Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to remove a shadow for an object. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access a shadow mask of the shadow in a first location. Further, the disclosed systems generate the modified digital image without the shadow by generating a fill for the first location that preserves a visible location of the first location. Moreover, the disclosed systems generate the digital image without the shadow for the object by combining the fill with the digital image.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that provides a graphical user interface experience to move objects and generate new shadows within a digital image scene. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems receive a selection to position an object in a first location within the scene. Further, the disclosed systems composite an image by placing the object at the first location within the scene of the digital image. Moreover, the disclosed systems generate a modified digital image having a shadow of the object where the shadow is consistent with the scene and provides the modified digital image to the client device.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to synthesize shadows for object(s). For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access an object mask of the object depicting in the digital image. The disclosed systems further combine the object mask, the digital image, and a noise representation to generate a combined representation. Moreover, the disclosed systems generate a shadow for the object from the combined representation and further generates the modified digital image by combining the shadow with the digital image.

Abstract: Various example embodiments of the disclosure relate to an electronic device and a wireless communication connection control method thereof.

Abstract: An automated driving system of a vehicle includes a driver monitoring unit configured to perform at least one of monitoring a gaze of a driver and determining a position of a hand of the driver, an automated-driving-level switching unit configured to determine whether a transition of a driving control over the vehicle is required based on whether driving environment of the vehicle has changed during the vehicle being driven under control of the automated driving system, and determine an automated driving level to be switched based on a driver monitoring result obtained by the drive monitoring unit, and an automated driving control unit configured to control the vehicle according to the automated driving level.

Abstract: An image encoding/decoding method, device and recording medium based sed on multiple compression levels disclosure may include extracting a region of interest for machine vision from an input image, determining a compression level of the region of interest, and encoding the compression level of the region of interest.

Abstract: An object-based image encoding/decoding method and device of the present disclosure may include an image partition step which partitions an image to obtain a first object region, a region scaling step which scales the first object region based on a scaling factor of the first object region to obtain a second objection region, a region merging step which merges the second object region with at least one of an object region different from the second object region or a non-object region to obtain a merged image, and an image reconstruction step which reconstructs the merged image.

Abstract: An imaging lens system includes a first lens having negative refractive power, a second lens having refractive power, a third lens having refractive power, a fourth lens having positive refractive power, a fifth lens having a concave object-side surface, a sixth lens having a convex object-side surface, a seventh lens having refractive power, and an eighth lens having refractive power, wherein the first to eighth lenses are sequentially disposed at intervals from the object-side surface, wherein the imaging lens system satisfies the following conditional expression: 0.170<ImgHT/TTL<0.182 where ImgHT is a height of an imaging plane and TTL is a distance from an object-side surface of the first lens to the imaging plane.

Abstract: An integrated circuit device includes a fin-type active region protruding from a top surface of a substrate and extending in a first direction parallel to the top surface of the substrate, a gate structure intersecting with the fin-type active region and extending on the substrate in a second direction perpendicular to the first direction, a source/drain region on a first side of the gate structure, a first contact structure on the source/drain region, and a contact capping layer on the first contact structure. A top surface of the first contact structure has a first width in the first direction, a bottom surface of the contact capping layer has a second width greater than the first width stated above in the first direction, and the contact capping layer includes a protruding portion extending outward from a sidewall of the first contact structure.

Abstract: An imaging lens system includes a first lens, a second lens, a third lens, a fourth lens, a fifth lens, a sixth lens, a seventh lens, and an eighth lens sequentially arranged at intervals from an object side. The first lens and the fifth lens have negative refractive power, the sixth lens has a concave object-side surface, and the imaging lens system satisfies the following conditional expression: ?1.0<f1/f2<?0.10 where f1 is a focal length of the first lens and f2 is a focal length of the second lens.

Abstract: The present disclosure relates to the composition and process for the production of an ultra-strong, biocompatible, electroconductive, and stretchable hydrogel, which comprises: a step (a) of physical or chemical modification of natural polymers e.g., preparation of silk nanofiber and double methacrylation of gelatin; a step (b) of graphene oxide (GO) carboxylation; a step (c) of carbodiimidation between methacrylated natural polymers of step (a) and carboxylated GO of step (b); and a step (d) of three dimensional (3D) bioprinting of step (c) with/without silk nanofiber. It was found that these steps in this disclosure give rise to a biocompatible hydrogel with high mechanical strength in the range of load-bearing soft tissue such as tendon and heart as opposed to conventional hydrogels.

Abstract: In this invention, we propose a convolutional neural network (CNN) based architecture designed for the ITM to HDR consumer displays, called ITM-CNN, and its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. To the best of our knowledge, this invention first presents the ITM problem using CNNs for HDR consumer displays, where the network is trained to restore lost details and local contrast. Our ITM-CNN can readily up-convert LDR images for direct viewing on an HDR consumer medium, and is a very powerful means to solve the lack of HDR video contents with legacy LDR videos.

Abstract: A method includes receiving an input image. The input image corresponds to one or more masked regions to be inpainted. The method includes providing the input image to a first neural network. The first neural network outputs a first inpainted image at a first resolution, and the one or more masked regions are inpainted in the first inpainted image. The method includes creating a second inpainted image by increasing a resolution of the first inpainted image from the first resolution to a second resolution. The second resolution is greater than the first resolution such that the one or more inpainted masked regions have an increased resolution. The method includes providing the second inpainted image to a second neural network. The second neural network outputs a first refined inpainted image at the second resolution, and the first refined inpainted image is a refined version of the second inpainted image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate refined depth maps of digital images utilizing digital segmentation masks. In particular, in one or more embodiments, the disclosed systems generate a depth map for a digital image utilizing a depth estimation machine learning model, determine a digital segmentation mask for the digital image, and generate a refined depth map from the depth map and the digital segmentation mask utilizing a depth refinement machine learning model. In some embodiments, the disclosed systems generate first and second intermediate depth maps using the digital segmentation mask and an inverse digital segmentation mask and merger the first and second intermediate depth maps to generate the refined depth map.

Abstract: An integrated circuit device includes a fin-type active region protruding from a top surface of a substrate and extending in a first direction parallel to the top surface of the substrate, a gate structure intersecting with the fin-type active region and extending on the substrate in a second direction perpendicular to the first direction, a source/drain region on a first side of the gate structure, a first contact structure on the source/drain region, and a contact capping layer on the first contact structure. A top surface of the first contact structure has a first width in the first direction, a bottom surface of the contact capping layer has a second width greater than the first width stated above in the first direction, and the contact capping layer includes a protruding portion extending outward from a sidewall of the first contact structure.

Abstract: An integrated circuit device includes a fin-type active region protruding from a top surface of a substrate and extending in a first direction parallel to the top surface of the substrate, a gate structure intersecting with the fin-type active region and extending on the substrate in a second direction perpendicular to the first direction, a source/drain region on a first side of the gate structure, a first contact structure on the source/drain region, and a contact capping layer on the first contact structure. A top surface of the first contact structure has a first width in the first direction, a bottom surface of the contact capping layer has a second width greater than the first width stated above in the first direction, and the contact capping layer includes a protruding portion extending outward from a sidewall of the first contact structure.

Abstract: Disclosed is an invention relating to a processor-executed method for outputting a hierarchical command menu on a display depending on user inputs.

Abstract: Disclosed is an invention relating to a processor-executed method for outputting a hierarchical command menu on a display depending on user inputs.

Abstract: Disclosed is an invention relating to a processor-executed method for outputting a hierarchical command menu on a display depending on user inputs.

Abstract: In this invention, we propose a convolutional neural network (CNN) based architecture designed for the ITM to HDR consumer displays, called ITM-CNN, and its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. To the best of our knowledge, this invention first presents the ITM problem using CNNs for HDR consumer displays, where the network is trained to restore lost details and local contrast. Our ITM-CNN can readily up-convert LDR images for direct viewing on an HDR consumer medium, and is a very powerful means to solve the lack of HDR video contents with legacy LDR videos.