Abstract: For enabling a change in lighting pattern in an image, adjustment of a shadow pattern is aimed at in an image. For this purpose, a parameter acquisition unit obtains weighting parameters representing the shadow pattern in a face region in the image by fitting to the face region detected by a face detection unit a mathematical model generated by a method of AAM using a plurality of sample images representing human faces in different lighting conditions. A shadow pattern adjustment unit adjusts the shadow pattern in the face region based on the weighting parameters having been obtained.

Abstract: An exposure value is determined based on a face region, with high accuracy and with less effect of a background region or density contrast caused by shadow. For this purpose, a face detection unit detects a face region from a face candidate region in a through image detected by a face candidate detection unit, by fitting to the face candidate region a mathematical model generated by a method of AAM using a plurality of sample images representing human faces. An exposure value determination unit then determines an exposure value for photography, based on the face region.

Abstract: In order to accurately remove an unnecessary periodic noise component from an image, a reconstruction unit generates a reconstructed image without a periodic noise component by fitting to a face region detected in an image by a face detection unit a mathematical model generated according a method of AAM using a plurality of sample images representing human faces without a periodic noise component. The periodic noise component is extracted by a difference between the face region and the reconstructed image, and a frequency of the noise component is determined. The noise component of the determined frequency is then removed from the image.

Abstract: In order to cause a structure such as a human face in an image to become unidentifiable while causing the image to have a natural finish, a parameter acquisition unit obtains weighting parameters representing a characteristic of an individual in a face region in the image by fitting to the face region detected by a face detection unit a mathematical model generated by a method of AAM using a plurality of sample images representing human faces. A reconstruction unit changes the weighting parameters to become smaller, and reconstructs the face region by using the parameters having been changed.

Abstract: An image processing apparatus comprises an image data obtaining section that obtains image data, an image analyzing section that analyzes an image represented by the image data obtained by the image data obtaining section, an image processing section that applies image processing to the image data obtained by the image data obtaining section in accordance with an analyzing result analyzed by the image analyzing section and a processing parameter for introducing processing contents from the analyzing result, and a parameter adjusting section that adjusts, prior to the image processing by the image processing section, the processing parameter in accordance with an operation.

Abstract: A luminance conversion function calculating circuit calculates a luminance conversion function in order that a specific image contained in an image represented by image data to be processed will be made to have a desired brightness. Saturation of the specific image in a case where the applied image data has been subjected to a luminance correction based upon the calculated luminance conversion function is calculated and upper-limit saturation is calculated based upon an input value from the user. If the saturation of the specific image after the luminance correction thereof exceeds the upper-limit saturation, then a target dynamic range revising circuit narrows the range width of a target dynamic range that stipulates the luminance conversion function. The luminance conversion function is revised in a luminance conversion function revising circuit so as to have the narrowed range width.

Abstract: On the basis of image data representing the area of a face image contained in an image represented by applied image data, a color correction value calculating circuit calculates a color correction value and a color image probability calculating circuit calculates the probability that the area of the face image is a color image. A color correction value adjusting circuit then adjusts the color correction value based upon the probability that the area of the face image is a color image. The adjusted color correction value is used in correction processing in an image correcting circuit.

Abstract: A trimmed image is obtained by trimming for cutting out from a photographic image obtained by taking a photograph of an object including a reference part, and one or more other part which has a common centerline with the reference part and is arranged in a direction in which the centerline of the reference part extends, an image of an area including a part or the whole of the reference part and said other part along a trimming centerline. The centerline of the reference part is obtained and the degree and the direction of inclination of said other part are detected from the photographic image. The trimming centerline is inclined by a larger angle away from the centerline toward the direction of the inclination of said other part as the degree of inclination increases.

Abstract: In an image processing method for carrying out an image quality correction for correcting an image quality on each of a plurality of images in an image group to obtain corrected images, an image processing method includes the steps of carrying out a temporary image quality correction for correcting an image quality on each images in the image group to obtain a temporarily corrected image. A characteristic value representing an image quality for each temporarily corrected image is calculated and a target characteristic value is calculated so that dispersion of each characteristic value to the target characteristic value is minimized on the basis of the characteristic value of each temporarily corrected image. A correction value of each of the plurality of images in the image group which corrects the characteristic value thereof to the target characteristic value is obtained, and the image is corrected with the correction value.

Abstract: Fed image data is used, to respectively calculate two color correction values in two correction value calculating circuits. Further, a target skin color chromaticity value and significance levels indicating which of the two color correction values should be attached importance to are inputted from an input device. Respective confidence levels for the two color correction values are calculated on the basis of the two color correction values and the inputted target skin color chromaticity value. On the basis of the calculated confidence levels, the inputted significance levels, and the two color correction values, both of the confidence levels and the significance levels are reflected, to calculate a new color correction value in a color correction value calculating circuit.

Abstract: A bright luminance value, a dark luminance value, and an average luminance value relating to a face image portion included in an image represented by fed image data are calculated in calculating circuits. Further, a target bright luminance value and a target dark luminance value are calculated in a calculating circuit on the basis of a target average luminance value and a dynamic range that are inputted from an input device and the calculated bright luminance value, dark luminance value, and average luminance value. Interpolation processing based on a correspondence between the calculated bright luminance value, dark luminance value, and average luminance value relating to the face image portion and the target bright luminance value, the target dark luminance value, and the target average luminance value respectively corresponding thereto is performed in a corrected value calculating circuit, to create a look-up table.

Abstract: An image processing system has a scale-down section for scaling down frames of a moving image to obtain thumbnail images, a temporary correction value calculation section for calculating a temporary correction value by employing a first feature quantity calculated from the thumbnail image by a feature quantity calculation section, and a temporary correction section for calculating a second feature quantity of a temporarily corrected thumbnail image obtained by correcting each thumbnail image with the temporary correction value.

Abstract: A single high resolution frame is obtained from a plurality of frames sampled from moving image data, regardless of movement of a subject included in the frames. Patches are provided in a plurality of frames including a reference frame and other frames. The patches are moved and/or deformed, and correspondent relationships are estimated for each frame. The other frames are coordinate converted based on the estimated correspondent relationships. Correlative values, which represent the degrees of correlation between the other frames and the reference frame, are calculated. The number of regions within the patches is changed, and the correlative values are calculated in the same manner. A synthesized frame is generated from the plurality of frames based on the correspondent relationship, which was estimated for the number of regions that yields the maximal correlation.

Abstract: To enable obtainment of a composite image including a movement trajectory of a moving body, even from a moving image including a background. Sampling means samples frames from moving image data. First mask frame calculating means calculates mask frames including mask regions, which correspond to positions of the moving body on the frames. Second mask frame calculating means accumulates and binarizes the mask frames to calculate a reference mask frame. Region cutout means masks the frames by using the mask frames and the reference mask frame to cut out moving body regions, which correspond to a moving body, from the frames. Composing means overwrites moving body regions on one of the frames in chronological order to obtain a composite image, which includes the movement trajectory of the moving body.

Abstract: A motion detecting apparatus includes a comparator and motion estimator. The comparator compares a threshold and an input absolute error, and selects as a selected motion vector, one of a predetermined motion vector and an input motion vector based on the comparison result. The motion estimator determines a reference video signal based on the selected motion vector, and determines an absolute error from the determined reference video signal and an input video signal to output the determined absolute error. Then, the motion estimator estimates a motion vector from the determined reference video signal and the input video signal such that the absolute error is minimum, to output a motion vector determined based on the estimated motion vector and the selected motion vector.

Abstract: To acquire a high-resolution frame from a plurality of frames sampled from a video image, it is necessary to obtain a high-resolution frame with reduced picture quality degradation regardless of motion of a subject included in the frame. Because of this, between a plurality of contiguous frames FrN and FrN+1, there is estimated a correspondent relationship. Based on the correspondent relationship, the frames FrN+1 and FrN are interposed to obtain first and second interpolated frames FrH1 and FrH2. Based on the correspondent relationship, the coordinates of the frame FrN+1 are transformed, and from a correlation value with the frame FrN, there is obtained a weighting coefficient &agr;(x°, y°) that makes the weight of the first interpolated frame FrH1 greater as a correlation becomes greater. With the weighting coefficient, the first and second interpolated frames are weighted and added to acquire a synthesized frame FrG.

Abstract: A motion detecting apparatus includes a comparator and motion estimator. The comparator compares a threshold and an input absolute error, and selects as a selected motion vector, one of a predetermined motion vector and an input motion vector based on the comparison result. The motion estimator determines a reference video signal based on the selected motion vector, and determines an absolute error from the determined reference video signal and an input video signal to output the determined absolute error. Then, the motion estimator estimates a motion vector from the determined reference video signal and the input video signal such that the absolute error is minimum, to output a motion vector determined based on the estimated motion vector and the selected motion vector.