Disclosed is a method for recognizing a marine object based on hyperspectral data including collecting target hyperspectral data; preprocessing the target hyperspectral data; and detecting and identifying an object included in the target hyperspectral data based on a marine object detection and identification model, trained through learning of the detection and identification of the marine object. According to the present invention, the preprocessing and processing of the hyperspectral data collected in real time according to a communication state may be performed in the sky or on the ground.

There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.

There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.

An image processing apparatus includes a first image generator and a second image generator. The first image generator generates a first image, including a predetermined ruled-line image and an inscription image, from a second sheet in a sheet group. The sheet group is obtained by stacking multiple sheets including a single first sheet and the second sheet. The first sheet has inscription information inscribed thereon. The second sheet has the inscription image corresponding to the inscription information transferred thereon and includes the ruled-line image. The second image generator generates a second image in which a surplus image is removed from the first image generated by the first image generator in accordance with a learning model that has learned to remove the surplus image different from the ruled-line image and the inscription image.

In an example method, a series of time-based clustering images is generated for a plurality of library fragments from a genome sample. Each time-based clustering image in the series is sequentially generated. To generate each time-based clustering image in the series: i) a respective sample is introduced to a flow cell, the respective sample including contiguity preserved library fragments of the plurality of library fragments, wherein the contiguity preserved library fragments are attached to a solid support or are attached to each other; ii) the contiguity preserved library fragments are released from the solid support or from each other; iii) the contiguity preserved library fragments are amplified to generate a plurality of respective template strands; iv) the respective template strands are stained; and v) the respective template strands are imaged.

In an example method, a series of time-based clustering images is generated for a plurality of library fragments from a genome sample. Each time-based clustering image in the series is sequentially generated. To generate each time-based clustering image in the series: i) a respective sample is introduced to a flow cell, the respective sample including contiguity preserved library fragments of the plurality of library fragments, wherein the contiguity preserved library fragments are attached to a solid support or are attached to each other; ii) the contiguity preserved library fragments are released from the solid support or from each other; iii) the contiguity preserved library fragments are amplified to generate a plurality of respective template strands; iv) the respective template strands are stained; and v) the respective template strands are imaged.

In implementations of the subject matter as described herein, there is provided a method for forgery detection of a face image. Subsequent to inputting a face image, it is detected whether a blending boundary due to the blend of different images exists in the face image, and then a corresponding grayscale image is generated based on a result of the detection, where the generated grayscale image can reveal whether the input face image is formed by blending different images. If a visible boundary corresponding to the blending boundary exists in the generated grayscale image, it indicates that the face image is a forged image; on the contrary, if the visible boundary does not exist in the generated grayscale image, it indicates that the face image is a real image.

A method for training an image segmentation model includes: acquiring target category feature information that represents category features of a training sample and a prediction sample, and associated scene feature information thereof; performing splicing processing on the target category feature information and the associated scene feature information; inputting first spliced feature information obtained by the splicing processing into an initial generation network to perform image synthesis processing; inputting a first synthesized image obtained by the synthesis processing into an initial determination network to determine authenticity; inputting the first synthesized image into a classification network of an initial image segmentation model to perform image segmentation, to obtain a first image segmentation result; and training the classification network of the initial image segmentation model based on a first image determination result, the first image segmentation result, and the target category feature information, so as to obtain a target image segmentation model.

An artificial intelligence-based image processing method implemented by a computer device is provided. The method includes: acquiring an image; performing element region detection on the image to determine an element region in the image; detecting a target element region in the image using an artificial intelligence-based technique; generating a target element envelope region by searching an envelope for the detected target element region; and fusing the element region and the target element envelope region to obtain a target element region outline.

Certain embodiments describe a system, method, and apparatus for multi-spectral photoacoustic imaging. A method, for example, can include receiving multi-spectral photoacoustic image data from a photoacoustic imaging system. The method can also include pre-processing the multi-spectral photoacoustic image data. The pre-processing can comprise determining a number of significant components above a noise floor of the multi-spectral photoacoustic image data. In addition, the method can include detecting tissue chromophores based on the number of significant components from the multi-spectral photoacoustic image data using an unsupervised spectral unmixing process. The unsupervised spectral unmixing process can include clustering and windowing of the multi-spectral photoacoustic image data. The method can further include displaying the detected tissue chromophores in an abundance map.