A computer-implemented method for surface modeling includes: receiving one or more polarization raw frames of a surface of a physical object, the polarization raw frames being captured with a polarizing filter at different linear polarization angles; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and detecting a surface characteristic of the surface of the physical object based on the one or more first tensors in the one or more polarization representation spaces.

Processor implemented systems and methods for matching color and appearance of a target coating are provided herein. A system includes a storage device for storing instructions, and one or more data processors. The data processor(s) are configured to execute instructions to receive a target image of a target coating. The data processor(s) are also configured to apply a feature extraction analysis process that divides the target image into a plurality of target pixels for image analysis.

The present invention provides an image processing apparatus for evaluating a surface of an object using a marker including marks whose positional relationship in a case where the marker is arranged in plane is known, the maker being arranged on the surface of the object. The image processing apparatus includes: a first acquisition unit configured to obtain image data obtained by capturing an image of the surface and an image of the marker arranged on the surface; a second acquisition unit configured to obtain information indicating positional relationship among the marks included in the marker; an extraction unit configured to extract the positional relationship among the marks included in the marker in an image indicated by the image data; and a correction unit configured to correct the image data based on the positional relationship indicated by the information and the extracted positional relationship.

A method for automated flushness measurement of point cloud rivets, including: extracting a rivet outline by adopting an RANSAC circle fitting algorithm, and determining a center, a radius and a normal vector of an outline circle; extracting point cloud of a rivet head for a single rivet outline; extracting point cloud around the rivet for the single rivet outline; and generating a distance color difference map reflecting rivet flushness according to the point cloud of the rivet head and the point cloud around the rivet. According to the present invention, the point cloud of the rivet head and the point cloud around the rivet can be respectively extracted, and the distance color difference map reflecting the rivet flushness is generated according to the point cloud of the rivet head and the point cloud around the rivet, so that the rivet flushness is rapidly and effectively measured.

Disclosed herein is a method for assessing a flow a sprayed coating, including the steps of spraying a coating onto a surface and capturing a plurality of images of the sprayed surface at a predetermined frequency within a predetermined interval of time, and a computer program product for assessing a flow of a sprayed coating.

Aspects described herein may facilitate an automated trade-in of a vehicle with limited human interaction. A server may receive a request to begin a value determination of a vehicle associated with the user. The server may receive first data comprising: vehicle-specific identifying information, and multimedia content showing a first aspect of the vehicle. The user may be directed to place the vehicle within a predetermined staging area. The server may receive, from one or more image sensors associated with the staging area, second data comprising multimedia content showing a second aspect of the vehicle. The server may create a feature vector comprising the first data and the second data. The feature vector may be inputted into a machine learning algorithm corresponding to the vehicle-specific identifying information of the vehicle. Based on the machine learning algorithm, the server may determine a value of the vehicle.

A vehicle imaging station for capturing images of scratches on a vehicle, the vehicle imaging station including a tunnel having an entrance and an exit, with one or more walls defining an enclosure between the entrance and exit to define a tunnel volume containing a vehicle pathway having a central axis. The station further includes a reflection source surface and a camera arranged with a field of view including an imaging volume of the tunnel volume in which an image defined by the reflection source surface will be reflected to be visible to the camera as a reflected image by a vehicle moving along the vehicle pathway. The station also includes a color modifier arranged to cause the reflection source surface to adopt a first color of a plurality of possible colors in response to a color control signal such that the reflected image has the first color.

A device to detect and analyze defects in a finished surface includes a supporting mechanism, a transmitting mechanism, a detecting mechanism, and a processor. The transmitting mechanism carries and transmits the product. The detecting mechanism includes a detecting frame, a light source assembly. The processor is used to connect to a first camera module and a second camera module, and preprocess the first image and the second image to obtain a detection and analysis of any defects of the front of the product.

The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle including preserving the quality of the input images of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle, including preserving the quality and/or resolution of the images of the damaged vehicle.

A system and method include receiving target image data associated with a target coating. A color model and a local color model are used to predict color differences between the target coating and a sample coating. The color model and local color model includes a feature extraction analysis process that determines image features by analyzing target pixel feature differences within the target coating. Performing an optimization routine upon the color differences for determining automotive paint components for spraying a substrate.