Abstract: An image processing apparatus comprises a video input unit, a region division unit configured to divide an image acquired by the video input unit into a plurality of regions each including pixels of similar attributes, a feature extraction unit configured to extract a feature from each divided region, a background model storage unit configured to store a background model generated from a feature of a background in advance, and a feature comparison unit configured to compare the extracted feature with a feature in the background model and determine for each of the regions whether the region is the background.

Abstract: A technique determines a depth measurement associated with a scene captured by an image capture device. The technique receives at least first and second images of the scene, in which the first image is captured using at least one different camera parameter than that of the second image. At least first and second image patches are selected from the first and second images, respectively, the selected patches corresponding to a common part of the scene. The selected image patches are used to determine which of the selected image patches provides a more focused representation of the common part. At least one value is calculated based on a combination of data in the first and second image patches, the combination being dependent on the more focused image patch. The depth measurement of the common part of the scene is determined from the at least one calculated value.

Abstract: An image processing apparatus comprises a video input unit, a region division unit configured to divide an image acquired by the video input unit into a plurality of regions each including pixels of similar attributes, a feature extraction unit configured to extract a feature from each divided region, a background model storage unit configured to store a background model generated from a feature of a background in advance, and a feature comparison unit configured to compare the extracted feature with a feature in the background model and determine for each of the regions whether the region is the background.

Abstract: An image processing apparatus comprises a video input unit, a region division unit configured to divide an image acquired by the video input unit into a plurality of regions each including pixels of similar attributes, a feature extraction unit configured to extract a feature from each divided region, a background model storage unit configured to store a background model generated from a feature of a background in advance, and a feature comparison unit configured to compare the extracted feature with a feature in the background model and determine for each of the regions whether the region is the background.

Abstract: An image processing apparatus comprises a video input unit, a region division unit configured to divide an image acquired by the video input unit into a plurality of regions each including pixels of similar attributes, a feature extraction unit configured to extract a feature from each divided region, a background model storage unit configured to store a background model generated from a feature of a background in advance, and a feature comparison unit configured to compare the extracted feature with a feature in the background model and determine for each of the regions whether the region is the background.

Abstract: Methods for determining a depth measurement of a scene which involve capturing at least two images of the scene with different camera parameters, and selecting corresponding image patches in each scene. A first approach calculates a plurality of complex responses for each image patch using a plurality of different quadrature filters, each complex response having a magnitude and a phase, assigns, for each quadrature filter, a weighting to the complex responses in the corresponding image patches, the weighting being determined by a relationship of the phases of the complex responses, and determines the depth measurement of the scene from a combination of the weighted complex responses.

Abstract: An aspect of the present invention provides a method of segmenting an input image into a plurality of segments. The method comprises the steps of: deriving an image representative of boundary strength of each of a plurality of pixels in the input image; adding a random noise pattern to at least a portion of the derived image; determining a plurality of local minima in the derived image with the random noise pattern added, each of the plurality of local minima comprising a point with a lowest measure of boundary strength within a pre-defined region in the derived image; and associating each of the plurality of pixels in the input image with one of the determined local minima to segment the image based on a geodesic distance transform of the measure between the determined local minima and the pixels.

Abstract: A method for determining a shift between two images, determining a first correlation in a first direction, the first correlation being derived from a first image projection characteristics and a second image projection characteristics, and a second correlation in a second direction, the second correlation being derived from the first image projection characteristics and the second image projection characteristics. The method determines a set of hypotheses from a first plurality of local maxima of the first correlation and a second plurality of local maxima of the second correlation. The method then calculates a two-dimensional correlation score between the first image and the second image based on a shift indicated in at least one of the set of hypotheses, and selecting one of the set of hypotheses as the shift between the first image and the second image based on the calculated two-dimensional correlation score.

Abstract: A method of determining a focus parameter for aligning a water in an exposure tool within a measurement tolerance required for the exposure tool, the exposure tool using a lens system for alignment. A test chart is provided having a sharp auto-correlation associated with the wafer. An image of the test chart is captured using a lens pupil mask having at least two phase ramps that are non-parallel. The captured image of the test charge is auto-correlated to determine the position of the test chart relative to a focal position of the lens system. The focus parameter for alignment of the wafer is determined using the determined position of the test chart, whereby the focus parameter is determined within the measurement tolerance required by the exposure tool.

Abstract: Methods for determining a depth measurement of a scene which involve capturing at least two images of the scene with different camera parameters, and selecting corresponding image patches in each scene. A first approach calculates a plurality of complex responses for each image patch using a plurality of different quadrature filters, each complex response having a magnitude and a phase, assigns, for each quadrature filter, a weighting to the complex responses in the corresponding image patches, the weighting being determined by a relationship of the phases of the complex responses, and determines the depth measurement of the scene from a combination of the weighted complex responses.

Abstract: A technique determines a depth measurement associated with a scene captured by an image capture device. The technique receives at least first and second images of the scene, in which the first image is captured using at least one different camera parameter than that of the second image. At least first and second image patches are selected from the first and second images, respectively, the selected patches corresponding to a common part of the scene. The selected image patches are used to determine which of the selected image patches provides a more focused representation of the common part. At least one value is calculated based on a combination of data in the first and second image patches, the combination being dependent on the more focused image patch. The depth measurement of the common part of the scene is determined from the at least one calculated value.

Abstract: A method for determining a shift between two images, determining a first correlation in a first direction, the first correlation being derived from a first image projection characteristics and a second image projection characteristics, and a second correlation in a second direction, the second correlation being derived from the first image projection characteristics and the second image projection characteristics. The method determines a set of hypotheses from a first plurality of local maxima of the first correlation and a second plurality of local maxima of the second correlation. The method then calculates a two-dimensional correlation score between the first image and the second image based on a shift indicated in at least one of the set of hypotheses, and selecting one of the set of hypotheses as the shift between the first image and the second image based on the calculated two-dimensional correlation score.

Abstract: Disclosed herein is a method of estimating a geometrical relationship between a first image (101) and a second image (102), wherein the second image (102) includes a noise component. The method determines a location and size of each one of a plurality of image patches (201), based on the noise component included in the second image (102) and correlation information derived from the first image (101). The method then identifies a plurality of first image areas in the first image and a corresponding plurality of second image areas in the second image, based on the location and size of each one of the plurality of image patches. Each first image area of the first image (101) corresponds to a related second image area of the second image (102).

Abstract: A method (500) of determining rotation and scale transformation parameters is disclosed. The method (500) determines a plurality of weight values associated with one or more parts of at least one of a first and second image (e.g., 154. 155). A representation of each of the first and second images (154. 155) is formed using the weight values, the representation being substantially invariant to translation of the first and second images (154. 155). Rotation and scale transformation parameters relating the first and second images (154. 155) are determined based on the representation of each of the first and second images (154. 155). The rotation and scale transformation parameters are stored.

Abstract: Disclosed is a method for determining a focus parameter for aligning a wafer (W) in an exposure tool (1000) within a measurement tolerance required for the exposure tool, the exposure tool using a lens system (EL) for alignment. The method provides a test chart (1410) associated with the wafer, the test chart having a sharp auto-correlation and then captures (1430), using a lens pupil mask (1420), an image of the test chart, the lens pupil mask having at least two phase ramps which are non-parallel. An auto-correlation (1520) is performed on the captured image (1510) of the test chart to determine the position of the test chart relative to a focal position of the lens system. The focus parameter is then determined (1530, 1540, 1550) for alignment of the wafer using the determined position of the test chart, whereby the focus parameter is determined within the measurement tolerance required by the exposure tool.

Abstract: A method (500) of determining rotation and scale transformation parameters is disclosed. The method (500) determines a plurality of weight values associated with one or more parts of at least one of a first and second image (e.g., 154. 155). A representation of each of the first and second images (154. 155) is formed using the weight values, the representation being substantially invariant to translation of the first and second images (154. 155). Rotation and scale transformation parameters relating the first and second images (154. 155) are determined based on the representation of each of the first and second images (154. 155). The rotation and scale transformation parameters are stored.

Abstract: Disclosed herein is a method of estimating a geometrical relationship between a first image (101) and a second image (102), wherein the second image (102) includes a noise component. The method determines a location and size of each one of a plurality of image patches (201), based on the noise component included in the second image (102) and correlation information derived from the first image (101). The method then identifies a plurality of first image areas in the first image and a corresponding plurality of second image areas in the second image, based on the location and size of each one of the plurality of image patches. Each first image area of the first image (101) corresponds to a related second image area of the second image (102).

Abstract: A method and system is disclosed for authenticating a physical medium (1405). The method starts by retrieving (1406) a reference digital signature (1412) previously determined (1403) based upon visual characteristics of an area (1402) of a reference physical medium (1400). A candidate digital signature (1415) is determined based upon visual characteristics of an area (1408) of the physical medium (1405), the area (1408) of the physical medium (1405) being at least partly corresponding to the area (1402) of the reference physical medium (1400). Weight values associated with different parts of at least one of the areas (1402 and 1408) are determined (201). Finally, the candidate digital signature (1415) is compared (1420) with the reference digital signature (1412), wherein the contributing of the parts in the comparison (1420) is based upon the weight values.