Seiko Epson Corporation

Ian Clarke

Eunice Poon

Hui Zhou

Megumi Kanda

Jeffrey Matthew Achong

Guoyi Fu

Mikhail Brusnitsyn

Yang Yang

Arash Abadpour

Ivo Moravec

Kazuo Nakamura

Hisanori Nakajima

Graham Sellers

Masahiro Hirose

Yury Yakubovich

Lihui Chen

Dan Rico

Derek Hang Chun Kwok

Puneet Khanduri

An adequate solution for computer vision applications is arrived at more efficiently and, with more automation, enables users with limited or no special image processing and pattern recognition knowledge to create reliable vision systems for their applications. Computer rendering of CAD models is used to automate the dataset acquisition process and labeling process. In order to speed up the training data preparation while maintaining the data quality, a number of processed samples are generated from one or a few seed images.

Method and apparatus for improved training of object detecting system

A method of estimating and compensating for jitter between two video frames in a digital video sequence comprises comparing a pixel subset in one video frame to candidate matching pixel subsets within a search area in the other video frame to obtain a preliminary matching pixel subset. Based on the location of the preliminary matching pixel subset, an adapted search area is formed and the comparing is continued within the adapted search area to obtain a final matching pixel subset. Motion is calculated between the video frames based on the final match thereby to estimate the jitter. The video frames are then transformed based on the estimated jitter.

Method and apparatus for estimating and compensating for jitter in digital video

An evaluating method of image information acquires image information, performs camera shake evaluation of the image information by determining a camera shake direction on the basis of the image information, and performs out-of-focus evaluation of the image information by using information concerning a camera shake direction determined in the camera shake evaluation.

Evaluating method of image information, storage medium having evaluation program stored therein, and evaluating apparatus

The present method of evaluating image information, includes: extracting, from image information to be evaluated, a plurality of edge pixels located in a boundary of an image expressed by the image information; calculating, for each of the edge pixels, a number of pixels that include the edge pixel targeted for calculation, that exist in the boundary, and that are arranged in a direction intersecting with the boundary; and performing out-of-focus blurring evaluation of the image information on the basis of the number of pixels that exist in the boundary and a number of the edge pixels.

Method of evaluating image information, storage medium, and image-information evaluating apparatus

An image correcting method includes: (a) acquiring original image information to be corrected; (b) setting a confirmation range for confirming an effect of a correction; (c) producing corrected image information; and (d) displaying, on an image display section, an original image based on the original image information and a corrected image based on the corrected image information. The confirmation range is set based on the original image information. The corrected image information is produced by correcting the original image information within the confirmation range.

Image correcting method, image correcting apparatus, and storage medium having program stored thereon

A method of enhancing contrast in a digital image comprises isolating pixels of the image having intensities falling within a low intensity range, determining at least one feature of the pixel distribution with the range and adjusting intensities of pixels in the image to make the pixels within the range more visually distinct. An apparatus for enhancing contrast in a digital image is also provided.

Method and apparatus for increasing contrast in a digital image

A system and method of recognizing characters in a captured image, includes selecting a portion of the image encompassing a string of characters in the captured image to be recognized. The selected portion is examined to identify segments that potentially include the characters of the string. During the examining multiple tests on pixels in the selected portion are performed. The identified segments are then examined to recognize the string of characters.

Method and system for recognizing a candidate character in a captured image

A method of fractionally resizing an image comprises locating areas of sensitivity in the image. Adjustment lines in the image are selected at least partially based on the location of the located areas of sensitivity. The image is then resized along the adjustment lines. An apparatus for fractionally resizing an image is also provided.

Method and apparatus for resizing images

An image capturing device is adapted to capture an object image and to generate an image data. Each of a plurality of image capturers is adapted to perform an image capturing of a part of the object image and to generate a partial image data which constitutes a part of the image data based on the part of the object image. Each of the image capturers has an optical axis. A detector is operable to detect movement of each of the image capturers in a direction perpendicular to the optical axis during the image capturing and to generate movement data based on the detected movement, for each of the image capturers. A processor is operable to correct the partial image data so as to compensate the movement during the image capturing based on the movement data.

Image capturing device, correction device, mobile phone, and correcting method

A method of estimating three-dimensional camera position information from a series of two-dimensional images that form a panorama employs common features in adjoining image pairs in the series to estimate a transform between the images in the pairs. The common features are subsequently employed to adjust an estimated rotational component of each transform by reducing error between coordinates corresponding to the common features in three-dimensional space in image pairs, on a pair-by-pair basis. A global optimization of the position estimation, used for long sequences of images such as 360 degree panoramas, refines the estimates of the rotational and focal length components of the transforms by concurrently reducing error between all 3D common feature coordinates for all adjoining pairs.

Method for determining camera position from two-dimensional images that form a panorama

In the present invention, an image evaluation method includes: (a) a step of acquiring image information to be evaluated; (b) a step of setting an evaluation area for the acquired image information, the evaluation area being smaller than an area corresponding to the image information; (c) a step of obtaining an individual evaluation on the acquired image information; (d) a step of obtaining another individual evaluation on the acquired image information; and (e) a step of obtaining a comprehensive evaluation based on the individual evaluation and the other individual evaluation. Herein, the individual evaluation is obtained by individually evaluating a predetermined item in the set evaluation area. The other individual evaluation is obtained by individually evaluating another predetermined item.

Image evaluation method, image evaluation device, and storage medium having program stored thereon

In one embodiment, a method of reducing brightness variations in a panorama comprises calculating a gamma plane based on the panorama, blurring and normalizing the gamma plane and then applying the blurred and normalized gamma plane to the panorama thereby to reduce brightness variations therein.

Method and apparatus for reducing brightness variations in a panorama

A method, apparatus and image capture device for limiting motion blur during capture of an image predetermines a relationship between movement of the image capture device, time and blur extent. A rate of movement of the image capture device is obtained and used in conjunction with the determined relationship and a blur extent limit as a basis for obtaining a maximum exposure time for the image capture device in order to limit blur extent. On the basis of the maximum exposure time obtained and a required image brightness, the image capture device is configured, and then an image is captured by the image capture device. Once the image has been captured, due to the possibility of under-exposure from having limited the exposure time, the image is processed as required to increase its intensity.

Determining maximum exposure time to limit motion blur during image capture

An evaluation on image information is indicated before operating a shutter operating section. For example, a method of evaluating image information includes: (a) a step of converting an optical image of an object to be photographed into electrical image information; (b) a step of evaluating the image information that has not yet been stored in a non-volatile memory, wherein the image information is evaluated in a state in which a shutter operating section, on which a predetermined operation is performed when storing the image information in the non-volatile memory, is not being subjected to the predetermined operation; and (c) a step of outputting an evaluation result.

Method of evaluating image information, photographing device, and storage medium having program stored thereon

A method of generating a high-resolution image from a set of source low-resolution images includes estimating a high-resolution image based on the set of source low-resolution images. The estimated high-resolution image is then transformed into a set of estimated low-resolution images. The set of source low-resolution images is compared with the set of estimated low-resolution images to generate a set of low-resolution errors. The set of low-resolution errors is transfomed into a set of high-resolution errors. A high-resolution error image based on the set of high-resolution errors is generated. The high-resolution error image is combined with the estimated high resolution image to yield an updated estimated high-resolution. The above steps are repeated until the updated estimated high-resolution image is of a desired quality.

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North York, Canada

Ian Clarke