Abstract: Disclosed a system for reconstructing an image of an object hidden by a flame. The system comprises a laser source emitting an infrared radiation and a lensless, off-axis interferometric arrangement that divides the infrared radiation into an object beam and a reference beam. The object beam is enlarged and then irradiates the object, that scatters it. The reference beam is enlarged and then interferes with the scattered object beam, so as to create a hologram. The system comprises an infrared detector which detects the hologram and a processor that reconstructs the image of the object by numerically processing the hologram.

Abstract: It is disclosed a system for reconstructing an image of an object hidden by a flame. The system comprises a laser source emitting an infrared radiation and a lensless, off-axis interferometric arrangement that divides the infrared radiation into an object beam and a reference beam. The object beam is enlarged and then irradiates the object, that scatters it. The reference beam is enlarged and then interferes with the scattered object beam, so as to create a hologram. The system comprises an infrared detector which detects the hologram and a processing unit which reconstructs the image of the object by numerically processing the hologram. The system therefore provides the object image based on digital holography at infrared wavelengths. Differently from known thermographic acquisition techniques, even if large portions of the object are hidden by the flame, the system allows to reconstruct an image of the whole object, with no blind areas.

Abstract: A method of determining segments in a series of images based on previous segmentation results and on motion estimation. A second segment (108) of a second image (106) is determined based on a first segment (102) of a first image (100), with the first segment (102) and the second segment (108) corresponding to one object (104). The method comprises a calculation step to compare values of pixels of the first image (100) and the second image (106) in order to calculate a motion model which defines the transformation of the first segment (102) into the second segment (108). An example of such a motion model allows only translation. Translation can be described with one motion vector (110).

Abstract: In a method of locating problem areas in an image signal (I), a motion vector field (Df) is estimated for the image signal (I), and edges are detected in the motion vector field (Df). In a corresponding method of interpolating images between existing images (I), image parts are interpolated (MCI) in dependence upon a presence of edges; preferably, an order statistical filtering (med) is used at edges.

Abstract: The invention relates to a motion-compensated interpolation of a data-signal, which data-signal comprises successive images wherein each image comprises groups of pixels, in which motion vectors are generated (18), each motion vector corresponding to a group of pixels of one image, between a group of pixels of said one image and a second group of pixels of another image in the data-signal, and interpolated results are obtained (16) as a function of these motion vectors. In accordance with the present invention, the reliability of each motion vector corresponding to a particular group of pixels is estimated (20), weights are calculated as a function of the reliability of the motion vectors, and interpolated luminous intensities of groups of pixels are generated for an interpolated image by calculating, on the basis of these weights, weighted averages of the interpolated results.

Abstract: The motion estimation unit (100) comprises a block-matcher (102) for calculating a start motion vector (110) by minimizing a predetermined cost function as a matching criterion for the block (116) of pixels with a further block of pixels (122) of a further image (120). The motion estimation unit (100) further comprises an optical flow analyzer (104) for calculating an update motion vector (111) based on the start motion vector (110) and which is designed to find the most appropriate set of optical flow equations corresponding to respective pixels of the block (116) of pixels. This is achieved by analyzing gradient vectors of optical flow equations for pixels of the block (116) of pixels. Finally the selector 106 of the motion estimation unit (100) selects the motion vector (126) by comparing the start motion vector (110) with the update motion vector (111).

Abstract: Nowadays, continuous media data processing is performed more and more by programmable components, rather than dedicated single-function components. To handle such data appropriately, a system must guarantee sufficient system resources for processing. The method according to the invention proposes scalable motion compensated up-conversion of the frame rate of video sequences. By computing suitable quality measures for the algorithms (404) that perform the up-conversion (402), the method according to the invention allows to predict the required resources given an input video (406). Furthermore, the visual quality of the output video sequence is predicted. Based upon these predictions, the method selects the best algorithm to perform the up-conversion thereby allowing an optimal resource utilization of the system, for example a programmable platform for media processing.

Abstract: The motion estimation unit (100) comprises a block-matcher (102) for calculating a start motion vector (110) by minimizing a predetermined cost function as a matching criterion for the block (116) of pixels with a further block of pixels (122) of a further image (120). The motion estimation unit (100) further comprises an optical flow analyzer (104) for calculating an update motion vector (111) based on the start motion vector (110) and which is designed to find the most appropriate set of optical flow equations corresponding to respective pixels of the block (116) of pixels. This is achieved by analyzing gradient vectors of optical flow equations for pixels of the block (116) of pixels. Finally the selector 106 of the motion estimation unit (100) selects the motion vector (126) by comparing the start motion vector (110) with the update motion vector (111).

Abstract: The motion estimation unit (100) comprises a block-matcher (102) for calculating a start motion vector (110) by minimizing a predetermined cost function as a matching criterion for the block (116) of pixels with a further block of pixels (122) of a further image (120). The motion estimation unit (100) further comprises an optical flow analyzer (104) for calculating an update motion vector (111) based on the start motion vector (110) and which is designed to minimize a sum of errors associated with a set of optical flow equations corresponding to respective pixels of the block (116) of pixels. Finally the selector 106 of the motion estimation unit (100) selects the motion vector (126) by comparing the start motion vector (110) with the update motion vector (111).

Abstract: A method of determining segments in a series of images based on previous segmentation results and on motion estimation. A second segment (108) of a second image (106) is determined based on a first segment (102) of a first image (100), with the first segment (102) and the second segment (108) corresponding to one object (104). The method comprises a calculation step to compare values of pixels of the first image (100) and the second image (106) in order to calculate a motion model which defines the transformation of the first segment (102) into the second segment (108). An example of such a motion model allows only translation. Translation can be described with one motion vector (110).

Abstract: In a method of locating problem areas in an image signal (I), a motion vector field (Df) is estimated for the image signal (I), and edges are detected in the motion vector field (Df). In a corresponding method of interpolating images between existing images (I), image parts are interpolated (MCI) in dependence upon a presence of edges; preferably, an order statistical filtering (med) is used at edges.

Abstract: In a method of locating problem areas in an image signal (I), a motion vector field (Df) is estimated for the image signal (I), and edges are detected in the motion vector field (Df). In a corresponding method of interpolating images between existing images (I), image parts are interpolated (MCI) in dependence upon a presence of edges; preferably, an order statistical filtering (med) is used at edges.

Abstract: The invention relates to a motion-compensated interpolation of a data-signal, which data-signal comprises successive images wherein each image comprises groups of pixels, in which motion vectors are generated (18), each motion vector corresponding to a group of pixels of one image, between a group of pixels of said one image and a second group of pixels of another image in the data-signal, and interpolated results are obtained (16) as a function of these motion vectors. In accordance with the present invention, the reliability of each motion vector corresponding to a particular group of pixels is estimated (20), weights are calculated as a function of the reliability of the motion vectors, and interpolated luminous intensities of groups of pixels are generated for an interpolated image by calculating, on the basis of these weights, weighted averages of the interpolated results.

Abstract: In a method of estimating motion, at least two motion parameter sets are generated (PE1-PEn) from input video data (n, n−1), a motion parameter set being a set of parameters describing motion in an image, by means of which motion parameter set motion vectors can be calculated. One motion parameter set indicates a zero velocity for all image parts in an image, and each motion parameter set has corresponding local match errors. Output motion data are determined from the input video data (n, n−1) in dependence on the at least two motion parameter sets, wherein the importance of each motion parameter set in calculating the output motion data depends on the motion parameter sets' local match errors.

Abstract: In a method of detecting covered and uncovered parts in an image to be interpolated between neighboring previous (I) and next (III) input images, backward motion vectors from the next input image to the previous input image, and having corresponding backward estimation errors, and forward motion vectors from the previous input image to the next input image, and having corresponding forward estimation errors, are determined, uncovered and covered parts are detected in the neighboring previous and next input images, respectively, in the thus detected uncovered parts, uncovered parts in the image to be interpolated are detected by determining second backward estimation errors by comparing both neighboring previous and next input images when partially shifted over the backward motion vectors to a temporal location of said image to be interpolated, and by comparing the second backward estimation errors to a threshold, and in the thus detected covered parts, covered parts in the image to be interpolated are detecte