Abstract: The invention provides an image identification device that classifies block images obtained by dividing a target image into predetermined categories, using a separating plane learning of which has been completed in advance for each of the categories. The image identification device includes a target image input unit inputs the target image, a block image generation unit divides the target image into blocks to generate the block images, a feature quantity computing unit computes feature quantities of the block images, and a category determination unit determines whether the block images are classified into one of the categories or not, using the separating plane and coordinate positions corresponding to magnitudes of feature quantities of the block images in a feature quantity space, wherein the feature quantity computing unit uses, as a feature quantity of a given target block image, local feature quantities and a global feature quantity.

Abstract: An image processing device includes: processing pixel group determination unit that determines a to-be-processed pixel group from a plurality of frame images of picture signals input based on a first predetermined rule and that determines a processed pixel group from the processed frame image stored based on a second predetermined rule; frequency component processing unit that extracts at least a temporal frequency component from the to-be-processed pixel group and the processed pixel group, that executes predetermined coefficient processing on a temporal high-frequency component of the temporal frequency component and that acquires the executed to-be-processed pixel group; processing completion frame generation unit that generates a processing completion frame image from the executed to-be-processed pixel group; and image output unit that outputs the processing completion frame image and that stores the processing completion frame image in the processed frame storage unit, in which the frequency component pr

Abstract: The invention provides an image identification device uses a separating plane to classify block images into the categories. The image identification device includes a target image input unit inputting a target image, a block image generation unit generates block images, a feature quantity computing unit computes feature quantities of the block images, and a category determination unit determines whether the block images are classified into the categories or not. The feature quantity computing unit uses local feature quantities of the block images and a global feature quantity of the target image as a whole, and also in a feature quantity space using features of the block images as coordinate axes, uses coordinate positions of feature quantity vectors optional areas in the feature quantity space to count the block images and causes the global feature quantity to include the number of the block images thus counted.

Abstract: An image identification method for classifying block images of input image data into one of predetermined categories; the method includes the steps of: dividing image data into multiple blocks to produce block images, processing the feature quantity of each block image by their color space information and frequency component, learning separating hyperplanes that indicate boundaries of each category by reading in training data image that have labeled categories for each block and processing image feature quantity for each block of an training data image, and classifying respective block image to a category according to the distance from the separating hyperplane of each category for a newly acquired image to obtain the image feature quantity of block images. An imaging apparatus implementing the image identification method noted above is also disclosed.

Abstract: The invention provides an image identification device that classifies block images obtained by dividing a target image into predetermined categories, using a separating plane learning of which has been completed in advance for each of the categories. The image identification device includes a target image input unit inputs the target image, a block image generation unit divides the target image into blocks to generate the block images, a feature quantity computing unit computes feature quantities of the block images, and a category determination unit determines whether the block images are classified into one of the categories or not, using the separating plane and coordinate positions corresponding to magnitudes of feature quantities of the block images in a feature quantity space, wherein the feature quantity computing unit uses, as a feature quantity of a given target block image, local feature quantities and a global feature quantity.

Abstract: The invention provides an image identification device uses a separating plane to classify block images into the categories. The image identification device includes a target image input unit inputting a target image, a block image generation unit generates block images, a feature quantity computing unit computes feature quantities of the block images, and a category determination unit determines whether the block images are classified into the categories or not. The feature quantity computing unit uses local feature quantities of the block images and a global feature quantity of the target image as a whole, and also in a feature quantity space using features of the block images as coordinate axes, uses coordinate positions of feature quantity vectors optional areas in the feature quantity space to count the block images and causes the global feature quantity to include the number of the block images thus counted.

Abstract: An image processing device includes: processing pixel group determination unit that determines a to-be-processed pixel group from a plurality of frame images of picture signals input based on a first predetermined rule and that determines a processed pixel group from the processed frame image stored based on a second predetermined rule; frequency component processing unit that extracts at least a temporal frequency component from the to-be-processed pixel group and the processed pixel group, that executes predetermined coefficient processing on a temporal high-frequency component of the temporal frequency component and that acquires the executed to-be-processed pixel group; processing completion frame generation unit that generates a processing completion frame image from the executed to-be-processed pixel group; and image output unit that outputs the processing completion frame image and that stores the processing completion frame image in the processed frame storage unit, in which the frequency component pr

Abstract: An image identification method for classifying block images of input image data into one of the multiple predetermined categories according to feature quantity in each block image; the method includes an image production step of dividing image data into multiple blocks to produce block images, an image feature quantity processing step of processing the feature quantity of each block image by their color space information and frequency component, a separating hyperplane processing step of learning separating hyperplanes that indicate boundaries of each category by reading in training data image that have labeled categories for each block and processing image feature quantity for each block of an training data image, and a category classification step of classifying respective block image to a category according to the distance from the separating hyperplane of each category by executing the block image production step and the image feature quantity processing step for a newly acquired image to obtain the image