A medical information processing apparatus according to an embodiment includes a storage and processing circuitry. The storage is configured to store therein, for each point of a frequency space represented by a plurality of pieces of first frequency component data acquired by applying frequency conversion to data inside regions of interest set to medical images, characteristic data representing a tendency of spectral values that appear at the point. The processing circuitry is configured to acquire second frequency component data by applying frequency conversion to a medical image to be processed, to determine similarity of a spectral value at each point of a frequency space represented by the second frequency component data, with the characteristic data, and to designate a target area in the frequency space represented by the second frequency component data based on the result of the determination.

A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.

A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.

A system for predicting occurrence of a defective image includes: an input device configured to input image data into an image forming apparatus; and a hardware processor configured to analyze a spatial frequency of gradient distribution of an image in accordance with a size of a density irregularity specific to the image forming apparatus with respect to the input image data and to calculate a probability of a conspicuous density irregularity of the size in regard to the image formed by the image forming apparatus based on the image data with reference to an index of correlation between an analysis result and an evaluation value of the density irregularity.

Apparatuses and methods are disclosed for generating a quantitative indication of the degree of oiliness of skin. In exemplary embodiments, a difference image generated from parallel- and a cross-polarized images of an area of skin is subjected to an intensity thresholding operation. An oiliness metric is generated based on the average intensity of those pixels whose intensities do not exceed the threshold, and/or on the average intensity of those pixels whose intensities exceed the threshold. An indication based on the metric is generated and output.

Apparatuses and methods are disclosed for generating a quantitative indication of the degree of oiliness of skin. In exemplary embodiments, a difference image generated from parallel- and a cross-polarized images of an area of skin is subjected to an intensity thresholding operation. An oiliness metric is generated based on the average intensity of those pixels whose intensities do not exceed the threshold, and/or on the average intensity of those pixels whose intensities exceed the threshold. An indication based on the metric is generated and output.

Methods of characterizing the topography of a surface of a creped material, devices for characterizing surface topography of a creped material, computer systems for characterizing surface topography of a creped material, and the like, are disclosed.

Methods of characterizing the topography of a surface of a creped material, devices for characterizing surface topography of a creped material, computer systems for characterizing surface topography of a creped material, and the like, are disclosed.

It is possible to consider an image as a composite wave that is the result of layering waves having differing periods and amplitudes. An image captured via a lens having a water droplet attached thereto has a higher occurrence of changes such as image blur than an image that does not have a water droplet attached thereto, which means that this type of change in an image results in changes in the composite wave thereof as well. Provided is a lens dirtiness detection device that suitably determines whether a lens is dirty without being affected by a background image by: focusing on the occurrence of large changes in the composite wave of an image that accompany an increase in the dirtiness of a camera lens; extracting each of the image frequency components that constitute the composite wave; and analyzing changes in the magnitude relation between the frequency components.

It is possible to consider an image as a composite wave that is the result of layering waves having differing periods and amplitudes. An image captured via a lens having a water droplet attached thereto has a higher occurrence of changes such as image blur than an image that does not have a water droplet attached thereto, which means that this type of change in an image results in changes in the composite wave thereof as well. Provided is a lens dirtiness detection device that suitably determines whether a lens is dirty without being affected by a background image by: focusing on the occurrence of large changes in the composite wave of an image that accompany an increase in the dirtiness of a camera lens; extracting each of the image frequency components that constitute the composite wave; and analyzing changes in the magnitude relation between the frequency components.