110results about How to "Valid description" patented technology

The invention belongs to the field of computer vision and pattern recognition and discloses a moving target tracking method based on an improved multi-example learning algorithm. Firstly, a random measurement matrix is designed according to the compression perception theory. Then a multi-example learning algorithm is used to sample an example in a current tracking result small neighborhood to form a positive package, and at the same time, sampling an example is carried out in a large neighborhood ring to obtain a negative package. For each example, the characteristic of a character target is extracted at an image surface, and the random measurement matrix is utilized to carry out dimensionality reduction on the characteristic. According to the extracted example characteristic, online learning weak classifiers are utilized, and weak classifiers with strong discrimination ability are selected from a weak classification pool to form a strong classifier. Finally, when a new target position is tracked, according to a similarity score of the current tracking result and a target template, the online adaptive adjustment of classifier update degree parameters is carried out. According to the method, a problem that a tracking result in the existing algorithm is easily affected by an illumination change, an attitude change, the interference of a complex background, target fast motion and the like is solved.

The invention provides a medical image registration method based on combination of mutual information and gradient information, comprising steps of performing weighting on mutual information similarity and adding difference values of a gradient model into gradient similarity, performing space changing on a floating image to be registered and calculating similarity measure of a reference image and the changed floating image. The invention provides a similarity measure function of weighting mutual information and gradient information. The similarity measure can attain a maximization value through constantly optimizing search and changing space, and the registration is successful. The similarity measure function is employed in the process of the registration, which can effectively solve the problems that the medical image registration algorism is low in accuracy and the robustness is not strong.

The invention relates to an audio/video keyword identification method based on decision-making level fusion. The method mainly includes the following steps that (1) a keyword audio/video is recorded, a keyword and non-keyword voice acoustic feature vector sequence and a visual feature vector sequence are obtained, and accordingly a keyword and non-keyword acoustic template and a visual template are trained; (2) acoustic likelihood and visual likelihood are obtained according to the audio/video in different acoustic noise environments, so that the acoustic mode reliability, visual mode reliability and optimal weight are obtained, and accordingly an artificial neural network can be trained; (3) secondary parallel keyword identification based on the acoustic mode and the visual mode is conducted on the audio/video to be detected according to the acoustic template, the visual template and the artificial neural network. According to the audio/video keyword identification method based on decision-making level fusion, the acoustic function and the visual function are fused at a decision-making level, the secondary parallel keyword identification based on the dual modes is conducted on the audio/video to be detected, the contribution of visual information in the acoustic noise environment is fully utilized, and therefore identification performance is improved.

The invention relates to a micro-CT technology-based reservoir core three-dimensional entity model reconstruction method. The method includes the following steps that: a micro-CT technology is utilized to scan a reservoir core, so that a sectional scanning image of the reservoir core can be obtained, so that a CT image of a real reservoir core can be obtained, and a watershed algorithm is adopted to perform image segmentation on the CT image, so that the three-dimensional data of the image can be obtained; a Marching Cubes algorithm is adopted to generate a three-dimensional surface model of the reservoir core; and with the three-dimensional surface model of the reservoir core adopted as constraints, a constrained Delaunay tetrahedralization algorithm is utilized to generate the three-dimensional entity model of the reservoir core. According to the method of the invention, the three-dimensional entity model of the real reservoir core is established based on the sectional scanning image of the reservoir core, so that the obtained three-dimensional entity model of the reservoir core is more approximate to the structure of a real reservoir core. The method of the invention has the advantages of high accuracy and high efficiency. With the method adopted, defects of poor accuracy and low efficiency in the reconstruction of the three-dimensional entity model of a reservoir core in the prior art can be eliminated, and an effective guarantee can be provided for reservoir core simulation characteristic research.

An infrared and visible light video fusion method based on moving target detection belongs to the technical field of video fusion. A moving target is detected through low-rank sparse representation, the target is extracted using the low-rank and sparse time slices along X-T and Y-T planes, the integrity of the target detected is ensured by spatial information, and the robustness and accuracy of detection are improved. Then, the structure information of an image can be better described through non-subsample shearlet transform with a good fusion effect. The method has the advantages of high computational efficiency, unlimited filtering direction, translational invariance, and the like, and improves the fusion effect. In addition, the low-frequency sub-band coefficients are fused according toa sparse representation rule, the high-frequency sub-band coefficients are fused according to a weighting rule taking a target saliency map as a weighting factor, and a better fusion effect is achieved. Therefore, the infrared and visible light video fusion method based on moving target detection presented by the invention is more robust, and has a better fusion effect.

The invention provides a method for judging typesetting directions of text regions, belonging to the OCR field. The method is characterized by carrying out statistic analysis according to the obtained projective histogram, finding out respective most representative characteristic data triples and judging the typesetting directions of the text regions with less than three character lines accordingto the length-width ratios of the external rectangles of the text regions; judging the typesetting directions of the text regions with not less than three character lines according to the number and statistical positions of abnormal projecting cylinders; judging the text typesetting directions which can not be judged by the above methods according to the first moment between normal projecting cylinders; judging the typesetting directions which can not be judged by the above methods according to the indent of the text characters; and giving up judging in the regions of which the typesetting directions still can not be judged at least. The method can accurately judge whether the normal text regions are horizontal or vertical and judge whether the text regions with low inclination angles or slight geometric distortion are horizontal or vertical and has good judgment effect, high speed and good application value.

The invention discloses a recording terminal clustering method based on voice time-frequency transform features and integral linear programming. The method comprises the following steps: a, carrying out preprocessing on read recording; b, extracting GTCC features from each frame of voices; c, constructing a depth self-coding network with a bottleneck layer, and extracting bottleneck features; d, constructing gaussian supervector features; e, carrying out spectrogram feature extraction on the voice obtained after preprocessing in step a; f, splicing the spectrogram features in step e to the gaussian supervector in step d, thus obtaining voice time-frequency transform features describing the features of the recording terminal finally; and g, carrying out clustering on the voice time-frequency transform features of all the recording samples by adopting the integral linear programming algorithm, wherein the clustering result is taken as the final classification basis. The feature extraction and clustering of the method provided by the invention are unsupervised, and compared with the existing supervising method, the method provided by the invention has better universality.

The invention provides a space cost distance calculating method based on the grid spatial data. The method inputs data representing space information through a ruled data format, the data describes the cost information of space access. Then on the input basis, the necessary smallest space cost distance value and path from an arbitrary position to another space position in the space is calculated. Through extending one grid value to nine values, on the basis of not changing the space position corresponding to the central grid, the cost relationship to the surrounding grids is intuitively described. The method can describe the passing distance differences between different directions in the hypsography areas; by using the calculating method, the conditions of mountainous areas, semi-closed roads, and one-way roads can be described and processed, therefore, the grid cost distance calculated result is closer to the fact, which meets the need of practical applications.

An opportunistic spectrum access method based on Markov chain and CSMA includes that the access predication statistical module method of the Markov chain is combined with the CSMA access protocol to adopt different access modes according to different induction areas, before the user access is recognized, the spectrum detection is carried out according to spectrum induction method, and the first and second dual thresholds Lambda 1 and Lambda 2 of the interference temperature of the master user, the judgment area whether the master user exists is divided according to the two thresholds, when the interference temperature value is less than the Lambda 1, the user is recognized and accessed directly; when the interference temperature value is more than the Lambda 2, the CSMA access mode is adopted for back-off access; when the interference temperature is between the Lambda 1 and Lambda 2, a Markov chain access predication statistical module with the master user as the priority is built in queuing and non-queuing modes, and the Markov chain module is adopted to describe the opportunistic spectrum access. During access process, the first and second interference temperature thresholds are regulated dynamically to realize the maximization of the whole network throughput.

The invention discloses a moving object segmenting method based on local space-time manifold learning. The method comprises the following steps: 1. segmentation of a space-time cube of an input video and extraction of an illuminant invariance feature; 2. establishment of a background model based on the local space-time manifold learning; and 3. segmentation of a moving object based on the local space-time manifold and on-line updating maintenance of a model. According to the moving object segmenting method based on the local space-time manifold learning, local space-time variation can be effectively described, the scale adaptability of an SIFT (scale invariant feature transform) feature point set in an extended image in the input video is treated, the problem that a dynamic background and illumination change can not be effectively eliminated when the moving object is segmented is solved, and the reliable and effective moving object can be provided for an intelligent monitoring platform.

The invention discloses a human body posture feature extracting method based on 3D joint point coordinates. Two features are extracted in total, one is the global feature based on the body part location (BPL), the other is the local feature based on the local joint structure (LJS), and the final feature is the feature formed after the two features are fused. The experiment proves that the extracted features can effectively describe human body postures, and the human body postures can be well recognized when the features are applied to a support vector machine classification model.

The invention provides a supervision cross-modal Hash search method based on a nonparametric Bayesian model, used for solving the technical problem that in the existing cross-modal Hash search method, the search accuracy is low. The method comprises the implementation steps of operating to acquire normalized training data and test data; classifying the normalized training data; operating to acquire three training data parameters of the normalized training data; operating to acquire the probability that normalized image training data and normalized text training data belong to the same class; operating to acquire the posteriori probability of the training data; operating to acquire a unified Hash code of the normalized image training data and the normalized text training data; operating to acquire the Hash code of the test data; operating to acquire a Hamming distance matrix of the Hash code of the test data and the unified Hash code of the normalized image training data and the normalized text training data; and operating to acquire a search result of the test data. The method provided by the invention is high in search accuracy, and can be applied to mutual search service for images and texts of mobile terminal equipment and the Internet of things.

The invention discloses an LBP (Local Binary Pattern) image and block encoding-based iris feature extracting method comprising the following steps of: firstly, adopting an LBP operator for a normalized iris image to obtain an LBP iris image; then, extracting an iris feature from the LBP image by using an iris statistical information based block encoding method; and finally, taking a Hamming distance as a classifier to obtain a recognition result. Parameters of the LBP operator and block encoding in the feature extracting method are obtained through training. Compared with the traditional feature extracting method, the LBP image and block encoding-based iris feature extracting method has higher recognition rate and strong robustness for illumination change.

The invention relates to an image processing method and device and an object detection method and system. The image processing method comprises a color classification step, a mask generation step and a feature extraction step, wherein the color classification step is used for classifying multiple pixels in an image region into at least one dominant color category; the mask generation step is used for generating a chromatic aberration mask for each dominant color category, and the chromatic aberration masks reflect the relations between the multiple pixels and the corresponding dominant color categories; the feature extraction step is used for extracting a feature vector from each corresponding chromatic aberration mask.

The invention discloses a visualization analysis method of a document hot point. The visualization analysis method mainly comprises the steps of 1) researching data acquisition and processing, particularly determining the field needed to be researched, crawling field periodical data, extracting a field needed to be researched, and processing an acquired keyword field; 2) researching subject detection; and 3) visually researching a subject and a subject network. Change is performed from a data source, a data acquisition method is changed, the field periodical is selected, data crawling is performed according to a set condition, a large of representative data relevant to research can be obtained, visualization is performed by a method combining a strategic map and the subject network, a result is analyzed from multiple angles, the accuracy of the analysis can be further improved, and effective analysis and explanation are expected to be performed on research trend in future.

The invention relates to a cross-scattering model-based polarization radar remote sensing image urban target decomposition method. The method includes the following steps that: a PolarSAR image single-view polarization scattering matrix is converted into a polarization covariance matrix or a polarization coherence matrix; a PolarSAR image pixel polarization azimuth is calculated on the basis of the polarization covariance matrix or the polarization coherence matrix; the cross-scattering model of a building is constructed on the basis of the polarization azimuth and a dihedral angle reflector;a polarization target decomposition solving equation is constructed according to a surface scattering model, an even-order scattering model, a volume scattering model, a spiral scattering model and the cross-scattering model, and the scattering coefficients of each model are solved; and the scattering weighting coefficients of each model are corrected, and surface scattering power, even-order scattering power, volume scattering power, spiral scattering power, and cross-scattering power are calculated on the basis of the scattering weighting coefficients of each model, and target decompositionis completed. With the cross-scattering model-based polarized radar remote sensing image urban target decomposition method of the present invention adopted, the HV scattering of the building can be effectively described.

The invention discloses a heterogeneous network community structure and a community discovery method based on the structure. A zero model of a heterogeneous network is provided, and a basic randomized network model is provided for research of a heterogeneous network structure in a complicated network; on this basis, modularity applicable to a function heterogeneous network of the heterogeneous network is provided, and a new method for evaluation of heterogeneous network communities is provided; on the basis of the heterogeneous network modularity, the heterogeneous network community structure is defined, and the heterogeneous network community discovery method based on maximization of the heterogeneous network modularity is provided on the basis of the community structure. The community structure in the heterogeneous network is discovered effectively. According to the structure and the method, through adoption of homogeneous and heterogeneous adjacent matrixes, a connection relationship of the heterogeneous network is effectively described.

The invention discloses a method for reducing the reconstruction distortion degree of a viewpoint in light field drawing, and the method comprises the following steps: 1) constructing a 3D scene witha shielding phenomenon; 2) carrying out quantitative description of shielding; 3) obtaining the camera position with the minimum shielding degree; and 4) reconstructing a new viewpoint at any positionand in any direction. According to the method, data acquisition and multi-view drawing in an actual complex scene can be realized; compensation of edge information loss in computational imaging and virtual reality caused by a shielding phenomenon can be solved, the method can even be applied to quantitative description of a three-dimensional scene shielding phenomenon of a 3D video technology, and the interactivity and the three-dimensional experience of the 3D video technology can be improved.

The invention provides a remote control robot for underwater precise measurement. Motion of four or more degrees of freedom of the underwater robot is achieved, the underwater robot has the capabilityof depth keeping hover, direct movement, rotation and lateral movement, and can maintain good communication with land control equipment, electronic components are all subjected to watertight pressure-resisting protection, and the remote control robot is suitable for passing through an underwater narrow area to conduct underwater detection and underwater measurement operation; the precise measurement underwater robot specially plans a path to improve the cruise and measurement efficiency; a multi-beam depth finder and a binocular precision measuring instrument are utilized to synergistically carry out underwater measurement, high-precision and high-density measurement of underwater detection targets is achieved through a multi-beam depth measuring technology, binocular precision measurement helps the underwater robot to execute certain complex underwater operation tasks, and two measurement methods synergistically complete high-quality underwater precision measurement; and the relativeerror of underwater measurement is small, and the operating requirements of the underwater robot for underwater target grabbing and the like are met.

The invention provides an image denoising method based on super pixel clustering and sparse representation to solve a technical problem of a low denoised image peak signal to noise ratio and detain information loss of an existing image donoising method. The method comprises the steps of (1) inputting an image to be denoised, (2) carrying out super pixel segmentation and super pixel clustering on the image and obtaining multiple clusters of similar super pixels, (3) carrying out image block extraction and dictionary training on each cluster of similar super pixels, (4) calculating the sparse coefficient of each image block under a corresponding dictionary, (5) searching the similar image block of each image block and calculating the sparse coefficient weighted sum of similar image blocks, (6) restraining the sparse decomposition process of each image block by using the sparse coefficient weighted sum of the similar image blocks, and obtaining a new sparse coefficient, (7) judging whether a current number of iterations is larger than a maximum number of iterations Lambda or not, executing a step (8), otherwise adding 1 to the number of iterations, and executing the step (5), and (8) reconstructing the image to be denoised , and obtaining a denoised image.

The invention discloses a method for predicting interaction between sRNA (small RNA) and target mRNA (messenger RNA) of sRNA in bacteria. The method includes the steps: 1) data collection and sorting;2) feature extraction, conversion of a data set into a matrix; 3) F-score feature optimization; and 4) training and construction of an SVM model, prediction and obtaining of a prediction result. Themethod for predicting interaction between sRNA and target mRNA of sRNA in bacteria can effectively characterize RNA sequence information and improve the prediction accuracy of sRNA-target mRNA interaction. At the same time, the method for predicting interaction between sRNA and target mRNA of sRNA in bacteria has the advantages of low cost, low time consumption and high prediction speed.