Morpho

Ntt Docomo, Inc.

Monolith Company, Ltd.

Kensuke Habuka

Masaki Hiraga

Takeshi Miura

Yukio Kamoshida

Toshiki Shiino

Yasushi Tanase

Yasuhiro Todoroki

Jun Nyukai

Tetsuya Hamada

Yasuyo Yasuda

Toshiki Shino

Go Abe

An image processing apparatus includes: a placement information acquisition section which acquires, for each image sensor, placement information of the image sensors with respect to the imaging section; a movement information acquisition section which acquires movement information of the imaging device; and an output area acquisition section which acquires, for every image sensor, position information of a second output area within the second frame image which corresponds to a first output area within the first frame image. The output area acquisition section acquires: a second output area of a base image sensor based on position information of a first output area determined by the placement information and based on the movement information; and a second output area of another image sensor based on the placement information of the base image sensor, the placement information of the other image sensor, and the movement information.

Apparatus, method and recording medium for image processing

An image processing device includes an image input portion, a motion vector acquisition portion and a distortion component estimation portion. The image input portion inputs a target image which is an image to be processed. The motion vector acquisition portion acquires a motion vector generated by relative movement with respect to a subject drawn in the target image when an imaging device which has generated the target image generates the target image. The distortion component estimation portion estimates a rolling shutter distortion component of the target image based on the motion vector.

Distortion estimating image processing device, method, and non-transitory storage medium

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.

Image identification device, image identification method, and recording medium

An image processing device includes an image input portion, a motion vector acquisition portion and a distortion component estimation portion. The image input portion inputs a plurality of frame images. The motion vector acquisition portion acquires an amount of parallel movement between two frame images. The distortion component estimation portion performs approximation such that a motion matrix indicating a motion between the frame images consists only of a parallel movement component and a rolling shutter distortion component, uses the amount of parallel movement to calculate, for each of the frame images, a tentative distortion coefficient included in the rolling shutter distortion component and uses the plurality of tentative distortion coefficients calculated to estimate a distortion coefficient.

Image processing device, image processing method and storage medium

An image data processing method for creating a clear blur-compensated image; the method calculates the motion data between a reference image and a target image, calculates difference in pixel value between the reference image and the target image for every block matched by the motion data, calculates, based on the difference in pixel value, the blending ratio between the reference image and the target image for each block, and creates a synthesized image by synthesizing the reference image and the target image according to the motion data and the blending ratio. An image processing apparatus implementing the image data processing method noted above is also disclosed.

Image data processing method and image processing apparatus

An image processing device that accumulatively generates a synthetic image by joining a first image, which is a single image or is constituted by a plurality of the images joined together, and a second image that is input, every time when the second image is input, the device comprising: a center-position obtainment portion () that obtains position information on a first center point being a center point of each of the images constituting the first image and position information on a second center point being a center point of the second image; and a synthetic image generation portion () that obtains the first center point of an image overlapping the second image among the images constituting the first image, and that joins the first image and the second image to generate the synthetic image, using perpendicular bisectors between the obtained first center points and the second center point as a joint between the first image and the second image, based on the obtained position information on the first center points and the obtained position information on the second center point.

Image processing device, image processing method and image processing program

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.

Image identification device, image identification method and recording medium

A panoramic image producing apparatus provides an intensity image production unit which produces intensity images from an input source image captured by an imaging device, a reference image data storage unit for storing a reference image which is used to compute the motion data of the intensity image and its absolute motion data, an absolute motion data calculation unit which uses the intensity image of the target image by first computing its relative motion data with respect to the reference image and using its result to compute the absolute motion data with respect to the target image, an image stitching unit to produce a panoramic image by using the absolute motion data computed above to stitch together the input source images, and a reference image setting unit which sets the target image as the new reference image under certain conditions.

Apparatus and program for producing a panoramic image

An image processing device that sequentially generates a synthetic image by joining a first image, which is a single image or constituted by a plurality of the images joined together, and a second image that is input, every time when the second image is input, includes: an overlapping region obtainment portionthat obtains a motion vector on the basis of the second image and an image input immediately before the second image among the images constituting the first image, and obtains an overlapping region where the image input immediately before and the second image overlap each other on the basis of the obtained motion vector; a brightness-value conversion factor calculation portionthat calculates a brightness conversion factor for converting a brightness value of the first image or a brightness value of the second image on the basis of the brightness value of the first image and the brightness value of the second image in the overlapping region so that a change in the brightness values between the first image and the second image becomes small; and a synthetic image generation portionthat generates the synthetic image by converting the first image or the second image using the brightness conversion factor and by joining the first image and the second image.

An image processing method for reducing noise of an image to be filtered; the method includes a step of creating shrunken image of shrinking ratio r&#xd7;r (where r is a natural number) by averaging the pixel values for each pixel in a block for every r pixels, a step of obtaining the pixel value in the shrunken image that correspond to the focus pixel in the source image by performing linear interpolation from the shrunken image pixels, a step of determining the noise reduction filter from the focus pixel and reference pixel in the shrunken image, and a step of computing the compensated pixel value of the focus pixel in the source image by weighted sum of each pixel value applying the filter to the focus pixel in the source image and the corresponding shrunken pixel.

Image processing method and imaging apparatus

Multi-resolution images of a reference image and a target image are generated. Then, whole-range matching is performed on an image of a lower resolution to detect a two-dimensional displacement between the images. Block matching is performed on an image of a higher resolution to detect a displacement at each feature point. The accuracy of motion data is increased by correcting the motion data with an image of a higher resolution by using the previously calculated motion data of the lowest resolution through higher resolutions as an initial value.

Multiple-resolution image processing apparatus

An image data processing method for sharpening a captured image; the method defines a first transformation equation to change the pixel values of a defocused image into a focused image using the coordinates of the defocused image and the focused image as arguments, calculates a DetailedFocus function from the optimal solution of the first transformation equation by giving the pixel values of the focused image and the defocused image as educational data, extracts a predetermined number of important points on the DetailedFocus function, defines a second transformation equation to change the pixel values of the important points of a defocused image into a focused image using the coordinates of the defocused image and the focused image as arguments, calculates a SmartFocus function from the second transformation equation, and produces a focused image from a defocused image using the SmartFocus function.

Image data processing method and imaging apparatus

A method for capturing an image using an image capturing module with multiple pixels configured in a 2-dimensional matrix and a focal plane electronic shutter function which sequentially slides the exposure timing by constant interval by pixel group which is a unit of predetermined number of the pixels while scanning; the method includes an image data input process for detecting shutter operation, obtaining a reference image while skipping one or more of the pixel groups by sequentially sliding the exposure timing by the constant interval, and obtaining a target image while not skipping pixel groups to be captured, a motion data detection process for calculating the motion data of the target image based on the reference image, and a distortion compensation process for compensating for distortion in the target image according to the motion data.

Method and apparatus for capturing an image

A motion vector calculation method includes: processing for calculating multi-resolution data up to a predetermined resolution level L from a plurality of inputted image data; and processing for estimating motion vectors per resolution executing a matching step for temporarily calculating motion vectors minimizing an energy function within a predetermined range of the input motion vectors and a smoothing step for finally calculating the motion vectors by averaging the surrounding data. By using the motion vectors of the resolution level L finally calculated as input motion vectors of a further higher resolution level L-1, the processing for estimating motion vectors per resolution is successively repeated on image data of a high resolution, thereby calculating the motion vectors.

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Tokyo, Japan

Kensuke Habuka