Embodiments described herein provide a system for generating semantically accurate synthetic images. During operation, the system generates a first synthetic image using a first artificial intelligence (AI) model and presents the first synthetic image in a user interface. The user interface allows a user to identify image units of the first synthetic image that are semantically irregular. The system then obtains semantic information for the semantically irregular image units from the user via the user interface and generates a second synthetic image using a second AI model based on the semantic information. The second synthetic image can be an improved image compared to the first synthetic image.

Embodiments described herein provide a system for generating semantically accurate synthetic images. During operation, the system generates a first synthetic image using a first artificial intelligence (AI) model and presents the first synthetic image in a user interface. The user interface allows a user to identify image units of the first synthetic image that are semantically irregular. The system then obtains semantic information for the semantically irregular image units from the user via the user interface and generates a second synthetic image using a second AI model based on the semantic information. The second synthetic image can be an improved image compared to the first synthetic image.

An image analyzing method of increasing analysis accuracy is applied to an image monitoring apparatus with a processor. The image analyzing method is executed by the first processor and includes receiving statistic data of an external electronic unit, transforming the statistic data into feedback data via specific algorithm, and generating a control parameter according to the feedback data. The control parameter is used to optimize image data output by the image monitoring apparatus.

An image analyzing method of increasing analysis accuracy is applied to an image monitoring apparatus with a processor. The image analyzing method is executed by the first processor and includes receiving statistic data of an external electronic unit, transforming the statistic data into feedback data via specific algorithm, and generating a control parameter according to the feedback data. The control parameter is used to optimize image data output by the image monitoring apparatus.

A method for automated analysis of data obtained from biologic, or non-biologic, material is provided. The method includes extracting, in a visualization of the material, first shapes that combine to form a target shape. The method also includes registering the first shape of the target shape to second shapes of a generic shape, and identifying variations between the first shapes and the second shapes. A system for analyzing biologic material is provided that includes an extraction engine a registration engine an identification engine a display an atlas adapted to provide the generic shape and a database for storing the visualization of the target shape. A non-transitory computer-readable medium storing a program for analyzing biologic material is provided. The program includes instructions that, when executed by a processor, causes a processor to execute the method.

In brain analysis, anatomical standardization is performed when analyzing a region of interest (ROI). There are individual differences in the shape and size of the brain and by converting the brain into a standard brain, these differences can be compared with each other and subjected to statistical analysis. When generating a standard brain analysis, a large number of pieces of image data are classified into a plurality of groups based on their anatomical features. An intermediate template that is an intermediate conversion image and a conversion map is calculated for each group, and the calculation of the intermediate template and the generation of the intermediate conversion image are repeated while gradually reducing the number of classifications, so that a final standard image is generated. Using the standard image and the intermediate template calculated during the generation of the standard image, spatial standardization of the measured image is performed.

Methods for co-registering images of different imaging modalities include obtaining a photograph and a medical image, maximizing mutual information between the photograph and the medical image, automatically locating features common to both the photograph and the medical image based on maximized mutual information between the photograph and the medical image, and automatically highlighting the located features within the photograph or medical image.

There is provided a subject tracking apparatus. An obtainment unit obtains first and second shot images, and depth direction distance information in a plurality of regions in each of the first and second shot images. A specifying unit specifies a subject to be tracked in the first and second shot images based on the distance information of the first and second shot images. The specifying unit specifies a region of the subject to be tracked in the second shot image corresponding to a region of the subject to be tracked in the first shot image by comparing histograms of the distance information of the first and second shot images.

There is provided a subject tracking apparatus. An obtainment unit obtains first and second shot images, and depth direction distance information in a plurality of regions in each of the first and second shot images. A specifying unit specifies a subject to be tracked in the first and second shot images based on the distance information of the first and second shot images. The specifying unit specifies a region of the subject to be tracked in the second shot image corresponding to a region of the subject to be tracked in the first shot image by comparing histograms of the distance information of the first and second shot images.

Embodiments of the present disclosure disclose a method and apparatus for positioning an autonomous vehicle. The method includes: matching a current point cloud projected image of a first resolution with a map of the first resolution to generate a first histogram filter based on the matching result; determining at least two first response areas in the first histogram filter based on a probability value of an element in the first histogram filter; generating a second histogram filter based on a result of matching a current point cloud projected image of a second resolution with a map of the second resolution and the at least two first response areas, the first resolution being less than the second resolution; and calculating a weighted average of probability values of target elements in the second histogram filter to determine a positioning result of the autonomous vehicle in the map of the second resolution.