Abstract: A method and system for processing image data to identify objects in an image. The method and system operate using various resolutions of the image to identify the objects. Information obtained while processing the image at one resolution is employed when processing the image at another resolution.

Abstract: A substrate inspection system is formed with an inspection device associated with a reflow process at the end of a series of production processes for substrates, image collectors each associated with a different one of the production processes upstream to the reflow process and an image display device for receiving images from the inspection device and the image collector and displaying the received images. The inspection device inspects a specified number of substrates to obtain measured values and calculates a margin for each component with respect to a specified reference value and transmits to the image collectors and the image display device the component code of the component with a small margin value. The image collectors save the image corresponding to the component code from the inspection device.

Abstract: Numerical image processing based on a model of medical image acquisition of two or more medical images to provide grayscale registration is described. The grayscale registered temporal images may then be displayed for visual comparison and/or further processed by a computer-aided diagnosis system for detection of medical abnormalities therein. A parametric method includes spatially registering two images and performing gray scale registration of the images. A parametric transform model, e.g., analog to analog, digital to digital, analog to digital, or digital to analog model, is selected based on the image acquisition method(s) of the images, i.e., digital or analog/film. Gray scale registration involves generating a joint pixel value histogram from the two images, statistically fitting parameters of the transform model to the joint histogram, generating a lookup table, and using the lookup table to transform and register pixel values of one image to the pixel values of the other image.

Abstract: The invention relates to an apparatus for segmenting a series of 2D or 3D images obtained by monitoring a patient's organ or other body part, wherein a first segmentation is carried out on a first image of the series of images and wherein the first segmentation is used for the subsequent segmentation of the remainder of images of the series of images. A series of transformations are carried out wherein each separate transformation embodies a fitting operation between two images of the series of images, and wherein substantially all images of the series of images are subject of such a transformation. The first segmentation on the first image of the series of images is modified and subsequently applied to any further image of the series of images according to the transformation or sequence of transformations that fits the first image to a further image of the series of images.

Abstract: Simultaneous grayscale and geometric registration of images, such as mammograms, facilitates temporal comparison and enhances the speed and reliability of computer aided diagnosis (CAD) detection of medical abnormalities. The method generally includes optimizing a merit function, e.g., sum of squared errors, containing parameters associated with a transformation function for simultaneous geometric and grayscale registering of the images, the optimizing of the merit function being performed by determining optimal values of the parameters using data in the images and registering one image to the other by applying the geometric and grayscale transformation function using the optimal values of the parameters. The optimizing may be performed iteratively from coarse to fine resolutions using a modified Levenberg-Marquardt method for optimizing nonlinear parameters with linear regression for optimizing linear parameters.

Abstract: Techniques are disclosed to provide more efficient and improved extraction of a portion of a scene without requiring excessive user interaction. More particularly, the extraction may be achieved by using iterated graph cuts. In an implementation, a method includes segmenting an image into a foreground portion and a background portion (e.g., where an object or desired portion to be extracted is present in the foreground portion). The method determines the properties corresponding to the foreground and background portions of the image. Distributions may be utilized to model the foreground and background properties. The properties may be color in one implementation and the distributions may be a Gaussian Mixture Model in another implementation. The foreground and background properties are updated based on the portions. And, the foreground and background portions are updated based on the updated foreground and background properties.

Abstract: When an output unit is capable of operation at a frame rate F and a resolution of x×y pixels, an imaging unit or an image input unit converts an image into an imaged frame in an internal data format having the frame rate F and a resolution of ix×jy pixels. An image converter converts the resolution of the imaged frame supplied from the imaging unit or the image input unit into a resolution that can be represented by the output unit, generating an output frame having x×y pixels. At this time, the image converter carries out predetermined resolution conversion based on a moving velocity of the image by blocks each having a predetermined size. Thus, such visual effect that an observer of the image of the output frame perceives the image at a resolution exceeding the actual resolution of the output frame is achieved.

Abstract: Image data of an image including an eye is input (S1), and band limitation is performed (S2). Then, a predetermined feature such as moment is extracted from the band-limited image data (S3), and recognition as to whether the eye projected in the image is a counterfeit eye or a living eye is performed based on the extracted feature (S4).

Abstract: Processing for obtaining processed compressed moving image data can be carried out efficiently through image enhancement processing on compressed moving image data. In a moving image processing unit of a relay server in a mobile phone system, a division unit divides compressed moving image data attached to an e-mail into a target part to be corrected and a non-target part not to be corrected. A decoding unit decodes the target part to generate decoded data. A correction unit carries out image enhancement processing on the decoded data, and obtains corrected decoded data. An encoding unit encodes the corrected decoded data, and obtains encoded corrected data. A combination unit combines the encoded corrected data with the non-target part, and obtains processed compressed moving image data.

Abstract: To provide a region detecting method capable of setting a proper threshold independently of a photographing condition and moreover, quickly and accurately detecting a specific region, which uses an image data storing section 10 for storing a sample image 12 and a purposed image 11, a first computing means 1 for obtaining the extraction-region-identifying threshold data for a plurality of sample images including a common extraction region but having average color values different from each other in accordance with a genetic algorithm and generating a threshold table 8 for the average color values, a second computing means 2 for adaptively computing the extraction-region-identifying threshold data for the purposed image 11 in accordance with the average color value of the purposed image and the threshold table 8, and a third computing means 3 for detecting an extraction region in accordance with the threshold data computed by the second computing means 12.

Abstract: A fast variational on-line learning technique for training a transformed hidden Markov model. A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, once the model has been initialized, an expectation-maximization (“EM”) algorithm is used to learn the one or more object class models, so that the video sequence has high marginal probability under the model. In the expectation step (the “E-Step”), the model parameters are assumed to be correct, and for an input image, probabilistic inference is used to fill in the values of the unobserved or hidden variables, e.g., the object class and appearance. In one embodiment of the invention, a Viterbi algorithm and a latent image is employed for this purpose. In the maximization step (the “M-Step”), the model parameters are adjusted using the values of the unobserved variables calculated in the previous E-step.

Abstract: In an image classification method, a plurality of grouping values are received. The grouping values each have an associated image. An average of the grouping values is calculated. A variance metric of the grouping values, relative to the average is computed. A grouping threshold is determined from the variance metric. Grouping values beyond the grouping threshold are identified as group boundaries. The images are assigned to a plurality of groups based upon the group boundaries.

Abstract: In a method for classifying a sequence of records into events based upon feature values, such as time and/or location, associated with each of the records, feature differences between consecutive records are determined. The feature differences are ranked. A sequence of three or more clusters of feature differences is computed. The clusters are arranged in decreasing order of relative likelihood of respective feature differences representing separations between events. The records can be inclusive of images.

Abstract: A facial identification system corrects lighting and pose in images prior to comparison with stored images. A three dimensional image is created from an original two dimensional image by combining the image with shape information. An iterative process is used to adjust the shape in order to match the original two dimensional image. A final image is rendered, with adjustments for lighting and pose, from the shape information.

Abstract: A display arrangement comprises an image display device having a first camera directed towards positions adopted by users viewing the display; one or more further cameras directed towards other respective locations; a face detector for detecting human faces in images captured by the cameras; and means for detecting those faces which appear in images captured by both the first camera and at least one of the further cameras.

Abstract: Automatic selection of best shots from among a plurality of photographic images is enabled, while retaining photographic images of scenes, for which a possibility exists that a user feels are necessary, regardless of image quality. A readout portion reads out a plurality of photographic images, which are recorded on a memory card. A classifying portion classifies these photographic images into similar photographic image groups, which include similar photographic images. A selection condition setting portion of an extracting portion sets selection conditions so that at least one qualified photographic image is selected from each similar photographic image group. The extracting portion extracts qualified photographic image groups form each similar photographic image group, based on the set selection conditions. A printing portion prints only the qualified photographic images, and a recording portion records the qualified photographic images and other photographic images in different folders of a CD-R.

Abstract: A method and system for fault isolation in semiconductor with devices thereon includes determining test data from a plurality of semiconductor devices and creating a failure bitmap of locations of the plurality of semiconductor devices and test data in a vector graphic CAD format. The vector graphic CAD format allows storage of test data on multiple layers.

Abstract: A method for measuring a position of an object according to an image of the object captured by a camera unit. The method includes the following steps: calculating a discrepancy of an incident beam of light penetrating a lens system of the camera unit relative to an optical center of the lens system, and compensating the position of the object according to the discrepancy.

Abstract: An image processing apparatus hierarchically compresses and codes image data by subjecting pixel values of the image data to a discrete wavelet transform, quantization and coding for each of one or a plurality of rectangular regions into which the image data is divided. The image processing apparatus includes a hierarchical coding unit to compress and code the image data in a state where the image data is divided for each hierarchical layer, to obtain compressed codes, and a distributively storing unit to distributively store the compressed codes that are divided for each hierarchical layer by the hierarchical coding unit.

Abstract: An image combination device acquires a first object image, a second object image, and a background image by first object image acquiring section (1), second object image acquiring section (3), and background image acquiring section (2) and by using one of the three images as a standard image, generates two corrected images by background correction amount calculating section (4) and corrected image generating section (5), so that background portions of the two images correspond. Superimposed image generating section (9) generates an image by superimposing the standard image and one or two of the corrected images. According to this arrangement, since the backgrounds of the superimposed images correspond to each other, the first object and the second object, which have been photographed separately, can be combined without causing an impression that these objects are combined to the background artificially.