Abstract: According to an exemplary embodiment of the present disclosure, a method of super resolution imaging based on deep learning performed by a computing device is disclosed. The method may include: receiving a first image and a second image having low resolution compared to the first image; training a first model so as to generate a low-resolution image corresponding to the second image based on the first image; and training a second model so as to generate a high-resolution image corresponding to the first image based on an output image of the first model. In this case, contents of the first image may not correspond to contents of the second image.

Abstract: A road gradient determining method includes obtaining a three-dimensional road image formed by a two-dimensional road image of a road and laser point cloud data of the road and selecting a plurality of nodes from the three-dimensional road image as control points. The method further includes generating, according to the control points, a first spline curve indicating a road elevation and converting the first spline curve into a second spline curve indicating a road gradient. Finally, the method includes obtaining location information and determining a first road gradient according to the location information and the second spline curve. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Abstract: This application provides a method for generating a high-resolution picture performed by a computer device. The method includes: acquiring at least one deep neural network model; acquiring a low-resolution picture; determining a corresponding deep neural network model according to the low-resolution picture; and converting the low-resolution pictures into a high-resolution picture through the deep neural network model, the deep neural network model including a plurality of non-linear conversion convolution layers that alternately use different parameter matrices as convolution template parameters.

Abstract: Implementations relate to improved crop field segmentation and crop classification in which boundaries between crop fields are more accurately detected. In various implementations, high-elevation image(s) that capture an area containing multiple demarcated fields may be applied as input across one or more machine learning models to generate a boundary enhancement channel. Each pixel of the boundary enhancement channel may be spatially aligned with a corresponding pixel of the one or more high-elevation images. Moreover, each pixel of the boundary enhancement channel may be classified with a unit angle to a reference location of the field of the multiple demarcated fields that contains the pixel. Based on the boundary enhancement channel, pixel-wise field memberships of pixels of the one or more high-elevation images in the multiple demarcated fields may be determined.

Abstract: A three-dimensional geometry measurement apparatus includes: a relationship identification part that identifies a combination of a first imaging pixel and a second imaging pixel corresponding to the same projection coordinate; a determination part that determines, whether or not at least one of the first imaging pixel or the second imaging pixel is a defective pixel on the basis of a distance between a projection pixel of the projection image; and a geometry measurement part that measures a geometry of an object to be measured using the first imaging pixel or the second imaging pixel corresponding to the combination, for which the first imaging pixel and the second imaging pixel that are determined to be not the defective pixel.

Abstract: A method for pose estimation includes receiving an image containing an object, a first pose of the object in the image, and 3D boundary features of a model corresponding to the object; computing a first pose confidence of the first pose based on the image, the 3D boundary features, and the first pose; perturbing the first pose to obtain a second pose; computing a second pose confidence of the second pose based on the image, the 3D boundary features, and the second pose; determining if the second pose confidence is greater than the first pose confidence, and; and outputting the second pose and the second pose confidence if the second pose confidence is greater than the first pose confidence.

Abstract: The present disclosure relates to imaging systems, aerial refueling aircraft, and methods relating to the processing of images. An example imaging system includes at least one camera, a display, and a controller. The controller includes at least one processor and a memory. The controller is configured to carry out operations. The operations include receiving at least one image from the at least one camera. The operations additionally include adjusting the at least one image to provide at least one adjusted image. Adjusting the at least one image includes applying: a local adaptive histogram equalization filter, a global gamma correction filter, and a local contrast filter. The operations also include outputting the at least one adjusted image to the display.

Abstract: The present disclosure relates to a system, method and storage medium for generating an image. At least one processor, when executing instructions, may perform one or more of the following operations. When raw data is received, a plurality of iterations may be implemented. During each iteration, a first voxel value relating to a first voxel in an image is calculated; at least a portion of a second voxel may be continuously changed with respect to at least a portion of the first voxel value; the image may be transformed to a projection domain to generate an estimated projection based on the first voxel value and the second voxel value; a projection error may be obtained based on the estimated projection and the raw data; and the image may be corrected or updated based on the projection error.

Abstract: The present application provides a volumetric measuring method and apparatus based on a TOF depth camera, which is achieved by: acquiring depth images; denoising the depth images; selecting a first feature area of the first depth image and a second feature area of the second depth image that have been denoised respectively, traversing each pixel point in the first feature area at multiple traversal speeds, and matching a first depth value of a current pixel point with a second depth value of a target pixel point corresponding to the current pixel point in the second feature area to acquire multiple total similarity values; determining a traversal speed corresponding to a maximum total similarity value as an instantaneous speed at a current moment; calculating a package volume according to the instantaneous speed. The present application effectively solves the problem that an accurate package volume cannot be obtained when a speed of the conveyor belt is not fixed, and high in feasibility.

Abstract: There is a need for more effective and efficient printed circuit board (PCB) design. This need can be addressed by, for example, solutions for performing automated PCB component estimation. In one example, a method includes identifying a PCB image of a PCB; performing chromaticity-based background subtraction on the PCB image to generate a background-subtracted PCB image; performing morphological noise removal on the background-subtracted PCB image to generate a noise-removed PCB image; and performing object localization on the noise-removed PCB image to identify one or more PCB component estimations within the noise-removed PCB image.

Abstract: A container crane has a trolley movable on a cross-member of a gantry. A harness for picking up and setting down a container and at least one laser scanner are arranged on the trolley. The laser scanner captures a depth image which, as a function of a first and a second angle, indicates the distance of object points detected by a laser beam. The captured depth image is evaluated. Based on the object points, objects are detected and their locations are determined. The objects comprise the harness and/or a container picked up by the harness, and further objects. Based on the detected object points, the contour of the harness and/or of the container picked up by the harness is determined. Detection of further objects within regions defined by the contour is suppressed. A control device takes the detected objects and their locations into account for controlling the container crane.

Abstract: The present invention discloses a region-growing motion tracking method based on Bayesian inference and polynomial fitting, including: determining a target region; selecting candidate points; calculating a posterior probability; determining a seed point by judging the seed point according to the maximum posterior probability threshold and the maximum absolute displacement difference; dividing a known-displacement point set (including an interior point (IP) set and a boundary point (BP) set) and an unknown-displacement point set of the target region; selecting an active growing point, with the maximum posterior probability value in the boundary point set; conducting guided search; updating the displacement value; conducting local polynomial fitting; updating the IP set and the BP set; calculating strain elastogram.

Abstract: A terminal for receiving streaming data may receive information of a plurality of different quality versions of an image content; request, based on the information, a server for a version of the image content from among the plurality of different quality versions of the image content; when the requested version of the image content and artificial intelligence (AI) data corresponding to the requested version of the image content are received, determines whether to perform AI upscaling on the received version of the image content, based on the AI data; and based on a result of the determining whether to perform AI upscaling, performs AI upscaling on the received version of the image content through a upscaling deep neural network (DNN) that is trained jointly with a downscaling DNN of the server.

Abstract: Provided is a decoding apparatus including: a communication interface configured to receive AI encoding data generated as a result of artificial intelligence (AI) down-scaling and first encoding of an original image; a processor configured to divide the AI encoding data into image data and AI data; and an input/output (I/O) device, wherein the processor is further configured to: obtain a second image by performing first decoding on a first image obtained by performing AI down-scaling on the original image, based on the image data; and control the I/O device to transmit the second image and the AI data to an external apparatus. In some embodiments, the external apparatus performs an AI upscaling of the second image using the AI data, and displays the resulting third image.

Abstract: A method for locating a three-dimensional target with respect to a vehicle is disclosed including capturing an image of the target, and from a three-dimensional mesh of the target, and from an estimation of the pose of the target, determining a set of projecting edges of the mesh of the target in the pose. The step of determining the projecting edges of the mesh of the target includes positioning the mesh of the target according to the pose, projecting in two dimensions the mesh so positioned, scanning the projection of the mesh in a plurality of scanning rows and, for each scanning row: defining a set of segments, each segment corresponding to the intersection of a face of the mesh with the scanning row and being defined by its ends, analyzing the relative depths of the ends of the segments, the depth being the position along a third dimension orthogonal to the two dimensions of the projection, in order to select a set of end points of segments corresponding to projecting edges of the mesh.

Abstract: An IC card includes a first visible layer comprising a nonmetallic natural material having a physical structure including a unique visual pattern. The IC card further includes a storage device configured to store a reference image of the unique visual pattern. The reference image is a scan of the unique visual pattern. The reference image is configured to be visually compared with the unique visual pattern for authentication. A second layer is included on a bottom surface of the first visible layer. The first visible layer and the second layer are laminated together. Each of the second layer and the nonmetallic natural material of the first visible layer have a same size to define a shape of the IC card.

Abstract: Provided is a decoding apparatus including: a communication interface configured to receive AI encoding data generated as a result of artificial intelligence (AI) down-scaling and first encoding of an original image; a processor configured to divide the AI encoding data into image data and AI data; and an input/output (I/O) device, wherein the processor is further configured to: obtain a second image by performing first decoding on a first image obtained by performing AI down-scaling on the original image, based on the image data; and control the I/O device to transmit the second image and the AI data to an external apparatus. In some embodiments, the external apparatus performs an AI upscaling of the second image using the AI data, and displays the resulting third image.

Abstract: The application provides an obstacle detecting method and obstacle detecting apparatus based on an unmanned vehicle, and a device, and a storage medium, where the method includes obtaining point cloud data collected by a detecting device, projecting the point cloud data onto a two-dimensional plane to obtain a two-dimensional projection grid graph, where the two-dimensional projection grid graph has multiple grids and two-dimensional data, and two-dimensional data is data obtained after the point cloud data is projected; generating multiple straight lines according to the two-dimensional projection grid graph, where each of the multiple straight lines has two-dimensional data, and each straight line has parameter information which represents the relationship between the straight line and other straight lines in the multiple straight lines; and determining orientation information of the obstacle according to the two-dimensional data and the parameter information of each of the multiple straight lines.

Abstract: Cameras capture time-stamped images of predefined areas. Individuals and items are tracked in the images. Occluded items detected in the images are preprocessed to remove pixels associated with occluded information and the pixels remaining associated with the items are cropped. The preprocessed and cropped images are provided to a trained machine-learning algorithm or a trained neural network trained to classify and identify the items. Output received from the trained neural network provides item identifiers for the items that are present in the original images.

Abstract: An image processing apparatus includes an obtaining unit configured to obtain a setting value for identifying an end line corresponding to (i) at least a part of a fisheye image and (ii) an end in a panoramic image serving as an image on which distortion correction processing has been performed, and a generating unit configured to generate a plurality of panoramic images in which division lines are harmonized with each other from the fisheye image on a basis of the setting value.