173 results about "Virtual sample" patented technology

The invention discloses a method for recognizing faces shaded by eyeglasses, which comprises the following steps of: inputting an image of an eyeglass-free face and a general face model to acquire corresponding points on a face image and the face model, and performing coordinate transformation on all peaks to adjust the peaks under the same coordinate system; processing the model to acquire a facade head model of which the posture is corrected; adding an eyeglass model on the head model and pasting textures; performing fuzzy processing and light reflecting processing on the lenses of the eyeglasses; and generating virtual samples in which the eyeglasses are worn under different conditions by using a projection model. The invention also provides a corresponding face recognition system. The method and the system have the advantages of wide simulation range, high generality, greatly improved recognition effect, real-time property, high practicability, high recognition rate and the like, can comprise more changes of the eyeglasses, are quick and are easy to operate.

The invention relates to a partial-compensation aspherical reflector surface shape detection method, which comprises the following steps of demarcating partial-compensation elements to obtain demarcated phase distribution Wb; using the demarcated phase distribution Wb, sphere theory wavefront Wq and aspherical theory wavefront Wf to obtain phase distribution Wy of digital virtual sample plates; measuring an aspherical reflector surface to be measured to obtain phase distribution Wa; and reducing the phase distribution Wy from the measured phase distribution Wa to achieve surface shape error distribution Wm of the aspherical surface. The partial-compensation aspherical reflector surface shape detection method can accurately measure optical aspherical surface shapes by using a single partial-compensation element, thereby reducing design difficulty of a compensator, simplifying structure of the compensator and being clear in physical definition, simple in data processing and arithmetical operation, simple and practical in experimental operation, low in detection cost and short in test time.

A computer-implemented graphics system that includes a rasterizer and a shader has a mode of operation in which primitive coverage information is generated for real sample locations and virtual sample locations for use in anti-aliasing. An individual pixel includes a single real sample location and at least one virtual sample location. In some instances, a primitive may cover only virtual sample locations and does not cover a real sample location. These instances can be identified in the coverage information sent from the rasterizer to the shader, so that the shader can determine whether or not it can write color information, depth information and/or stencil information for the real sample location to a framebuffer.

Provided is a multiple-sparse-representation face recognition method for solving the small sample size problem. In the method, two modes are adopted to solve the small sample size problem during face recognition, one mode is that given original training samples produce 'virtual samples' so as to increase the number of the training samples, and the other mode is that three nonlinear feature extraction methods, namely a kernel principle component analysis method, a kernel discriminant analysis method and a kernel locality preserving projection algorithm method are adopted to extract features of the samples on the basis that the virtual samples are produced. Therefore, three feature modes are obtained, sparse-representation models are established for each feature mode. Three sparse-representation models are established for each sample, and finally classification is performed according to representation results. By means of the multiple-sparse-representation face recognition method, virtual faces are produced through mirror symmetry, and then norm L1 based multiple-sparse-representation models are established and classified. Compared with other classification methods, the multiple-sparse-representation face recognition method is good in robustness and classification effect and is especially suitable for a lot of classification occasions with high data dimensionality and few training samples.

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 relates to a power transmission line defect detection method and system and electronic equipment. The method comprises the steps of constructing a power transmission line defect detection model based on virtual and real sample integration and transfer learning according to image sample data of power transmission line elements, The method specifically comprises the following steps: step a, constructing a virtual and real integrated virtual sample generation and labeling model, integrating rich ground object information in virtual data and real image data, and fusing the virtual data and the real data; B, constructing a deep learning transfer learning model, and completing model optimization based on transfer learning; And step c, training a deep learning model based on a target detection algorithm, and carrying out abnormity diagnosis on the power transmission line element on the basis of target detection of deep learning. Through deep learning distributed training of massimage samples, the power transmission line defect detection model based on multi-image fusion of visible light, infrared light, ultraviolet light and the like is established, the defect recognition accuracy can be improved, and the inspection working efficiency and quality are improved.

For use in acoustic micro imaging, a method (and implementing apparatus) is disclosed for creating a 4D virtual sample data memory. The method comprises employing a pulsed ultrasonic microscope probe to interrogate a sample at three-dimensionally varied locations in the sample. Data developed by the microscope probe includes for each location interrogated a digitized A-scan for that location. The developed data is stored in a data memory.

The invention discloses a face image virtual sample generating method. The facial image virtual sample generating method comprises the steps of conducting control point calibration on a source posture face image training sample and a target posture face image training sample, then using a source posture face image training sample set and a target posture face image training sample set as input and output of an RBF neural network, and obtaining an RBF neural network fitting model through training; generating a source coordinate matrix according to source posture face images, inputting the source coordinate matrix into the RBF neural network fitting model to obtain a coordinate transformation matrix, conducting textural feature mapping according to the source coordinate matrix and the coordinate transformation matrix, then conducting interpolation on textural feature deficiency points to obtain a target posture face image virtual sample and finally conducting normalization operation and saving on the target posture face image virtual sample. The face image virtual sample generating method adopts the RBF neural network fitting model and enables the generated face image virtual sample to be approximate to a real sample, and further face recognition rate is further improved.

A graphics system has a mode of operation in which real samples and virtual samples are generated for anti-aliasing pixels. Each virtual sample identifies a set of real samples associated with a common primitive that covers a virtual sample location within a pixel. The virtual samples provide additional coverage information that may be used to adjust the weights of real samples.

The invention discloses a mill load parameter soft measuring method based on a virtual sample. The mill load parameter soft measuring method comprises the steps of acquiring a multi-dimension time domain sub-signal of mill cylinder vibration and vibration sound sample signal by means of ensemble empirical mode decomposition (EEMD) technology, and performing further processing for obtaining high-dimension spectral data with different time dimensions; then constructing a feasibility-based planning (FBP) model based on the high-dimension spectral data according to an improved selective integrated kernel partial least squares (IGASEN-KPLS) method, and generating a new virtual sample based on priori knowledge and an FBP model; then obtaining a mixed sampling model after mixing the new virtual sample with a true training sample, performing adaptive selection of a multiple-dimension spectral characteristic by means of a mutual information (MI) based characteristic selecting method, constructing a soft measuring model by means of the selected spectral characteristics and performing soft measurement.

A computer-implemented graphics system has a mode of operation in which primitive coverage information is generated for real sample locations and virtual sample locations for use in anti-aliasing. An individual pixel includes a single real sample location and at least one virtual sample location. A block of real sample locations can be selected to delineate and encompass a region containing a number of virtual sample locations. Pixel attribute values (e.g., z-depth or stencil values) associated with the block of selected real sample locations can be used to associate each virtual sample location within the region with one of the selected real sample locations. The virtual sample location assumes the pixel attribute value of the real sample location with which it is associated.

The invention provides a pedestrian re-identification method and device based on a semi-supervised training mode and a storage medium. The method comprises the following steps: constructing a trainingsample set by using the collected target domain sample set and open source data set based on a semi-supervised training mode; and training a deep residual network by using the training sample set toobtain a trained deep residual network model, identifying the acquired pedestrian images by using the trained deep residual network model to obtain feature values of the pedestrian images, and determining whether the pedestrian images belong to the same person according to cosine distances among the feature values. According to the invention, the virtual sample is generated; a smoothing function is constructed when a virtual sample is generated; and meanwhile, a DBSCAN clustering algorithm is used for adding pseudo tags to the virtual samples, local features and global features are used in thedeep neural network, and a joint loss function of different weight combinations is adopted, so that the trained deep neural network is accurate and more reliable in recognition.

A multi-grade chemical process soft sensor modeling method automatically generating samples comprises the following steps: (1) dividing the data collected from the multi-grade chemical process into atraining set and a test set as raw data; (2) establishing a generative antagonistic network AGAN based on gradient penalty and Wasserstein distance, and inputting the divided training set into the generative antagonistic network to train the network; (3) using the trained AGAN to generate virtual samples, and forming a new training set together with the original training set; (4) using the new training set as the driving data, training the soft sensor modeling, adjusting the parameters of the soft sensor model to adapt to the new training set, and using the trained soft sensor model to predictthe key quality variables of the multi-brand chemical process. The invention utilizes automatic generation of antagonistic network to generate data to make up the deficiency of data quantity, and improves the prediction accuracy of the soft sensor model based on data driving.

A computer-implemented graphics system has a mode of operation in which primitive coverage information is generated by a rasterizer for real sample locations and virtual sample locations for use in anti-aliasing. An individual pixel includes a single real sample location and at least one virtual sample location. If the coverage information cannot be changed by a pixel shader, then the rasterizer can write the coverage information to a framebuffer. If, however, the coverage information can be changed by the shader, then the rasterizer sends the coverage information to the shader.

The invention discloses a virtual sample based kernel discrimination method for face recognition, which is a virtual sample based quick kernel method. The method comprises the following steps of: constructing a virtual sample set for a training sample set at one time before constructing a kernel matrix for the training sample set; and training/testing through a kernel matrix theory based on the virtual sample set. Since the virtual sample set is an aggregate of a characteristic sample set (MES) and a public vector sample set (MCS) of the training sample set, the virtual sample set has extremely high description capacity for both a known training sample set and an unknown test sample set. Experimental verification of the method on an FERET database shows that the method is quick and effective; the computation speed of the kernel method is greatly increased by using the method; and meanwhile, compared with the conventional kernel method, the recognition rate is also increased.