Techniques for texture-based authentication of digital identity documents are disclosed. The disclosed techniques include authentication based on global texture information and/or on local texture information. Global texture-based authentication may include generating a global texture profile for an identity document image and comparing the global texture profile with a stored profile associated with a jurisdiction class of the identity document. Local texture-based authentication may include generating one or more local texture patches representative of texture information of one or more select local blocks of the ID. The one or more local texture patches are provided as input to one or more local detectors each trained to detect the presence of a forgery based on a target manipulation space.

Methods and systems are disclosed for performing operations for transferring garments in a video from one real-world object to another in real time. The operations comprise receiving a first video that includes a depiction of a first person wearing a first garment in a first pose and obtaining a second video that includes a depiction of a second person wearing a second garment in a second pose. The operations comprise modifying a pose of the second person to match the first pose of the first person depicted in the first video. The operations comprise generating a whole-body segmentation of the second garment which the second person is wearing in the second video and changing an appearance of the first person from wearing the first garment to wearing the second garment based on the whole-body segmentation of the second garment which the second person is wearing in the second video.

Methods and systems are disclosed for performing operations for transferring garments in a video from one real-world object to another in real time. The operations comprise receiving a first video that includes a depiction of a first person wearing a first garment in a first pose and obtaining a second video that includes a depiction of a second person wearing a second garment in a second pose. The operations comprise modifying a pose of the second person to match the first pose of the first person depicted in the first video. The operations comprise generating a whole-body segmentation of the second garment which the second person is wearing in the second video and changing an appearance of the first person from wearing the first garment to wearing the second garment based on the whole-body segmentation of the second garment which the second person is wearing in the second video.

Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin puffiness. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin puffiness model, trained with the pixel data, is operable to output, across a range of a skin puffiness scale, skin puffiness values associated with a degree of skin puffiness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin puffiness model, to determine a user-specific skin puffiness value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem configured for determining a global texture characteristic of an image of a specimen and one or more local characteristics of a localized area in the image. The system also includes one or more components executed by the computer subsystem. The component(s) include a machine learning model configured for determining information for the specimen based on the global texture characteristic and the one or more local characteristics. The computer subsystem is also configured for generating results including the determined information. The methods and systems may be used for metrology (in which the determined information includes one or more characteristics of a structure formed on the specimen) or inspection (in which the determined information includes a classification of a defect detected on the specimen).

Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem configured for determining a global texture characteristic of an image of a specimen and one or more local characteristics of a localized area in the image. The system also includes one or more components executed by the computer subsystem. The component(s) include a machine learning model configured for determining information for the specimen based on the global texture characteristic and the one or more local characteristics. The computer subsystem is also configured for generating results including the determined information. The methods and systems may be used for metrology (in which the determined information includes one or more characteristics of a structure formed on the specimen) or inspection (in which the determined information includes a classification of a defect detected on the specimen).

Methods, apparatus, and other embodiments predict response to pemetrexed based chemotherapy. One example apparatus includes an image acquisition circuit that acquires a radiological image of a region of tissue demonstrating NSCLC that includes a region of interest (ROI) defining a tumoral volume, a peritumoral volume definition circuit that defines a peritumoral volume based on the boundary of the ROI and a distance, a feature extraction circuit that extracts a set of discriminative tumoral features from the tumoral volume, and a set of discriminative peritumoral features from the peritumoral volume, and a classification circuit that classifies the ROI as a responder or a non-responder using a machine learning classifier based, at least in part, on the set of discriminative tumoral features and the set of discriminative peritumoral features.

Apparatuses, systems, and techniques for texture synthesis from small input textures in images using convolutional neural networks. In at least one embodiment, one or more convolutional layers are used in conjunction with one or more transposed convolution operations to generate a large textured output image from a small input textured image while preserving global features and texture, according to various novel techniques described herein.

Apparatuses, systems, and techniques for texture synthesis from small input textures in images using convolutional neural networks. In at least one embodiment, one or more convolutional layers are used in conjunction with one or more transposed convolution operations to generate a large textured output image from a small input textured image while preserving global features and texture, according to various novel techniques described herein.

A method for non-destructive ripeness identification of kiwifruit based on machine vision learning may include: collecting kiwifruit data to obtain an original data set by collecting images of 40-80 kiwifruits in the same period of time over 3-6 days, recording a label, which comprises ripeness information for each of the images, and saving each of the images with the label; extracting the color and the texture of a kiwifruit skin from each of the images in the original data set; and training a deep learning model to learn a connection between the color and the texture of the kiwifruit skin and the ripeness information of the corresponding kiwifruit using the color and the texture of the kiwifruit skin extracted from each of the images and the label.