An orientation device, an orientation system and an orientation method are provided. The orientation device includes a seat body, a pressure sensor, and a computing unit. The seat body includes a bearing surface, and the seat body is non-directional. The pressure sensor is disposed below the bearing surface. The pressure sensor is configured to obtain a plurality of pressure data of the bearing surface when an object is disposed on the bearing surface. The computing unit is coupled to the pressure sensor. The computing unit is configured to analyze the pressure data to obtain a direction data. The direction data is configured to determine a first direction of the seat body.

A hologram detection method according to an aspect of the disclosure, includes: inputting a first image, obtained by capturing a detection object on the basis of first flash intensity, to a neural network model to obtain a first detection result value representing the detection or not of a hologram for each of predetermined at least one detection unit regions; and comparing a threshold value with the first detection result value obtained for each detection unit region to determine the detection or not of a hologram in the first image and a first detection unit region where a hologram is detected.

Methods and systems for identifying a region of interest in breast tissue utilize artificial intelligence to confirm that a target lesion identified during imaging the breast tissue using a first imaging modality (e.g. x-ray imaging) has been identified using a second imaging modality (e.g. ultrasound imaging). A computing system operating a lesion matching engine utilizes a machine learning classifier algorithm trained on cases of x-ray images and corresponding ultrasound images in which lesions were identified for further analysis. The lesion matching engine analyzes a target lesion identified with x-ray imaging and a potential lesion identified with ultrasound imaging to determine a likelihood that the target lesion is the same as the potential lesion. A confidence level indicator for the lesion match is presented on a display of a computing device to aid a healthcare provider in locating a lesion in breast tissue.

The present disclosure provides an image processing apparatus and an image processing method. The method comprising: performing a target detection on a first image to generate a first to-be-processed image, the first to-be-processed image has a size smaller than the size of the first image; performing the target detection on a second image to generate a second to-be-processed image, the second to-be-processed image has a size smaller than the size of the second image, the first image and the second image constitute an image pair containing the target, and a disparity exists between the first image and second image; and calculating, based on the first to-be-processed image and the second to-be-processed image, a disparity value of the target in the first to-be-processed image and the second to-be-processed image.

The embodiments are an aircraft return control method and device, an aircraft and a storage medium. The method includes: determining the location of a return target region according to the time and the phase of a return signal; and when flying to the return target region, according to a matching result between an image of a current region and a pre-collected image of the return target region, adjusting flight parameters to land at the return target. Embodiments of the present invention solve the technical problem in the prior art that the aircraft cannot be accurately landed at the return target due to the movement of the return target, and achieve the technical effect of controlling the aircraft to accurately and safely land at the return target on the return target region.

An apparatus for training a model to identify abnormal medical/image data comprises processing circuitry configured to: receive medical/image data; obtain a local region and a context region from the medical/image data; generate abnormal medical/image data using the local region and/or the context region; train a model using the medical/image data and the generated abnormal medical/image data to identify abnormal medical/image data.

A learning data generation device of an embodiment includes a detection unit that detects a changed region in a space between a first map and a second map based on first map data which indicate the first map and second map data which indicate the second map which indicates a substantially same space as the first map and represents the space at a different time point from the first map and a changed region image acquisition unit that acquires images which correspond to the changed region detected by the detection unit from a first image in which the space represented by the first map is captured and a second image in which the space represented by the second map is captured.

The present invention provides a processing apparatus (10) including: an acquisition unit (11) that acquires an image generated by a plurality of cameras for photographing a product picked up by a customer; a recognition unit (12) that recognizes the product, based on each of a plurality of images generated by the plurality of cameras; and a determination unit (13) that determines a final recognition result, based on a plurality of recognition results based on each of the plurality of images, and a size of a region where the product is present within each of the plurality of images.

The present technique relates to an information processing device, an information processing method, and a program that can improve tracking performance.

A feature information extracting unit extracts feature information about an object for each frame image, and a tracking unit tracks a vehicle in the frame image by using the feature information. The present technique is applicable to a driving support device with an onboard camera, for example.

A gas detection device that gives a notification of a detected gas on the basis of a captured image obtained by capturing an image of a monitoring target, the gas detection device including: a gas detection unit that detects gas on the basis of the captured image and gives a notification of the detected gas; an input unit that receives input information from a user; a mask candidate region extraction unit that extracts a mask candidate region that is a candidate region of a mask region for which a notification of gas detection is suppressed; and a mask generation unit that generates mask data indicating the mask region, in which the gas detection unit gives a notification of a gas detected outside the mask region, and the mask generation unit generates, as the mask data, a region in which first mask candidate region information input from the input unit matches second mask candidate region information extracted by the mask candidate region extraction unit.