482 results about "Local binary patterns" patented technology

The invention relates to a face recognition method based on Gabor wavelet transform and local binary pattern (LBP) optimization. Two-dimensional Gabor wavelet transform can associate pixels of adjacent areas so as to reflect the change conditions of image pixel gray values in a local range from different frequency scales and directions. The feature extraction and the classification recognition are carried out on the basis of a face image two-dimensional Gabor wavelet transform coefficient. For a high-dimensional Gabor wavelet transform coefficient, overall histogram features are extracted by adopting the LBP, and then the image is blocked by utilizing priori knowledge to extract the features of each piece of LBP local histogram. The method has better recognition rate, better robustness to illumination and wide using prospect in the fields of biometric recognition and public security monitoring.

The invention provides a face detecting and tracking method and a device. The method comprises the steps of inputting a face image or a face video, preprocessing the face image or the face video in an illumination mode, detecting a face by usage of an Ada Boost algorithm, confirming an initial position of the face, and tracking the face by the usage of a Mean Shift algorithm. According to the face detecting and tracking method and the device, a self-adaptation local contrast enhancement method is provided to enhance image detail information in the period of image preprocessing, in order to increase robustness under different illumination conditions, face front samples under different illumination are added to training samples and accuracy of the face detection is increased by adoption of the Ada Boost algorithm in the period of face detection, in order to overcome the defect that using color of the Mean Shift algorithm is single, grads features and local binary pattern length between perpendiculars (LBP) vein features are integrated by adoption of the Mean Shift tracking algorithm in the period of face tracking, wherein the LBP vein features further considers using LBP local variance for expressing change of image contrast information, and accuracy of the face detection and the face tracking is improved.

A statistical (a) Computer Aided Diagnostic (CAD) technique is described for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular risk score computation. We demonstrate this for longitudinal Ultrasound, CT, MR modalities and extendable to 3D carotid Ultrasound. The on-line system consists of Atherosclerotic Wall Region estimation using AtheroEdge™ for longitudinal Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a feature extraction processor which uses the combination: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. Another combination uses: (a) Local Binary Pattern; (b) Law's Mask Energy and (c) Wall Variability. Another combination uses: (a) Trace Transform; (b) Fuzzy Grayscale Level Co-occurrence Matrix and (c) Wall Variability. The output of the Feature Processor is fed to the Classifier which is trained off-line.

The invention relates to a living iris detection method, which comprises the following steps of: S1, pre-treating a living iris image and an artificial iris image in a training image library; performing multi-scale characteristic extraction of local binary mode in an obtained interested area, and selecting the preferable one from the obtained candidate characteristics by using an adaptive reinforcement learning algorithm, and establishing a classifier for living iris detection; and S2, pre-treating the randomly input test iris image, and calculating the preferable local binary-mode characteristic in the obtained interested area; inputting the calculated characteristic value into the classifier for living iris detection obtained in step S1, and judging whether the test image is from the living iris according to the output result of the classifier. The invention can perform effective anti-forgery detection and alarm for the iris image and reduce error rate in iris recognition. The invention is widely applicable to various application systems for identification and safety precaution by using iris recognition.

The invention belongs to the technical field of digital image processing and mode recognition, in particular to a face recognition method based on blocking consistency LBP (Local Binary Pattern) and sparse coding. The face recognition method comprises the steps of: firstly, segmenting a face image into 16 subdomains which are same in size according to a mode of 4*4, calculating a consistency LBP histogram with one pixel radius and 8 neighbors, connecting LBP histograms of the 16 subdomains into a column vector to be used as a characteristic vector of a face image; and representing images to be tested into a most sparse linear combination on a training set, and recognizing the face image. Compared with the traditional characteristic extraction and clustering algorithm. According to the invention, structure information of a face can be well extracted, and under the condition of a single training sample and shielding, higher recognition rate and robustness are shown.

The invention discloses a crowd exceptional event detecting method based on an LBP (Local Binary Pattern) weighted social force model. The method comprises the following steps of: computing a light stream vector of a sampled point based on a block-matching method; extracting dynamic textures of the sampled point by a time-space domain local binary pattern, and performing spectral analysis of Fourier transform; computing social force of the sampled point based on LBP weighted social force model; performing histogram quantization on the social force and performing classification on the video sequence based on a support vector machine to detect the exceptional behavior. Through combination of light stream and LBP frequency spectrum, the method provided by the invention innovatively calculates the social force to detect the exceptional behavior of the crowd, thus avoiding background modeling, prospect detection and detection and track of target, improving robustness and reduce calculated amount, and being particularly fit for occassions with large density of the crowd and complicated environment.

The invention discloses a newborn-painful-expression recognition method based on dual-channel-characteristic deep learning. The newborn-painful-expression recognition method includes the steps that firstly, newborn facial images are grayed, and a Local Binary Pattern (LBP) specific chromatogram is extracted; secondly, grayscale images of the parallelly-input newborn facial images and the characteristics of two channels of LBP characteristic images of the grayscale images are deeply learned with a dual-channel convolutional neural network; finally, the fusion characteristics of the two channels are subjected to expression classification through a classifier based on a softmax, and expressions are divided into the calmness expression, the crying expression, the mild pain expression and the acute pain expression. According to the newborn-painful-expression recognition method, the grayscale images and the characteristic information of the two channels of the LBP characteristic images of the grayscale images are combined, the expressions such as the calmness expression, the crying expression, the mild pain expression and the acute pain expression can be effectively recognized, the quite-good robustness of the illumination problem, the noise problem and the shielding problem of the newborn facial images is achieved, and a new method and way are provided for developing a newborn-painful-expression recognition system.

The invention discloses a moving object detecting method based on a Bayesian frame and an LBP (Local Binary Pattern), which relates to the technical field of intellectualized video monitoring. The method comprises the following steps of: 1. extracting a video frame in a video stream; 2. preprocessing the video frame and eliminating the interference of fine light ray change and other disturbance; 3. carrying out moving object detection based on a background model and the Bayesian frame; before detecting, filtering a pixel point with unchanged time difference and background difference and degrading color dimensionality in modeling, wherein the speed of the whole method is improved, and the background model comprises a color characteristic and a symbiotic color characteristic and can favorably detect a moving and static object in a video; and 4. removing a shadow, detecting the texture information of the obtained moving object area and the texture information of a background image corresponding area by utilizing an LBP descriptor and comparing the differences of the texture information for removing the shadow in moving object detection.

The invention discloses a multi-feature fusion-based deep learning face recognition method. The multi-feature fusion-based deep learning face recognition method comprises performing feature extractionon images in ORL (Olivetti Research Laboratory) through a local binary pattern and an oriented gradient histogram algorithm; fusing acquired textual features and gradient features, connecting the twofeature vectors into one feature vector; recognizing the feature vector through a deep belief network of deep learning, and taking fused features as input of the deep belief network to layer by layertrain the deep belief network and to complete face recognition. By fusing multiple features, the multi-feature fusion-based deep learning face recognition method can improve accuracy, algorithm stability and applicability to complex scenes.

The invention discloses a method and a system for identifying an offline handwritten signature. The method comprises the following steps of S1, preprocessing an offline signature sample in an offline signature sample library; S2, extracting a plurality of characteristics for a preprocessed signature image, wherein the characteristics include moment characteristics, local binary pattern characteristics, gray-level co-occurrence matrix characteristics and pulse coupled neural network characteristics; S3, training the plurality of extracted characteristics of the offline signature sample to obtain a trained standard sample library; S4, acquiring a signature to be detected, and preprocessing the signature to be detected to obtain the plurality of characteristics of the signature to be detected; and S5, matching the plurality of characteristics of the signature to be detected with corresponding characteristics of the offline signature sample in the standard sample library, and identifying whether the signature to be detected is a genuine signature or a forged signature. The method and the system can effectively and accurately identify an offline signature to be detected.

The invention discloses a CT image liver segmentation method and system based on characteristic learning, being able to effectively improve the segmentation precision of the liver in a CT image. The method comprises the steps: S101: reading a training image set and an image to be segmented, wherein the training images in the training image set and the image to be segmented are the CT images of a belly; S102: extracting the Haar characteristic of the training images and the image to be segmented, the local binary pattern characteristic, the directional gradient histogram and the co-occurrence matrix characteristic; S103: utilizing a principal component analysis method to perform characteristic fusion on all the extracted characteristics so as to acquire more effective characteristics; S104: utilizing a classifier to classify the characteristics of each pixel of the image to be segmented to obtain a liver probability graph; and S105: combining the liver probability graph with the image to be segmented, modifying the graph model weight of a random walk segmentation algorithm to realize segmentation of the liver.

A image verification method, medium, and apparatus using a local binary pattern (LBP) discriminant technique. The verification method includes generating a kernel fisher discriminant analysis (KFDA) basis vector by using the LBP feature of an input image, obtaining a Chi square inner product by using the LBP feature of an image registered in advance and a kernel LBP feature and projecting to a KFDA basis vector, obtaining a Chi square inner product by using the LBP feature of a query image and a kernel LBP feature and projecting to a KFDA basis vector, and obtaining the similarity degree of the target image and the query image that are obtained as Chi square inner product results, and projected to the KFDA basis vector. According to the method, medium, and apparatus, the KFDA based LBP shows superior performance over conventional LBP, KFDA, and biometric experimentation environment (BEE) baseline algorithms.

The invention discloses an iris recognition method based on local two-value mode feature and graph matching. Firstly local two-value mode code is picked up according to the ordinal relation of each two pixel grey-scale values in an iris image neighborhood for describing the iris oriented texture feature having a constant characteristic under light irradiation; secondly the iris image is divided into a plurality of image pieces, and a local two-value mode histogram in each piece is calculated to describe the iris oriented texture statistical characterization having robustness on translation and deformation; each image piece is viewed as a node, the local two-value mode histogram in each piece is viewed as the attribute of the node, and the feature of each iris image is presented as a graph mode; during iris recognition, the graph matching method is used for searching matching node pairs in two graph modes; image recognition and registration of the number of the matching node pairs in the graph modes are carried out to show the similarity between the two graph mode, thereby judging the ID of a user. The invention is used for automatic identification in the application fields such as entrance guard, attendance check and clearance.

The invention requests to protect an identification method for a human facial expression based on two-step dimensionality reduction and parallel feature fusion. The adopted two-step dimensionality method comprises the following steps: firstly, respectively performing the first-time dimensionality reduction on two kinds of human facial expression features to be fused in the real number field by using a principal component analysis (PCA) method, then performing the parallel feature fusion on the features subjected to dimensionality reduction in a unitary space, secondly, providing a hybrid discriminant analysis (HDA) method based on the unitary space as a feature dimensionality reduction method of the unitary space, respectively extracting two kinds of features of a local binary pattern (LBP) and a Gabor wavelet, combining dimensionality reduction frameworks in two steps, and finally, classifying and training by adopting a support vector machine (SVM). According to the method, the dimensions of the parallel fusion features can be effectively reduced; besides, the identification for six kinds of human facial expressions is realized and the identification rate is effectively improved; the defects existing in the identification method for serial feature fusion and single feature expression can be avoided; the method can be widely applied to the fields of mode identification such as safe video monitoring of public places, safe driving monitoring of vehicles, psychological study and medical monitoring.

The invention provides a dense population estimation method and a system based on multi-feature fusion. The method comprises the following steps: partitioning an image into N equal sub-blocks; performing hierarchical background modeling on the image by using a method based on a CSLBP (Center-Symmetric Local Binary Pattern) histogram texture model and mixture Gaussian background modeling, extracting the foreground area of each sub-block subjected to perspective correction, detecting the edge density of each sub-block in combination with an improved Sobel edge detection operator, and extracting four important texture feature vectors in different directions for describing image texture features in combination with CSLBP transform and a gray-level co-occurrence matrix; performing dimension reduction processing on the extracted population foreground partition feature vectors and texture feature vectors through main component analysis; inputting the dimension-reduced feature vectors into an input layer of a nerve network model, and acquiring the population estimation of each sub-block through an output layer; adding to obtain the total population. The dense population estimation method and system have high accuracy and high robustness, and a good effect is achieved in the population counting experiment of subway station monitoring videos.

The invention discloses a face recognition method based on reference features. The method comprises the following steps that: scale invariant features and local binary pattern features of a face image to be recognized are extracted; a principal component analysis method is utilized for dimensionality reduction to obtain the image features of the face image to be recognized; the similarity of the image features to a cluster center is calculated by utilizing the obtained image features to obtain the reference features of the face image to be recognized; and the similarity of the reference features of the face image to be recognized and the reference features of training data concentration is calculated to obtain an analysis result. The reference features of the face image provided by the invention comprise texture information and structure information of the face image, so that the method provided by the invention can more comprehensively represent the face compared with the method in the prior art, which only represents the texture information or the structure information of the face. The process of feature extraction is simple and easy to realize; the recognition result is highly precise; high recognition rate of different facial gestures of the same person is realized.

The present invention provides a binocular vision depth feature and apparent feature-combined face living body detection method. The method includes the following steps that: step 1, a binocular vision system is established; step 2, a face is detected through the binocular vision system, so that a plurality of key points can be obtained; step 3, a binocular vision depth feature and a classification score corresponding to the binocular vision depth feature are obtained; step 4, a complete face area is intercepted from a left image, the complete face area is normalized to a fixed size, and a local binary pattern (LBP) feature is extracted so as to be adopted as an apparent feature descriptor; step 5, a face living body detection score corresponding to a micro-texture feature is obtained; and step 6, the classification score corresponding to the binocular vision depth feature obtained in the step 3 and the face living body detection score corresponding to the micro-texture feature obtained in the step 5 are fused in a decision-making layer, so that whether an image to be detected is a live body can be judged. The binocular vision depth feature and apparent feature-combined face living body detection method of the invention has the advantages of simple algorithm, high operation speed, high precision and the like. With the method adopted, a new and reliable method can be provided for living body face detection.

The invention relates to a human face image fusing method based on laplacian-pyramid. On a basis of the laplacian-pyramid decomposition, the invention applies a partial binary mode arithmetic operator into a fusion rule. As for infrared and visible-light human face images in each layer of the laplacian-pyramid, average gradient of the image, standard deviation of an image histogram and Chi square statistic similarity measurement are used for determining the selection of respective weighting coefficients of the infrared and visible-light human face images, and effective fusion is performed by utilizing the determined weighting coefficients so as to obtain a fused image which is rich in detail information, clear and stable and facilitates the observation of human eyes. Compared with visible-light images and near-infrared images, the fused information provided by the invention has greater comentropy but smaller average cross-entropy and mean square root cross-entropy and has more remarkable effect of image fusion.

An expression recognition method based on AVR and enhanced LBP in the technical field of model recognition comprises the following steps: collecting an original image; expanding virtual samples; carrying out wavelet decomposition on human image; extracting local binary pattern characteristic LBP; calculating an enhanced variance ratio AVR characteristic value and adding with a penalty factor, then extracting a plurality of groups of characteristic values of different dimensionality discriminated by AVR value, carrying out support vector machine classification accuracy test, and taking the characteristic dimensionality with the highest accuracy and corresponding characteristic value as the LBP characteristic. The method of the invention integrates image acquisition, human face test and human eye test, enhances the LBP characteristic by wavelet decomposition, and effectively improves the accuracy by adopting AVR method to extract effective characteristic.

The invention discloses a method and a system for detecting the pedestrian in front of a vehicle. The method comprises the steps of image acquisition and preprocessing, image scaling, LBP (Local Binary Pattern) and HOG (Histogram of Oriented Gradient) feature extraction, region of interest extraction, target identification, and target fusion and early warning. A driver is reminded timely at the presence of the pedestrian in front of the vehicle. The system for detecting the pedestrian in front of the vehicle comprises three portions which are an image acquisition unit, an SOPC (System on Programmable Chip) unit and an ASIC (Application Specific Integrated Circuit) unit, wherein the image acquisition unit is a camera unit, the SOPC unit comprises an image preprocessing unit, a region of interest extraction unit, a target identification unit, and a target fusion and early warning unit, and the ASIC unit comprises an image scaling unit, an LBP feature extraction unit and an HOG feature extraction unit. According to the invention, LBP features and HOG features are used in a joint manner, and two-level detection improves the accuracy of pedestrian detection on the whole; and HOG feature extraction is dynamically adjusted according to classification conditions of an LBP based SVM (Support Vector Machine), the calculation amount is reduced, the calculation speed is improved, and the driving safety of the vehicle is improved.

The invention provides a face authentication method and device, and belongs to the field of image processing and pattern recognition. The method comprises the following steps: extracting Gabor texture features of an acquired face image sample pair through a Gabor filter, and separating real parts from virtual parts of the extracted Gabor texture features to obtain a plurality of feature graphs; summarizing the plurality of obtained feature graphs through an LBP (Local Binary Pattern) statistical histogram to serve as face feature expression of the face image pair; performing feature selection on the face feature expression of the face image sample pair through an AdaBoost (Adaptive Boosting) algorithm; and performing feature dimension reduction, feature judgment processing and the like on the face feature expression of the face image sample pair subjected to the feature selection through a PCA (Principal Components Analysis) algorithm and an LDA (Linear Discriminant Analysis) algorithm in sequence. Compared with the prior art, the face authentication method provided by the invention has the advantages of full extraction of sample texture information, low sample quantity demand, short algorithm time and low space complexity.

The invention discloses a nuclear coordinated expression-based hyperspectral image classification method. The method comprises the following steps: carrying out feature selection by adopting a waveband selection strategy with strong operability; carrying out local binary-pattern space feature extraction on the basis of selected different feature groups; carrying out nuclear coordinated expression classification; and finally fusing classification results corresponding to the groups of features and a residual-level fusion strategy and obtaining the final high-precision classification result. According to the method, the textural features of data extracted by a LBP operator are combined, the LBP has the remarkable advantages of rotation invariance and grey level invariance, and the calculation is simple, so that the robustness of the features to be classified is further increased. Finally, a nuclear coordinated expression classifier is used for classification, the calculation efficiency is better than that of the traditional sparse manner and the nonlinear space data can be classified, so that the application range is wider and the application performance is more excellent.

System, apparatus, and method embodiments are provided for detecting the presence of a pedestrian in an image. In an embodiment, a method for determining whether a person is present in an image includes receiving a plurality of images, wherein each image comprises a plurality of pixels and determining a modified center symmetric local binary pattern (MS-LBP) for the plurality of pixels for each image, wherein the MS-LBP is calculated on a gradient magnitude map without using an interpolation process, and wherein a value for each pixel is a gradient magnitude.

The invention relates to an accurate moving shadow detection method based on multi-feature fusion, belonging to the field of video image processing. Firstly, the foreground image in a video is extracted, and six features of brightness, color and texture of the foreground image are extracted. In order to describe the features as roundly as possible, under the brightness constraint, the color information of multiple color spaces and multi-scale images are extracted. Meanwhile, the texture information is respectively described through entropy and a local binary pattern. Secondly, the features are fused to generate a feature pattern. Then, a moving shadow can be roughly determined on the feature pattern. Finally, in order to obtain an accurate shadow detection result, misclassified pixels are corrected through space adjustment. A lot of experiment and comparison results express that the method has good performance and is superior to the current shadow detection method.

The invention relates to a video target tracking method under the circumstance of scale change and shielding and belongs to the field of image processing. The invention aims at solving the problem of non-ideal tracking effect under the circumstance of the scale change and shielding of an LBP (local binary pattern) tracking algorithm. The invention provides an optimization objective tracking method based on combining the LBP algorithm, NMI (normalized moment of inertia) features and kalman filtering. The NMI features are used for determining an update strategy of a module and solving the problem of target loss caused by target rotation, scale change and the like. The kalman filtering is used for overcoming the defect that a target is easy to lose under the circumstance of shielding.