Computer program products, methods, systems, apparatus, and computing entities are provided for an augmented reality display using an actual image of the item. Additionally, the present disclosure provides for a proportionally dimensioned representation of the item in the augmented reality display. In some aspects, a beacon/tag/sensor-based approach may be used. In some aspects, a marker-based approach may be used.

An information processing apparatus (10) is for supporting work by a user who uses drawings for a plant. The information processing apparatus (10) includes a controller (15). The controller (15) is configured to convert a drawing including elements configuring the plant into an abstract model represented by element information indicating the elements and connection information indicating a connection relationship between the elements. The controller (15) is configured to generate display information, when it is judged that a difference exists between one abstract model based on one drawing and another abstract model based on another drawing, for displaying the differing portion in a different form than another portion.

An information processing apparatus (10) is for supporting work by a user who uses drawings for a plant. The information processing apparatus (10) includes a controller (15). The controller (15) is configured to convert a drawing including elements configuring the plant into an abstract model represented by element information indicating the elements and connection information indicating a connection relationship between the elements. The controller (15) is configured to generate display information, when it is judged that a difference exists between one abstract model based on one drawing and another abstract model based on another drawing, for displaying the differing portion in a different form than another portion.

A face image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

The present disclosure relates to methods and apparatus for display processing. For example, disclosed techniques facilitate region of interest tracking for in-motion frames. Aspects of the present disclosure can identify a layer of interest of a plurality of layers associated with a frame. Aspects of the present disclosure can also determine coordinates for a region of interest based on characteristics associated with the identified layer of interest. Further, aspects of the present disclosure can perform display processing on the region of interest of the frame based on the coordinates for the region of interest.

The present disclosure relates to an image processing apparatus and method capable of suppressing an increase in load of inverse adaptive color transform processing while suppressing an increase in distortion of coefficient data subjected to inverse adaptive color transform.

The coefficient data subjected to the lossless adaptive color transform is clipped at a level based on a bit depth of the coefficient data, and the coefficient data clipped at the level is subjected to lossless inverse adaptive color transform. The present disclosure may be applied to, for example, an image processing apparatus, an image coding device, an image decoding device, a transmission device, a reception device, a transmission/reception device, an information processing device, an imaging device, a reproduction device, an electronic device, an image processing method, an information processing method, or the like.

The present disclosure relates to an image processing apparatus and method capable of suppressing an increase in load of inverse adaptive color transform processing while suppressing an increase in distortion of coefficient data subjected to inverse adaptive color transform.

The coefficient data subjected to the lossless adaptive color transform is clipped at a level based on a bit depth of the coefficient data, and the coefficient data clipped at the level is subjected to lossless inverse adaptive color transform. The present disclosure may be applied to, for example, an image processing apparatus, an image coding device, an image decoding device, a transmission device, a reception device, a transmission/reception device, an information processing device, an imaging device, a reproduction device, an electronic device, an image processing method, an information processing method, or the like.

A server includes a feature point position estimation section that estimates positions of feature points of a plurality of respective input maps if a position correction section has failed in position correction for an input map, an accumulated data generation section that accumulates the positions of the feature points of the plurality of respective input maps to generate accumulated data, a convergence determination section that determines whether the positions of the feature points have converged based on the accumulated data, a difference data generation section that generates, if it is determined that the positions of the feature points have converged, difference data from the positions of the feature points that have converged, and a difference data reflection section that reflects the difference data in the reference map to update the reference map.

A hazard visualization system that can use artificial intelligence to identify locations at which hazards have occurred and a cause therein and to predict locations at which hazards may occur in the future is described herein. As a result, the hazard visualization system may reduce the likelihood of structural damage and/or loss of life that could otherwise occur due to natural disasters or other hazards. For example, the hazard visualization system can train an artificial intelligence model to predict the date, time, type, severity, path, and/or other conditions of a hazard that may occur at a geographic location. As another example, the hazard visualization system can train an artificial intelligence model to identify equipment or other infrastructure depicted in geographic images.

A hazard visualization system that can use artificial intelligence to identify locations at which hazards have occurred and a cause therein and to predict locations at which hazards may occur in the future is described herein. As a result, the hazard visualization system may reduce the likelihood of structural damage and/or loss of life that could otherwise occur due to natural disasters or other hazards. For example, the hazard visualization system can train an artificial intelligence model to predict the date, time, type, severity, path, and/or other conditions of a hazard that may occur at a geographic location. As another example, the hazard visualization system can train an artificial intelligence model to identify equipment or other infrastructure depicted in geographic images.