Image processing apparatus and image processing method
AssigneeCanon Kabushiki Kaisha
InventorAtsushi Nagao
Life of this patent
Estimating the term…
Abstract
An image processing apparatus that estimates a vector representing a relative positional relationship between parts of a subject using machine learning is disclosed. The image processing apparatus, using a convolutional neural network (CNN), detects, from an image, a first part and a second part of a specific subject, and a relative position vector from the first part to the second part. The CNN is configured to generate a feature map indicating an approximate direction of the relative position vector. The image processing apparatus trains the CNN using a loss function pertaining to the relative position vector and a loss function pertaining to the feature map.
Claims10
An image processing apparatus comprising:
- one or more processors that execute a program stored in a memory and cause the image processing apparatus to:
- detect, using a convolutional neural network (CNN), from an image, a first part and a second part of a specific subject, and a relative position vector from the first part to the second part;
- and
- train the CNN,
- wherein:
- the CNN is configured to generate a feature map indicating an approximate direction of the relative position vector,
- the CNN is trained using a loss function pertaining to the relative position vector and a loss function pertaining to the feature map,
- the feature map is indicated by an angular range having a predetermined magnitude as the approximate direction of the relative position vector, and
- the feature map has a channel for each of the angular ranges, and each of the channels indicates a likelihood that the second part corresponding to the first part is present.
An image processing method executed by an image processing apparatus, the image processing apparatus including a convolutional neural network (CNN), that detects, from an image, a first part and a second part of a specific subject, and a relative position vector from the first part to the second part, the image processing method comprising:
- generating, by the CNN, a feature map indicating an approximate direction of the relative position vector;
- and
- training the CNN using a loss function pertaining to the relative position vector and a loss function pertaining to the feature map,
- wherein the feature map is indicated by an angular range having a predetermined magnitude as the approximate direction of the relative position vector, and
- wherein the feature map has a channel for each of the angular ranges, and each of the channels indicates a likelihood that the second part corresponding to the first part is present.
A non-transitory computer-readable medium that stores a program, which when executed by a computer, causes the computer to function as an image processing apparatus and to execute an image processing method comprising:
- using a convolutional neural network (CNN), detects, from an image, a first part and a second part of a specific subject, and a relative position vector from the first part to the second part;
- and
- training the CNN,
- wherein;
- the CNN is configured to generate a feature map indicating an approximate direction of the relative position vector,
- the CNN is trained using a loss function pertaining to the relative position vector and a loss function pertaining to the feature map,
- the feature map is indicated by an angular range having a predetermined magnitude as the approximate direction of the relative position vector, and
- the feature map has a channel for each of the angular ranges, and each of the channels indicates a likelihood that the second part corresponding to the first part is present.
Dependencies are inferred from the claim text.