63 results about "Quaternion matrix" patented technology

The invention discloses a color image quality evaluation method based on HVSs and quaternions and belongs to the technical field of image processing and computer vision. The method includes the steps that firstly, mathematic evaluation models of an original reference image and a distorted image to be evaluated are established through analysis of human vision features, wherein the mathematic evaluation models comprise spatial location functions Q<L>, local variances Q<V>, texture edge complexity functions Q<TE> and color functions Q<C> of the images; secondly, quaternion arrays of the original reference image and the distorted image to be evaluated are established and singular value decomposition is conducted on the quaternion arrays, so that singular value feature vectors of the images can be acquired; thirdly, the image distortion degree is measured through the Euclidean distance of the singular value feature vectors of the original reference image and the distorted image to be evaluated. According to the method, the human vision features and the quaternions are combined, brightness and chromaticity information of the images is extracted, the spatial location functions, the texture edge complexity functions and the local variances are established through the human vision features, and an evaluation result accords with a result generated in the mode that the images are sensed by the human eyes.

The invention discloses a multi-focus image fusion method based on quaternion wavelet transform in order to more accurately integrate the information in a source image into a fusion image. The method comprises steps of: 1) subjecting two multi-focus images to be fused to quaternion wavelet transform in order to obtain a low-frequency sub-band (LL) and three high-frequency sub-bands (LH, HL, HH) of each layer, wherein each sub-band corresponds to four quaternion coefficient sub-bands, the four quaternion coefficient sub-bands can be transformed to an amplitude sub-band and three phase sub-bands by quaternion algebraic operation; 2) subjecting the low-frequency sub-band to fusion by a fusion rule based on improved phase gradient characteristic value maximization, and subjecting the high-frequency sub-bands to fusion by a fusion method based on quaternion matrix comprehensive multi-features; and 4) subjecting the fused high and low frequency sub-band coefficients to quaternion wavelet transform to obtain a fused image. The method can fully extract the features of the source image and accurately represent the source image so as to avoid introducing error information into the fused image and to improve a visual effect, and greatly improves the quality of the fused image compared with a traditional fusion method.

The invention discloses a quaternion field colored image compressed sensing recovery method based on a Quasi Newton algorithm. The quaternion field colored image compressed sensing recovery method comprises the following steps of: converting a colored image two-dimensional matrix to a two-dimensional matrix of the quaternion field; and carrying out compressed sensing on the two-dimensional matrix of the quaternion field once to recover the original colored image, so that the operation time is saved in comparison with a conventional method of carrying out compressed sensing on the three vectors, i.e. RGB (Red, Green, Blue) of the colored image respectively. According to the quaternion field colored image compressed sensing recovery method based on the Quasi Newton algorithm disclosed by the invention, a quaternion matrix signal is written into an Euler form of a quaternion, the amplitude and the phase are adopted as novel bound terms of compressed sensing optimization problems, therefore, the recovery effect is better in comparison with the conventional method which converts the data of the three channels, i.e. RGB of the colored image into three real-number two-dimensional matrixes for being processed respectively. According to the quaternion field colored image compressed sensing recovery method based on the Quasi Newton algorithm disclosed by the invention, the result image is a mean value of an image obtained by carrying out compressed sensing decoding in rows and an image obtained by carrying out compressed sensing decoding in columns, therefore, the obtained image is smoother than the image which is recovered by being processed only in rows or in columns.

The invention discloses an expression recognition method and an expression recognition system for privacy protection. The method comprises the following steps: obtaining a plurality of color face images, and carrying out the preprocessing of the plurality of color face images, and obtaining the color components of the plurality of color face images; taking the color components as imaginary parts of a quaternion matrix, constructing a first quaternion matrix, and calculating a local variance of the first quaternion matrix; constructing a second quaternion matrix by taking the local variance asa real part of the quaternion matrix and the color component as an imaginary part of the quaternion matrix; performing encryption processing on the second quaternion matrix by adopting quaternion Gyrator transformation to generate a real number matrix; randomly scrambling the real number matrix so as to train the convolutional neural network, and using the trained convolutional neural network to identify and verify the color face image. The method has the advantages of stronger recognition performance, higher safety and revocation.

The invention provides a biological feature recognition method and device, and relates to the technical field of image processing and recognition. The method comprises the steps of obtaining a to-be-detected image; Projecting the first full quaternion matrix representing the to-be-detected image on a preset feature vector to obtain a projection matrix; Wherein the preset feature vector is a feature vector of a scrambling matrix of a second full quaternion matrix of the training image set; And using the obtained projection matrix to classify and identify the obtained to-be-detected image, and obtaining an identification result. According to the method, the image information amount borne by the quaternion matrix is increased, and the face color image can be expressed more accurately, so thatthe recognition precision is further improved; The realization process of the scrambling matrix is irreversible, and even if the registered or stored biological characteristic data is lost or stolen,only the biological characteristic data needs to be registered or stored again, so that the security and privacy of the biological characteristic data such as a color face image are effectively protected.

The invention discloses an image fuzziness assessment method based on a quaternary phase congruency model. The method is represented by a color image based on a quaternion matrix, the color image is processed as a whole in the form of a vector signal and a quaternary phase congruency picture is calculated, and the quaternary phase congruency picture is combined with a traditional complex number phase congruency picture to form a significance picture which can be used to describe the structure contour clear degree of the image, that is a mixed phase congruency picture. According to the invention, histogram analysis is performed on the calculated significance pictures at the same time, the weighted average of phase congruency is taken as the index for measuring the image clear degree, and finally, the fuzzy coefficient of the image is calculated. The method of the invention is a significance method capable of reflecting the contour structure information of the color image, by effectively utilizing the correlation between color channels and the human visual characteristics, the color image fuzziness assessment accuracy rate can be improved.

The invention discloses a method for recovering a quaternion field color image in a low sampling frequency, and belongs to the technical field of digital picture processing. Firstly, a color image is expressed in a quaternion field, namely, values of an R component, a G component and a B component of the color image are respectively placed in three imaginary parts of a quaternion; and then downsampling is conducted to an original whole color image by utilizing an even stochastic matrix, and part of the sampled color image is obtained; furthermore, part of the color image is stored or conveyed; and finally the original color image is recovered through the part of the sampled color image by utilizing a real number expression form of the quaternion and quaternion matrix filling theory. The method for recovering the quaternion field color image in the low sampling frequency improves precision of recovering of the color image in the low sampling frequency and meanwhile simplifies complexity of sampling and storage content of data by utilizing a characteristic that a quaternion algorithm is good in binding force.

The invention discloses a color image quality evaluation method based on HVSs and quaternions and belongs to the technical field of image processing and computer vision. The method includes the steps that firstly, mathematic evaluation models of an original reference image and a distorted image to be evaluated are established through analysis of human vision features, wherein the mathematic evaluation models comprise spatial location functions Q<L>, local variances Q<V>, texture edge complexity functions Q<TE> and color functions Q<C> of the images; secondly, quaternion arrays of the original reference image and the distorted image to be evaluated are established and singular value decomposition is conducted on the quaternion arrays, so that singular value feature vectors of the images can be acquired; thirdly, the image distortion degree is measured through the Euclidean distance of the singular value feature vectors of the original reference image and the distorted image to be evaluated. According to the method, the human vision features and the quaternions are combined, brightness and chromaticity information of the images is extracted, the spatial location functions, the texture edge complexity functions and the local variances are established through the human vision features, and an evaluation result accords with a result generated in the mode that the images are sensed by the human eyes.

The invention discloses a color face recognition method based on quaternion non-convex penalty sparse principal component analysis, and belongs to the field of mode recognition and artificial intelligence. The method comprises the following steps: firstly, encoding red, green and blue channels of a color image by utilizing quaternion, constructing a complex representation form of a quaternion matrix, then calculating a quaternion covariance matrix, and carrying out characteristic decomposition on the quaternion covariance matrix to obtain a principal component vector; and introducing a non-convex penalty L1 / 2 norm as a sparse constraint term to obtain a new quaternion non-convex penalty sparse principal component analysis (QHSPCA) optimization model, and solving a sparse solution of the model by adopting a coordinate descent method and an immobile point iteration method, and finally realizing face recognition by using a nearest neighbor classifier. Experiments on a Geoga Tech face database show that the QHSPCA method provided by the invention has relatively good recognition performance, and the calculation efficiency is also improved.