Abstract: A computer-implemented is disclosed. The method includes: obtaining three-dimensional geometric scan data and first texture data for a defined space, the defined space defined by interior surfaces including at least one wall; detecting an object occluding a first one of the interior surfaces of the defined space based on the three-dimensional geometric scan data; in response to detecting the object: identifying a corresponding portion of the first interior surface occluded by the detected object; and estimating texture data for the corresponding portion of the first interior surface based on the first texture data for the defined space, and outputting texture data for the interior surfaces based on the first texture data for the defined space and the estimated texture data for the corresponding portion of the first interior surface.

Abstract: A method for operating a mobile terminal including an image recording device includes capturing at least one recording of a scene and checking at least one of optionally several recordings checked for the presence of a light source. Subsequently, a position of the light source is determined relative to an optical center and a shape, an intensity and/or a color is determined for the light source. An algorithm trained in relation to imaging properties of a given optical unit is then used to generate a flare image of a lens flare for this light source and the given optical unit using the position of the light source. The flare image is subsequently combined with the recording to form a combination image and the combination image is stored in a memory component.

Abstract: A system and method are described, for automatically analyzing and understanding individual and group activities and interactions. The method includes receiving at least one image from a video of a scene showing one or more individual objects or humans at a given time; applying at least one machine learning or artificial intelligence technique to automatically learn a spatial, temporal or a spatio-temporal informative representation of the image and video content for activity recognition; and identifying and analyzing individual and group activities in the scene.

Abstract: Imaging a distribution of one or more optical sources includes: receiving respective optical signals from a spatial mode sorter during each of two or more detection intervals of time; after each of the two or more detection intervals of time, processing information based at least in part on: (1) the respective optical signal received in the corresponding detection interval of time, and (2) a first set of models comprising a set of distributions related to one or more optical sources, each model corresponding to a different number of optical sources in the distribution, and configuring the spatial mode sorter based at least in part on the processing; and providing an estimated measurement characterizing the distribution of one or more optical sources based at least in part on the processed information. The processing after at least one of the two or more detection intervals of time includes computing an eigen-projection.

Abstract: Artifacts in the reconstruction of images are to be reduced. For this purpose, a method in which a line signal of an object is captured with an imaging modality is provided. The line signal represents a truncated projection through a portion of the object. The line signal is extrapolated with a profile function that represents a cross-sectional profile of the object. For the extrapolation, three points into which the profile function is placed, such as the two end points of the line signal and a third point having a line index that corresponds to the line index of a maximum of the line signal, are selected.

Abstract: A low resolution image frame is received. A past reference low resolution image frame and its super resolution result are retained. Motion vectors between the input low resolution image frame and reference low resolution frame are computed. For each pixel in the low resolution image frame, a reference pixel is determined based on the computed motion vectors and the reference low resolution frame. If the reference pixel equals the corresponding input pixel, the reference super resolution result pixel is rendered for this pixel, and otherwise a corresponding pixel from the current input frame is rendered.

Abstract: [Problem] The present disclosure has an object of correcting the orientation of a captured image captured during flight. [Solving Means] An information processing system includes: a captured image acquisition unit that acquires a captured image captured during flight by an imaging device mounted on an airplane; a relevant information acquisition unit that acquires information detected using a sensor mounted on the airplane; and an image correction unit that acquires, by correcting the captured image on a basis of information acquired using the relevant information, a correction image in which a predetermined direction in a state where the image is erected and a reference direction in the image are matched to each other.

Abstract: A method of processing information acquired by imaging performed by a medical image diagnostic apparatus, the method including but not limited to at least one of (A) acquiring a training image volume including at least one three-dimensional object having an embedded three-dimensional feature having a first cross-sectional area in a first three-dimensional plane; selecting a second cross-sectional area in a second three-dimensional plane containing the embedded three-dimensional feature, wherein the second cross-sectional area is larger than the first cross-sectional area; and training an untrained neural network with an image of the second cross-sectional area generated from the training image volume; and (B) acquiring a first set of training data; determining a first distribution of tissue density information from the first set of training data; generating from the first set of training data a second set of training data by performing at least one of a tissue-density shifting process and a tissue-density sca

Abstract: A system for inspecting and assessing the condition of the pavement of a roadway segment using at least one drone configured to capture image data of the pavement, operational data of the drone and its components, and environmental data. Image data portions comprising pixels corresponding to candidate pavement defects are extracted from the captured image data and independently processed by multiple AI-based computer-vision algorithms. Candidate pavement defects are confirmed based on concordant pavement defect detections generated by the algorithms. Pavement defects are geopositioned and measured, and a condition measure of the pavement (e.g., PCI) is determined. The at least one drone follows controlled flight trajectories to reduce or eliminate image quality deficiencies and cover the entire roadway segment with minimal passes without compromising the accuracy of the pavement condition assessment.

Abstract: Techniques for generating high resolution images based on ToF sensor data are discussed herein. A vehicle may generate a composite image by combining multiple images, such as a first image and a second image. Since the images may be captured at different times and/or positions in the environment, to accurately combine the images, the ToF device may compensate for the motion of the vehicle. Thus, the ToF device may unproject the pixels of the first image into a three-dimensional point cloud. Further, the ToF device may update the position of the points in the point cloud. Based on the updated point cloud, the ToF device can determine a motion vector which may be applied to the first image to generate an updated image. The ToF device can determine a composite image by combining the updated image with the second image. The vehicle can be controlled based on the composite image.

Abstract: An image processing device according to an embodiment includes: a depth estimation unit that, when a size of a differential region between a first frame and a second frame at a timing later than a timing of the first frame in chronological order in a moving image satisfies a predetermined condition and is large, estimates a depth of at least a region corresponding to the differential region in the second frame; and a depth estimation result update unit that updates an estimation result of the depth of the first frame by using the region corresponding to the differential region to output the updated estimation result as a new estimation result of the depth of the second frame.

Abstract: A method of determining a steering angle of an aircraft landing gear including: obtaining an image of the aircraft landing gear; performing edge detection on the image; determining, based at least in part on an edge obtained by the edge detection, a relative position of a component of the aircraft landing gear; and determining, based at least in part on the relative position of the component, the steering angle of the aircraft landing gear.

Abstract: One embodiment provides a computer-implemented method that includes receiving content having a first resolution. The processor splits the content into most significant bits and least significant bits. The processor further performs processing of the most significant bits of the content while preserving the least significant bits. The processor additionally performs a linear combination of outputs of the processing of the most significant bits with the preserved least significant bits for improving quality of the content.

Abstract: An image depth prediction method acquires image frames of containing a dynamic object by a monocular camera, extracts a continuous of object frames and reference frames from the image frames, reconstructs the object frames to obtain reconstructed frames according to the reference frames and a preset depth estimation model, obtains a reconstruction error between the object frames and the reconstructed frames, processes the image frames to obtain point cloud data and instance segmentation data, fuses the point cloud data with the instance segmentation data to obtain mask data, obtains a loss function according to the reconstruction error and the mask data, and trains the depth estimation model based on the loss function until the loss function converges. The method can obtain more accurate depth estimation results for dynamic scenes. An electronic device and a non-transitory storage recording the method are also disclosed.

Abstract: An image analysis method includes the steps of obtaining a saturation channel image and a value channel image according to an original image; obtaining a relative deviation value image based on the saturation channel image and the value channel image, wherein the relative deviation value image represents relative deviations between saturation and value; extracting several contours from the relative deviation value image; determining target parameter values corresponding to each contour; and Determining according to analysis results of the target parameter values corresponding to each contour whether there is a non-working region in the original image. A related image analysis apparatus, computer device, and computer readable storage medium are also disclosed.

Abstract: A dynamic and selective tone mapping system that receives digital imagery content and adjusts the digital imagery content using defined perceptual point parameters that correspond to particular luminance points or regions of the digital imagery content. The defined perceptual point parameters include a first white reference value, wherein the tone mapping system adjusts the digital imagery content by compressing luminance values above the first white reference value at different compression rates to produce digital imagery content that is compatible with various media viewing devices with varying display brightness capabilities while preserving influence over the intended look of the displayed image resulting from the digital imagery content.

Abstract: Described herein are methods, systems and computer-program products for intra- and inter-gait classification for both symmetric and asymmetric gait patterns using images.

Abstract: Aspects of the subject disclosure relate to stereo vision-based height clearance detection, of which a device includes a processor that obtains first data and second data from different cameras of a vehicle. The processor generates a depth map based on a disparity between the first data and the second data. The processor determines a road segmentation based at least in part on the depth map. The processor determines road surface height information for corresponding portions of the road segmentation. The processor determines a height estimation of a non-road object corresponding to at least one portion of the road segmentation. The processor provides an indication of a height clearance estimate for the at least one portion of the road segmentation based on the road surface height information and the height estimation of the non-road object.

Abstract: A control method, device, equipment and storage medium for interactive reproduction of a target object is provided. The control method comprises: acquiring a control instruction of the target object, the control instruction including a plurality of direction control parameters; acquiring target image information of the target object according to the combination of the plurality of direction control parameters, the target image information being acquired through a preset three-dimensional reproduction method; performing interactive reproduction on the target object based on the direction control parameters and the target image information.

Abstract: Techniques for spatially preserving flattening in deep learning neural networks are provided. In one aspect, a spatially preserving flattening module includes: a predictor for generating image feature maps from at least one convolutional layer of a feature extraction phase of a deep learning neural network applied to input image data; an auto-encoder for producing encodings of the image feature maps that preserve location and shape information associated with objects in the input image data; and a flattener for concatenating the encodings of the image feature maps to form a spatially preserving flattened encoding vector. A deep learning neural network that includes the present spatially preserving flattening module is also provided, as is a method for spatially preserving flattening.