An abnormal physical condition determination system includes: an extraction unit that extracts a plurality of feature quantities indicating a condition of a target person from an image of the target person; an accumulation unit that accumulates the plurality of feature quantities as time series data; a calculation unit that calculates a relationship between each feature quantity from the plurality of feature quantities accumulated in the accumulation unit; and a determination unit that determines an abnormal physical condition of the target person on the basis of the relationship. According to such an abnormal physical condition determination system, it is possible to appropriately determine the abnormal physical condition of the target person.

An abnormal physical condition determination system includes: an extraction unit that extracts a plurality of feature quantities indicating a condition of a target person from an image of the target person; an accumulation unit that accumulates the plurality of feature quantities as time series data; a calculation unit that calculates a relationship between each feature quantity from the plurality of feature quantities accumulated in the accumulation unit; and a determination unit that determines an abnormal physical condition of the target person on the basis of the relationship. According to such an abnormal physical condition determination system, it is possible to appropriately determine the abnormal physical condition of the target person.

The disclosed feature makes it possible to accurately determine a change that has occurred in a tissue. The feature includes: a binarizing section (41) that generates, from an image to be analyzed, a plurality of binarized images having respective binarization reference values different from each other; a Betti number calculating section (42) that calculates, for each of the plurality of binarized images, a one-dimensional Betti number indicating the number of hole-shaped regions each of which is surrounded by pixels each having a first pixel value obtained by binarization and is constituted by pixels each having a second pixel value obtained by binarization; and a determining section (44) that determines a change that has occurred in the tissue, based on a binarization reference value and a one-dimensional Betti number in a binarized image in which the one-dimensional Betti number is maximized.

The coordinate system fixing unit uses the displacement of an object under measurement between photographed images in chronological order to generate fixed-coordinate chronological images. The displacement calculation unit uses the fixed-coordinate chronological images to calculate a two-dimensional spatial distribution of the displacement of the surface of the object under measurement. The displacement difference calculation unit calculates a two-dimensional displacement difference distribution by removing an error component from the two-dimensional spatial distribution. The depth movement amount calculation unit calculates a depth movement amount from the two-dimensional displacement difference distribution. The displacement separation unit calculates in-plane displacement from the two-dimensional displacement difference distribution. The determination unit uses the in-plane displacement and/or the depth movement amount to determine whether there is an abnormality in the object under measurement.

An object of the invention is to configure an object search device capable of expressing information on shapes and irregularities as features only by images, in a search for an object that is characteristic in shape or irregularity, and performing an accurate search.

The object search device includes: an image feature extraction unit that is configured with a first neural network, and is configured to input an image to extract an image feature; a three-dimensional data feature extraction unit that is configured with a second neural network, and is configured to input three-dimensional data to extract a three-dimensional data feature; a learning unit that is configured to extract an image feature and a three-dimensional data feature from an image and three-dimensional data of an object obtained from a same individual, respectively, and update an image feature extraction parameter so as to reduce a difference between the image feature and the three-dimensional data feature; and a search unit that is configured to extract image features of a query image and a gallery image of the object by the image feature extraction unit using the updated image feature extraction parameter, and calculate a similarity between the image features of both images to search for the object.

A method and apparatus for detecting a digital stamp pattern are disclosed. Keypoints and descriptors are extracted from an original template pattern image. A low resolution original document and at least one lower resolution template pattern image are template-matched to detect a matched region based on match correlation coefficients. This region is cropped out of a full resolution original document. Keypoints and descriptors are extracted from the cropped region, and are matched with stamp pattern keypoints and descriptors using feature based pattern matching. A transformation matrix is used to detect scaling, rotation, and translation of a detected digital stamp pattern in the cropped region. A number of qualified matches determined using feature based pattern matching or the transformation matrix are checked against a pre-set threshold. If a pre-set threshold is exceeded, an alert is generated for a possible security issue. Otherwise, a no security issues signal may be generated.

A method and apparatus for detecting a digital stamp pattern are disclosed. Keypoints and descriptors are extracted from an original template pattern image. A low resolution original document and at least one lower resolution template pattern image are template-matched to detect a matched region based on match correlation coefficients. This region is cropped out of a full resolution original document. Keypoints and descriptors are extracted from the cropped region, and are matched with stamp pattern keypoints and descriptors using feature based pattern matching. A transformation matrix is used to detect scaling, rotation, and translation of a detected digital stamp pattern in the cropped region. A number of qualified matches determined using feature based pattern matching or the transformation matrix are checked against a pre-set threshold. If a pre-set threshold is exceeded, an alert is generated for a possible security issue. Otherwise, a no security issues signal may be generated.

There are provided an information processing apparatus, an information processing system, an information processing method, and a program which can intuitively grasp a state of a region of interest.

An information processing apparatus includes at least one processor; and at least one memory that stores a command for the at least one processor to execute, in which the at least one processor acquires an analysis result relating to a characteristic of a region of interest of an image, and generates a diagram on the basis of the analysis result. The diagram expresses a state of the region of interest having an exclusive relationship by at least one of a spread direction or an area.

There are provided an information processing apparatus, an information processing system, an information processing method, and a program which can intuitively grasp a state of a region of interest.

An information processing apparatus includes at least one processor; and at least one memory that stores a command for the at least one processor to execute, in which the at least one processor acquires an analysis result relating to a characteristic of a region of interest of an image, and generates a diagram on the basis of the analysis result. The diagram expresses a state of the region of interest having an exclusive relationship by at least one of a spread direction or an area.

An object detection method and apparatus are disclosed. The object detection method may include acquiring identification information of an object detected from each of N (N being a preset integer of 3 or more) images continuous in order of capture from the output of a classification layer of an object detection deep learning network and correcting, based on the identification information of the object detected from each of at least two images that have been captured early in order of time, among the N images, the identification information of the object detected from an image that has been captured later in order of time than the at least two images that have been captured early in order of time, among the N images.