Learning image generation device, learning image generation method, learning image generation program, learning method, learning device, and learning program

A learning image generation method comprising:

• acquiring supervised data including a learning image and a correct region defined in the learning image as a pair;
• acquiring a limitation range of allowable pixel values for a first designated pixel belonging to the correct region in the learning image;
• generating a plurality of variable pixel values for the first designated pixel belonging to the correct region in the learning image according to the limitation range;
• and
• generating a plurality of variation learning images, wherein a pixel value of a second designated pixel in each of the plurality of variation learning images is respectively a different pixel value among the plurality of variation pixel values, and wherein the first designated pixel in the learning image and the second designated pixel in each of the plurality of variation learning images have same pixel coordinates.

A non-transitory computer recording medium storing a learning image generation program causing a computer to:

• acquire supervised data including a learning image and a correct region defined in the learning image as a pair;
• acquire a limitation range of allowable pixel values for a first designated pixel belonging to the correct region in the learning image;
• generate a plurality of variable pixel values for the first designated pixel belonging to the correct region in the learning image according to the limitation range;
• and
• generate a variation learning images, wherein a pixel value of a second designated pixel in each of the plurality of variation learning images is respectively a different pixel value among the plurality of variation pixel values, and wherein the first designated pixel in the learning image and the second designated pixel in each of the plurality of variation learning images have same pixel coordinates.

A learning method comprising:

• learning a model by using
• one or more first supervised data including a learning image and a correct region defined in the learning image as a pair, and
• one or more second supervised data including a plurality of variation learning images and a correct region defined in the learning image as a pair,
• wherein the plurality of variation learning images are generated by acquiring a limitation range of allowable pixel values for a first designated pixel belonging to the correct region in the learning image and generating a plurality of variable pixel values for the first designated pixel in the learning image according to the limitation range, wherein a value of a second designated pixel in each of the plurality of variation learning images is respectively a different pixel value among the plurality of variation pixel values, and wherein the first designated pixel in the learning image and the second designated pixel in each of the plurality of variation learning images have same pixel coordinates.

A learning device comprising:

• a memory;
• and
• a processor coupled to the memory, the processor configured to:
• acquire one or more first supervised data including a learning image and a correct region defined in the learning image as a pair;
• acquire one or more second supervised data including a plurality of variation learning images and a correct region defined in the learning image as a pair,
• wherein the plurality of variation learning images are generated by acquiring a limitation range of allowable pixel values for a first designated pixel belonging to the correct region in the learning image and generating a plurality of variable pixel values for the first designated pixel in the learning image according to the limitation range, wherein a value of a second designated pixel in each of the plurality of variation learning images is respectively a different pixel value among the plurality of variation pixel values, and wherein the first designated pixel in the learning image and the second designated pixel in each of the plurality of variation learning images have same pixel coordinates;
• and
• learn a model by using the acquired one or more first supervised data and the acquired one or more second supervised data.

A learning device comprising:

• a memory;
• and
• a processor coupled to the memory, the processor configured to:
• acquire one or more first supervised data including a learning image and a correct region defined in the learning image as a pair;
• acquire one or more second supervised data including a plurality of variation learning images and a correct region defined in the learning image as a pair,
• wherein the plurality of variation learning images are generated by acquiring a limitation range of allowable pixel values for a first designated pixel belonging to the correct region in the learning image and generating a plurality of variable pixel values for the first designated pixel in the learning image according to the limitation range, wherein a value of a second designated pixel in each of the plurality of variation learning images is respectively a different pixel value among the plurality of variation pixel values, and wherein the first designated pixel in the learning image and the second designated pixel in each of the plurality of variation learning images have same pixel coordinates;
• and
• learn a model by using the acquired one or more first supervised data and the acquired one or more second supervised data,
• wherein the model is learned by using a plurality of the first supervised data in the first learning, and the model is learned by replacing at least one first supervised data among the plurality of first supervised data with the second supervised data in second and subsequent learning.

A non-transitory computer recording medium storing a learning program causing a computer to:

• acquire one or more first supervised data including a learning image and a correct region defined in the learning image as a pair;
• acquire one or more second supervised data including a plurality of variation learning images and a correct region defined in the learning image as a pair,
• wherein the plurality of variation learning images are generated by acquiring a limitation range of allowable pixel values for a first designated pixel belonging to the correct region in the learning image and generating a plurality of variable pixel values for the first designated pixel in the learning image according to the limitation range, wherein a value of a second designated pixel in each of the plurality of variation learning images is respectively a different pixel value among the plurality of variation pixel values, and wherein the first designated pixel in the learning image and the second designated pixel in each of the plurality of variation learning images have same pixel coordinates;
• and
• learn a model by using the one or more first supervised data and the one or more second supervised data acquired by the supervised data acquisition unit.