41 results about "Covariant transformation" patented technology

A method, apparatus, and article of manufacture provide the ability to process three-dimensional image data. A color transformation to be applied to image data is received from the user and concatenated with previous transformations. The concatenated transformations are applied to pixel values for the image data. Thereafter, the values of various parameters are evaluated to obtain and display updated pixel values. A matte may also be extracted and/or defined. For example, a reference color and various parameters may be obtained and used to calculate a transformation that transforms the reference color to an specified point (e.g., an origin) of the 3D space. The transformation may then be applied to each pixel so that each pixel is assigned a matte value according to its transformed values (and selected parameters), and therefore according to its position with respect to the specified reference color.

The invention belong to the technical field of communication and particularly relates to a low complexity three-dimensional (3D) beam forming algorithm based on angle-domain transformation. In allusion to single-cell multi-user-multiple input multiple output (MU-MIMO) downlink broadcast channel scenes, a 3D beam forming problem is modeled to form an optimization model of a minimum base station transmitting power, then the original problem is converted to an equivalence optimization problem on an angle domain through angle-domain transformation, the problem is subjected to dimensionality reduction relaxation by means of angle-domain sparse characteristics of 3D-MIMO channels, thereby, the calculating complexity of solution is reduced, and the algorithm complexity is never increased with increasing of the number of base station antennae. A simulation result shows that the angle-domain dimensionality reduction optimization algorithm can excellently approach original space all-dimension optimization algorithm.

The invention discloses a blind recognition method of convolutional coding parameters. The method has the following beneficial effects: matrix transformation is carried out on convolutional code sequences after determining the output convolutional code length n0 and the output codon start point through linear transformation, thus determining the check matrix of the convolutional code and further obtaining the generator matrix through analysis; the problem of determination of the output convolutional code length and output codon start point is better solved; a method for obtaining the generator matrix from the check matrix of the convolutional code with rate not being 1/n0 is also provided; blind recognition of the convolutional coding parameters can be realized only through the communication content; and the method has the characteristics of simple and direct algorithm, clear process, high recognition speed and the like. The blind recognition method is applicable to the fields of intelligent communication, information processing and the like.

The invention discloses a four-point transformation based image adaptive-correction method, which is easy to implement and transplant, simple in operation, convenient to use and high in correction precision. The image correction method disclosed by the invention is implemented through acquiring double vision images, taking one of the double vision images as an original image, respectively acquiring four points on the original image and extracting an original image point; connecting the four points so as to form an arbitrary quadrilateral; through a mapping relation, obtaining corresponding four points, an original image point and an arbitrary quadrilateral on the other image and corresponding and traversing the image through double mice, thereby realizing the correction of the image, wherein in the process of building the arbitrary quadrilateral, a situation that no blind point or blind area occurs in areas outside the arbitrary quadrilateral is ensured by using an external expansion two-time method or an indentation two-point method, thereby realizing the adaptive correction, so that the image is corrected efficiently and accurately. The method disclosed by the invention can be applied to the field of image correction.

An output color profile, such as an ICC output profile, contains a look-up table to separate the color of a pixel, for example a CIELAB color, into a set of colorant values, for example CMYK colorants. In practice, most CMYK images have been separated using one of a limited set of standardized output profiles. The invention deals with the problem of guessing which one of these standardized output profiles has been used to separate a specific image. For this purpose the CMYK values of the image are analyzed. The analysis involves calculating a distance in the CMYK space between the CMYK values of an image, and the CMYK values produced by different output profiles. A profile is selected based on these distances, for example by selecting the profile yielding the shortest distance.

The invention discloses a method for color transformation of the color spaces based on the CIE Lab (International Commission on Illumination Laboratory). The method comprises the steps of: transforming original image data two-dimensional color space planes Ln and Ln-1 into target two-dimensional color space planes Ln' and Ln-1', calculating Lx', which exists between Ln' and Ln-1', under the same brightness level as that of Lx according to any brightness plane Lx between Ln and Ln-1, calculating a transformation relational matrix between Lx and Lx', calculating a target color after color transformation regulation based to the color of any point of the original image data on the plane Lx, and completing the color space of the target color. The invention further discloses a device for the color transformation of the color spaces based on the CIE Lab and a liquid crystal display device. According to the invention, based on the manner in such a way, an inverse transformation model can be established more easily, and an algorithm is simple to implement and high in running speed, so that the color performance is closer to the color of a real object or is closer to a preset effect than the color of the real object.

A feature vector is encoded into a sparse binary vector. The feature vector is retrieved, for example from storage or a feature vector generator. The feature vector represents a media object or other data object. One or more permutations are generated, the dimensionality of the generated permutations equivalent to the dimensionality of the feature vector. The permutations may be generated randomly or formulaically. The feature vector is permuted with the one or more permutations, creating one or more permuted feature vectors. The permuted feature vectors are truncated according to a selected window size. The indexes representing the maximum values of the permuted feature vectors are identified and encoded using one -hot encoding, producing one or more sparse binary vectors. The sparse binary vectors may be concatenated into a single sparse binary vector and stored.; The sparse binary vector may be used in the similarity search, indexing or categorization of media objects.

The invention discloses an image lossless compression method based on a discrete Tchebichef orthogonal polynomial, and belongs to the technical field of digital image compression. According to encoding and decoding methods, two-dimensional forward/backward orthogonal transformation is performed through two-dimensional integer forward/backward discrete Tchebichef orthogonal polynomial transformation instead of other integer transformation methods adopted in the prior art, so that lossless compression is realized; the problem of mismatch of an encoder can be solved effectively; lossless encoding is realized; and relatively high compression performance and higher extensibility are achieved. Mapping from integers to integers is realized in matrix transformation, and in-situ calculation is performed, so that images are reconstructed completely; hardware resource consumption is lowered; and hardware implementation is facilitated.

A system, method, and computer program product are provided for generating a range of ordinals corresponding to key values represented in a B-Tree data structure. This allows for a boundary point, usable, for example, for partitioning the B-Tree for parallel processing, to be specified using an ordinal value of the range of ordinals. Then an ordinal-to-key transformation can be performed in order to identify a key value corresponding to the boundary point. Additionally, ordinal values can be identified through the use of key-to-ordinal transformations, allowing for specification of ranges, for example, in terms of key values.

The invention relates to a dynamic model allocating method based on matrix transformation. The method comprises the steps of firstly, transforming and generating complex models into model files which can be loaded conveniently by programs according to dynamic model operation requirements in expense analysis and assessment processes, and designing transformation matrixes to be adapted to uncertain input and output parameters of different models; dynamically calling successfully registered models, analyzing input matrix content and loading corresponding data for calculation; and finally, graphically displaying and saving output results according to analysis of the output matrix content. By means of the dynamic model allocating method, dynamic model allocation based on matrix transformation is designed and achieved, all kinds of models in expense analysis and assessment processes can be integrated and called widely, the problem that staff members fail to use complex models is solved effectively, the complexity and workloads of expense analysis and assessment through models are reduced for staff members, and the optimum and scientific application of expense analysis and assessment models is achieved.

The invention discloses a human body behavior recognition method based on a history motion graph and R transformation. According to the method, a depth video is used as a recognition basis, firstly, the minimum enclosure rectangle of human body motion is calculated according to a foreground segmentation technology, then the history motion graph is extracted within a depth video area limited by the minimum enclosure rectangle, motion intensity constraint is exerted on the extracted history motion graph, so that a motion energy diagram is obtained, R transformation is calculated on the obtained motion energy graph, and therefore a characteristic vector used for behavior recognition is obtained. A method of a support vector machine is adopted for training and recognition processes. The minimum enclosure rectangle of human body behavior motion is adopted for preprocessing, and behavior characteristic extraction is accelerated; a method of history motion graph sequences is adopted for reducing influences of noise in depth graphs; characteristics are extracted through performing R transformation on the energy graph, so that calculation speed is high.

The invention discloses a scrambling and recovery method for a GIS (Geographic Information System) vector data line and plane graphic layer and belongs to the field of geographic information security. The method is based on quasi-affine transformation in a finite integer field and mainly comprises the following processes: 1) a scrambling process, which comprises the steps of constructing a vector data finite field scrambling transformation space, determining transformation rules, generating transformation parameters, performing global scrambling, removing virtual points, forming scrambled vector data and the like; 2) a recovery process, which comprises the steps of generating inverse transformation parameters, performing global anti-scrambling, forming and displaying recovered vector data and the like. The method disclosed by the invention has the characteristics of randomness, reversibility and the like, the recovery transformation has a simple analytical expression, recovery can be realized without performing periodic iteration and effectively technical means are provided for safe transmission, packaging and storage of geographic spatial data.

The invention provides a planarization pre-processing method comprising the following steps: calculating the covariance matrix of a sampling point; calculating the characteristic value and characteristic vector of the covariance matrix; determining the direction of z' axis according to obtained minimum characteristic value; determining the directions of x' axis and y' axis according to the directions of z axis and z' axis; calculating a coordinate transformation matrix; carrying out matrix transformation on the sampling point; and removing the direction information of z' axis to finish projection. The projection algorithm can determine a projection plane only through the respective statistical characteristic of the sampling point of a handwritten character track so that the determined projection plane changes in adaptability along with the change of an writing angle so as to generate optimal projection effect. Therefore, a writer can perform spatial handwriting with more free postureswithout influencing identification effect.

Techniques for performing mesh-based video compression/decompression with domain transformation are described. A video encoder partitions an image into meshes of pixels, processes the meshes of pixels to obtain blocks of prediction errors, and codes the blocks of prediction errors to generate coded data for the image. The meshes may have arbitrary polygonal shapes and the blocks may have a predetermined shape, e.g., square. The video encoder may process the meshes of pixels to obtain meshes of prediction errors and may then transform the meshes of prediction errors to the blocks of prediction errors. Alternatively, the video encoder may transform the meshes of pixels to blocks of pixels and may then process the blocks of pixels to obtain the blocks of prediction errors. The video encoder may also perform mesh-based motion estimation to determine reference meshes used to generate the prediction errors.

A video encoding method is disclosed. The method comprises the steps of firstly performing macroblock prediction and compensation of the image; then performing 4 x 4 DCT like integer transformation of the predicted difference values; and then performing quantification and entropy encoding of the transformation coefficients. The method adopts a new 4 x 4 DCT-like integer transformation method and the corresponding inverse transformation, quantification, inverse quantification methods. The computation complexity of the transformation method is lower since the transformation process only relates to addition and multiplication. The transformation results of the invention greatly approximate to the 4 x 4 DCT transformation, and the decorrelation performance is as good as DCT transformation, therefore a better video compression effect is achieved.