22 results about "Gaussian image" patented technology

The invention provides a digital human body modeling system and method based on space-time consistency Gaussian sputtering, and the system comprises a Gaussian set construction module which is used for constructing an initial Gaussian set based on a collected monocular RGB video sequence of human body motions; the non-rigid deformation module is used for updating a pre-constructed standard Gaussian set by adopting a Gaussian decoder based on the initial Gaussian set to obtain a dynamic Gaussian set containing non-rigid deformation; the space-time constraint module is used for applying space-time consistency constraint to the dynamic Gaussian set to obtain the dynamic Gaussian set of the space-time consistency constraint; the rigid deformation module is used for obtaining an attitude-driven Gaussian set based on the rigid deformation of the linear binding skeleton; and the projection rendering module is used for rendering the attitude-driven Gaussian set into a Gaussian image under the current frame attitude. According to the method, a space-time consistency mechanism of motion perception is combined, and the consistency problem of non-rigid deformation in a monocular video is effectively solved.

The invention relates to the technical field of image processing, in particular to a mining truck edge detection method based on a multi-scale edge membership map. The method comprises the following steps: 1, constructing a multi-scale image pyramid: acquiring a grayscale image as input, performing multi-scale pixel sampling, and performing fuzzy processing by using a Gaussian function to construct a Gaussian image pyramid; 2, gradient calculation: respectively calculating the gradient of the image in the x direction and the y direction by using a Scharr operator; 3, the standard deviation of the local gradient difference is calculated, edge pixels and noise points are further effectively distinguished by calculating the standard deviation of the local gradient difference through the statistical characteristics of the local gradient difference, and therefore the edge detection precision is improved; 4, calculating an edge membership degree; 5, fusing a multi-scale edge membership map; 6, adaptive lagging connection: adopting lagging connection to further process the multi-scale edge membership fusion image so as to improve edge continuity and positioning accuracy; according to the invention, the reliability and safety of automatic loading operation are improved.

A telecentric interferometer, a method for determining characteristics of an input light field, and an interferometer assembly are disclosed. The telecentric interferometer includes a front side optic having an exit pupil, a first optical system having a first interferometer arm, a second optical system having a second interferometer arm, a detector, and a displacement unit located between the front side optic and the detector. The first optical system and the second optical system have a same object-side focal length, a same Gaussian image distance, and a same object-side main plane and a same object-side optical axis. The exit pupil of the front-side optic is the object-side focal length from the object-side main plane. The shifting unit shifts an image-side main plane of the first optical system and / or an image-side main plane of the second optical system such that the image-side main plane of the first optical system is different from the image-side main plane of the second optical system. And the optical path length of the first interferometer arm is equal to the optical path length of the second interferometer arm.

The invention discloses an SIFT (Scale Invariant Feature Transform) algorithm hardware circuit implementation, which optimizes the hardware circuit implementation of an original SIFT algorithm, and solves the problems of slow operation of the SIFT algorithm and large resource consumption when the SIFT algorithm is deployed on an FPGA (Field Programmable Gate Array). According to the specific implementation scheme, the method comprises the steps that a one-layer six-group Gaussian pyramid is built, and an independent precalculated 21 * 21 Gaussian filtering kernel is used in each group; extreme points of three adjacent groups of differential pyramids are detected by adopting a threshold method, and edge response points are eliminated; a CORDIC algorithm is adopted to calculate the gradient direction and amplitude of a Gaussian image where the feature points are located, and a 16-row 16-column calculation result is output for gradient histogram statistics and descriptor generation; and normalization of a 128-dimensional descriptor is realized by using a single divider. The method has the advantages of low resource consumption, high real-time performance and the like, and is suitable for scenes, such as an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit) and the like, needing hardware acceleration of the SIFT algorithm.

The present invention discloses a visible light and infrared image fusion method based on feature extraction. First, the infrared image is preprocessed by a hybrid filtering algorithm. Secondly, the gray histogram of the image is extended to fill the entire gray range, highlighting the infrared target and weakening the influence of the background. Then, Gaussian image pyramids are processed for different spectral images respectively, and the significant regions of the images at different scales are obtained through Laplacian pyramids. Next, the infrared region of interest is calculated, and through the dominant information comparison strategy, the significant regions of different spectra are compared to obtain the infrared image regions of interest at each scale. Finally, the infrared regions of interest at different scales are fused into the visible light image to obtain the fusion results of different spectra. The multi-spectral image fusion method of the present invention retains complete detail information and has strong robustness, solving the problem of limited spectral detection range of a single visible light sensor.

This application provides a Gaussian image rendering method and apparatus. Starting with multi-view image rendering based on an initial Gaussian loss function, color and depth images from different viewpoints are gradually acquired. Multiple loss functions are then used to specifically optimize the properties of the initial Gaussian loss function, resulting in a first Gaussian loss function. Based on the first Gaussian loss function, rendering continues from more viewpoints, and a third loss function, formed by comparing with the real image, is used to further optimize the Gaussian loss function, ultimately yielding a second Gaussian loss function for rendering images from new viewpoints. This entire process ensures that the rendered fifth color image achieves good results in terms of accuracy, realism, and detail rendering, more realistically restoring the visual appearance of the target scene from new viewpoints and reducing problems such as blurring, distortion, and unreasonable object structures in the image.

The embodiment of the invention discloses an automatic image registration method and device for tissue slices and electronic equipment. The method comprises the following steps: acquiring a target dyed image obtained by preprocessing a tissue slice, and generating a honeycomb image corresponding to the tissue slice; constructing a multi-layer Gaussian image pyramid for the target dyed image and the cellular image; and carrying out angle and position search layer by layer from a low-resolution layer to a high-resolution layer in the multi-layer Gaussian image pyramid, determining a target registration parameter corresponding to the highest-resolution layer, and carrying out registration on the target dyed image and the honeycomb image according to the target registration parameter. In the embodiment of the invention, the optimal registration parameter can be comprehensively evaluated layer by layer through a multi-dimensional scoring mode, the limitation of a traditional correlation method is overcome, the algorithm adaptability is better, and the precision of automatic registration is higher.

The application discloses a dense optical flow-based optical remote sensing image uniform dense matching method and system, equipment and a storage medium, and belongs to the technical field of satellite remote sensing image processing. The application solves the technical problem that the prior art lacks a dense optical flow-based optical remote sensing image uniform dense matching method which can recognize sufficient and uniformly distributed matching points in various complex scenes and has high positioning accuracy. The application confirms a remote sensing image overlap area, divides the remote sensing image overlap area into a plurality of remote sensing image blocks, constructs a displacement field solving model and a Gaussian image pyramid, obtains a displacement field of the remote sensing image overlap area, acquires a pre-matching matching point pair set, distributes the pre-matching matching point pair set into corresponding remote sensing image overlap area image blocks, and screens a final matching point set. The application is used for realizing a dense optical flow-based optical remote sensing image uniform dense matching method which can recognize sufficient and uniformly distributed matching points in various complex scenes and has high positioning accuracy.

The application discloses an image edge detection method and device, equipment and a storage medium, and relates to the technical field of computer vision. The method comprises the following steps: if an initial image input by a user is received, performing Gaussian filtering on the initial image to obtain a Gaussian image; gradient information of each pixel point in the Gaussian image is acquired; a target edge threshold corresponding to the Gaussian image is determined according to the gradient information; each edge point is screened out from the pixel points according to the target edge threshold; and the edge image of the initial image is obtained by connecting the edge points. The application solves the technical problem of low image edge detection efficiency.

The embodiments of the present invention provide a dispensing method, apparatus, equipment and storage medium, which obtain a Gaussian image by performing illumination normalization processing and Gaussian blur smoothing processing on a training image; generate multiple reference circles with different radii; extract features from the image through a feature extraction module; predict multiple image features based on the coordinates and radius of the reference circle to obtain multiple prediction results, and fuse the multiple prediction results to obtain a fusion result; train a trained positioning model based on the fusion result; control a dispensing machine to perform dispensing based on the trained positioning model; solve the problem of uneven image illumination through illumination normalization technology; the positioning model has the advantage of being lightweight and can quickly complete the dispensing positioning task; and improve the accuracy of positioning prediction by setting a reference circle and fusing multiple prediction results obtained by the feature extraction module shared by multiple parameters.

The invention discloses a three-dimensional scene reconstruction method, electronic equipment and a vehicle, and the method comprises the steps: obtaining an initial four-dimensional Gaussian according to an initial all-around image collected by a camera of a vehicle, a camera pose corresponding to the initial all-around image, and a camera internal reference; according to the position information of the vehicle, the camera pose, the camera internal reference and the acquisition time of the initial look-around image, obtaining an initial motion field; according to the initial four-dimensional Gaussian and the initial motion field, obtaining a rendering result corresponding to the collection moment; adjusting parameters in the initial four-dimensional Gaussian and the initial motion field according to the rendering result and the initial look-around image to obtain a target four-dimensional Gaussian and a target motion field; and the target three-dimensional scene is constructed according to the target four-dimensional Gaussian and the target motion field, so that the three-dimensional reconstruction of the dynamic scene is carried out only through the image acquired by the camera of the vehicle and the calibration parameter of the camera without a point cloud and a three-dimensional marking frame, and the three-dimensional scene reconstruction efficiency is improved.

The invention relates to the technical field of infrared image enhancement, in particular to an infrared spliced image multi-scale enhancement method based on fusion wavelet denoising. Comprising the following steps: S1, acquiring an original infrared spliced image, and constructing a Gaussian image pyramid by using a processing result of the original infrared spliced image; s2, a Laplacian image pyramid is constructed; s3, performing first-layer wavelet denoising processing on the Laplacian image pyramid to obtain the Laplacian image pyramid after the first-layer wavelet denoising processing is performed on the Laplacian image pyramid; s4, performing contrast stretching on the Laplacian image pyramid after the first layer of wavelet denoising to obtain a Laplacian image pyramid after detail enhancement; and S5, reconstructing the high-resolution image pyramid to obtain a multi-scale enhanced infrared spliced image. The image contrast can be effectively improved.

This invention relates to the field of image data processing technology and discloses an image data processing method based on AOI defect feature extraction, comprising: constructing a Gaussian image sequence of the image to be detected using a preset step size; calculating the Hessian matrix of the pixel position for each scale image and extracting the principal feature vector, and determining the inter-scale directional coherence coefficient characterizing the geometric orientation stability; sampling along the normal space of the principal feature vector, calculating the discrete measure characterizing the isolation degree of feature distribution, and suppressing periodic background texture; and modulating the response intensity using the directional coherence coefficient. This invention utilizes the geometric stability and spatial distribution density differences during the scale evolution process to achieve the essential separation of industrial background texture from weak structural defects, effectively suppressing background random noise interference, and improving the linear separability and detection stability of defect features in the feature space.

A telecentric interferometer, a method for determining characteristics of an incident light field, and an interferometer assembly are disclosed. The telecentric interferometer includes a front optical element having an exit pupil, a first optical system having a first interferometer arm, a second optical system having a second interferometer arm, a detector, and a shift unit positioned between the front optical element and the detector. The first optical system and the second optical system have identically positioned object-side principal planes with the same object-side focal length, the same Gaussian image distance, and the same object-side optical axis. The exit pupil of the front optical element is separated from the object-side principal plane by the object-side focal length. The shift unit shifts the image-side principal plane of the first optical system and / or the image-side principal plane of the second optical system so that the image-side principal plane of the first optical system is different from the image-side principal plane of the second optical system and the optical path length of the first interferometer arm is equal to the optical path length of the second interferometer arm.

The invention provides a three-dimensional asset library construction method. The three-dimensional asset library construction method comprises the following steps: S1, acquiring an advanced multi-source data set; s2, acquiring a triangular mesh data set, a plurality of 3D Gaussian images and a plurality of rendering images corresponding to the advanced multi-source data set by adopting an SfM algorithm and a 3D Gaussian sputtering model; s3, acquiring a geometric structure data set, a rendering parameter set and a three-dimensional dense point cloud set; aligning the rendering parameter set with the multispectral information of the remote sensing image to obtain a material information set; s4, performing three-dimensional reconstruction by using the geometric structure data sets of the plurality of rendered images to obtain a plurality of reconstruction scenes; identifying and marking a plurality of monomers and a plurality of components in each reconstruction scene to obtain a label information set of each reconstruction scene; s5, constructing the triangular mesh data set, the geometric structure information set and the three-dimensional dense point cloud set into a space entity asset set; constructing the rendering parameter set into a color texture asset set; and constructing the plurality of reconstruction scenes, the material information set and the label information set into a ground object entity asset set, and forming a three-dimensional asset library.

The invention discloses a bridge vibration detection method and system based on a visual technology, and belongs to the technical field of bridge vibration detection, and the method comprises the following steps: S1, obtaining vibration displacement data of a target bridge; s2, analyzing vibration displacement data; and S3, evaluating the structural performance and the safety state of the bridge. The speed of the NCC algorithm is increased by using the Gaussian image pyramid method, the accuracy of the NCC algorithm is improved by using the template matching algorithm improved by the quadric surface fitting algorithm, and the vibration of the bridge can be monitored, analyzed and identified in real time, so that the evaluation of the structural performance and the safety state of the detected bridge is realized.

A system-on-chip, an electronic device, and an operating method of a processor for reducing noise in an image based on an image pyramid are provided. The system-on-chip includes a pyramid generation module outputting a Gaussian image of a highest layer and a Laplacian pyramid, based on an input image, a denoising process module denoising the Gaussian image of the highest layer and outputting a denoised image of the highest layer, and a pyramid reconstruction module receiving the Laplacian pyramid and the denoised image of the highest layer, generating a denoised image and edge grade information for each layer based on a Laplacian image of each layer, a denoised image of an upper layer that is higher than each layer, and edge grade information of the upper layer, and outputting an output image.

The invention provides a method and a device for detecting a pipeline welding seam image, relates to the technical field of image processing, and can improve the accuracy of pipeline welding seam detection. The method comprises the following steps: acquiring an initial pipeline welding seam image; gaussian blur processing is carried out on the basis of the initial pipeline welding seam image, a first Gaussian image is obtained, the first Gaussian image comprises low-frequency region information, and the low-frequency region information is information with the change amplitude, changed along with the spatial position, in the initial pipeline welding seam image smaller than a preset amplitude threshold value. And performing logarithmic transformation based on the first Gaussian image to obtain a first logarithmic image, and performing logarithmic transformation on the initial pipeline welding seam image to obtain a second logarithmic image. Signal separation is conducted on the basis of the first logarithmic image and the second logarithmic image, a target image is obtained, the pipeline welding seam is detected on the basis of the target image, the target image comprises high-frequency area information, and the high-frequency area information is image information of the pipeline welding seam in the initial pipeline welding seam image.

The invention provides a long-distance monocular depth estimation method for various illumination driving scenes, and the method comprises the steps: carrying out the multi-scale decomposition of an input driving scene image based on Gaussian smoothing and downsampling, and obtaining a multi-layer Gaussian image; performing high-frequency information enhancement on the multi-layer Laplacian image to obtain a multi-layer Laplacian image, and performing image reconstruction on the multi-layer Laplacian image to obtain a reconstructed high-frequency information enhancement image; a potential encoder is adopted to encode the reconstructed high-frequency information enhancement graph, and low-dimensional potential space representation is obtained; taking the low-dimensional potential space representation as input, and generating depth features by adopting a diffusion model which gradually introduces residual errors; and decoding the depth features by using a potential decoder to obtain a final depth map. According to the invention, the distance of a distant object can be measured more accurately, and accurate distance measurement is provided especially in different illumination environments.

The embodiment of the invention provides a Gaussian image rendering method and device. The method comprises the following steps: starting from multi-view image rendering based on initial Gaussian, gradually obtaining color images and depth images under different views, so as to construct a plurality of loss functions to carry out targeted optimization on attributes of the initial Gaussian, thereby obtaining a first Gaussian; and rendering is continued under more visual angles based on the first Gaussian, and Gaussian attributes are further optimized by using a third loss function formed by comparison with a real image, and finally a second Gaussian is obtained for rendering an image under a new visual angle. According to the whole set of process, the rendered fifth color image can achieve better effects in the aspects of accuracy, authenticity, detail presentation and the like, the visual appearance of the target scene under a new visual angle can be restored more vividly, and the problems of blurring, distortion, unreasonable object structure and the like in the image are reduced.

The invention discloses an optical remote sensing image uniform and dense homonymy point matching method, system and device based on dense optical flow, and a storage medium, belongs to the technical field of satellite remote sensing image processing, and solves the problem that sufficient and uniformly distributed homonymy points can not be identified in various complex scenes in the prior art. The invention aims to solve the technical problems in the prior art and to provide the dense optical flow-based uniform and dense homonymy point matching method for the optical remote sensing image. Confirming a remote sensing image overlapping region, and dividing the remote sensing image overlapping region into a plurality of remote sensing image blocks; constructing a displacement field resolving model and a Gaussian image pyramid to obtain a displacement field of the remote sensing image overlapping region; and obtaining a pre-matched homonymy point pair set, distributing the pre-matched homonymy point pair set to the corresponding remote sensing image overlapping region image blocks, and screening to obtain a final matched homonymy point set. According to the optical remote sensing image uniform and dense homonymy point matching method based on the dense optical flow, sufficient and uniformly distributed homonymy points can be identified in various complex scenes, and the positioning precision is high.

The present invention belongs to the field of machine vision technology, and specifically relates to a low-altitude, slow, small target tracking method that integrates millimeter-wave radar and visual sensor. The method comprises: using millimeter-wave radar scanning to obtain target information, and filtering the obtained target information to obtain filtered target information; using a visual sensor to obtain an optical image as a search image, mapping the filtered target information to the optical image, and then generating a Gaussian image to obtain a heat map (Heat Map) containing target position information; constructing a twin network target tracking model, intercepting the first frame image with a millimeter-wave radar-marked target position in the optical image as a template image, fusing the search image and the radar heat map through a spatial attention mechanism, and using the twin network target tracking model to achieve stable tracking of the target; the present invention achieves reliable target tracking by fusing millimeter-wave radar and visual sensor data.