Abstract: Provided is an image processing apparatus including a parameter storage that stores, in association with a plurality of attributes, a plurality of image processing parameters for respectively rendering subject images of different attributes from each other, into high image quality; and an image generating section that generates a high quality image of a subject image included in an input image, using the plurality of image processing parameters stored in the parameter storage. The image generating section may select at least one image from high quality images of the subject image included in the input image using respectively different combinations of the plurality of image processing parameters, based on comparison with the input image, and generate the selected image as the high quality image.

Abstract: An image processing apparatus includes a characteristic region detecting section that detects a plurality of characteristic regions in an image, a condition storing section that stores thereon assignment conditions differing in accordance with characters of characteristic regions, so that different compression strengths are assigned in accordance with the characters of the characteristic regions, a compressing section that respectively compresses a plurality of characteristic region images which are images of the plurality of characteristic regions, and a compression control section that controls compression strengths at which the compressing section respectively compresses the plurality of characteristic region images in accordance with characters of the plurality of characteristic regions, with reference to the conditions stored on the condition storing section.

Abstract: When obtaining a final result from a discriminator group arranged in a predetermined order, an evaluation value obtaining unit successively evaluates each discriminator from a first discriminator and if it is determined that a discrimination result represented by an overall evaluation value obtained by evaluating the first to Ith discriminators remains unchanged even if evaluation values of I+1th and subsequent discriminators are added to the overall evaluation value, a discrimination result determination unit instructs the evaluation value obtaining unit to terminate the evaluation of the I+1th and subsequent discriminators, and determines the discrimination result according to the overall evaluation value obtained by evaluating the first to Ith discriminators as a final result.

Abstract: An image processing apparatus includes a characteristic region detecting section that detects a characteristic region in an image, an image dividing section that divides the image into the characteristic region and a background region other than the characteristic region, and a compressing section that compresses a characteristic region image which is an image of the characteristic region and a background region image which is an image of the background region at different strengths from each other.

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 ?(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: The image processing apparatus and method, and the program and the recording medium according to the present invention can make the coefficient vector into high precision by noise elimination or correction utilizing the mutual correlation of the divided image areas in the intermediate eigenspace, and allows relaxation of the input condition and robustness. The high correlation in the divided image areas in the intermediate eigenspace can reduce the divided image areas to be processed, and actualize reduction in processing load and enhancement of the processing speed.

Abstract: An image compression apparatus capable of compressing an input image that includes a predetermined target object at a high compression ratio while allowing high quality image restoration. In the apparatus, the input image is reduced and compressed. A region of interest corresponding to a predetermined target object is set in the input image. A partial area image of an expanded image of the compressed reduced image corresponding to the region of interest is converted to a high resolution image by applying a prediction process that uses a learning result obtained by learning the predetermined object in advance. With respect to the region of interest portion, a differential image between the image converted to the high resolution and the input image is generated and encoded. Reduced image compression data and differential image encoded data are outputted.

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 ? (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: An image processing apparatus includes an encoding manner storing section that stores encoding manners in association with quantities of characteristics of objects, a characteristic region detecting section that detects a plurality of characteristic regions from an image, and a compressing section that compresses the images of the plurality of characteristic regions by encoding manners stored in the encoding manner storing section in association with the quantities of characteristics of objects included in the plurality of characteristic regions respectively.

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 ?(xo, yo) 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: When a processed image obtained by performing image processing on an original image is presented, an option desired by a user is presented. A plurality of object regions is detected from an original image to detect the imaged state of each of the object regions when the original image was obtained. Then, an attention level for each object region is calculated based on the imaged state of the object region, and an output condition and an image processing condition are set based on the calculated attention level. Thereafter, a processed image is generated by performing image processing according to the image processing condition, and the processed image is outputted according to the output condition.

Abstract: In an image processing device and method, program, and recording medium of the present invention, high frequency components of a low quality image and a high quality image included in a studying image set are extracted, and an eigenprojection matrix and a projection core tensor of the high frequency components are generated in a studying step. In a restoration step, a first sub-core tensor and a second sub-core tensor are generated based on the eigenprojection matrix and the projection core tensor of the high frequency components, and a tensor projection process is applied to the high frequency components of an input image to generate a high quality image of the high frequency components. The high quality image of the high frequency components is added to an enlarged image obtained by enlarging the input image to the same size as an output image.

Abstract: An eigenprojection matrix (#14) is generated by a projection computation (#12) using a local relationship from a studying image group (#10) including a pair of a high quality image and a low quality image to create a projection core tensor (#16) defining a correspondence between the low quality image and an intermediate eigenspace and a correspondence between the high quality image and the intermediate eigenspace. A first sub-core tensor is created (#24) from the projection core tensor based on a first setting, and an inputted low quality image (#20) is projected (#30) based on the eigenprojection matrix and the first sub-core tensor to calculate a coefficient vector in the intermediate eigenspace. The coefficient vector is projected (#34) based on a second sub-core tensor (#26) created by a second setting from the projection core tensor and based on the eigenprojection matrix to obtain a high quality image (#36).

Abstract: A tentative eigenprojection matrix (#12-a) is provisionally generated from a studying image group (#10) including a pair of a high quality image and a low quality image, and a tentative projection core tensor (#12-b) defining a correspondence between the low quality image and an intermediate eigenspace and a correspondence between the high quality image and the intermediate eigenspace is created. A first sub-core tensor is created from the tentative projection core tensor based on a first setting (#12-c), and the studying image group is projected based on the tentative eigenprojection matrix and the first sub-core tensor (#15-a) to calculate an intermediate eigenspace coefficient vector (#15-b).

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: An image processing apparatus includes: an image obtaining section obtaining an input image; a first image processing section generating a predetermined quality image resulting from rendering the input image in high image quality using a predetermined image processing parameter; a display section displaying the predetermined quality image; a characteristic region identifying section identifying a plurality of characteristic regions in the input image; a parameter determining section determining, respectively for the plurality of characteristic regions, image processing parameters for rendering images of the plurality of characteristic regions, in higher image quality; a second image processing section generating a plurality of high quality characteristic-region images respectively of the plurality of characteristic regions, using the image processing parameters determined by the parameter determining section; and a display control section replacing the plurality of characteristic regions in the predetermined

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: A detector configuration apparatus for configuring a detector that performs detection through a plurality of detection stages with different resolutions capable of objectively determining a detection target modality type to be detected in each stage. The detector is configured to detect to which of a plurality of attribute values an attribute of an object included in input data corresponds with respect to each of a plurality of modality types. A variation amount calculation unit obtains, based on a plurality of teacher data corresponding to each modality type used for training the detector, a representative value of variation between a plurality of teacher data with respect to each modality type, and a detection stage determination unit determines in which stage of the plurality of detection stages each modality type is to be detected based on the representative value of variation between the teacher data.

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 ?(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: 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.