Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for predicting scene transitions. A computer system receives an input sequence of images of a scene with each image corresponding to a different time point in an observation time sequence. For each time point, the system processes the corresponding image using a decomposition neural network to generate one or more feature representations. The system processes the feature representations for the time points using an alignment neural network to generate a set of aligned sequences of feature representations. The system further processes the set of aligned sequences of feature representations using a transition neural network to predict, for each of the aligned sequences of feature representations, one or more feature representations that represent predicted features of the object represented by the aligned sequence at one or more successive time points.

Abstract: A method for detecting optical defects within a windshield. The method takes, as input, a digital image map of the intensity of optical power of a windshield, and provides, as output, a digital image map of optical defects. The method includes (a) an image processing of the digital image map of optical powers for detecting and delimiting regions that differs in intensity of optical power; (b) computing, for each detected regions, a representative geometric distance and a representative value of the optical power; (c) computing an image map of the detected regions for which the product between the representative geometric distance and the representative value of the optical power is equal or superior to 2.9·10?4.

Abstract: Disclosed herein are methods, systems, and aircraft for verifying performing automated refueling data. A method includes receiving a two-dimensional (2D) image from a camera, cropping the 2D image based on predefined feature areas of interest of a target object to produce a plurality of cropped images, resizing one or more of the cropped images responsive to the target object being greater than a threshold distance from the camera to produce one or more resized images, determining 2D keypoints of the target object within the one or more resized images or the plurality of cropped images, estimating a 6 degrees-of-freedom (6DOF) pose based on the 2D keypoints and a three-dimensional (3D) model of the target object to produce an estimated 6DOF pose, and outputting the 6DOF pose.

Abstract: Systems and methods for analyzing image data to assess property damage are disclosed. According to certain aspects, a server may analyze segmented digital image data of a roof of a property using a convolutional neural network (CNN). The server may extract a set of features from a set of regions output by the CNN. Additionally, the server may analyze the set of features using an additional image model to generate a set of outputs indicative of a confidence level that actual hail damage is depicted in the set of regions.

Abstract: The present invention provides a high-speed automatic film-foreign substance detection apparatus, including: a stage on which a sample is mounted; a plurality of side light units arranged on an upper side surface of the stage to irradiate side light toward a sample during an operation of a low-magnification optical module; a low-magnification optical module installed at an upper side of the stage and generating a low-magnification image by capturing the sample so that the foreign substance can be detected from the sample using the light scattering of the foreign substance by the side light irradiated from the side light unit; a high-magnification optical module installed at one side of the stage, having a high-magnification driving unit installed and connected thereto to drive in directions of an x-axis, a y-axis and a z-axis, and generating a high-magnification image by capturing the sample; and a user device for controlling the operation of the low-magnification optical module and the high-magnification opt

Abstract: To provide a technique which makes it possible to suitably estimate an important region and a non-important region in an image, an information processing apparatus includes: an obtaining means for obtaining input data which includes at least one of image data and point cloud data; an estimating means for estimating levels of importance with respect to a respective plurality of regions which are included in a frame indicated by the input data; a replacing means for generating replaced data by replacing at least one of the plurality of regions, which are included in the input data, with alternative data in accordance with the levels of importance; an evaluating means for deriving an evaluation value by referring to the replaced data; and a training means for training the estimating means with reference to the evaluation value.

Abstract: Computer vision systems and methods for noisy contour alignment are provided. The system generates a loss function and trains a convolutional neural network with the loss function and a plurality of images of a dataset to learn to align contours with progressively increasing complex forward and backward transforms over increasing scales. The system can align an attribute of an image of the dataset by the trained neural network.

Abstract: An electronic device for generating an image with an enhanced depth as perceived by a viewer includes at least one memory storing instructions, and at least one processor configured to execute the instructions to receive an input image, receive a control parameter for adjusting a first level of at least one pictorial depth cue included in the input image, and generate an output image including the at least one pictorial depth cue at a second level that is adjusted from the first level based on the control parameter using a deep neural network.

Abstract: A state determination apparatus acquires each of time-series fixed images obtained by shooting a rotating object via a first optical device that rotates and emits incident light; calculates a displacement amount of a plurality of specific points on the object, and calculates a difference between the displacement amount of a reference specific point and the displacement amount of each specific point other than the reference specific point; calculates, as an out-of-plane displacement, a displacement of a surface of the object on an image plane following movement of the object in a normal direction of the image plane, using the difference; calculates, as an in-plane displacement, a displacement of the surface of the object on the image plane following movement of the surface of the object; and specifies an abnormality of the object, based on a distribution of the in-plane displacement or a temporal change of the out-of-plane displacement.

Abstract: This image quality evaluation device comprises: a conversion unit that converts the data of an image to two-dimensional array data of the luminance value of each pixel in the image; a processing unit that executes an averaging process, based on a plurality of filter sizes, on each pixel of the two-dimensional array data; and an evaluation unit that evaluates the quality of the image using the processing results of the processing unit.

Abstract: A method for measuring volume of pomelo based on three view image contour fitting is provided, for measuring the volume of the pomelo based on images, includes: a number of image capturing devices capturing a number of surface images of a pomelo to be measured; calibrating a position of an image capturing device for capturing a top view image of the pomelo to be measured; performing enhanced extraction processing on the number of surface images, and obtaining a standard contour image; obtaining a size of each pomelo slice based on the standard contour image, and obtaining a B-spline curve by fitting a contour of the pomelo slice; and calculating volume of each pomelo slice according to the B-spline curve, and obtaining total volume of the pomelo to be measured by calculating a sum of the volume of all of the pomelo slices.

Abstract: A computing system including a processing circuit in communication with a camera having a field of view. The processing circuit is configured to perform operations related to detecting, identifying, and retrieving objects disposed amongst a plurality of objects. The processing circuit may be configured to perform operations related to object recognition template generation, feature generation, hypothesis generation, hypothesis refinement, and hypothesis validation.

Abstract: An aspect of the present invention is a learning method for learning a model which reconstructs data and acquires reconstructed data, the learning method includes a plurality of stages, and the learning method includes first sub-processing which is based on an iterative calculation algorithm in which a Lagrange multiplier is not used, second sub-processing which is based on an iterative calculation algorithm in which the Lagrange multiplier is used, and weighting sub-processing which determines processing in each of the plurality of stages based on a weight for determining usage of the first sub-processing and the second sub-processing in each stage.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide self-supervised object discovery systems that combine motion and appearance information to generate segmentation masks from a digital image or digital video and delineate one or more salient objects within the digital image/digital video. The disclosed systems utilize a neural network encoder to generate a fully connected graph based on image patches from the digital input, incorporating image patch feature and optical flow patch feature similarities to produce edge weights. The disclosed systems partition the generated graph to produce a segmentation mask. Furthermore, the disclosed systems iteratively train a segmentation network based on the segmentation mask as a pseudo-ground truth via a bootstrapped, self-training process.

Abstract: A traffic light recognizing apparatus includes: an imager configured to capture a color image of driving environment; a lighting region detector detecting a lighting region in a traffic light body on the image; a first lamp detector detecting a lamp in an ON state in the traffic light body based on the lighting region; a first search area setter setting search areas having the same dimensions as the lamp and horizontally and vertically aligned with the lamp on the image, in accordance with a lighting color of the lighting region; a second lamp detector evaluating luminances at an edge and in a center of each search area, and detecting lamps in an OFF state in the traffic light body; and an orientation determiner determining whether the traffic light body is horizontally or vertically disposed based on the arrangement of the lamps in the ON and OFF states.

Abstract: The present disclosure relates to a method of inferring an epipolar plane image and extrapolating a view of a target image using the epipolar plane image. A view extrapolation method using an epipolar plane image according to an embodiment of the present disclosure includes: creating an Epipolar Plane Image (EPI) using a target image and a depth map corresponding to the target image; creating a super-resolution EPI and a disparity of the EPI on the basis of the EPI; creating an EPI mask by warping the super-resolution EPI in accordance with the disparity; and creating a restored EPI by applying the EPI mask to the warped super-resolution EPI, and creating an extrapolated image for the target image using the restored EPI.

Abstract: An image processing device configured to convert image data of a first resolution into image data of a second resolution higher than the first resolution includes at least one memory, and at least one processor in communication with the at least one memory and configured to cooperate with the at least one memory to calculate a direction and an intensity of an edge from the image data of the first resolution, and determine a pattern for the image data of the second resolution to be replaced by pixels of the image data of the first resolution, based on the direction and the intensity of the edge.

Abstract: A system for detecting an event occurrence period of cyclical event includes an image monitoring device and a period judgment device. The image monitoring device captures monitoring images respective in image capturing times. The period judgment device extracts feature vectors from the monitoring images to calculate vector values to accordingly generate a vector value time domain signal, executes a short-time Fourier transform (STFT) with a window width time to transform the vector value time domain signal to a spectrogram, makes the spectrogram be denoised to generate a representative frequency time domain signal, and calculates an event-cycle achievement rate and a product term accumulation number after each window width time, so as to accordingly calculate event occurrence time and the event occurrence period.

Abstract: A system and corresponding method perform image super-resolution (SR). The system comprises an element-unshuffled downsampler and an image SR module. The image SR module performs image SR on a low-resolution (LR) representation of a high-resolution (HR) original image. The HR original image is at a higher resolution relative to a resolution of the LR representation. The image SR module produces a reconstructed version of the HR original image via the image SR performed. The image SR is based on element-unshuffled downsampling of the LR representation. The element-unshuffled downsampler performs the element-unshuffled downsampling. The image SR module outputs the reconstructed version produced. The system performs the image SR with fewer parameters and less computation cost relative to conventional image SR.

Abstract: An image deblurring method and apparatus are provided. The image deblurring method includes generating a primary feature representation on a first blur point in an input image and offset information on similar points of the first blur point by encoding the input image by implementing an encoding model, generating secondary feature representations on the similar points by applying the offset information to the primary feature representation, and generating an output image, based on the secondary feature representations and the offset information, by implementing an implicit function model.