Embodiments of the disclosure provide an image fitting method. The method includes: providing a chip plate and a plurality of photographing assemblies, where the chip plate is used to place a chip tray, and the photographing assemblies are used to capture images of the chip plate; acquiring a chip plate image captured by each photographing assembly, where the chip plate image is an image of the chip plate with a partial area and the chip tray placed on the chip plate; acquiring a chip tray image included in each chip plate image, where the chip tray image is an image of the chip tray with a partial area; and fitting the plurality of chip tray images to acquire a chip image, where the chip image is an image of an entire chip tray.

A radiation imaging control apparatus according to an aspect of the present invention includes an image acquisition unit configured to acquire a captured image obtained by capturing an image of a subject, a foreign matter detection unit configured to detect, in a case where a radiation image of the subject is to be acquired, a foreign matter which is likely to appear in the radiation image from the captured image acquired by the image acquisition unit, a warning information generation unit configured to generate warning information regarding image capturing of a radiation image based on the foreign matter detection unit having detected a foreign matter in the captured image, and a display control unit configured to issue a notification based on the warning information.

A method of removing a mobile airborne device from a field of view of a primary camera. A sensor associated with a primary camera detects a mobile airborne device within a field of view of the primary camera, where a three-dimensional physical space is within the field of view of the primary camera. A signal generator generates a signal that, when received by the mobile airborne device, causes the mobile airborne device to exit the three-dimensional physical space. A transmitter transmits the signal to the mobile airborne device to cause the mobile airborne device to exit the three-dimensional physical space that is within the field of view of the primary camera.

A dynamic image processing apparatus includes a hardware processor. The hardware processor extracts (i) a region of interest and/or (ii) a frame image of interest from a series of frame images obtained by dynamic imaging of a subject. Further, the hardware processor stores, of the series of the frame images, only (i) the extracted region of interest, (ii) the extracted frame image of interest or (iii) the extracted region of interest in the extracted frame image of interest in a storage.

A system and a method for recognition of an orchard on a geographic area are provided. The system includes a pre-processing module for deriving a target section of an aerial image containing a parcel of an orchard, an image optimization module for performing customized image processing on the target section of the aerial image, and a recognition module for determining a type and a border of the orchard present on the target section of the aerial image with a deep learning mechanism. Accordingly, farmers and agricultural entities can effectively monitor orchards within different geographic areas so as to yield better fruit production and conduct better fruit quality control and land utilization.

A method and a system described herein provide sensor-level based data stream processing. In particular, concepts of enabling low level sensor fusion by lightweight semantic segmentation on sensors generating point cloud as generated from LIDAR, radar, cameras and Time-of-Flight sensors are described. According to the present disclosure a computer-implemented method for sensor-level based data stream processing comprises receiving a first data stream from a LIDAR sensor, removing a ground from the point cloud, performing clustering on the point cloud, and feature processing on the point cloud. The point cloud represents a set of data points in space.

Techniques and systems are described for automatic artifact removal in a digital image. A segmentation map is generated that describes a magnitude of difference among pixels in a digital image. Contours may be generated that describe boundaries of objects described in the segmentation map. The contours may be filtered according to two-dimensional and three-dimensional cues to identify contours corresponding to artifacts in the digital image. For each contour corresponding to an artifact, an object mask and a sampling mask may be generated. The object mask and the sampling mask may be utilized as part of a content filling operation upon the digital image to remove the artifact, and a corrected digital image is generated that does not include the artifact.

An information processing program including: operating on one terminal device; transmitting an approval confirmation requesting approval of external output of an image to one or multiple other terminal devices other than the one terminal device; and outputting the image externally on the basis of a response to the approval confirmation from the other terminal device.

An imaging apparatus provided inside a vehicle is provided. The imaging apparatus is capable of capturing images with different degrees of sensitivity, and the imaging apparatus captures an image for recognizing an inside of the vehicle with high sensitivity, and captures an image for recognizing an outside of the vehicle with low sensitivity.

An AI-based object classification method and apparatus, a computer-readable storage medium, and a computer device. The method includes: obtaining a target image to be processed, the target image including a target detection object; separating a target detection object image of the target detection object from the target image; inputting the target detection object image into a feature object prediction model to obtain a feature object segmentation image of a feature object in the target detection object image; obtaining quantitative feature information of the target detection object according to the target detection object image and the feature object segmentation image; and classifying the target detection object image according to the quantitative feature information to obtain category information of the target detection object in the target image.