Abstract: An image processing apparatus includes a change detection unit configured to detect phase changes in multiple predetermined directions from among phase changes in a luminance image in units of mutually different resolutions, and a reliability estimation unit configured to estimate reliability of the detected phase change based on temporal amplitude change information in the multiple directions determined in the luminance image. The reliability estimation unit may estimate the reliability using an amplitude change of multiple resolutions and using a value of an amplitude change equal to or greater than a predetermined threshold value among images having multiple resolutions. The reliability may become a greater value as the amplitude change becomes larger.

Abstract: An image processing apparatus includes a change detection unit configured to detect a direction change, which is a temporal fluctuation in a plausible direction orthogonal to an edge determined for each pixel in an edge image indicating a high frequency component in an image that is an image processing target, and a phase change, which is a temporal fluctuation of a phase of the high frequency component according to the direction change, and a reliability estimation unit configured to estimate reliability indicating that the detected phase change is not a change caused by noise based on a variance value of the direction change per unit time.

Abstract: A subject tracking device includes a first histogram generation unit configured to generate a first histogram representing an appearance frequency of an image feature quantity of a subject region, for each segment and for each auxiliary segment of the image feature quantity, a second histogram generation unit configured to generate, for each candidate region, a second histogram representing an appearance frequency of the image feature quantity of an image of a candidate region for each segment and for each auxiliary segment of the image feature quantity, a difference derivation unit configured to compare the first histogram with the second histogram for each segment and for each auxiliary segment, and derive, for each candidate region, a total difference value of the appearance frequencies of the image feature quantities of the first histogram and the second histogram; and a region selection unit configured to select the candidate region having a minimum total difference value from among the candidate regions

Abstract: A learning device includes: a time-series information generation unit that obtains a first image group including a plurality of successive time-series images including a reference image and generates first time-series information based on a difference between the reference image and each of the images in the first image group other than the reference image; and a first learning unit that performs machine learning using the reference image and the first time-series information, thereby obtaining a first learning result used for estimating depth information on a target image, which is an image to be processed, and silhouette information on a subject captured in the target image based on the target image and second time-series information generated from a second image group including a plurality of successive time-series images including the target image.

Abstract: An image processing apparatus includes a change detection unit configured to detect color or luminance change for each of different resolutions among changes in color or luminance in a color or luminance image, and a reliability estimation unit configured to estimate reliability of the detected color or luminance change based on temporal color or luminance change in the color or luminance image. The reliability estimation unit estimates the reliability based on a diffusion result of the temporal color or luminance change in the color or luminance image. The reliability has a greater value when the diffusion result shows anisotropy.

Abstract: An image processing apparatus includes a change detection unit that detects phase changes in multiple predetermined directions from among phase changes in a luminance image for each of the mutually different resolutions, and a reliability estimation unit that estimates reliability of the detected phase change based on temporal phase changes in the multiple directions determined in the luminance image. The reliability estimation unit may estimate the reliability based on a diffusion result of the temporal phase changes in the direction. The reliability may become a greater value as the diffusion result indicates more anisotropic.

Abstract: An image processing apparatus includes a change detection unit configured to detect phase changes in multiple predetermined directions from among phase changes in a luminance image in units of mutually different resolutions, and a reliability estimation unit configured to estimate reliability of the detected phase change based on temporal amplitude change information in the multiple directions determined in the luminance image. The reliability estimation unit may estimate the reliability using an amplitude change of multiple resolutions and using a value of an amplitude change equal to or greater than a predetermined threshold value among images having multiple resolutions. The reliability may become a greater value as the amplitude change becomes larger.

Abstract: An image processing apparatus includes a change detection unit configured to detect a direction change, which is a temporal fluctuation in a plausible direction orthogonal to an edge determined for each pixel in an edge image indicating a high frequency component in an image that is an image processing target, and a phase change, which is a temporal fluctuation of a phase of the high frequency component according to the direction change, and a reliability estimation unit configured to estimate reliability indicating that the detected phase change is not a change caused by noise based on a variance value of the direction change per unit time.

Abstract: A subject tracking device includes a first histogram generation unit configured to generate a first histogram representing an appearance frequency of an image feature quantity of a subject region, for each segment and for each auxiliary segment of the image feature quantity, a second histogram generation unit configured to generate, for each candidate region, a second histogram representing an appearance frequency of the image feature quantity of an image of a candidate region for each segment and for each auxiliary segment of the image feature quantity, a difference derivation unit configured to compare the first histogram with the second histogram for each segment and for each auxiliary segment, and derive, for each candidate region, a total difference value of the appearance frequencies of the image feature quantities of the first histogram and the second histogram; and a region selection unit configured to select the candidate region having a minimum total difference value from among the candidate regions

Abstract: A learning device includes: a time-series information generation unit that obtains a first image group including a plurality of successive time-series images including a reference image and generates first time-series information based on a difference between the reference image and each of the images in the first image group other than the reference image; and a first learning unit that performs machine learning using the reference image and the first time-series information, thereby obtaining a first learning result used for estimating depth information on a target image, which is an image to be processed, and silhouette information on a subject captured in the target image based on the target image and second time-series information generated from a second image group including a plurality of successive time-series images including the target image.