178 results about "Haar wavelet" patented technology

Described is an active learning system for fingerprinting an object identified in an image frame. The active learning system comprises a flow-based object segmentation module for segmenting a potential object candidate from a video sequence, a fixed-basis function decomposition module using Haar wavelets to extract a relevant feature set from the potential object candidate, a static classifier for initial classification of the potential object candidate, an incremental learning module for predicting a general class of the potential object candidate, an oriented localized filter module to extract features from the potential object candidate, and a learning-feature graph-fingerprinting module configured to receive the features and build a fingerprint of the object for tracking the object.

A system and process for identifying corresponding points among multiple images of a scene is presented. This involves a multi-view matching framework based on a new class of invariant features. Features are located at Harris corners in scale-space and oriented using a blurred local gradient. This defines a similarity invariant frame in which to sample a feature descriptor. The descriptor actually formed is a bias/gain normalized patch of intensity values. Matching is achieved using a fast nearest neighbor procedure that uses indexing on low frequency Haar wavelet coefficients. A simple 6 parameter model for patch matching is employed, and the noise statistics are analyzed for correct and incorrect matches. This leads to a simple match verification procedure based on a per feature outlier distance.

The invention discloses a consistent lighting method under a falsehood-reality fused environment, comprising the following steps: shooting a high dynamic range image reflecting a real environment, of a small mirror-surface sphere in a real scene, and acquiring an environmental lighting map of the real scene; preprocessing the environmental lighting map of the real scene and surface reflection attributes of a virtual object; preprocessing the environmental lighting of the real scene to generate environment lighting on a unit small sphere under local coordinates corresponding to all normal vectors to obtain Haar wavelet coefficients of the environment lighting; preprocessing a bidirectional reflection distribution function to generate slices of the bidirectional reflection distribution function on the unit hemisphere under all sight directions to obtain Haar wavelet coefficients of the slices; and drawing a falsehood-reality fused environment, indexing the wavelet coefficients of the environment lighting and the slices of the bidirectional reflection distribution function according to a normal vector of an apex of the virtual object and the sight directions, calculating a vector product of the coefficients to obtain an apex drawing color, and meanwhile, accelerating by adopting GPU (Graphics Processing Unit). The consistent lighting method can be used for efficiently realizing afull-range environment lighting drawing effect under the falsehood-reality fused environment.

The invention discloses a novel digital image encryption method belonging to the field of DNA calculation and image encryption, and provides a digital image encryption method based on a DNA sequence and multi-chaotic mapping. The traditional encryption algorithm based on chaos has the defects that a key space is small, a chaos system is easy to analyze and forecast, and the like; and almost of the current encryption systems based on DNA need complex biological operation and are hard to realize. In order to overcome the defects, the invention provides the digital image encryption method based on a DNA sequence and multi-chaotic mapping, comprising the following steps of: firstly, scrambling the positions of pixels of images by utilizing a two-dimension chaotic sequence generated by Cubic mapping and Haar wavelet functions, and then disrobing the pixel values of the images under the action of a chaotic sequence generated by the DNA sequence and the Logistic mapping. The experimental result shows that the algorithm is easy to realize, has better encryption effect on images, large key space and high sensitivity to keys and can effectively resist exhaustive attack and statistical attack.

A 3D face recognition method based on intermediate frequency information in a geometric image as follows: (1) preprocessing a library and test models of 3D faces, including 3D face area cutting, smoothing processing and point cloud thinning, and discarding the lower portion of the face; (2) mapping the remainder of the face to a 2D grid using grid parameters, and performing linear interpolation on the 3D coordinates of the grid top to acquire the 3D coordinate attributes and generating a geometric image of a 3D face model; (3) performing multi-scale filtering with a multi-scale Haar wavelet filter to extract horizontal, vertical, and diagonal intermediate frequency information image images as invariable facial features; (4) calculating the similarity between the test model and the library set model with a wavelet domain structuring similarity algorithm; and (5) judging the test and library set model models with the maximum similarity belong to the same person.

The invention relates to a multi-resolution network characteristic registration-based method for sorting face values and face directions of notes in a sorter, which comprises the following steps of: firstly, before performing image recognition and image measurement, balancing the luminance of different points on sampling images; secondly, performing Haar wavelet transform on note images, and extracting approximate picture contents of the note images; thirdly, extracting network characteristics; fourthly, analyzing the separability of the network characteristics on different note images; and finally, establishing a comprehensive sorter by using the network characteristics, wherein a structural risk minimization-based Gaussian mixture model is applied in the sorter. By the method, the real-time processing requirement of the note sorter can be met, and the requirement of processing 1,000 notes per minute can be met. Simultaneously, the defect of inconsistent note images in the same currency and face direction caused by abrasion and printing can be overcome effectively by adopting local characteristic registration so as to perform better recognition.

The invention discloses a multi-resolution path planning method for a micro unmanned aerial vehicle under the influence of a wind field. The multi-resolution path planning method includes six steps as follows: firstly, obtaining an environmental zone for the path planning of the aerial vehicle; secondly, selecting a basic function for wavelet transformation; thirdly, creating a flying environment cost function under the influence of the wind field; fourthly, reconstructing a multi-resolution flying environment cost function by using a Haar wavelet grid decomposition method; fifthly, generating an environmental weighted graph by using a multi-resolution environment cost function value; and sixthly, looking for the optimal path. According to the multi-resolution path planning method, the factors such as the computing power of an airborne computer, the detection range of a sensor, the distribution of an obstacle, the distribution and intensity of the wind field and the like are considered in the process of determining different resolution levels in the environmental zone, so that the path planning efficiency is improved. Moreover, the multi-resolution path planning method is simple, reliable and practical, and is suitable for the online path planning of the low-altitude micro unmanned aerial vehicle under the influence of the wind field.

The invention relates to a continuous chemical process fault detection method. The continuous chemical process fault detection method comprises the following steps that (1) a linear regression model of a vector Xj and a vector Y is built, and a regression constraint function is introduced; (2) data compression is carried out through haar wavelet transformation to improve computational efficiency; (3) a regression constraint construction sparse pivot element model with the addition of 1-norm and 2-norm is built, and an optimal solution of a sparse pivot element is worked out through derivation of the SPCA algorithm; (4) the optimal threshold value of the T2 statistic and the optimal threshold value of the SPE statistic are estimated through the kernel density estimation (kde) method; (5) calculation of the T2 statistic and the SPE statistic is conducted on fault data, and the value of the T2 statistic and the value of the SPE statistic of the fault data are obtained in sequence; (6) whether a fault exists in the data is detected. According to the continuous chemical process fault detection method, the data size related to a pivot element after sparsity is reduced, so that the calculated quantity is reduced, the computation time is shortened, real-time performance of monitoring is improved, and accuracy and efficiency of fault detection can be improved.

The invention discloses a segmentation method of a watershed texture image on the basis of morphological Haar wavelet gradient extraction, which relates to the technical filed of image processing and aims at solving the excessive segmentation problem of watershed texture image segmentation. The method not only can reduce excessive segmentation, but also is much better than other post processing methods solving the problem of excessive segmentation in arithmetic speed. The steps for realizing the method are as follows: (1) a texture gradient image TG is extracted by carrying out morphological Haar wavelet conversion to the original texture image; (2) after circumrotating by 90 degrees, the original image is used for extracting a texture gradient image RTG with a morphological Haar wavelet; (3) the texture gradient image RTG and the texture gradient image TG are subjected to circumrotation weighting to obtain a texture gradient image G; (4) morphological filtering is carried out to the texture gradient image G to obtain a texture gradient image OCG after being smoothed; and (5) marker-watershed segmentation is carried out to the texture gradient image OCG to obtain the final texture segmentation result. Compared with the image segmentation standards, the image segmentation result of the method is validated to basically meet standards.

The invention relates to a binocular vision-based unmanned aerial vehicle aerial autonomous refueling fast docking navigation method and belongs to the machine vision and image processing field. The method includes the following steps that: a binocular camera system with an optical filter shoots a refueling taper sleeve with an optical marker lamp, so that a left image and a right image are obtained; gray processing, binarization and median-value filtering are performed on the left image and the right image sequentially, all communicated regions in the left image and the right image are searched and marked through adopting a regional growth method, and the centroids of each of the communicated regions are calculated and are adopted as feature points; improved haar wavelet transform is adopted to describe all the feature points, and the description vectors of all the feature points are obtained, the feature points of the left image and the right image are matched through adopting the minimum Euclidean distance method, so that the feature point pars of the left image and the right image are obtained; and the three-dimensional coordinates of the feature points are calculated by using a binocular vision principle, and the least squares method is adopted to perform spatial circle fitting on the three-dimensional coordinates, and the end surface circle curve of the refueling taper sleeve is obtained, and the center, normal vector and radius of the circle are obtained through calculation.

The invention relates to a face detection and identification method. The face detection and identification method includes the steps that 1, face detection is carried out on an image obtained by a camera through a face detector based on the haar characteristic and an Adaboost cascade classifier, the eyes of the detected face are located through an eye classifier based on RNDA and Adaboost, geometric correction and histogram modification are carried out, and then a modification face sample is obtained; 2, the image is preprocessed through haar wavelets, and low-frequency components of the image are reserved as the input of a later feature extraction algorithm; 3, characteristic training is carried out on the obtained low-frequency components according to an improved direct linear identification and analysis method, and an optimal characteristic projection matrix is obtained, wherein the improved direct linear identification and analysis method is based on a new identification rule; 4, the low-frequency components of the face are projected into the optimal characteristic projection matrix, face images are matched according to a nearest neighbor classification method, and measurement is carried out by the utilization of the Euclidean distance. The face detection and identification method is rapid and accurate.

The invention discloses a device capable of improving measurement precision of gas analysis instrument, which comprises a filter wheel which is provided with a plurality of infrared filters; an electric motor which is used to drive the an infrared stationary light source and a monolithic system, wherein, a plurality of infrared filters are arranged on the filter wheel, the electric motor is used to drive the filter wheel to rotate against a preset frequency; an infrared stable light source which is used to irradiate a detecting gas cell through the infrared filters and is provided with an infrared sensor behind the detecting gas cell to receive the beam from the infrared stable light source; a single chip system which is used to control the drive of the electric motor and conduct wavelet contraction thresholds filtering on the signals received by the infrared sensor. The method and device of the invention are optimized by decomposing Haar wavelet and rebuilding wavelet, multiplication is transferred to addition in wavelet transform and meanwhile section wavelet filtering is adopted, which reduces operation volume, can be applied on a single chip system or imbedded system, effectively reducing the noise signals of gas analysis instrument, improving the measurement precision and decreasing the dependence on the hard circuit filter and model selection.

The invention discloses a grain size nondestructive evaluation method based on haar wavelet. The method comprises the following steps: data acquisition is conducted on a reference test block, time-size distribution is obtained by utilizing haar wavelet transform, furthermore, the average multi-scale attenuation coefficient of all reference blocks is calculated, an ultrasonic multi-scale attenuation evaluation model for the average grain size is built according to the preset size assembly and the preset normalized weight, and finally grain size evaluation of the test block of which the grain size is unknown is realized through the built ultrasonic multi-scale attenuation evaluation model of the average grain size. The method reduces system errors in grain size measurement; for test blocks of which the average grain size is 103.5 [mu]m measured by adopting metallography, the evaluation result is 101.7 [mu]m, and the error is controlled to be +/-2%. Therefore, through multi-scale analysis of original ultrasonic A signals, the method provided by the invention can discover more rich grain size information in the original ultrasonic A signals, and further improves the nondestructive evaluation precision of the grain size.

A method for image reconstruction includes receiving under-sampled k-space data, determining a data fidelity term of a first image of the under-sampled k-space data in view of a second image of the under-sampled k-space data, wherein a time component separated the first image and the second image, determining a spatial penalization on redundant Haar wavelet coefficients of the first image in view of the second image, and optimizing the first image according the data fidelity term and the spatial penalization, wherein the spatial penalization selectively penalizes temporal coefficients and an optimized image of the first image is output.

The present invention provides an iris preprocessing algorithm based on space distance circle marking edge detection. The method comprises a first step of performing image acquisition on an iris of a human eye through a photographing device, thus to obtain an original gray level image; a second step of carrying out noise processing on the acquired image by using a median filtering algorithm; a third step of decomposing the iris image into four subband images by utilizing Haar wavelets; a fourth step of carrying out edge detection on an inner edge of the iris by using Canny operators; a fifth step of carrying out precision positioning of an inner circuit of the iris by using a sub pixel circle positioning algorithm; and a sixth step of carrying out accurate positioning on an outer circuit by using a method of increasing searching radius step length. According to the iris preprocessing algorithm based on the space distance circle marking edge detection, the Canny operators are utilized for filtering, enhancement and detection, the sub pixel circle positioning algorithm is utilized for carrying out accurate positioning of the inner circle of the iris, and the efficiency of circle detection operators is promoted by using the method of increasing the searching radius step length, thereby realizing accurate positioning of the outer circle of the iris, raising the detection precision, and promoting the detection speed.

The invention discloses a switching circuit fault classifying method based on wavelet transform and ICA feature extraction. The method comprises the following steps: (1) generating a pseudo random signal as a test stimulation signal; (2) defining a fault mode; (3) acquiring the original response data of the circuit; (4) pre-treating the original response data by a Haar wavelet orthogonal filter; (5) extracting the fault feature parameters, and calculating the entropy and kurtosis as well as fuzzy sets thereof of low-frequency approximate information and high-frequency detail information for the pre-treated signal respectively; and (6) constructing a fault dictionary based on the extracted fault feature parameters so as to realize fault classification of the switching circuit. The method disclosed by the invention has the advantages of skillful concept, easiness in implementation and simulation proof and can distinguish the fault types more accurately than the existing method.

The invention provides a method for realizing quantum circuit design through quantum Haar wavelet transformation, and belongs to the field of quantum information processing. According to the method, the existing quantum Fourier transform implementing technology is perfected and improved, and implementing circuits of two pieces of multi-layer quantum Haar wavelet transformation and two pieces of multi-layer quantum Haar wavelet inverse transformation are established separately by using extended tensor product and basic quantum bit gate (comprising quantum bit controlled door and single quantum bit gate). Based on analysis of the complex rates of the implementing circuits of the quantum Haar wavelet transformation and the quantum Haar wavelet inverse transformation, the complex rates of the implementing circuits of the two pieces of multi-layer quantum Haar wavelet transformation and the two pieces of multi-layer quantum Haar wavelet inverse transformation are Theta(n2) for one data set with 2n elements, and other typical and rapid Haar wavelet transformation cannot achieve the aim. The method is suitable for the fields of algorithms such as compression, denoising, encryption and decryption of images of actual information processing application, and has important significance in popularization of perfection and application of a quantum computing theory.

The invention discloses a material surface defect detection method based on compressed sensing. The method comprises the following steps: first, a to-be-detected material surface low-resolution image acquisition model is established; then, super-resolution reconstruction is carried out on a material surface image, a Haar wavelet is adopted as the basis function of a representation function, and transform basis matrix design is carried out on the basis function by the use of discrete cosine transform and discrete wavelet transform; next, during super-resolution reconstruction of the material surface image, an orthogonal matching pursuit algorithm is adopted to orthogonalize a selected bionic cell in the iterative process to enable the reconstruction process to be converged in finite steps; and finally, macro block processing is introduced to complete reconstruction of the to-be-detected material surface image. Based on the principle of compressed sensing, super-resolution reconstruction is carried out by the use of a general sparse transform domain, and target recognition and detection are achieved on the basis.

The invention belongs to the field of video and image processing, and provides a stereoscopic video objective quality evaluation method which is more suitable to the human vision system related characteristic and effective. A new stereoscopic video objective quality evaluation method can more accurately evaluate the quality of the stereoscopic video, and pushes the development of the stereoscopic imaging technology at the same time. An application method of a vision multichannel model in stereoscopic video quality objective evaluation comprises the following steps: modelling the vision multichannel model by use of haar wavelets, firstly performing three-level wavelet transformation on an absolute difference map of a key frame so as to simulate the human eye multichannel characteristic by resolving the absolute difference map into different directions and frequency ingredients; and evaluating the different components obtained through transformation by applying SSIM so as to obtain the corresponding objective quality; obtaining the single quality of each key frame by use of the wavelet inverse transformation; and finally weighting the quality of the key frame by use of the weight of the key frame so as to obtain the complete stereoscopic video quality. The application method of the vision multichannel model is mainly applied to the video and image processing.

A system and method are provided for summarizing dynamic data from distributed sources through the use of wavelets. The method comprises receiving a first data signal at a first location, where the first data signal is dynamic, determining a first array sketch of the first data signal and constructing a first wavelet representation by manipulating the first array sketch with a B-term wavelet expansion to produce a first representation. The method further comprises receiving a second data signal at a second location, where the second data signal is dynamic and where the second location is distinct from the first location, determining a second array sketch of the second data signal, and constructing a second wavelet representation by manipulating the second array sketch with a B-term wavelet expansion to produce a second representation. In one embodiment, the method further comprises obtaining first and second array sketches from first and second locations respectively, and constructing a wavelet representation of a linear combination of the first and second array sketches. In one embodiment, the expansion is done using a Haar wavelet.