Abstract: First, a face within an image, which is a target of detection, is detected. Detection data of the face is employed to detect eyes which are included in the face. Detection data of the eyes are employed to detect the inner and outer corners of the eyes. Detection data of the inner and outer corners of the eyes is employed to detect characteristic points of the upper and lower eyelids that represent the outline of the eyes.

Abstract: First, a face within an image, which is a target of detection, is detected. Detection data of the face is employed to detect eyes which are included in the face. Detection data of the eyes are employed to detect the inner and outer corners of the eyes. Detection data of the inner and outer corners of the eyes is employed to detect characteristic points of the upper and lower eyelids that represent the outline of the eyes.

Abstract: First, a face within an image, which is a target of detection, is detected. Detection data of the face is employed to detect eyes which are included in the face. Detection data of the eyes are employed to detect the inner and outer corners of the eyes. Detection data of the inner and outer corners of the eyes is employed to detect characteristic points of the upper and lower eyelids that represent the outline of the eyes.

Abstract: A compensation table for compensating interference signal obtained by OCT measurement is generated easily. When a reflection body interference signal is obtained by OCT measurement, the reflection body interference signal is compensated by a compensation table (Formula (1)) to obtain an evaluation output signal. Then, a spectrum of the evaluation output signal is calculated to obtain an evaluation value of the spectrum. Thereafter, a model coefficient is updated such that the evaluation value becomes small.

Abstract: An index representing the probability that a fraction image is a face image including a face in an input image is calculated for each of the positions of the face to be detected on the basis of a feature value. When the sum of the indexes of the fraction images is not smaller than the first threshold value, the image formed by the fraction images is determined to be a face image.

Abstract: Faces are detected efficiently, while preventing deterioration of face detection accuracy due to brightness during photography or the type of illumination which is employed during photography. When an image is obtained by an imaging means, an illumination judging means judges whether the illumination employed during photography is provided tungsten light source. In the case that the illumination is provided by a tungsten light source, face detection is performed based on a red signal image. In the case that the illumination is not provided by a tungsten light source, face detection is performed based on a green signal image.

Abstract: Deterioration of the resolution of tomographic images obtained by optical tomography measurement is prevented. Interference signals, which are obtained when a light beam L is emitted, are divided into a plurality of divided interference signals each having different wavelength bands. Spectral analysis is administered for each of the plurality of divided interference signals, to obtain a plurality of pieces of intermediate tomographic data. The plurality of pieces of intermediate tomographic data are employed to obtain tomographic data, a tomographic image is generated based on the tomographic data, then displayed.

Abstract: A plurality of different facial images is used to cause a face classification apparatus to learn a characteristic feature of a face by using a machine-learning method. Each of the facial images includes a face which has the same direction and the same angle of inclination as those of a face included in each of the other facial images and each of the facial images is limited to an image of a specific facial region. For example, the facial region is a predetermined region including only a specific facial part other than a region below an upper lip to avoid an influence of a change in facial expressions. Alternatively, if the apparatus is used to detect a frontal face and to perform refined detection processing on the extracted face candidate, a region including only an eye or eyes, a nose and an upper lip is used as the facial region.

Abstract: To detect a face image in an inputted image, predetermined-size partial images are cut out at different positions in the inputted image. An indicator value indicating a probability of each partial image being the face image is calculated. The partial images having the indicator values not less than a first threshold are extracted as candidate face images. Each candidate is set as a candidate of interest. If any nearby candidate is present within a predetermined coordinate distance from the candidate of interest, the candidate of interest and the nearby candidate are set in one candidate group. For each candidate group, an integrated indicator value reflecting the indicator values calculated for the candidates forming the candidate group is calculated. Then, an image within a predetermined area in the inputted image containing the candidate group having the integrated indicator value not less than a second threshold is extracted as the face image.

Abstract: In a method of detection of different objects in an input image by application, to partial images cut at different positions in the input image, of a plurality of weak classifiers that evaluate whether a detection target image is an image of a predetermined object based on a histogram of values of characteristic quantities calculated from a plurality of sample images representing the predetermined object, the histogram is extended to multi-dimensions and a criterion for the evaluation by the weak classifiers is a multi-dimensional histogram representing histograms for the different objects in the form of vectors.

Abstract: Usability of a face detecting apparatus is improved, by enabling selection of a detecting mode optimal for an intended purpose, when detecting facial images from within images. During detection of images of forward facing faces, switching of the detecting mode to one of: a detection rate mode; a false positive detection rate mode; and a processing speed mode is enabled. Face detection focused on optimizing detection performance for each detecting mode is performed.

Abstract: In a method of detecting a face in various directions in a target image with use of detectors, a partial image cut sequentially from the target image is subjected to mirror reversal processing and rotation processing in 90 degree increments for generating reversed/rotated images of the partial image. The detectors of predetermined types judge whether the respective images represent face images in predetermined face directions and orientations. Based on combinations of the types of the detectors and the types of the input images, faces in various face directions and orientations can be judged.

Abstract: Red-eye area detection accuracy is improved according to a pattern of appearance of red-eye areas generated frequently in an actual photography environment. A red-eye candidate area finding unit finds red-eye candidate areas in a digital photograph image, by using reference data. A display unit displays the image with the red-eye candidate areas having been marked and preliminary corrected. A user can specify an unfound red-eye area and an erroneously specified area by using a specification unit 22. An update unit 24 updates the reference data by learning a characteristic of the unfound red-eye area and the erroneously specified area so that a probability becomes higher regarding detection of an area having a characteristic similar to the unfound red-eye area as a red-eye candidate area while becomes lower regarding detection of an area having a characteristic similar to the erroneously specified area as a red-eye area.

Abstract: A moving image generating apparatus for generating three-dimensional moving image to display a subject as a three-dimensional object, comprises a plurality of two-dimensional moving image generators, and each of the two-dimensional moving image generators is provided to each position, of which relative positions with respect to each of the two-dimensional moving image generators is predetermined respectively, and captures the subject at different timing intermittently, so that each of the two-dimensional moving image generators generating two-dimensional captured moving image respectively, and a three-dimensional moving image generator operable to generate the three-dimensional moving image of which frame rate is higher than a frame rate of the two-dimensional captured moving image, based on both the relative positions of the plurality of two-dimensional moving image generator and the two-dimensional captured moving images generated by the two-dimensional moving image generators.

Abstract: Structural element candidates, estimated to be predetermined structural elements of a predetermined subject, are detected from an image that includes the subject. The subject that includes the structural element candidates is detected from the image in the vicinity of the detected structural element candidates. The characteristics of the structural elements are discriminated from the image in the vicinity of the structural element candidates, at a higher accuracy than when the structural elements were detected. In the case that the characteristics of the structural elements are discriminated, the structural element candidates are confirmed as being the predetermined structural elements.

Abstract: A compensation table for compensating interference signal obtained by OCT measurement is generated easily. When a reflection body interference signal is obtained by OCT measurement, the reflection body interference signal is compensated by a compensation table (Formula (1)) to obtain an evaluation output signal. Then, a spectrum of the evaluation output signal is calculated to obtain an evaluation value of the spectrum. Thereafter, a model coefficient is updated such that the evaluation value becomes small.

Abstract: First, a face within an image, which is a target of detection, is detected. Detection data of the face is employed to detect eyes which are included in the face. Detection data of the eyes are employed to detect the inner and outer corners of the eyes. Detection data of the inner and outer corners of the eyes is employed to detect characteristic points of the upper and lower eyelids that represent the outline of the eyes.

Abstract: Deterioration of the resolution of tomographic images obtained by optical tomography measurement is prevented. Interference signals, which are obtained when a light beam L is emitted, are divided into a plurality of divided interference signals each having different wavelength bands. Spectral analysis is administered for each of the plurality of divided interference signals, to obtain a plurality of pieces of intermediate tomographic data. The plurality of pieces of intermediate tomographic data are employed to obtain tomographic data, a tomographic image is generated based on the tomographic data, then displayed.

Abstract: In a method of detecting a face in various directions in a target image with use of detectors, a partial image cut sequentially from the target image is subjected to mirror reversal processing and rotation processing in 90 degree increments for generating reversed/rotated images of the partial image. The detectors of predetermined types judge whether the respective images represent face images in predetermined face directions and orientations. Based on combinations of the types of the detectors and the types of the input images, faces in various face directions and orientations can be judged.

Abstract: Faces are detected efficiently, while preventing deterioration of face detection accuracy due to brightness during photography or the type of illumination which is employed during photography. When an image is obtained by an imaging means, an illumination judging means judges whether the illumination employed during photography is provided tungsten light source. In the case that the illumination is provided by a tungsten light source, face detection is performed based on a red signal image. In the case that the illumination is not provided by a tungsten light source, face detection is performed based on a green signal image.