Abstract: A method determines the region where a vehicle can travel without changing clarity of the position information of the object detected by a sensor. An in-vehicle control device includes an object detection unit detecting the position of an object from image information captured by an image pickup device, an object information storage unit stores a pre-processing grid map including a position of a detected object set as an object occupied region and a position where no object has been detected is as an object unoccupied region, an information processing unit generates a determination grid map in which part of the object unoccupied region of the map is replaced with the object occupied region, and a road surface region determination unit that generates an automatic driving grid map in which a closed space surrounded by the object occupied region of the determination grid map is set as a road surface region.

Abstract: An image processing method includes: receiving an input image; performing a low-frequency image regulating operation to regulate the local intensity of the image of pixel unit(s) according to low-frequency information of the image of pixel unit(s) of the input image; performing a high-frequency image regulating operation to improve the details of the image of pixel unit(s) according to high-frequency information of the image of pixel unit (s) of the input image; and, generating an output image according to the input image, the low-frequency image regulating operation, and the high-frequency image regulating operation.

Abstract: An image processing device according to one embodiment estimates first optical flow information of a pixel unit on the basis of consecutive frames, and estimates a polynomial model corresponding to the optical flow information. The image processing device estimates second optical flow information of holes included in the frames on the basis of the polynomial model, and inpaints an input image on the basis of the first optical flow information and/or the second optical flow information.

Abstract: In examples, image data representative of an image of a field of view of at least one sensor may be received. Source areas may be defined that correspond to a region of the image. Areas and/or dimensions of at least some of the source areas may decrease along at least one direction relative to a perspective of the at least one sensor. A downsampled version of the region (e.g., a downsampled image or feature map of a neural network) may be generated from the source areas based at least in part on mapping the source areas to cells of the downsampled version of the region. Resolutions of the region that are captured by the cells may correspond to the areas of the source areas, such that certain portions of the region (e.g., portions at a far distance from the sensor) retain higher resolution than others.

Abstract: Embodiments of this specification provide methods and apparatuses for processing images, devices, and storage media. A method includes: obtaining an image for processing; generating a first mask image corresponding to the image based on a machine-learning model and determining whether the image has a foreground object; in response to determining that the image has the foreground object, setting pixels corresponding to the foreground object to a first grayscale value range, and setting pixels corresponding to non-foreground objects in the first mask image to a second grayscale value range; determining an outline corresponding to the foreground object in the image according to a grayscale value range difference between the first grayscale value range of the foreground object and the second grayscale value range of the non-foreground objects in the first mask image; and sending, to a client device for display, the image with the determined outline for interaction by a user.

Abstract: An embodiment method includes performing first convolutional filtering on a first tensor constructed using a current frame and reference frames (or digital world reference images) of the current frame in a video, to generate a first estimated image of the current frame having a higher resolution than an image of the current frame. The method also includes performing second convolutional filtering on a second tensor constructed using the first estimated image and estimated reference images of the reference frames, to generate a second estimated image of the current having a higher resolution than the image of the current frame. The estimated reference images of the reference frames are reconstructed high resolution images of the reference images.

Abstract: Apparatuses and computer-implemented methods for compression and extraction of three-dimensional (3D) volumetric imaging data, including in particular medical and dental imaging data, that may receive the 3D volumetric image data and preparing compressed 3D volumetric image data for compression by analyzing a relevant region of the 3D volumetric image data. The method may further include compressing, into compressed 3D volumetric image data, the pre-processed static 3D volumetric image data using a video compression scheme. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A detection unit configured to detect a detection target from an image to be processed by detection processing capable of adjusting at least one setting, a correction cost estimation unit configured to estimate a correction cost needed for correction needed in a case where a result of detection by the detection unit is corrected, an adjustment cost estimation unit configured to estimate an adjustment cost needed in a case where the setting of the detection processing is adjusted to the detection target, and a display control unit configured to perform control to display the correction cost and the adjustment cost on a display unit in a comparable manner are included.

Abstract: To provide evaluation by using a virtual video image generated by performing an optical image processing for lightness and distortion required by an evaluation object. An evaluation apparatus for a camera system including a camera for capturing a video image of outside view includes: a video generation unit for generating a simulation video image simulating the video image of outside view captured by the camera; an optical filter unit which performs an optical image processing on the simulation video image in order to simulate the video image outputted from the camera; and a camera image processing unit which generates a vehicle control signal by performing a recognition processing by using the processed video image.

Abstract: A vehicular vision system includes a camera disposed at a vehicle and capturing image data. The system, via processing at an electronic control unit (ECU) of image data captured by the camera, detects an object at a first orientation relative to camera. A first local binary pattern represents in binary form a first portion of the image data that includes the detected object. The system detects the object at a second orientation relative to camera. A second local binary pattern represents in binary form a second portion of the image data that includes the detected object. The second orientation is different from the first orientation and the second local binary pattern is different than the first local binary pattern. The system groups the first and second local binary patterns into a common histogram bin. The system classifies the detected object based at least in part on the common histogram bin.

Abstract: A method and system are provided. The method includes obtaining a difference map between a reference frame and a non-reference frame, estimating a noise variance of the obtained difference map, obtaining merging weights based on the estimated noise variance and the obtained different map, and merging the non-reference frame with the reference frame using the obtained merging weights.

Abstract: Methods and apparatus for blending unknown pixels in overlapping images. In one embodiment, an action camera captures two hyper-hemispherical fisheye images that are stitched to a 360° panorama. In order to remove exposure differences between the two cameras, the images are pre-processed prior to multiband blending. The pre-processing leverages image information from pixels to make informed guesses about pixels that were not captured. In particular, various pixels with different knowability (e.g., known, unknown, consistent, and conflicting) may be handled differently so as to emphasize/de-emphasize their importance in pre-processing.

Abstract: A method includes obtaining at least one 2-D image dataset of semiconductor structures formed on a wafer including one or more defects during a wafer run of a wafer using a predefined fabrication process. The method also includes determining, based on at least one machine-learning algorithm trained on prior knowledge of the fabrication process and based on the at least one 2-D image dataset, one or more process deviations of the wafer run from the predefined fabrication process as a root cause of the one or more defects. A 3-D image dataset may be determined as a hidden variable.

Abstract: A system and method for augmenting an original OCT (optical coherence tomography) image includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The system includes one or more learning modules selectively executable by the controller. The learning modules are trained by a training network with a training dataset having a plurality of training ultrasound bio-microscopy images and respective training OCT images. Execution of the instructions by the processor causes the controller to obtain the original OCT image, captured through an OCT device. The controller is configured to generate an augmented OCT image based in part on the original OCT image, by executing the (trained) learning modules. The augmented OCT image at least partially extends a peripheral portion of the original OCT image.

Abstract: A method includes defining, in an image, bounding boxes about an electrical connector assembly, identifying an edge of each of the bounding boxes, and determining one or more metrics based on the edge of each the bounding boxes, where the one or more metrics indicate a positional relationship between the edges of the bounding boxes. The method includes determining a state of the electrical connector assembly based on the one or more metrics and transmitting a notification based on the state.

Abstract: A system and method for detecting motion with respect an object includes providing a processor and at least one video sensor; synchronizing the at least one video sensor to a dynamic event associated with the object; recording, by the at least one video sensor, a plurality of data sets including the object, each data set of the plurality of data sets being synchronized with respect to the dynamic event associated with the object; averaging, by the processor, the plurality of data sets to provide an averaged synchronized data set; and calculating, by the processor, a motion with respect to the object based on the averaged synchronized data set.

Abstract: A system includes: a depth module including an encoder and a decoder and configured to: receive a first image from a first time from a camera; and based on the first image, generate a depth map including depths between the camera and objects in the first image; a pose module configured to: generate a first pose of the camera based on the first image; generate a second pose of the camera for a second time based on a second image; and generate a third pose of the camera for a third time based on a third image; and a motion module configured to: determine a first motion of the camera between the second and first times based on the first and second poses; and determine a second motion of the camera between the second and third times based on the second and third poses.

Abstract: This disclosure relates generally to system and method for forecasting location of target in monocular first person view. Conventional systems for location forecasting utilizes complex neural networks and hence are computationally intensive and requires high compute power. The disclosed system includes an efficient and light-weight RNN based network model for predicting motion of targets in first person monocular videos. The network model includes an auto-encoder in the encoding phase and a regularizing layer in the end helps us get better accuracy. The disclosed method relies entirely just on detection bounding boxes for prediction as well as training of the network model and is still capable of transferring zero-shot on a different dataset.

Abstract: The in-vehicle device transfers the selected image selected by the user from the photographed images stored in the storage device and the selected vehicle state, which is the vehicle state when the selected image is captured, to the form terminal. The portable device transmits the selected image and the selected vehicle state to the image management server only when the transmission is permitted by the user. The image management server determines whether or not the selected image is a rare image using the selected image and the selected vehicle state, and stores the selected image in the image storage device when the selected image is determined to be a rare image.

Abstract: A method for photoacoustic imaging is performed by measuring RF time samples from transducer elements, and reconstructing an estimated initial pressure image from the measured RF time samples and a pre-calculated forward model matrix. The reconstructing involves minimizing the difference between the pre-calculated forward model matrix applied to the image estimate and the measured RF time samples by implementing a superiorized modified conjugate gradient least squares algorithm to minimize the total variation norm. The model matrix factors each of the transducer elements into sub-wavelength mathematical elements.