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.

Abstract: An image registration method and apparatus, an electronic apparatus, and a storage medium are provided. The image registration method comprises: extracting edge pixels of the binocular image, and determining high-confidence parallax points in the edge pixels and parallax; projecting each non-high-confidence parallax point to the triangular mesh in a direction parallel with a parallax dimension to obtain a triangular face; determining a parallax search range of parallax of each non-high-confidence parallax point, calculating a matching cost corresponding to all parallax in each parallax search range, and determining that parallax with the smallest matching cost is the parallax of the corresponding non-high-confidence parallax point; and determining a depth boundary point in the edge pixels, determining a parallax boundary point of each depth boundary point in a target direction, and setting parallax of pixels between the depth boundary point and the parallax boundary point to a target value.

Abstract: Methods and apparatus are disclosed herein to synthesize six degree-of-freedom views from sparce RGB-depth inputs. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to reproject a first red-green-blue (RGB) depth view and a second RGB depth view to a target camera position to obtain a first reprojected view and a second reprojected view, combine the first reprojected view and the second reprojected view into a blended view data based on a first weight map and a second weight map, the blended view including missing RGB depth information due to at least one of an occlusion or a disocclusion, and generate a six degree-of-freedom synthesized view of the blended view data, the synthesized view including the missing RGB depth information.

Abstract: A neural network training method, includes: obtaining an input feature map of a training image; performing feature extraction processing on the input feature map by using a feature extraction core of a neural network to obtain a first candidate feature map; adding the first candidate feature map and a second candidate feature map to obtain an output feature map, where the second candidate feature map is a feature map obtained after a value corresponding to each element in the input feature map is increased by N times, and N is greater than 0; determining an image processing result of the training image based on the output feature map; and adjusting a parameter of the neural network based on the image processing result.

Abstract: An object is to obtain three-dimensional shape data with high accuracy from three-dimensional shape data representing an approximate shape of an object. Three-dimensional shape data of an object captured in a plurality of captured images whose viewpoints are different is obtained. Further, surface three-dimensional information on the object is derived based on the plurality of captured images. Then, the derived surface three-dimensional information is selected based on a distance from the shape surface of the object represented by the three-dimensional shape data.

Abstract: A fully automated and reliable picking of a diverse range of unseen objects in clutter is a challenging problem. The present disclosure provides an optimum grasp pose selection to pick an object from a bin. Initially, the system receives an input image pertaining to a surface. Further, a plurality of sampled grasp poses are generated in a random configuration. Further, a depth difference value is computed for each of a plurality of pixels corresponding to each of the plurality of sampled grasp poses. Further, a binary map is generated for each of the plurality of sampled grasp poses and a plurality of subregions are obtained. Further, a plurality of feasible grasp poses are selected based on the plurality of subregions and a plurality of conditions. Further, the plurality of feasible grasp poses are refined and an optimum grasp pose is obtained based on a Grasp Quality Score (GQS).

Abstract: A neural light field (NeLF) that runs real-time on mobile devices for neural rendering of three dimensional (3D) scenes, referred to as MobileR2L. The MobileR2L architecture runs efficiently on mobile devices with low latency and small size, and it achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world 3D scenes on mobile devices. The MobileR2L has a network backbone including a convolutional layer embedding an input image at a resolution, residual blocks uploading the embedded image, and super-resolution modules receiving the uploaded embedded image and rendering an output image having a higher resolution than the embedded image. The convolution layer generates a number of rays equal to a number of pixels in the input image, where a partial number of the rays is uploaded to the super-resolution modules.