A part inspection system includes a vision device configured to image a part being inspected and generate a digital image of the part. The system includes a part inspection module communicatively coupled to the vision device and receives the digital image of the part as an input image. The part inspection module includes a defect detection model. The defect detection model includes a template image. The defect detection model compares the input image to the template image to identify defects. The defect detection model generates an output image. The defect detection model configured to overlay defect identifiers on the output image at the identified defect locations, if any.

A part inspection system includes a vision device configured to image a part being inspected and generate a digital image of the part. The system includes a part inspection module communicatively coupled to the vision device and receives the digital image of the part as an input image. The part inspection module includes a defect detection model. The defect detection model includes a template image. The defect detection model compares the input image to the template image to identify defects. The defect detection model generates an output image. The defect detection model configured to overlay defect identifiers on the output image at the identified defect locations, if any.

This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images using class-specific generators for objects of different classes. The disclosed system modifies a synthesized digital image by utilizing a plurality of class-specific generator neural networks to generate a plurality of synthesized objects according to object classes identified in the synthesized digital image. The disclosed system determines object classes in the synthesized digital image such as via a semantic label map corresponding to the synthesized digital image. The disclosed system selects class-specific generator neural networks corresponding to the classes of objects in the synthesized digital image. The disclosed system also generates a plurality of synthesized objects utilizing the class-specific generator neural networks based on contextual data associated with the identified objects. The disclosed system generates a modified synthesized digital image by replacing the identified objects in the synthesized digital images with the synthesized objects.

This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images using class-specific generators for objects of different classes. The disclosed system modifies a synthesized digital image by utilizing a plurality of class-specific generator neural networks to generate a plurality of synthesized objects according to object classes identified in the synthesized digital image. The disclosed system determines object classes in the synthesized digital image such as via a semantic label map corresponding to the synthesized digital image. The disclosed system selects class-specific generator neural networks corresponding to the classes of objects in the synthesized digital image. The disclosed system also generates a plurality of synthesized objects utilizing the class-specific generator neural networks based on contextual data associated with the identified objects. The disclosed system generates a modified synthesized digital image by replacing the identified objects in the synthesized digital images with the synthesized objects.

An information processing apparatus includes a processor configured to: obtain a video and an instruction to generate a still image from the video, the video being a video in which a work target is photographed, the work target being a target on which to work; generate the still image in response to the instruction, the still image being cut from the video including the work target; specify the work target in the video, position information, and a superimposition area by using the still image, the position information describing a position of the work target, the superimposition area being an area in which an image is superimposed, the image being obtained by using the position of the work target as a reference; receive instruction information indicating an instruction for work on the work target; and superimpose and display an instruction image in the superimposition area in the video, the instruction image being an image according to the instruction information.

An information processing apparatus includes a processor configured to: obtain a video and an instruction to generate a still image from the video, the video being a video in which a work target is photographed, the work target being a target on which to work; generate the still image in response to the instruction, the still image being cut from the video including the work target; specify the work target in the video, position information, and a superimposition area by using the still image, the position information describing a position of the work target, the superimposition area being an area in which an image is superimposed, the image being obtained by using the position of the work target as a reference; receive instruction information indicating an instruction for work on the work target; and superimpose and display an instruction image in the superimposition area in the video, the instruction image being an image according to the instruction information.

The present disclosure describes devices and methods for generating RGB images from Bayer filter images using adaptive sub-pixel spatiotemporal interpolation. An electronic device includes a processor configured to estimate green values at red and blue pixel locations of an input Bayer frame based on green values at green pixel locations of the input Bayer frame and a kernel for green pixels, generate a green channel of a joint demosaiced-warped output RGB pixel from the input Bayer frame based on the green values at the green pixel locations, the kernel for green pixels, and an alignment vector map, and generate red and blue channels of the joint demosaiced-warped output RGB pixel from the input Bayer frame based on the estimated green values at the red and blue pixel locations, kernels for red and blue pixels, and the alignment vector map.

A video content selecting apparatus for augmented reality is provided. The apparatus includes a communication interface; and an operation processor configured to perform: (a) collect a plurality of video contents through the Internet; (b) extract feature information and metadata for each of the plurality of video contents, and generate a hash value corresponding to the feature information by using a predetermined hashing function; (c) manage a database to include at least the hash value and the metadata of each of the plurality of video contents; (d) receive object information corresponding to an object in a real-world environment from a user terminal through the communication interface; (e) search the database based on the object information and select a video content corresponding to the object information from among the plurality of video contents; and (f) transmit the metadata of the selected video content to the user terminal through the communication interface.

A video content selecting apparatus for augmented reality is provided. The apparatus includes a communication interface; and an operation processor configured to perform: (a) collect a plurality of video contents through the Internet; (b) extract feature information and metadata for each of the plurality of video contents, and generate a hash value corresponding to the feature information by using a predetermined hashing function; (c) manage a database to include at least the hash value and the metadata of each of the plurality of video contents; (d) receive object information corresponding to an object in a real-world environment from a user terminal through the communication interface; (e) search the database based on the object information and select a video content corresponding to the object information from among the plurality of video contents; and (f) transmit the metadata of the selected video content to the user terminal through the communication interface.

An embodiment for generating an electronic coupon based on user preferences is provided. The embodiment may include receiving real-time and historical data relating to one or more reward coupons. The embodiment may also include identifying a contextual situation of the user and one or more preferences of the user regarding a coupon reward type. The embodiment may further include identifying one or more vendors that match with the one or more preferences of the user. The embodiment may also include generating one or more electronic coupons and presenting the one or more generated electronic coupons to the user. The embodiment may further include in response to determining the one or more generated electronic coupons match at least one preference of the user, adding the one or more generated electronic coupons that match the at least one preference of the user to an account of the user.