A computer-implemented method for high resolution image inpainting comprising the following steps: providing a high resolution input image, providing at least one inpainting mask, selecting at least one rectangular sub-region of the input image and at least one aligned rectangular subregion of the inpainting mask such that the rectangular subregion of the input image encompasses at least one set of pixels to be removed and synthetized, the at least one sub-region of the input image and its corresponding aligned subregion of the inpainting mask having identical minimum possible size and a position for which a calculated information gain does not decrease, processing the sub-region of the input image and its corresponding aligned subregion of the inpainting mask by a machine learning model, generating an output high resolution image comprising the inpainted sub-region.

Methods, systems, and devices for equipment reading in a factory or plant environment are described, including: capturing an image of an environment including a measurement device; detecting a target region included in the image, the target region including at least a portion of the measurement device; determining identification information associated with the measurement device based on detecting the target region; and extracting measurement information associated with the measurement device based on detecting the target region. In some aspects, detecting the target region may include: providing the image to a machine learning network; and receiving an output from the machine learning network in response to the machine learning network processing the image based on a detection model, the output including the target region.

A method and an apparatus for detecting a defect based on a phased pass/fail determination are disclosed. According to at least one aspect of the present disclosure, a method comprising: a process of acquiring a product image which is an image of the product; a first determination process of inputting the product image into a first determination model to perform a pass/fail determination for the product; and a second determination process of inputting the product image into a second determination model to perform a pass/fail determination for the product when the product is determined to be undeterminable as a result of the pass/fail determination of the first determination process.

The disclosed computer-implemented method may include (i) conditionally operating, at a first frequency, a first stage of an eye-tracking system processing pipeline that detects a region of interest and (ii) operating, at a second frequency that is substantially greater than the first frequency, a second stage of the eye-tracking system processing pipeline that predicts a gaze orientation based at least in part on the detected region of interest. Various other methods, systems, and computer-readable media are also disclosed.

An image analyzation method and an image analyzation device are disclosed. The method includes: obtaining a first image which presents at least a first object and a second object; analyzing the first image to detect a first central point between a first endpoint of the first object and a second endpoint of the second object; determining a target region based on the first central point as a center of the target region; capturing a second image located in the target region from the first image; and analyzing the second image to generate status information which reflects a gap status between the first object and the second object.

An image matching system includes a non-transitory computer-readable medium and a processor. The non-transitory computer-readable medium is configured to store information of a plurality of images. The processor is configured to identify an object area in an original image that illustrates an object. The processor is configured to normalize the object area, resulting in a normalized image. The processor is configured to calculate a shape vector and a color vector from the normalized image. The processor is configured to calculate a match score using the shape vector and the color vector. The processor is configured to determine if the non-transitory computer-readable medium stores an identical match for the original image based on the match score.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image rendering. In one aspect, a method comprises receiving a plurality of observations characterizing a particular scene, each observation comprising an image of the particular scene and data identifying a location of a camera that captured the image. In another aspect, the method comprises receiving a plurality of observations characterizing a particular video, each observation comprising a video frame from the particular video and data identifying a time stamp of the video frame in the particular video. In yet another aspect, the method comprises receiving a plurality of observations characterizing a particular image, each observation comprising a crop of the particular image and data characterizing the crop of the particular image. The method processes each of the plurality of observations using an observation neural network to determine a numeric representation as output.

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.

Disclosed herein are system, method, and computer program product embodiments for generating a Virtual Reality (VR) simulation. By combining physical and simulated data from a physical, virtual mobile device and virtual environment and rendering that within a VR simulation, the system can give the developer a more hands-on accurate representation and feel for how their mobile app can work at a real-world environment. This VR simulation assists a developer to cut down on iteration and travel time when developing, debugging, and testing their mobile apps/games for real-world environments, location-based entertainment (LBE) venues including indoor or outdoor installations.

There are provided methods and systems for media processing, comprising: providing at least one media asset source selected from a media asset sources library, the at least one media asset source comprising at least one source video, via a network to a client device; receiving via the network or the client device a media recording comprising a client video recorded by a user of the client device; transcoding the at least one source video and the client video which includes parsing the client video and the source video, respectively, to a plurality of client video frames and a plurality of source video frames based on the matching; segmenting one or more frames of the plurality of source video frames to one or more character frames; detecting one or more face images in one or more frames of the plurality of client video frames and provide face markers; resizing the one or more character frames according to the face markers compositing the resized character frames with the background frames using one or more blending methods to yield a mixed media asset frames; and encoding the mixed media asset frames to yield a mixed media asset video.