Abstract: A system comprises an input component, a feature extractor, an object classifier, an adaptation component and a calibration tool. The input component is configured to receive one or more images, and the feature extractor is configured to extract features for one or more objects in the one or more images, the extracted features comprising at least one view-independent feature. The object classifier is configured to classify the one or more objects based at least in part on the extracted features and one or more object classification parameters, and the adaptation component is configured to adjust the classification of at least one of the objects based on one or more contextual parameters. The calibration tool is configured to adjust one or more of the object classification parameters based on likelihoods for characteristics associated with one or more object classes.

Abstract: One method for estimating the extracorporeal blood volume in a portion of a physical sample includes: extracting a feature from a portion of an image of the sample; tagging the portion of the image of the sample with a blood volume indicator according to the extracted feature; and estimating the extracorporeal blood volume in at least the portion of the physical sample, associated with the portion of the image of the sample, according to the blood volume indicator.

Abstract: Techniques and tools for high dynamic range (HDR) image rendering and generation. An HDR image generating system performs motion analysis on a set of lower dynamic range (LDR) images and derives relative exposure levels for the images based on information obtained in the motion analysis. These relative exposure levels are used when integrating the LDR images to form an HDR image. An HDR image rendering system tone maps sample values in an HDR image to a respective lower dynamic range value, and calculates local contrast values. Residual signals are derived based on local contrast, and sample values for an LDR image are calculated based on the tone-mapped sample values and the residual signals. User preference information can be used during various stages of HDR image generation or rendering.

Abstract: The present invention relates to a method for the detection of egg yolk in albumen, for example, but not exclusively, at least one yolk spot, comprising, —breaking an egg, —separating albumen and yolk, —collecting albumen and yolk separately in a yolk cup and in an albumen cup, —imaging of the albumen cup substantially from above with a camera, whereby an albumen image is obtained, and —imaging, substantially from above, of the yolk cup, having yolk area Ay, a preselected subarea Py, whereby a yolk image in a preselected color system Sy is obtained.

Abstract: An object detection apparatus for detecting a target object in an input image. The apparatus includes a storage storing, for each of a plurality of part areas forming an area subject to image recognition processing, image recognition dictionaries used to recognize a target object and typed according to variations in appearance of a part of the target object to be detected in the part area. A part score calculator calculates, for each of the part areas, a part score indicative of a degree of similarity between the part area and each of at least some of the image recognition dictionaries. An integrated score calculator calculates an integrated score that is a weighted sum of the part scores for the respective part areas. A determiner determines, on the basis of the integrated score, whether or not the target object is present in the subject area.

Abstract: A user authentication apparatus and method using movement of a pupil is capable of rapidly and accurately performing authentication with high security by storing the respective frequencies of objects mechanically moving on a screen and security keys corresponding thereto, comparing a frequency detected from movement of a pupil gazing at any object with a frequency of the object, and performing the authentication based on a corresponding security key when the detected frequency is included in a predetermined range.

Abstract: A method for separating tissue classes in MR images is presented. The method includes acquiring a plurality of magnetic resonance images of a subject with different acquisition parameters and generating a multi-dimensional intensity distribution model from the plurality of magnetic resonance images. The multi-dimensional intensity distribution model represents a distribution of intensities of voxels in each magnetic resonance image of the plurality. The method also includes identifying clusters of correlated intensities in the multi-dimensional intensity distribution model and assigning the clusters into one or more tissue classes based on the correlated intensities of each of the cluster.

Abstract: Systems and methods are disclosed for evaluating cardiovascular treatment options for a patient. One method includes creating a three-dimensional model representing a portion of the patient's heart based on patient-specific data regarding a geometry of the patient's heart or vasculature; and for a plurality of treatment options for the patient's heart or vasculature, modifying at least one of the three-dimensional model and a reduced order model based on the three-dimensional model. The method also includes determining, for each of the plurality of treatment options, a value of a blood flow characteristic, by solving at least one of the modified three-dimensional model and the modified reduced order model; and identifying one of the plurality of treatment options that solves a function of at least one of: the determined blood flow characteristics of the patient's heart or vasculature, and one or more costs of each of the plurality of treatment options.

Abstract: A computer system running image processing software receives an identification of a desired geographical area to be imaged and collected into an oblique-mosaic image; creates a mathematical model of a virtual camera looking down at an oblique angle, the mathematical model having an oblique-mosaic pixel map of the desired area encompassing multiple source images; assigns surface locations to pixels included in the oblique-mosaic pixel map; creates a ground elevation model of the ground and vertical structures within the oblique-mosaic pixel map using overlapping source images of the desired geographical area, wherein the source oblique images were captured at an oblique angle and compass direction similar to that of the virtual camera; and reprojects, with the mathematical model, source oblique image pixels of the overlapping source images for pixels included in the oblique-mosaic pixel map using the ground elevation model to thereby create an oblique-mosaic image of the desired area.

Abstract: A system and method for restricting the number of layout patterns by pattern identification, matching and classification, includes decomposing the pattern windows into a low frequency component and a high frequency component using a wavelet analysis for an integrated circuit layout having a plurality of pattern windows. Using the low frequency component as an approximation, a plurality of moments is computed for each pattern window. The pattern windows are classified using a distance computation for respective moments of the pattern windows by comparing the distance computation to an error value to determine similarities between the pattern windows.

Abstract: A method for biometric recognition may be employed on multiple biometrics, including irises and ears. The method includes decomposing the radiometric profile of each line of an image into a plurality of wavelets using a wavelet transform process. A unique signal is calculated using the plurality of wavelets. A template is assembled using the signal and areas of interest in the template are identified. The template is then compared to a second template using the plurality of areas of interest.

Abstract: A number of images of a scene are captured and stored. The images are captured over a range of values for an attribute (e.g., a camera setting). One of the images is displayed. A location of interest in the displayed image is identified. Regions that correspond to the location of interest are identified in each of the images. Those regions are evaluated to identify which of the regions is rated highest with respect to the attribute relative to the other regions. The image that includes the highest-rated region is then displayed.

Abstract: An image analysis method includes acquiring fluorescent images of frames in time-series. Each fluorescent image comprises pixels in which pixel data are acquired in the time-series. The method further includes setting analysis areas to the acquired fluorescent images, calculating a classification value of the analysis areas, classifying the images into one or more groups on the basis of the classification value, calculating an average image of the analysis area every group, subtracting the average image from each image of the analysis area every group to calculate a new image of the analysis area, and calculating a correlation value on the basis of the new images every group.

Abstract: An image analysis method includes acquiring fluorescent images of frames in time-series. Each fluorescent image comprises pixels in which pixel data are acquired in the time-series. The method further includes setting analysis areas to the fluorescent images, selecting the fluorescent images of two or more frames to be used in analysis, extracting data pairs each comprising two pixels in which acquisition time intervals are the same in the analysis area of each of the selected fluorescent images, and performing product sum calculation of each of the data pairs for all of the selected images to calculate a correlation value.

Abstract: A method of autonomously monitoring a remote site, including the steps of locating a primary detector at a site to be monitored; creating one or more geospatial maps of the site using an overhead image of the site; calibrating the primary detector to the geospatial map using a detector-specific model; detecting an object in motion at the site; tracking the moving object on the geospatial map; and alerting a user to the presence of motion at the site. In addition thermal image data from a infrared cameras, rather than optical/visual image data, is used to create detector-specific models and geospatial maps in substantially the same way that optical cameras and optical image data would be used.

Abstract: An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.

Abstract: A method for processing image data corresponding to temporally successive motions of an object, the method including adjusting a range of a first occlusion region which is estimated according to whether position changes occur in sub-blocks forming temporally successive first and second frames among image frames which display the motions of the object, by using Motion Vectors (MVs) mapped to the sub-blocks and detecting a second occlusion region of a third frame which displays the object that moves between the first frame and the second frame, by using the adjusted range of the first occlusion region.

Abstract: A method of image processing within an image acquisition device comprises: acquiring an image including one or more face regions and identifying one or more eye-iris regions within the one or more face regions. The one or more eye-iris regions are analyzed to identify any eye-iris region comprising an eye-iris pattern of sufficient quality to pose a risk of biometrically identifying a person within the image. Responsive to identifying any such eye-iris region, a respective substitute eye-iris region comprising an eye-iris pattern sufficiently distinct from the identified eye-iris pattern to avoid identifying the person within the image is determined, and the identified eye-iris region is replaced with the substitute eye-iris region in the original image.

Abstract: A second form of image data is determined from a first form of image data of an examination object in a radiological imaging system. A set of a defined plurality of input pixels in the image data of the first form is determined. In addition, a set of target form parameters of a target form model with a defined plurality of target form parameters is prognostically determined by way of a data-driven regression method from the plurality of input pixels. The number of target form parameters is smaller than the number of input pixels. The second form of image data is determined from the set of target form parameters. There is also described a method in radiological imaging for determining the geometric position of a number of target objects in a second form of image data and an image processing workstation for determining a second form of image data from a first form of image data as well as an imaging device.

Abstract: One exemplary embodiment involves receiving, at a computing device comprising a processor, a test image having a candidate object and a set of object images detected to depict a similar object as the test image. The embodiment involves localizing the object depicted in each one of the object images based on the candidate object in the test image to determine a location of the object in each respective object image and then generating a validation score for the candidate object in the test image based at least in part on the determined location of the object in the respective object image and known location of the object in the same respective object image. The embodiment also involves computing a final detection score for the candidate object based on the validation score that indicates a confidence level that the object in the test image is located as indicated by the candidate object.