6919 results about "Reconstruction method" patented technology

The invention discloses a data validation, mirroring and error / erasure correction method for the dispersal and protection of one and two-dimensional data at the micro level for computer, communication and storage systems. Each of 256 possible 8-bit data bytes are mirrored with a unique 8-bit ECC byte. The ECC enables 8-bit burst and 4-bit random error detection plus 2-bit random error correction for each encoded data byte. With the data byte and ECC byte configured into a 4 bit×4 bit codeword array and dispersed in either row, column or both dimensions the method can perform dual 4-bit row and column erasure recovery. It is shown that for each codeword there are 12 possible combinations of row and column elements called couplets capable of mirroring the data byte. These byte level micro-mirrors outperform conventional mirroring in that each byte and its ECC mirror can self-detect and self-correct random errors and can recover all dual erasure combinations over four elements. Encoding at the byte quanta level maximizes application flexibility. Also disclosed are fast encode, decode and reconstruction methods via boolean logic, processor instructions and software table look-up with the intent to run at line and application speeds. The new error control method can augment ARQ algorithms and bring resiliency to system fabrics including routers and links previously limited to the recovery of transient errors. Image storage and storage over arrays of static devices can benefit from the two-dimensional capabilities. Applications with critical data integrity requirements can utilize the method for end-to-end protection and validation. An extra ECC byte per codeword extends both the resiliency and dimensionality.

A limitation in the physical resolution of an image sensor offers a motivation to improve the resolution of an image. Super-resolution is mainly applied to gray scale images, and it has not been thoroughly investigated yet that a high resolution color image is reconstructed from an image sensor having a color filter array. An object of the invention is to directly reconstruct a high resolution color image from color mosaic obtained by an image sensor having a color filter array. A high resolution color image reconstruction method according to the invention is based on novel technique principles of color image reconstruction that an increase in resolution and demosaicing are performed at the same time. The verification and effective implement of the invention are also described.

Methods for determining a mechanical parameter of a sample include detecting shear waves that have been generated in the sample by an applied shear wave source. A time of peak displacement of the shear waves for a plurality of sample positions is determined. At least one mechanical parameter of the sample based on the time of peak displacement for the plurality of sample positions is determined.

The invention discloses a human face super-resolution reconstruction method based on a generative adversarial network and sub-pixel convolution, and the method comprises the steps: A, carrying out the preprocessing through a normally used public human face data set, and making a low-resolution human face image and a corresponding high-resolution human face image training set; B, constructing the generative adversarial network for training, adding a sub-pixel convolution to the generative adversarial network to achieve the generation of a super-resolution image and introduce a weighted type loss function comprising feature loss; C, sequentially inputting a training set obtained at step A into a generative adversarial network model for modeling training, adjusting the parameters, and achieving the convergence; D, carrying out the preprocessing of a to-be-processed low-resolution human face image, inputting the image into the generative adversarial network model, and obtaining a high-resolution image after super-resolution reconstruction. The method can achieve the generation of a corresponding high-resolution image which is clearer in human face contour, is more specific in detail and is invariable in features. The method improves the human face recognition accuracy, and is better in human face super-resolution reconstruction effect.

A semi-automatic reconstruction method of 3-D building models using building outline segments is introduced. The core technology of the present invention is called the “Split-Merge-Shape” algorithm. The Split and Merge processes sequentially reconstruct the topology between any roof-edges of the buildings and then reform them as enclosed regions. The Shape process uses height information and consecutive-coplanar analysis to determine the shapes and heights of the roofs. After generating polyhedral building models, prismatic building models can also be generated by using a semi-automatic procedure. An existing digital topographic map of buildings can be directly used to reconstruct their 3-D models without any excess stereo-measurements. In addition to cost reduction, high efficiency, high quality, and minimization of manual operations, the integration of photogrammetric mapping with 3-D building modeling in one procedure is possible, which is the most cost-effective approach for 3-D mapping.

An apparatus for encoding an audio signal to obtain an encoded audio signal to be used by a decoder having a high frequency reconstruction module for performing a high frequency reconstruction for a frequency range above a crossover frequency includes, a core encoder for encoding a lower frequency band of the audio signal up to the crossover frequency, the crossover frequency being variable, and the core encoder being operable on a block-wise frame by frame basis, and a crossover frequency control module for estimating, dependent on a measure of the degree of difficulty for encoding the audio signal by the core encoder and / or a boarder between a tonal and a noise-like frequency range of the audio signal, the crossover frequency to be selected by the core encoder for a frame of a series of subsequent frames, so that the crossover frequency is variable adaptively over time for the series of subsequent frames.

A spinal disc annulus repair stent for repair and reconstruction of the spinal disc wall (annulus) after surgical invasion or pathologic rupture, which may incorporate suture closure or other means of stent insertion and fixation, designed to reduce the failure rate of conventional surgical procedures on the spinal discs. In an illustrative embodiment, the design of the spinal disc annulus stent advantageously allows ingrowth of normal cells of healing in an enhanced fashion strengthening the normal reparative process.

The invention discloses a single image super-resolution reconstruction method based on a conditional generative adversarial network. A judgment condition, namely an original real image, is added intoa judger network of the generative adversarial network. A deep residual error learning module is added into a generator network to realize learning of high-frequency information and alleviate the problem of gradient disappearance. The single low-resolution image is input to be reconstructed into a pre-trained conditional generative adversarial network, and super-resolution reconstruction is performed to obtain a reconstructed high-resolution image; learning steps of the conditional generative adversarial network model include: learning a model of the conditional adversarial network; inputtingthe high-resolution training set and the low-resolution training set into a conditional generative adversarial network model, using pre-trained model parameters as initialization parameters of the training, judging the convergence condition of the whole network through a loss function, obtaining a finally trained conditional generative adversarial network model when the loss function is converged,and storing the model parameters.

The invention belongs to crossing field of computer vision and intelligent robots and discloses a variational mechanism-based large-area indoor scene reconstruction method. The method comprises the following steps: step 1, acquiring calibration parameters of a camera, and building an aberration correcting model; step 2, building a camera position and gesture depiction and camera projection model; step 3, utilizing an SFM-based monocular SFM (Space Frequency Modulation) algorithm to realize camera position and gesture estimation; step 4, building a variational mechanism-based depth map estimation model, and performing solving on the model; and step 5, building a key frame selection mechanism to realize three-dimensional scene renewal. According to the invention, an RGB (Red Green Blue) camera is adopted to acquire environmental data, and a variational mechanism-based depth map generation method is proposed through utilizing a high-precision monocular positioning algorithm, so that quick large-area indoor three-dimensional scene reconstruction is realized, and problems of three-dimensional reconstruction algorithm cost and real-time performance are effectively solved.

A camera calibration method and medium and a 3-dimensional (3D) object reconstruction method and medium using the camera calibration method are provided. The camera calibration method includes setting a feature track by tracking and matching features in an input image sequence; estimating 3-dimensional (3D) points in relation to the features by initializing a structure and motion of the camera; estimating a pose of the camera by using the 3D points and refining the structure of the camera based on the estimated pose; and refining the pose of the camera.

A method for reconstructing a high quality image from undersampled image data is provided. The image reconstruction method is applicable to a number of different imaging modalities. Specifically, the present invention provides an image reconstruction method that incorporates an appropriate prior image into the image reconstruction process. Thus, one aspect of the present invention is to provide an image reconstruction method that requires less number of data samples to reconstruct an accurate reconstruction of a desired image than previous methods, such as, compressed sensing. Another aspect of the invention is to provide an image reconstruction method that produces a time series of desired images indicative of a higher temporal resolution than is ordinarily achievable with the imaging system. For example, cardiac phase images can be produced with high temporal resolution (e.g., 20 milliseconds) using a CT imaging system with a slow gantry rotation speed.

The invention discloses a three-dimensional reconstruction method based on coding structured light, comprising the following steps: 1) projecting structured light to an object to be measured, and capturing an image modulated by the object to be measured by a camera; 2) matching an optical template, comprising: (2.1) positioning the optical strip boundary, scanning along each row of the image, determining a pixel point with strong gray variation as a candidate marginal point, and searching a local domain; and (2.2) matching the optical strip: adopting a color cluster method to build a color matching proper vector, comparing image color with a projected color, and defining Euclidean distance between the color proper vector and the cluster center to distribute the colors of red, green, blue and white of the candidate optical strip; and 3) using a calibrated system parameter for three-dimensional reconstruction of the object to be measured, determining the relation between a space point coordinate and the image coordinate point thereof by the calibrated conversion matrix parameter; and restoring three-dimensional spatial coordinate from the image coordinate of a feature point. The invention can simplify calculation process and has high matching precision and high reconstruction precision.

The invention discloses a deep learning super-resolution reconstruction method based on residual sub-images; residual sub-images are effectively combined with deep learning method based on convolutional neural network, super-resolution reconstructed images are clearer, and reconstruction speed is higher. By increasing the depth of convolutional neural network, a network model acquired by learning has higher nonlinear expression capacity and image reconstructing capacity; in addition, by introducing residual sub-image process, preprocessing based on traditional interpolation algorithm is removed, and fuzzy effect due to the interpolation algorithm is avoided. By making ingenious use of residual sub-images, it is possible to transfer deep learning convolutional operation from high-resolution space to low-resolution space, and accordingly reconstruction efficiency of super-resolution algorithm is increased at the premise of improving super-resolution reconstruction effect.

The invention discloses a single-image super-resolution reconstruction method based on a symmetric depth network and belongs to the image processing technology field. The method mainly comprises the following steps of 1, making a high resolution image block and low resolution image block training set; 2, constructing a symmetric convolution-deconvolution depth network used for model training; 3, based on the constructed depth network and the made data set, carrying out network model training; and 4, based on a learned model parameter, inputting one low resolution image, wherein acquired output is a reconstructed high resolution image. In the invention, a convolution layer and a deconvolution layer are combined and simultaneously a network depth is increased; the network depth is used to increase network performance; a reconstruction capability of an image detail portion is enhanced and a good image super-resolution reconstruction effect is acquired. The method has a wide application prospect in fields of image high definition displaying, medical imaging, a remote sensing image and the like.

The invention relates to a three-dimensional model reconstruction method and system. The method comprises the following steps: S1, performing image acqusition on a target by using at least one depth camera to acquire a depth image of the target; S2, preprocessing the acquired depth image; S3, acquiring dense point cloud data according to the depth image of the target to reconstruct a target depth information point cloud grid; S4, combining and registering the multiple frames of reconstructed depth images to obtain a three-dimensional model. By implementing the method and the system, the accurate three-dimensional model of the target can be acquired without manually marking the target.

An iterative image reconstruction method used with an imaging system that generates projection data, the method comprises: collecting the projection data; choosing a polar or cylindrical image definition comprising a polar or cylindrical grid representation and a number of basis functions positioned according to the polar or cylindrical grid so that the number of basis functions at different radius positions of the polar or cylindrical image grid is a factor of a number of in-plane symmetries between lines of response along which the projection data are measured by the imaging system; obtaining a system probability matrix that relates each of the projection data to each basis function of the polar or cylindrical image definition; restructuring the system probability matrix into a block circulant matrix and converting the system probability matrix in the Fourier domain; storing the projection data into a measurement data vector; providing an initial polar or cylindrical image estimate; for each iteration; recalculating the polar or cylindrical image estimate according to an iterative solver based on forward and back projection operations with the system probability matrix in the Fourier domain; and converting the polar or cylindrical image estimate into a Cartesian image representation to thereby obtain a reconstructed image.

The invention discloses a regional depth edge detection and binocular stereo matching-based three-dimensional reconstruction method, which is implemented by the following steps: (1) shooting a calibration plate image with a mark point at two proper angles by using two black and white cameras; (2) keeping the shooting angles constant and shooting two images of a shooting target object at the same time by using the same camera; (3) performing the epipolar line rectification of the two images of the target objects according to the nominal data of the camera; (4) searching the neighbor regions of each pixel of the two rectified images for a closed region depth edge and building a supporting window; (5) in the built window, computing a normalized cross-correlation coefficient of supported pixels and acquiring the matching price of a central pixel; (6) acquiring a parallax by using a confidence transmission optimization method having an acceleration updating system; (7) estimating an accurate parallax by a subpixel; and (8) computing the three-dimensional coordinates of an actual object point according to the matching relationship between the nominal data of the camera and the pixel and consequently reconstructing the three-dimensional point cloud of the object and reducing the three-dimensional information of a target.

The invention belongs to the field of image processing, aims at restoring a high-resolution depth image and utilizing the great learning capacity of convolutional neural networks to solve the defects that the conventional algorithm is high in computational complexity and high in actual application cost and cannot effectively extract features, and provides the technical scheme of a depth map super-resolution reconstruction method based on the convolutional neural networks. The convolutional neural networks (CNN) combining a convolutional layer and a deconvolutional layer is utilized to extract the depth image features of low-resolution sample depth image block and a high-resolution sample depth image block, and then the nonlinear mapping relation between the depth image features is learnt so as to restore the high-resolution depth image. The depth map super-resolution reconstruction method based on the convolutional neural networks is mainly applied to the occasion of image processing.

Embodiments of the present invention relate to a Surrounding Neighbors based Autocalibrating Partial Parallel Imaging (SNAPPI) approach to MRI reconstruction. Several 2D PPI reconstruction methods may be provided by applying SNAPPI to recover the partially skipped k-space data along two PE directions separately or non-separately, in k-space or in the hybrid k-space and image-space.

A transport protocol data flow reconstruction method delays determination that a missing packet is lost for a period of time. For an evaluated TCP packet in a first direction, the method determines if a TCP packet is missing in a second direction, in which case the method stores the evaluated TCP packet in a list and creates an acknowledgement timer indicating a maximum time to wait until treating the missing TCP packet as lost. Expiration of the acknowledgment timer indicates a missing packet in the second direction. The method determines if a TCP packet is missing in the first direction, in which case the method stores the evaluated TCP packet in the list and creates a retransmission timer indicating a maximum time to wait until treating the missing TCP packet as lost. Expiration of the retransmission timer indicates a missing packet in the first direction.

A method for multi-slice magnetic resonance imaging, in which image data is acquired simultaneously from multiple slice locations using a radio frequency coil array, is provided. By way of example, a modified EPI pulse sequence is provided, and includes a series of magnetic gradient field “blips” that are applied along a slice-encoding direction contemporaneously with phase-encoding blips common to EPI sequences. The slice-encoding blips are designed such that phase accruals along the phase-encoding direction are substantially mitigated, while providing that signal information for each sequentially adjacent slice location is cumulatively shifted by a percentage of the imaging FOV. This percentage FOV shift in the image domain provides for more reliable separation of the aliased signal information using parallel image reconstruction methods such as SENSE. In addition, the mitigation of phase accruals in the phase-encoding direction provides for the substantial suppression of pixel tilt and blurring in the reconstructed images.

The invention discloses a face image super-resolution reconstruction method based on a discriminable attribute constraint generative adversarial network, and belongs to the field of digital images / video signal processing. The method comprises the following steps: firstly, designing a processing flow of face detailed information enhancement; secondly, designing a network structure according to theflow, and acquiring an HR image from an LR image through the network; and lastly, performing face verification accuracy evaluation on the HR image through a face recognition network. Through adoptionof the method, enhancement including LR face image detailed information can be completed, and the accuracy of face verification is increased. Secondly, the generative network completes compensation ofimage high-frequency information firstly, then completes image amplification by subpixel convolution, and finally completes stepwise image amplification through a cascade structure, thereby completing enhancement of image detailed information. An attribute constraint module are trained together with a perception module and an adversarial model in order to perform fine adjustment of the performance of a network reconstructed image. Finally, a reconstructed image of the generative network is input into a face verification network, so that the accuracy of face verification is increased.

The invention discloses a high-resolution dictionary based sparse representation image super-resolution reconstruction method. The method comprises the following steps of: (1) constructing a high-resolution brightness image library; (2) generating a sample training set; (3) learning an over-complete dictionary; (4) primarily establishing a high-resolution image brightness space; (5) establishing an image sample test set; (6) updating the high-resolution image brightness space; (7) calculating a weight sparse matrix; (8) reupdating the high-resolution image brightness space; (9) judging whether to repeat execution; and (10) outputting a high-resolution image. The high-resolution over-complete dictionary learned by the invention can be applied to different amplification factors. Sparse representation, non-local prior and data fidelity constraint are fully utilized, so that local information and global information can be comprehensively utilized. The method has higher super-resolution capacity; and the reconstructed image is closer to an actual image.

The invention provides a contact network three-dimensional reconstruction method based on SIFT and LBP point cloud registration. The method comprises the first step of obtaining initial three-dimensional point cloud data of the environment where parts of a contact network to be reconstructed are located through motion-sensing peripheral Kinect for Windows, and conducting denoising, simplifying, partitioning clustering, fusing and other preprocessing operations on the initial three-dimensional point cloud data to obtain single-view-angle point cloud data of the parts of the contact network to be reconstructed, the second step of extracting key points through an SIFT algorithm, constructing description vectors of the key points by means of LBP features of uniform patterns and determining the corresponding relations between the key points in different point clouds according to the distances between the vectors, the third step of completing point cloud registration through a rough registration method and an ICP fine registration method and obtaining the complete three-dimensional point cloud data of the parts of the contact network to be reconstructed, and the fourth step of completing three-dimensional reconstruction through the Poisson surface reconstruction method and obtaining a three-dimensional model. According to the method, the key factor is point cloud registration which is the key step influencing the three-dimensional reconstruction speed; the description vectors of the key points are constructed by means of the LBP features of the uniform patterns, so that vector dimensions are reduced, the matching speed of the corresponding relations is increased, registration is accelerated, and the three-dimensional reconstruction speed is increased.

The invention discloses a multi-operating process monitor method based on principal component analysis and support vectors data. The method establishes a uniform PCA model to various operating mixed data firstly, puts score vectors of principal component space and residual space to high dimension characteristic space. Two new statistics are established in the characteristic space for monitoring the principal component space and residual space. When the process goes wrong, a fault reconstruction method based on SVDD identifies fault. The method establishes two SVDD statistics monitor model to various operating based that the principal analysis method is used for reducing process variable dimension, reduces statistics limit of processing monitor, increases sensitivity of processing monitoring. In addition, the invention provides a fault reconstruction and identifying method aiming at detected process fault which can locate source of fault commendably, is benefit to removing fault as soon as possible, returns process to normal operation.

The invention relates to a color image three-dimensional reconstruction method based on three-dimensional matching, comprising the following steps of: (1) simultaneously and respectively taking an image from proper angles by using two color cameras; (2) respectively calibrating the internal parameter matrixes and the external parameter matrixes of the two cameras; (3) carrying out polar line correction and image transformation according to calibrated data; (4) working out matching cost for each pixel point in the two corrected images by applying a self-adaption weight window algorithm and acquiring an initial parallax image; (5) marking the reliability coefficient of the pixel initial matching result by adopting matching cost reliability detection and left and right consistency verification; (6) carrying out color segmentation on the images through a Mean-Shift algorithm; (7) carrying out global optimization by a selective confidence propagation algorithm on the basis of color segmentation and pixel reliability classification results to obtain a final parallax image; and (8) working out the three-dimensional coordinates of actual object points on the images according to the calibrated data and the matching relation, thereby reconstructing the three-dimensional point cloud of an object.

The invention provides a method for representing a device color gamut as a detail directed hierarchical distance field. A distance field representing the device color gamut is enclosed with a bounding box. The enclosed distance field is partitioned into a plurality of cells. Each cell has a size corresponding to detail of the continuous distance field and a location with respect to the bounding box. A set of values of the enclosed distance field is sampled for each cell. A method for reconstructing the portion of the distance field enclosed by the cell is specified. The size, the location, the set of values, and the method for reconstructing is stored in a memory to enable reconstruction of the device color gamut by applying the reconstruction methods of the cells to the values.

The invention discloses a deep learning-based multiview face three-dimensional model reconstruction method, and belongs to the field of computer vision. The method comprises the following steps of: generating a multi-illumination multiview virtual face image; generating a depth map of a face front view; training a plurality of independent and parallel convolutional neural networks; training a neural network in which weights of various views are distributed; and restoring depth maps output by the networks into a face three-dimensional grid model and carrying out peak coloring. According to themethod, multiview images are independently trained to restore depth maps, and each view weight distribution map is trained to carry out deep integration, so that the face three-dimensional model reconstruction precision is improved under the premise of ensuring the efficiency.

The invention relates to a compressed sensing reconstructing method of a sparse signal with the unknown block sparsity, belonging to the technical field of compressed sensing, in particular to a reconstruction method of a block sparse signal. The method comprises the steps of finding out one subset of a signal support set by initializing block sparsity k and iterating each block sparse signal, increasing the block sparsity while keeping iteration and finally finding out the support set of the whole source signal x so as to achieve the purpose of reconstructing the source signal x. The invention has high reconstruction precision by iterating and modifying the support set many times, and has high probability for reconstructing block sparse signals without the overmatching phenomenon compared with the traditional block sparsity matching and tracking and orthogonal matching and tracking method. The invention does not need the block sparsity as the priori knowledge and is particularly suitable for the reconstruction field of signals with unknown block sparsity.