Abstract: An information processing apparatus inputs an image, detects the face of a person from the input image, and calculates a feature amount associated with the open/closed state of eyes of the detected face. In addition, the information processing apparatus calculates, as a feature-change amount, the difference between the calculated feature amount and a predetermined feature amount, and calculates the eye open/closed degree of eyes of the detected face on the basis of the feature amount and the feature-change amount.

Abstract: In an image capturing apparatus, a video input unit (2) captures the image of an object and sequentially acquires image data associated with the image capturing, a model data memory (6) stores model data associated with the first feature quantity calculated from a feature point of the object in a model image, a principal object detection unit (3) calculates the second feature quantity from a feature point of the object in the acquired image data, a state change estimation unit (4) estimates, on the basis of the second feature quantity and the model data, the timing when the object satisfies a predetermined condition, and an image input processing control unit (7) stores the image data corresponding to the estimated timing in an image recording unit (5). This configuration makes the image capturing apparatus acquire an image in a more proper state without large-capacity memory.

Abstract: A feature point detection unit (153) and feature amount extraction unit (154) extract a plurality of features of an object from input image data. When there are unextracted features of the plurality of features, a weight setting unit (155) sets weights for the extracted features. A facial expression determination unit (156) executes recognition processing of the object based on the features weighted by the weight setting unit (155).

Abstract: A feature point detection unit (153) and feature amount extraction unit (154) extract a plurality of features of an object from input image data. When there are unextracted features of the plurality of features, a weight setting unit (155) sets weights for the extracted features. A facial expression determination unit (156) executes recognition processing of the object based on the features weighted by the weight setting unit (155).

Abstract: The present invention facilitates the setting of a condition for the execution of predetermined processing and enables the execution of processing conforming to a user's intention. In an image processing apparatus, a predetermined object is detected in an image selected based on an instruction from a user, a determination is made regarding an attribute of the detected object, and the determined attribute is set as an execution condition used when executing predetermined processing on an image.

Abstract: An information processing apparatus inputs an image, detects the face of a person from the input image, and calculates a feature amount associated with the open/closed state of eyes of the detected face. In addition, the information processing apparatus calculates, as a feature-change amount, the difference between the calculated feature amount and a predetermined feature amount, and calculates the eye open/closed degree of eyes of the detected face on the basis of the feature amount and the feature-change amount.

Abstract: The present invention refers to an information processing apparatus comprising: an obtaining unit adapted to obtain an image of an object; a face region detection unit adapted to detect a face region of the object from the image; an eye region detection unit adapted to detect an eye region of the object; a generation unit adapted to generate a high-resolution image and low-resolution image of the face region detected by the face region detection means; a first extraction unit adapted to extract a first feature amount indicating a direction of a face existing in the face region from the low-resolution image; a second extraction unit adapted to extract a second feature amount indicating a direction of an eye existing in the eye region from the high-resolution image; and an estimation unit adapted to estimate a gaze direction of the object from the first feature amount and the second feature amount.

Abstract: At least one exemplary embodiment is directed to an imaging processing method where a plurality of images are picked up while an imaging direction of a camera is being changed. The images are combined into a composite image. A target region is specified within the composite image. An imaging direction and an imaging magnification for imaging an object included in the target region in a predetermined size are calculated based on a position and a size of the target region in the composite image and an imaging parameter set for the camera when the plurality of images are picked up. Accordingly, the imaging direction and the imaging magnification for imaging the object to be imaged can be set easily.

Abstract: The present invention provides an agent which induces acceleration of hard tissue formation, acceleration of cell differentiation and increase in cellular alkaline phosphatase activity, by directly acting on the cell. Specifically, it provides a hard tissue formation promoter, a cell differentiation inducer and a cellular alkaline phosphatase activity reinforcing agent comprising, as an active ingredient, a glycosaminoglycan or a salt thereof that keeps sulfate group and has the characteristics of the following (1) and (2): (1) a basic structure is a disaccharide repeating structure consisting of a hexuronic acid residue and a glucosamine residue, (2) one or less of the position among a 2-position hydroxyl group of a hexuronic acid residue, a 6-position hydroxyl group of a glucosamine residue and a 2-position amino group of the glucosamine residue in the basic structure of the aforementioned (1) does not have the sulfate group.

Abstract: An image processing apparatus acquires a plurality of input images which are serially photographed, detects a plurality of object regions from each input image, and associates object regions in a past input image with object regions in a current input image. If any object region that has not been appropriately associated exists in the object regions detected from the past input image, the apparatus calculates object region movement information for each size of the object region based on the object regions mutually associated between the past input image and the current input image, and estimates a position of the object region of the current input image that has not been associated based on the movement information for each size of the object region and a size of the object region that has not been associated.

Abstract: An image capturing apparatus includes an image input unit which photo-electrically converts an object image to generate an image signal, a status determination unit which determines an object status in an image generated by the image input unit, an automatic image capturing instruction unit which starts an automatic image capturing operation as an operation to automatically store the image generated by the image input unit in the first storage unit when the object status determined by the status determination unit satisfies a predetermined automatic image capturing condition, and a shooting control unit which automatically stores the image generated by the image input unit in the second storage unit when the object status does not satisfy the automatic image capturing condition even upon the lapse of a predetermined time after the automatic image capturing instruction unit designates the automatic image capturing operation.

Abstract: An eye region is extracted from an input image. A poor pupil hue quality region is extracted from the eye region. A high brightness region is extracted from the poor pupil hue quality region. A region obtained by excluding the high brightness region from the poor pupil hue quality region is extracted as a red-eye region. The high brightness region is corrected by using a method different from that for the red-eye region.

Abstract: In an image capturing apparatus, a video input unit (2) captures the image of an object and sequentially acquires image data associated with the image capturing, a model data memory (6) stores model data associated with the first feature quantity calculated from a feature point of the object in a model image, a principal object detection unit (3) calculates the second feature quantity from a feature point of the object in the acquired image data, a state change estimation unit (4) estimates, on the basis of the second feature quantity and the model data, the timing when the object satisfies a predetermined condition, and an image input processing control unit (7) stores the image data corresponding to the estimated timing in an image recording unit (5). This configuration makes the image capturing apparatus acquire an image in a more proper state without large-capacity memory.

Abstract: A facial expression recognition apparatus (10) detects a face image of a person from an input image, calculates a facial expression evaluation value corresponding to each facial expression from the detected face image, updates, based on the face image, the relationship between the calculated facial expression evaluation value and a threshold for determining a facial expression set for the facial expression evaluation value, and determines the facial expression of the face image based on the updated relationship between the facial expression evaluation value and the threshold for determining a facial expression.

Abstract: A plurality of kinds of feature amounts are collected from image information and voice information on a person existing in a space, valid values of the collected feature amounts are calculated, feature amounts to be used for personal recognition are determined in the collected feature amounts on the basis of the calculated valid values, and personal recognition is performed by using the determined feature amounts.

Abstract: A face detecting unit detects a person's face from input image data, and a parameter setting unit sets parameters for generating a gradient histogram indicating the gradient direction and gradient magnitude of a pixel value based on the detected face. Further, a generating unit sets a region (a cell) from which to generate a gradient histogram in the region of the detected face, and generates a gradient histogram for each such region to generate feature vectors. An expression identifying unit identifies an expression exhibited by the detected face based on the feature vectors. Thereby, the facial expression of a person included in an image is identified with high precision.

Abstract: A facial expression recognition apparatus includes an image input unit configured to sequentially input images, a face detection unit configured to detect faces in images obtained by the image input unit, and a start determination unit configured to determine whether to start facial expression determination based on facial image information detected by the face detection unit. When the start determination unit determines that facial expression determination should be started, an acquisition unit acquires reference feature information based on the facial image information detected by the face detection unit and a facial expression determination unit extracts feature information from the facial image information detected by the face detection unit and determines facial expressions of the detected faces based on the extracted feature information and the reference feature information.

Abstract: In an image capturing apparatus, a video input unit (2) captures the image of an object and sequentially acquires image data associated with the image capturing, a model data memory (6) stores model data associated with the first feature quantity calculated from a feature point of the object in a model image, a principal object detection unit (3) calculates the second feature quantity from a feature point of the object in the acquired image data, a state change estimation unit (4) estimates, on the basis of the second feature quantity and the model data, the timing when the object satisfies a predetermined condition, and an image input processing control unit (7) stores the image data corresponding to the estimated timing in an image recording unit (5). This configuration makes the image capturing apparatus acquire an image in a more proper state without large-capacity memory.

Abstract: A peptide having any one of the amino acid sequences of SEQ ID NO: 1 or 13, preferably a peptide having any one of the amino acid sequences of SEQ ID NOS: 2 to 9 or a peptide having any one of the amino acid sequences of SEQ ID NOS: 10 and 15 to 17, is used as an active ingredient of an agent for promoting growth or differentiation of cells such as osteoblasts, chondroblasts, cementoblasts, bone marrow-derived mesenchymal stem cells and periodontal ligament-derived cells.

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.