Abstract:
There is provided a method for processing a variable length code encoded image comprising a plurality of scan lines, wherein each scan line comprises a number of original code blocks comprising a differentially encoded parameter. The method comprises defining a subarea of the image comprising parts of a number of said scan lines, extracting the subarea, generating a set of auxiliary code blocks comprising an auxiliary differentially encoded parameter based on the first differentially encoded parameter of a first code block of a first current scan line in the subarea, and associating the auxiliary code blocks with the subarea. There is also provided an apparatus and a computer program product thereof.
Abstract:
The invention is related to a device for analysing an encoded image and to a corresponding method. Said device comprises a decoder (VIDDEC), means (ED) for providing information on encoding parameters, means (ROIDET) for determining a region of interest in the decoded image and means (PA) for comparing said region of interest with the encoding parameters. Comparison of the determined region of interest with the encoding parameters allows for determining whether and to which degree the determined region of interest was favoured by being coded with more bits during the encoding process.
Abstract:
The invention relates to an automatic detection method in a source image, of at least one area called a layout area comprising at least one layout, such as a logo and/or a score. According to the invention, the layout areas of a source image are detected using the salience of source image pixels. The detection is carried out in specific areas of the source image saliency map, usually in the areas corresponding to the corners of the image or to the bands in the upper part and lower part of the image. In these areas, two points are sought having maximum salience values and distant by at least p points from each other. These two points corresponding to the beginning and end of a layout area. The window bounding these two points then corresponds to a layout area.
Abstract:
A composition determination device includes: a subject detection unit configured to detect a subject in an image based on acquired image data; an actual subject size detection unit configured to detect the actual size which can be viewed as being equivalent to actual measurements, for each subject detected by the subject detection unit; a subject distinguishing unit configured to distinguish relevant subjects from subjects detected by the subject detection unit, based on determination regarding whether or not the actual size detected by the actual subject size detection unit is an appropriate value corresponding to a relevant subject; and a composition determination unit configured to determine a composition with only relevant subjects, distinguished by the subject distinguishing unit, as objects.
Abstract:
A moving image sequence is automatically reframed for a small display. The framing is adjusted in dependence upon foreground and background segment weights derived for pixels in images in the sequence. Reframed images are formed from only those input pixels which fall within a reframing window within the input image area and the position or size of the reframing window is adjusted so as to maximise a weighted total of the sum of the foreground weights of pixels within the window and the sum of the background weights of pixels outside the window.
Abstract:
A registration process that allows for assessment of deformation in the gastrointestinal region is provided. The registration process includes a classification process that classifies image data into the type of material imaged. The registration process further includes an automated segmentation process that allows for identification of the materials in the imaging region and allows for removal of objects, such as stool, from imaging data to allow for registration of images.
Abstract:
A method for computer-aided four-dimensional (4D) modeling of an anatomical object comprises acquiring a set of three-dimensional (3D) models representing a plurality of static states of the object throughout a cycle. A 4D correspondency estimation is performed on the set of 3D models to determine which points of the 3D models most likely correspond to each other, wherein the 4D correspondency estimation includes one or more of (i) defining a reference phase, (ii) performing vessel-oriented correspondency estimation, and (iii) post-processing of 4D motion data. The method further comprises automatic 3D modeling with a front propagation algorithm.
Abstract:
The semi-automatic extraction and delineation of the cardiac region of interest in computer tomographic angiography images is time consuming and requires an experienced operator. According to the present invention, a completely automatic delineation and extraction of the CROI is provided, wherein the chest wall is detected and the region where the CROI is attached to the chest wall. Then, the descending aorta is detected. After that a circular initialization of a closed contour around a part of the CROI is performed, which is optimized in a subsequent step. Then, a propagation is performed through all slices of the CTA image, where the preceding contour of the preceding slice image is used for an actual contour optimization in the actual slice image. Advantageously, a fully automatic delineation and extraction of the CROI is provided within very short time.
Abstract:
The method 1 according to the invention is preferably practiced in real time and directly after a suitable acquisition 3 of the multi-dimensional dataset, which is accessed at step 5 and the images constituting the multi-dimensional dataset are classified at step 8. Preferably, for reducing an amount of data to be processed at step 6 the image data is subjected to a restrictive region of interest determination. At step 9 the classified cardiac images are subjected to a an image thinning operator so that the resulting images comprise a plurality of connected image components which are further analyzed at step 14. After the thinning step 9 a labeling step 11 is performed, where different connected components in the multi-dimensional dataset are accordingly labeled. This step is preferably followed by a region growing step 13, which is constrained by binary threshold used at step 8b. For each connected image component a factor F is computed at step 14. The anatomic structure is segmented at step 16 by selecting the connected image component with factor F meeting a pre-determined criterion. After this, the segmented anatomic structure is stored in a suitable format at step 18. The invention further relates to an apparatus, a working station, a viewing station and a computer program.