Abstract: A remittance instruction apparatus includes: a key information storage configured to store each of key information for a given user to use each remittance execution service in association with each of remittance execution services; an identification unit configured to identify the key information in the key information unit based on the remittance execution service designated by the user; and a remittance instruction unit configured to, based on remittance information in which a remittance amount in a digital currency and a remittance destination are specified, give a remittance instruction to the remittance execution service, the remittance instruction to remit to a remittance destination address corresponding to the specified remittance destination using the identified key information, wherein, each of the plurality of key information includes one or more keys that differ for each remittance execution service and includes information that can uniquely identify the given user in the remittance execution serv

Abstract: An object detection method includes an image acquisition step of acquiring an image including a target object, a layer image generation step of generating a plurality of layer images by one or both of enlarging and reducing the image at a plurality of different scales, a first detection step of detecting a region of at least a part of the target object as a first detected region from each of the layer images, a selection step of selecting at least one of the layer images based on the detected first detected region and learning data learned in advance, a second detection step of detecting a region of at least a part of the target object in the selected layer image as a second detected region, and an integration step of integrating a detection result detected in the first detection step and a detection result detected in the second detection step.

Abstract: An object detection method includes an image acquisition step of acquiring an image including a target object, a layer image generation step of generating a plurality of layer images by one or both of enlarging and reducing the image at a plurality of different scales, a first detection step of detecting a region of at least a part of the target object as a first detected region from each of the layer images, a selection step of selecting at least one of the layer images based on the detected first detected region and learning data learned in advance, a second detection step of detecting a region of at least a part of the target object in the selected layer image as a second detected region, and an integration step of integrating a detection result detected in the first detection step and a detection result detected in the second detection step.

Abstract: An object detection apparatus includes a first detection unit configured to detect a first portion of an object from an input image, a second detection unit configured to detect a second portion different from the first portion of the object, a first estimation unit configured to estimate a third portion of the object based on the first portion, a second estimation unit configured to estimate a third portion of the object based on the second portion, a determination unit configured to determine whether the third portions, which have been respectively estimated by the first and second estimation units, match each other, and an output unit configured to output, if the third portions match each other, a detection result of the object based on at least one of a detection result of the first or second detection unit and an estimation result of the first or second estimation unit.

Abstract: A learning apparatus comprises a plurality of detection units configured to detect a part or whole of a target object in an image and output a plurality of detection results; an estimation unit configured to estimate a state of the target object based on at least one of the plurality of detection results; a classification unit configured to classify the image into a plurality of groups based on the state of the target object; and a weight calculation unit configured to calculate weight information on each of the plurality of detection units for each of the groups based on the detection results.

Abstract: In an image within which a face pattern is detected, when a ratio of a skin color pixel is equal to or smaller than a first threshold value in a first region and a ratio of a skin color pixel is equal to or greater than a second threshold value in a second r region, the vicinity of the first region is determined to be a face candidate position at which the face pattern can exist. Face detection is carried out on the face candidate position. The second region is arranged in a predetermined position relative to the first region.

Abstract: An object detection method includes an image acquisition step of acquiring an image including a target object, a layer image generation step of generating a plurality of layer images by one or both of enlarging and reducing the image at a plurality of different scales, a first detection step of detecting a region of at least a part of the target object as a first detected region from each of the layer images, a selection step of selecting at least one of the layer images based on the detected first detected region and learning data learned in advance, a second detection step of detecting a region of at least a part of the target object in the selected layer image as a second detected region, and an integration step of integrating a detection result detected in the first detection step and a detection result detected in the second detection step.

Abstract: An information processing method comprises the steps of: inputting an input information of a multi-dimensional array; calculating an accumulated information value corresponding to a position of each element of the input information; and holding the accumulated information value in a buffer having a size of predetermined bits, wherein in the holding step, when an accumulated information value calculated in the calculating step overflows with respect to the size, a part not more than the predetermined bits of the calculated accumulated information value is held as the accumulated information value.

Abstract: A pattern recognition method, applicable to input information including a plurality of regions, includes obtaining a certainty at which each region of the input information includes a pattern, selecting one or more regions having a relatively high-level certainty among the plurality of regions, and performing pattern detection processing on the selected region or regions.

Abstract: An object detection apparatus includes a first detection unit configured to detect a first portion of an object from an input image, a second detection unit configured to detect a second portion different from the first portion of the object, a first estimation unit configured to estimate a third portion of the object based on the first portion, a second estimation unit configured to estimate a third portion of the object based on the second portion, a determination unit configured to determine whether the third portions, which have been respectively estimated by the first and second estimation units, match each other, and an output unit configured to output, if the third portions match each other, a detection result of the object based on at least one of a detection result of the first or second detection unit and an estimation result of the first or second estimation unit.

Abstract: An object detection method includes an image acquisition step of acquiring an image including a target object, a layer image generation step of generating a plurality of layer images by one or both of enlarging and reducing the image at a plurality of different scales, a first detection step of detecting a region of at least a part of the target object as a first detected region from each of the layer images, a selection step of selecting at least one of the layer images based on the detected first detected region and learning data learned in advance, a second detection step of detecting a region of at least a part of the target object in the selected layer image as a second detected region, and an integration step of integrating a detection result detected in the first detection step and a detection result detected in the second detection step.

Abstract: In a pattern identification method in which input data is classified into predetermined classes by sequentially executing a combination of a plurality of classification processes, at least one of the classification processes includes a mapping step of mapping the input data in an N (N?2) dimensional feature space as corresponding points, a determination step of determining whether or not to execute the next classification process based on the corresponding points, and selecting step of selecting a classification process to be executed next based on the corresponding points when it is determined in the determination step that the next classification process should be executed.

Abstract: A learning apparatus comprises a plurality of detection units configured to detect a part or whole of a target object in an image and output a plurality of detection results; an estimation unit configured to estimate a state of the target object based on at least one of the plurality of detection results; a classification unit configured to classify the image into a plurality of groups based on the state of the target object; and a weight calculation unit configured to calculate weight information on each of the plurality of detection units for each of the groups based on the detection results.

Abstract: A learning apparatus for a pattern detector, which includes a plurality of weak classifiers and detects a specific pattern from input data by classifications of the plurality of weak classifiers, acquires a plurality of data for learning in each of which whether or not the specific pattern is included is given, makes the plurality of weak classifiers learn by making the plurality of weak classifiers detect the specific pattern from the acquired data for learning, selects a plurality of weak classifiers to be composited from the weak classifiers which have learned, and composites the plurality of weak classifiers into one composite weak classifier based on comparison between a performance of the composite weak classifier and performances of the plurality of weak classifiers.

Abstract: In an image within which a face pattern is detected, when a ratio of a skin color pixel is equal to or smaller than a first threshold value in a first region and a ratio of a skin color pixel is equal to or greater than a second threshold value in a second r region, the vicinity of the first region is determined to be a face candidate position at which the face pattern can exist. Face detection is carried out on the face candidate position. The second region is arranged in a predetermined position relative to the first region.

Abstract: In an image within which a face pattern is detected, when a ratio of a skin color pixel is equal to or smaller than a first threshold value in a first region and a ratio of a skin color pixel is equal to or greater than a second threshold value in a second r region, the vicinity of the first region is determined to be a face candidate position at which the face pattern can exist. Face detection is carried out on the face candidate position. The second region is arranged in a predetermined position relative to the first region.

Abstract: A method includes detecting a feature of an input pattern using a plurality of feature detectors, selecting at least one of the feature detectors based on their output values, and calculating a feature quantity of the input pattern based on an output value from at least one selected feature detector.

Abstract: A plurality of pieces of learning data, each associated with a class to which the piece of the learning data belong, are input. In each piece of the learning data, a statistical amount of attribute values of elements in each of specific k parts, k being equal to or larger than 1, is calculated. Each piece of the learning data is mapped in a k-dimensional feature space as a vector having the calculated k statistics amounts as elements. Based on each piece of the mapped learning data and the classes to which the pieces of learning data belong, parameters for classifying input data into one of the plurality of classes are learned in the k-dimensional feature space. By using the parameters, pattern classification can be performed with high speed and high accuracy.

Abstract: There is disclosed an information processing apparatus which is handy for a user. In the information processing apparatus, a user interface of a control program for controlling a peripheral is automatically formed in accordance with a function obtained from the peripheral.

Abstract: An information processing method comprises the steps of: inputting an input information of a multi-dimensional array; calculating an accumulated information value corresponding to a position of each element of the input information; and holding the accumulated information value in a buffer having a size of predetermined bits, wherein in the holding step, when an accumulated information value calculated in the calculating step overflows with respect to the size, a part not more than the predetermined bits of the calculated accumulated information value is held as the accumulated information value.