Abstract: A method of generating lane information using a neural network includes generating a lane probability map based on an input image, generating lane feature information and depth feature information by applying the lane probability map to a second neural network, generating depth distribution information by applying the depth feature information to a third neural network, generating spatial information based on the lane feature information and the depth distribution information, generating offset information including a displacement between a position of a lane and a reference line by applying the spatial information to a fourth neural network, and generating three-dimensional (3D) lane information using the offset information.

Abstract: Disclosed is an image processing method, electronic device and storage medium. The method includes obtaining feature information of first region in a current image frame, wherein first region includes a region that is determined by performing motion estimation on the current and previous image frames based on optical flow; obtaining feature information of second region in the current image frame, wherein second region includes a region corresponding to pixel points among first pixel points of the current image frame, where its association with pixel points among second pixel points of the previous image frame satisfies a condition; and based on the feature information of first region and that of second region, fusing the previous and current image frames to obtain a processed current image frame, which is used as a previous image frame for a next image frame.

Abstract: Provided is an object detection device or the like which efficiently generates good-quality training data. This object detection device is provided with: a detection unit which uses a dictionary to detect objects from an input image; a reception unit which displays, on a display device, the input image accompanied by a display emphasizing partial areas of detected objects, and receives, from one operation of an input device, a selection of a partial area and an input of the class of the selected partial area; a generation unit which generates training data from the image of the selected partial area and the inputted class; and a learning unit which uses the training data to learn the dictionary.

Abstract: An information processing device of the present invention includes a detection means that detects the content of an image, a determination means that determines a processing mode for the image based on the result of detection of the content of the image, and an execution means that executes processing for a captured image corresponding to the processing mode.

Abstract: A method to train a model for feature point detection involves obtaining a first image and a second image. The method involves generating a first score map for the first image and a second score map for the second image using the model. The method involves selecting a first plurality of interest points in the first image based on the first score map. The method involves selecting a second plurality of interest points in the second image based on the second score map. Pairwise matching of a first interest point among the first plurality of interest points with a second interest point among the second plurality of interest points is performed. Correctness of the pairwise matches is checked based on a ground truth, to generate a reward map. The score map and the reward map are compared and used to update the model.

Abstract: An image processing method and apparatus, a device, and a storage medium. The image processing method includes: performing preliminary segmentation recognition on a raw image by using a first segmentation model to obtain a candidate foreground image region and a candidate background image region of the raw image; recombining the candidate foreground image region, the candidate background image region, and the raw image to obtain a recombined image, pixels in the recombined image being in a one-to-one correspondence with pixels in the raw image; and performing region segmentation recognition on the recombined image by using a second segmentation model to obtain a target foreground image region and a target background image region of the raw image.

Abstract: There are provided a learning recommendation apparatus and method for detecting a problem from an image through character recognition and providing at least one sub-topic learning among a plurality of sub-topic learnings related to the detected problem. The provided learning recommendation apparatus recommends, as a recommendation target, a plurality of learning topics including the concept of a formula which has been read through the character recognition for an image, wherein a priority order is set to the plurality of learning topics based on the concept distance between the learning topic and the learning history, and the learning topics are recommended so that the learning topic having a higher priority order is located at a higher position.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: Devices, methods, and systems for occupancy detection are described herein. One device includes instructions to receive an image of a portion of a facility captured by an imaging device, the image defined by a field of view, and set a first occupancy detection threshold for a first part of the field of view and a second occupancy detection threshold for a second part of the field of view.

Abstract: According to some embodiments, the process includes the steps of: a) taking a sagittal preoperative x-ray of the vertebral column of the patient to be treated, extending from the cervical vertebrae to the femoral heads; b) on that x-ray, identifying points on S1, S2, T12 et C7; c) depicting, on the said x-ray, curved segments beginning at the center of the plate of S1 et going to the center of the plate of C7; e) identifying, on that x-ray, the correction(s) to be made to the vertebral column, including the identification of posterior osteotomies to make; f) pivoting portions of said x-ray relative to other portions of that x-ray, according to osteotomies to be made; g) performing, on said x-ray, a displacement of the sagittal curvature segment extending over the vertebral segment to be corrected; h) from a straight vertebral rod (TV), producing the curvature of that rod according to the shape of said sagittal curvature segment in said displacement position.

Abstract: In learning processing performed before sample observation processing (steps S705 to S708), the sample observation device acquires a low-picture quality learning image under a first imaging condition for each defect position indicated by defect position information, determines an imaging count of a plurality of high-picture quality learning images associated with the low-picture quality learning image for each defect position and a plurality of imaging points based on a set value of the imaging count, acquires the plurality of high-picture quality learning images under a second imaging condition (step S702), learns a high-picture quality image estimation model using the low-picture quality learning image and the plurality of high-picture quality learning images (step S703), and adjusts a parameter related to the defect detection in the sample observation processing using the high-picture quality image estimation model (step S704).

Abstract: A first artificial neural network (ANN) model implemented on a memory device can be executed on first data from an imaging device corresponding to a first image. A second ANN model implemented on the memory device can be executed on second data from the imaging device corresponding to a second, subsequent image. Whether an accuracy value of results yielded from the execution of the second ANN model on the second data is less than a threshold accuracy value can be determined by the memory device. Responsive to determining that the accuracy value is less than the threshold accuracy value, the first ANN model can be executed on third data from the imaging device corresponding to a third image subsequent to the second image. Such selection of ANN models can reduce excess power consumption of a memory device on which the ANN models are implemented.

Abstract: Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images and video with multiple subjects. Some of these subjects may vary in priority for various tasks. It may be desired to increase or decrease the compression on each subject in order to more efficiently store the image data. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. Inference data can be associated with the type, motion and/or priority of the subjects as desired. This inference data can be utilized along with other subject data to generate one or more variable compression regions within the image data. The image data can be subsequently processed to compress different areas of the image based on a desired application. The variably compressed image can reduce file sizes and allow for more efficient storage and processing.

Abstract: There is provided an information processing device, an information processing method, and a computer program capable of highly accurately designating a discretionary position in a three-dimensional model.

Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to receive, at a deep neural network, an image captured by an image capture device. The processor is also programmed to generate a predicted distortion map based on the image and update at least one weight of the deep neural network based on a loss function of the deep neural network.

Abstract: A LiDAR-based object detection method includes clustering a point cloud acquired from LiDAR, selecting a to-be-divided cluster among clusters generated in the clustering, and selecting division points according to a geometrical feature formed with adjacent points from among points belonging to the to-be-divided cluster, and dividing the to-be-divided cluster based on a representative point determined by at least some of the division points.

Abstract: An information processing system (10) comprises: a distance acquisition unit (110) that specifies an iris area containing an iris of a target from a visible-light image of the target, and acquires an iris distance that is a distance to the iris area; an iris image acquisition unit (120) that acquires an iris image of the target by changing a focal length according to the iris distance; a score computing unit (130) that calculates a score relating to deviation of a focus in the iris image, based on the iris image; and a correlation update unit (140) that updates correlation between the iris distance and the focal length at a moment of acquisition of the iris image, based on the score. According to such an information processing system, since the correlation is updated with good accuracy, it is possible to acquire the appropriate iris image.

Abstract: Various factors may cause an error between the shape of a workpiece machined by an industrial machine and the target shape of the workpiece. An image analysis device includes a first image generating section that generates first image data indicating a first distribution of locations on a workpiece of an error between a shape of the workpiece machined by an industrial machine and a pre-prepared target shape of the workpiece; a second image generating section that generates second image data indicating a second distribution of locations on the workpiece of an error between a command transmitted to the industrial machine for machining the workpiece and feedback from the industrial machine corresponding to the command; and a correlation acquisition section that obtains a correlation between the first distribution and the second distribution, based on the first image data and the second image data.

Abstract: An image processing apparatus includes an acquisition unit configured to acquire a read image obtained by reading a printed document, and a determination unit configured to determine presence or absence of a streak included in the read image, by tracking a contour of the printed document from each of a plurality of points that are both end points of the read image in a direction orthogonal to a reading direction of the read image.

Abstract: A camera pose information detection method, includes collecting a plurality of images by means of a multi-lens camera, and determining a road surface region image based on the plurality of images; and projecting points in the road surface region image onto a camera coordinate system, so as to obtain camera pose information.