the absolute light intensity information.

An image positioning system based on upsampling and a method thereof are provided. The image positioning method based on upsampling is to fetch a region image covering a target from a wide region image, determine a rough position of the target, execute an upsampling process on the region image based on neural network data model for obtaining a super-resolution region image, map the rough position onto the super-resolution region image, and analyze the super-resolution region image for determining a precise position of the target. The present disclosed example can significantly improve the efficiency of positioning and effectively reduce the required cost of hardware.

Methods, apparatus, systems and articles of manufacture are disclosed for augmented reality vehicle condition inspection. An example apparatus disclosed herein includes a location analyzer to determine whether a camera is at an inspection location and directed towards a first vehicle in an inspection profile, the inspection location corresponding to a location of the camera relative to the first vehicle, an interface generator to generate an indication on a display that the camera is at the inspection location, the indication associated with an inspection image being captured, and an image analyzer to compare the inspection image captured at the inspection location with a reference image taken of a reference vehicle of a same type as the first vehicle, and determine a vehicle part condition or a vehicle condition based on the comparison of the inspection image and the reference image.