A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry detects, for each of target points corresponding to feature points, a reference point having a spatial correlation with the target point in a medical image. The processing circuitry generates candidate points corresponding to the target point for each of the target points by using a detection model. The processing circuitry selects, for each of the target points, a candidate point based on a position feature indicating a spatial position relationship between the target point and the reference point. The processing circuitry selects, for each of a plurality of candidate point combinations, a candidate point combination based on a structural feature indicating a spatial structural relationship between the target points. The processing circuitry outputs feature points in the medical image based on the selected candidate point and candidate point combination.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry detects, for each of target points corresponding to feature points, a reference point having a spatial correlation with the target point in a medical image. The processing circuitry generates candidate points corresponding to the target point for each of the target points by using a detection model. The processing circuitry selects, for each of the target points, a candidate point based on a position feature indicating a spatial position relationship between the target point and the reference point. The processing circuitry selects, for each of a plurality of candidate point combinations, a candidate point combination based on a structural feature indicating a spatial structural relationship between the target points. The processing circuitry outputs feature points in the medical image based on the selected candidate point and candidate point combination.

A data generation apparatus (2) has: an obtaining unit (21) that obtains real data (D_real); a fake data generating unit (22) that generates fake data (D_fake) that imitates the real data; and a mix data generating unit (23) that generates mix data (D_mix) by mixing the real data and the fake data at a desired mix ratio (a), the mix data generating unit changes the mix ratio that is used to generate a data element of the mix data based on a position of the data element in the mix data.

An object recognition device 80 includes a scene determination unit 81, a learning-model selection unit 82, and an object recognition unit 83. The scene determination unit 81 determines, based on information obtained during driving of a vehicle, a scene of the vehicle. The learning-model selection unit 82 selects, in accordance with the determined scene, a learning model to be used for object recognition from two or more learning models. The object recognition unit 83 recognizes, using the selected learning model, an object in an image to be photographed during driving of the vehicle.

The present disclosure provides an apparatus and method to automatically detect vehicle damage which minimises the time taken to detect damage per vehicle. For example, the action of damage detection is also performed with an action of causing the fixing of any detected damage, where appropriate. A damage detection unit is configured and arranged to detect damage on a vehicle. The damage detection unit includes a receiving unit arranged to receive an image from a camera and a determining unit arranged to determine damage on the vehicle based on the received image. Moreover, the damage detection unit includes a storing unit arranged to store information about damage previously determined to be present on the vehicle and a comparing unit arranged to compare the damage determined by the determining unit with the information about previous damage as stored by the storing unit.

The present disclosure provides an apparatus and method to automatically detect vehicle damage which minimises the time taken to detect damage per vehicle. For example, the action of damage detection is also performed with an action of causing the fixing of any detected damage, where appropriate. A damage detection unit is configured and arranged to detect damage on a vehicle. The damage detection unit includes a receiving unit arranged to receive an image from a camera and a determining unit arranged to determine damage on the vehicle based on the received image. Moreover, the damage detection unit includes a storing unit arranged to store information about damage previously determined to be present on the vehicle and a comparing unit arranged to compare the damage determined by the determining unit with the information about previous damage as stored by the storing unit.

Provided in the present disclosure are an insect identification method and system. The system comprises a client and a server, and the client is connected to the server. The method comprises: the client acquires an image photograph that is captured by a target user and that contains an insect to be identified, acquires an insect region in the image photograph, slices the insect region to obtain an insect image, and sends the insect image to the server; the server identifies the insect image by invoking an insect species identification model deployed in the server, and sends the identification result to the client; and the client pushes the identification result to the target user. By applying the solution provided in the present disclosure, the problem in the prior art in which insect identification is very difficult and has low accuracy may be solved.

According to an embodiment of the present disclosure, there may be provided an operation method of a server for estimating the size of damage in disaster affected areas. In this instance, the operation method of the server may include acquiring at least one first disaster image, deriving an affected area from each of the at least one first disaster image, acquiring affected area related information through labeling based on the derived affected area, and training a first learning model using the at least one first disaster image and the affected area related information.

This disclosure describes systems, methods, and devices related to automatic annotation. A device may capture data associated with an image comprising an object. The device may acquire input data associated with the object. The device may estimate a plurality of points within a frame of the image, wherein the plurality of point constitute a 3D bounding to around the object. The device may transform the plurality of points to two or more 2D points. The device may construct a bounding box that encapsulates the object using the two or more 2D points. The device may create a segmentation mask of the object using morphological techniques. The device may perform annotation based on the segmentation mask.

A system and method for translating audio, and video when desired. The translations include synthetic media and data generated using AI systems. Through unique processors and generators executing a unique sequence of steps, the system and method produces more accurate translations that can account for various speech characteristics (e.g., emotion, pacing, idioms, sarcasm, jokes, tone, phonemes, etc.). These speech characteristics are identified in the input media and synthetically incorporated into the translated outputs to mirror the characteristics in the input media. Some embodiments further include systems and methods that manipulate the input video such that the speakers' faces and/or lips appear as if they are natively speaking the generated audio.