30 results about "Log Gabor filter" patented technology

The invention discloses a fingerprint identification method. A modified fingerprint partitioning algorithm is provided in order to overcome the defects of a variance method used for carrying out fingerprint partitioning, an appropriate Log Gabor filter is built to achieve the reinforcement of a fingerprint image, and finally a node relationship representation method under a curvilinear coordinate system is provided. The modified fingerprint partitioning algorithm is provided in order to overcome the defects of the variance method used for carrying out fingerprint partitioning, and the fingerprint partitioning algorithm makes full use of the advantages of a simple algorithm and high calculation speed of the variance method, overcomes the defect that the variance method is easily affected by noise, and obtains a more accurate fingerprint portioning image. The appropriate Log Gabor filter is built to achieve the reinforcement of the fingerprint image, and the node relationship representation method under the curvilinear coordinate system can be effectively matched with deformed fingerprints. The system distinguishes the identification of each person by utilizing the characteristics such as uniqueness and forgery difficulty of the fingerprints, and has the advantages that allograph is avoided and paper is not used.

The invention discloses a method for realizing image registration of a synthetic aperture radar (SAR) by using three components of monogenic signals, belonging to the technical field of image processing and registration of SARs. The conventional gradient information-based detection method and cross correlation matching method have the defects of excessive detected characteristic points, overlong detection time, low registration accuracies and the like when applied to image registration of an SAR. A method for matching a detection algorithm of monogenic signal phase congruency with monogenic signal phase correlation is given, and three orthogonal monogenic signal component signals are generated by designing a frequency domain Log-Gabor filter. One path of the monogenic signal component signals is transmitted to a characteristic detector, i.e., a local amplitude and a local phase are resolved by using three components to construct a monogenic signal phase congruency function for detecting the phase congruency characteristic. Another path of the monogenic signal component signals is transmitted to a matcher, and a characteristic description vector is constructed by using three components of the monogenic signals; a characteristic vector correlation matrix is obtained by calculating the characteristic vector correlation of a reference image and characteristic points in an image to be registered; and largest line elements and column elements in the characteristic vector correlation matrix are searched and are indexed to a coarsely-matched characteristic point pair, so that coarse characteristic matching is realized. An affine basic matrix is fitted by using an RANSAC (Random Sample Consensus Algorithm), so that accurate matching of characteristic points is completed. An affine conversion model is used for realizing image registration of the SAR. The algorithm disclosed by the invention has the advantages of realization of automatic registration of SAR images, high registration speed, small influence by speckle noise, high registration accuracy and popularization and application values.

The invention discloses a no-reference stereo image quality evaluation method based on dictionary learning and machine learning. The method comprises the following steps of: firstly, performing log-Gabor filtering for left and right viewpoint images, obtaining respective amplitude and phase information, then, performing local binarization operation for the amplitude and the phase information, and obtaining a local binarization mode feature image of the left and right viewpoint images; secondly, using a binocular energy model to fuse the amplitude and the phase information of the left and right viewpoint images, obtaining binocular energy information, and acquiring a local binarization mode feature image of the binocular energy information; then, using a coordination representation algorithm to perform dictionary learning for the local binarization mode feature images of the left and right viewpoint images and the binocular energy information, obtaining binocular visual perception sparse feature information, and finally, obtaining an objective quality evaluation predicted value of a to-be-evaluated distorted stereo image. The method has the advantages of being capable of fully considering stereo visual perception characteristics, and being capable of effectively improving correlation between objective evaluation result and subjective perception.

The invention relates to a method for recognizing iris on the basis of odd-symmetry 2D (two-dimensional) Log-Gabor filter and Adaboost combinations. The method has the advantages that the problem of partial non-effective feature points when iris features are extracted by 2D Log-Gabor filters can be solved by odd-symmetry 2D Log-Gabor filters, phase and amplitude signals of iris textures are analyzed at different frequencies in different directions, redundant information is eliminated by the aid of feature fusion technologies, accordingly, feature codes can be formed and can be trained by the aid of Adaboost algorithms to form the optimal classifiers, and features can be recognized.

The invention relates to an image strong and weak edge detection method based on spatio-temporal information responded by dot matrix nerve cells. Firstly, a multi-direction Log-Gabor filtering result of an image in a view is utilized and edge information of the image is reconstructed; a reconstruction result serves as input of the dot matrix nerve cells; time in releasing action potentials of all the nerve cells is recorded to form a time matrix; a constructed reception field window slides on the time matrix, the improved variance is calculated according to a time order of all time elements and a center point of the window undergoes assignment and thus, a variance matrix containing nerve cell responding time and the spatio-temporal information is obtained; afterwards, the reception field window continues sliding on the variance matrix, the side direction rejection characteristic of the nerve cells in space is achieved and an edge matrix is obtained; finally, the edge matrix is mapped into a result image inversely. According to the image strong and weak edge detection method based on the spatio-temporal information responded by the dot matrix nerve cells, the spatio-temporal information responded by the dot matrix nerve cells is taken into consideration, the edges of the image can be detected and additionally, a strong and week relationship of the edges can be effectively reflected.

The invention provides a filter bank-based gait phase recognition method for children suffering from cerebral palsy. The method comprises the stages of original signal obtaining, wherein original signals of surface electromyogram of the children suffering from the cerebral palsy are obtained; preprocessing, wherein the received original signals are preprocessed; feature library establishing, wherein on the basis of a log-gabor filter bank, surface electromyogram signals obtained after preprocessing are subjected to multi-band filtering, and a feature library is established by conducting feature extraction on the basis of linear discriminant analysis; classification, wherein an extreme learning machine is adopted as a classifier, feature subsets picked from the feature library and corresponding label information serve as training samples, and ELM modeling is conducted, so that gait phase classification in the normal walking process of the children suffering from the cerebral palsy is evaluated.

The invention discloses a no-reference stereo image quality evaluation method based on dictionary learning and machine learning. The method comprises the following steps of: firstly, performing log-Gabor filtering for left and right viewpoint images, obtaining respective amplitude and phase information, then, performing local binarization operation for the amplitude and the phase information, and obtaining a local binarization mode feature image of the left and right viewpoint images; secondly, using a binocular energy model to fuse the amplitude and the phase information of the left and right viewpoint images, obtaining binocular energy information, and acquiring a local binarization mode feature image of the binocular energy information; then, using a coordination representation algorithm to perform dictionary learning for the local binarization mode feature images of the left and right viewpoint images and the binocular energy information, obtaining binocular visual perception sparse feature information, and finally, obtaining an objective quality evaluation predicted value of a to-be-evaluated distorted stereo image. The method has the advantages of being capable of fully considering stereo visual perception characteristics, and being capable of effectively improving correlation between objective evaluation result and subjective perception.

The invention discloses a high-resolution remote sensing image segmentation method combining a phase and a spectrum. For the limitation existing in the aspects of weak edge detection and the like dueto the fact that gradient information is excessively depended during segmentation of a traditional method, advantage complementation is conducted by introducing phase information, and firstly, phase consistency information is extracted through Log Gabor filtering; on the basis, a local homogeneity index J-value in a traditional JSEG algorithm is adopted to optimize an edge detection result, and anobjective function optimization strategy based on inter-scale mutual information minimization is provided, so that a parameter adaptive optimal parameter combination and response model is constructed, and finally, the multi-scale region segmentation and region merging is carried out based on the model to obtain a final segmentation result.

The invention discloses a 3D image quality evaluation method based on visual saliency and depth map. Use the stereo matching algorithm to obtain the disparity map; use the spectral residual algorithm to obtain the saliency map of the image, use the structural similarity algorithm to obtain the similarity of brightness, contrast, and contrast, and use the Gaussian color model to obtain metrics such as chroma similarity features; Finally, the Log Gabor filter is used to extract multi-scale features from the reference image and the distorted image, and the edge texture features of the left and right images of multi-scale and multi-directional are obtained, and the similarity calculation is performed to obtain the index features of image quality evaluation, and the support vector machine is used to carry out Regression prediction, get the objective quality score, complete the mapping to the stereoscopic image quality, and get the final stereoscopic image quality evaluation. The objective evaluation and subjective evaluation of the full-reference image quality evaluation method proposed by the present invention have good consistency, and are superior to the traditional three-dimensional image quality evaluation method.

The invention relates to a local feature description method, system and device of a rotation-invariant multi-source image. This method is based on multi-scale and multi-directional log Gabor filters to construct a descriptor for local feature matching of rotation-invariant multi-source images. This descriptor can not only adapt to the differences between multi-source images caused by nonlinear brightness changes, but also has certain noise At the same time, it can also adapt to geometric changes between images, such as translation and rotation, so as to ensure that the descriptor has stronger robustness, making the descriptor more accurate for local feature description methods that support rotation invariance and efficient.

The invention discloses a high-precision palmprint recognition method based on two-dimensional orthogonal Log-Gabor filtration, which uses two two-dimensional Log-Gabor filters which are mutually orthogonal on directions to constitute the two-dimensional orthogonal Log-Gabor filter OLG (f, Theta, Theta 0, Sigma Theta, f0, Sigma f), thus carrying out the filtration of a palmprint image after the pre-treatment; then the palmprint image OF after the filtration adopts the phase coding mode to extract the orthogonal phase characteristic POPF which is equal to (FR, FI) of the palmprint image; then the Hamming distance matching algorithm is adopted to carry out the matching of orthogonal phase characteristics POPF1 and POPF2 of two palmprint images, thus obtaining the matching result. The algorithm is less affected by the conditions of the collected light and has low computational complexity and high recognition precision.

The invention provides a texture image segmentation method based on a level set model. The texture image segmentation method comprises following steps of: 1, carrying out Log-Gabor filtering on an input image and outputting the filtered image; 2, extracting an LSS descriptor from the filtered image so as to obtain an image formed by a descriptor component; 3, constructing a texture energy item on the image formed by the descriptor component; 4, constructing an LGDF energy item on the input image; 5, integrating the two kinds of energy items in steps 3 and 4 so as to form an energy function of the method; and 6, by maximizing the energy function in the step 5, solving a segmentation curve of the energy function and using a Lattice Boltzmann method to accelerating the solving process. According to the invention, based on the level set model, a new texture energy item is designed by use of the LSS descriptor and then is combined with a traditional LGDF energy item, a good segmentation effect is achieved for each kind of texture image, so the method has robustness for noise and uneven gray levels, and through optimization of the Lattice Boltzmann method, the image segmentation efficiency is improved.

The invention discloses a method for realizing image registration of a synthetic aperture radar (SAR) by using three components of monogenic signals, belonging to the technical field of image processing and registration of SARs. The conventional gradient information-based detection method and cross correlation matching method have the defects of excessive detected characteristic points, overlong detection time, low registration accuracies and the like when applied to image registration of an SAR. A method for matching a detection algorithm of monogenic signal phase congruency with monogenic signal phase correlation is given, and three orthogonal monogenic signal component signals are generated by designing a frequency domain Log-Gabor filter. One path of the monogenic signal component signals is transmitted to a characteristic detector, i.e., a local amplitude and a local phase are resolved by using three components to construct a monogenic signal phase congruency function for detecting the phase congruency characteristic. Another path of the monogenic signal component signals is transmitted to a matcher, and a characteristic description vector is constructed by using three components of the monogenic signals; a characteristic vector correlation matrix is obtained by calculating the characteristic vector correlation of a reference image and characteristic points in an image to be registered; and largest line elements and column elements in the characteristic vector correlation matrix are searched and are indexed to a coarsely-matched characteristic point pair, so that coarse characteristic matching is realized. An affine basic matrix is fitted by using an RANSAC (Random Sample Consensus Algorithm), so that accurate matching of characteristic points is completed. An affine conversion model is used for realizing image registration of the SAR. The algorithm disclosed by the invention has the advantages of realization of automatic registration of SAR images, high registration speed, small influence by speckle noise, high registration accuracy and popularization and application values.

The invention discloses a universal no-reference image quality evaluation method based on a phase selection mechanism. The method comprises the steps of firstly implementing Log-Gabor filtering on a distorted image to be evaluated, thus obtaining a multi-scale and multi-directional phase image; comparing the pixel value of each pixel point in the phase image with the pixel values of surrounding pixel points to obtain a local feature map; then obtaining the local feature mode chart of the local feature map by a rotational invariance method, and performing statistics on the local feature mode chart by a histogram statistic method, thus obtaining the histogram statistic feature vector of the distorted image to be evaluated; and at last obtaining the objective quality evaluation predicted value of the distorted image to be evaluated according to the distance between the histogram statistic feature vector of the distorted image to be evaluated and the histogram statistic feature vector of each distorted image in a training set. The method has the advantages that the influence of the change of the phase information on the visual quality can be fully considered, and the correlation between the objective evaluation result and subjective perception can be effectively improved.

The invention relates to an image strong and weak edge detection method based on spatio-temporal information responded by dot matrix nerve cells. Firstly, a multi-direction Log-Gabor filtering result of an image in a view is utilized and edge information of the image is reconstructed; a reconstruction result serves as input of the dot matrix nerve cells; time in releasing action potentials of all the nerve cells is recorded to form a time matrix; a constructed reception field window slides on the time matrix, the improved variance is calculated according to a time order of all time elements and a center point of the window undergoes assignment and thus, a variance matrix containing nerve cell responding time and the spatio-temporal information is obtained; afterwards, the reception field window continues sliding on the variance matrix, the side direction rejection characteristic of the nerve cells in space is achieved and an edge matrix is obtained; finally, the edge matrix is mapped into a result image inversely. According to the image strong and weak edge detection method based on the spatio-temporal information responded by the dot matrix nerve cells, the spatio-temporal information responded by the dot matrix nerve cells is taken into consideration, the edges of the image can be detected and additionally, a strong and week relationship of the edges can be effectively reflected.