Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating harmonized digital images utilizing an object-to-object harmonization neural network. For example, the disclosed systems implement, and learn parameters for, an object-to-object harmonization neural network to combine a style code from a reference object with features extracted from a target object. Indeed, the disclosed systems extract a style code from a reference object utilizing a style encoder neural network. In addition, the disclosed systems generate a harmonized target object by applying the style code of the reference object to a target object utilizing an object-to-object harmonization neural network.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.

Abstract: Devices, systems, and methods of the present disclosure are directed to accurate and non-invasive assessments of anatomic vessels (e.g., the internal jugular vein (IJV)) of vertebrates. For example, a piezoelectric crystal may generate a signal and receive a pulse echo of the signal along an axis extending through the piezoelectric crystal and an anatomic vessel. A force sensor disposed relative to the piezoelectric crystal may measure a force exerted (e.g., along skin of the vertebrate) on the anatomic vessel along the axis. The pulse echo received by the piezoelectric crystal and the force measured by the force sensor may, in combination, non-invasively and accurately determine a force response of the anatomic vessel. In turn, the force response may be probative of any one or more of a variety of different characteristics of the anatomic vessel including, for example, location of the anatomic vessel and pressure of the anatomic vessel.

Abstract: Various embodiments describe facilitating real-time crops on an image. In an example, an image processing application executed on a device receives image data corresponding to a field of view of a camera of the device. The image processing application renders a major view on a display of the device in a preview mode. The major view presents a previewed image based on the image data. The image processing application receives a composition score of a cropped image from a deep-learning system. The image processing application renders a sub-view presenting the cropped image based on the composition score in a preview mode. Based on a user interaction, the image processing application renders the cropped image in the major view with the sub-view in the preview mode.

Abstract: Various embodiments describe using a neural network to evaluate image crops in substantially real-time. In an example, a computer system performs unsupervised training of a first neural network based on unannotated image crops, followed by a supervised training of the first neural network based on annotated image crops. Once this first neural network is trained, the computer system inputs image crops generated from images to this trained network and receives composition scores therefrom. The computer system performs supervised training of a second neural network based on the images and the composition scores.

Abstract: Various embodiments describe facilitating real-time crops on an image. In an example, an image processing application executed on a device receives image data corresponding to a field of view of a camera of the device. The image processing application renders a major view on a display of the device in a preview mode. The major view presents a previewed image based on the image data. The image processing application receives a composition score of a cropped image from a deep-learning system. The image processing application renders a sub-view presenting the cropped image based on the composition score in a preview mode. Based on a user interaction, the image processing application renders the cropped image in the major view with the sub-view in the preview mode.

Abstract: Various embodiments describe using a neural network to evaluate image crops in substantially real-time. In an example, a computer system performs unsupervised training of a first neural network based on unannotated image crops, followed by a supervised training of the first neural network based on annotated image crops. Once this first neural network is trained, the computer system inputs image crops generated from images to this trained network and receives composition scores therefrom. The computer system performs supervised training of a second neural network based on the images and the composition scores.

Abstract: Various embodiments describe view switching of video on a computing device. In an example, a video processing application executed on the computing device receives a stream of video data. The video processing application renders a major view on a display of the computing device. The major view presents a video from the stream of video data. The video processing application inputs the stream of video data to a deep learning system and receives back information that identifies a cropped video from the video based on a composition score of the cropped video, while the video is presented in the major view. The composition score is generated by the deep learning system. The video processing application renders a sub-view on a display of the device, the sub-view presenting the cropped video. The video processing application renders the cropped video in the major view based on a user interaction with the sub-view.

Abstract: Various embodiments describe view switching of video on a computing device. In an example, a video processing application receives a stream of video data. The video processing application renders a major view on a display of the computing device. The major view presents a video from the stream of video data. The video processing application inputs the stream of video data to a deep learning system and receives back information that identifies a cropped video from the video based on a composition score of the cropped video, while the video is presented in the major view. The composition score is generated by the deep learning system. The video processing application renders a sub-view on a display of the device, the sub-view presenting the cropped video. The video processing application renders the cropped video in the major view based on a user interaction with the sub-view.

Abstract: Devices, systems, and methods of the present disclosure are directed to accurate and non-invasive assessments of anatomic vessels (e.g., the internal jugular vein (IJV)) of vertebrates. For example, a piezoelectric crystal may generate a signal and receive a pulse echo of the signal along an axis extending through the piezoelectric crystal and an anatomic vessel. A force sensor disposed relative to the piezoelectric crystal may measure a force exerted (e.g., along skin of the vertebrate) on the anatomic vessel along the axis. The pulse echo received by the piezoelectric crystal and the force measured by the force sensor may, in combination, non-invasively and accurately determine a force response of the anatomic vessel. In turn, the force response may be probative of any one or more of a variety of different characteristics of the anatomic vessel including, for example, location of the anatomic vessel and pressure of the anatomic vessel.