173 results about "Image stack" patented technology

A system, method, and computer program product are provided for generating a focus sweep to produce a focus stack. In use, an image is sampled as image data. Next, a first focus region is identified and a second focus region is identified. Next, first focus target information corresponding to the first focus region is determined and second focus target information corresponding to the second focus region is determined. Further, a focus is adjusted, based on the first focus target information and at least one first image is captured based on the first focus target information. Additionally, the focus is adjusted, based on the second focus target information and at least one second image is captured based on the second focus target information. Lastly, the at least one first image and the at least one second image are saved to an image stack. Additional systems, methods, and computer program products are also presented.

The invention discloses a house full life cycle intelligent monitoring method based on machine vision, particularly relates to the field of machine vision, and is used for solving the problems of insufficient image alignment precision, aging change recognition lag and incapability of accurately quantifying life cycle evolution trend in existing house structure aging detection. The method comprises the following steps of: acquiring an image sequence by setting a shooting point position of a house facade component, performing spatial composite alignment by utilizing affine transformation and feature edge fitting, extracting component key points in an aligned image, dividing the component key points into segmented regions, and constructing a structure life cycle image stacking group; then calculating color distribution dispersion and texture roughness entropy of each section of region, establishing an aging evolution feature sequence, executing trend mutation point detection, extracting a material state jump section, and constructing an aging trend jump map; and finally, based on the map, identifying a jump risk area and outputting house component aging information, thereby realizing accurate monitoring and trend study and judgment of the house outer wall structure state.

Methods and systems for providing multi-plane viewing of a digital image of a subject matter which do not require viewing multiple images taken at multiple image planes. Pseudo-focus images are generated from a flattened, merged image of a three-dimensional subject matter. The merged image is generated by merging a plurality of images taken at different focal planes of the subject matter which form a through-focus image stack. The merged image comprises the best focus pixels originating from different respective image planes. A depth map is also generated from the multiple images which associates each best focus pixel with a pixel depth value. A plane of interest (POI) is selected from the image planes. A pseudo-focus image is then generated using the merged image and pixel depth map by generating a blurred focus value for each best focus pixel based on the focus offset of each pixel from the POI.

Method (50) for measuring a surface of a workpiece (12) with an optical sensor (14) of a measuring device (10), comprising the following steps: - Capturing (54) a plurality of images of the surface of the workpiece (12) using the optical sensor (14), wherein each image represents a region of the surface of the workpiece (12), wherein each image is assigned a defined 6-DOF pose of the optical sensor (14) relative to the workpiece (12) and a defined focal plane position of the optical sensor (14), and wherein the captured images form an image stack; - Determining (62) a sharpness value of each pixel of each image of the image stack in order to generate a sharpness map for each image of the image stack, wherein the sharpness maps of the images form a sharpness map stack; - Transforming (64) the focus maps of the focus map stack into a defined reference system based on the respective associated 6-DOF pose and focal plane position of the optical sensor (14) to generate a focus cloud; and - Generating (66) a surface profile of the workpiece (12) based on the sharpness cloud, in particular based on local sharpness maxima of the sharpness cloud, wherein the method (50) further comprises the following step: - Moving (56) the optical sensor (14) and the workpiece (12) relative to each other during the sensing step (54) to change the 6-DOF pose of the optical sensor (14) in space relative to the workpiece (12), wherein in the moving step (56) the optical sensor (14) is moved rotationally, in particular tilted and / or rotated.

The invention discloses a total primary productivity estimation method and system based on multi-model coupling deep learning, and the method comprises the steps: obtaining the multi-source data of meteorological data, remote sensing images, latent heat flux, sensible heat flux and solar radiation, carrying out the quality control, missing value processing and nearest neighbor interpolation of different data sources, and carrying out the prediction of the total primary productivity. Unifying to a target spatial resolution and a time resolution; in a light energy utilization rate (LUE) model family, a solar radiation phase factor is introduced into a photosynthetically active radiation absorption ratio (FPAR) to obtain a phase modulation type FPAR, drought duration is introduced into a water stress function f (W) to obtain an exponential decay type f (W), and a GPP time sequence of a plurality of improved mechanism models is calculated according to the exponential decay type f (W); extracting spatial texture features from a remote sensing image stack by using a convolutional neural network (CNN), and performing cross-modal fusion on the spatial features and the GPP estimated by the plurality of improved mechanism models in a gating mode to form fusion representation; carrying out learning and collaborative optimization on the fusion representation of the GPP and CNN spatial features estimated by the plurality of improved mechanism models by adopting a gradient lifting tree model; and high-precision estimation of the GPP is realized through multi-model collaborative optimization. The method aims at solving the problem that a traditional light energy utilization rate model is insufficient in response under the extreme environment conditions of drought and intense radiation, the adaptability limitation of a traditional single model under the complex environment is broken through, and therefore high-precision GPP estimation under the complex environment is achieved.

The invention discloses an adjacent foundation pit deformation prediction method for a dense building group and a support deformation prediction method, and the method comprises the steps: obtaining SAR satellite images of a to-be-processed region in a period of time sequence, and generating an image stack after registration; screening points of which the amplitude deviation indexes are lower than a threshold value as PS points; calculating the relative displacement sequence of each PS point, inverting the displacement variation and accumulating to obtain the accumulated settlement; before construction, a digital surface model (DSM) is generated through aerial survey of an unmanned aerial vehicle, feature points with stable displacement are recognized by combining SAR images, and the feature points are anchored to an absolute coordinate system of the unmanned aerial vehicle for correction. Therefore, permanent scatterer (PS) identification and time sequence analysis are carried out by utilizing SAR images of a long time sequence, absolute geographic reference is provided by a high-precision digital surface model (DSM) generated by aerial survey of an unmanned aerial vehicle, and reference correction is carried out on a relative displacement field acquired by the InSAR.

The invention relates to a real-time three-dimensional reconstruction method based on intracavity ultrasound and fistula radiography, which comprises the following steps of: extracting a fistula channel projection path through a fistula expansion outline after a contrast agent is injected during radiography; the ultrasonic probe is linked to detect the tissue interface change in the image, and reverse matching is carried out on the tissue interface change and a high-density area of the radiography image; establishing a spatial perspective calibration system based on an imaging cone projection relation, and estimating a rotation / translation pose of the intracavity ultrasound image ICE in a three-dimensional space; outputting a spatial state sequence of the ICE frame; performing curved surface resampling on the ICE image, and establishing a sectioned image stack; continuously receiving a new ICE image and a contrast frame; performing model fine tuning through a local deformation field; operating a spatial feature extraction network on the fistula volume model; judging local expansion / contraction and curvature change in the three-dimensional model to identify a fistula opening point; a fistula main channel and branch paths are analyzed, and the fistula complexity level is judged through topology analysis; and comparing with a preoperative map to assist in preoperative pre-judgment and intra-operative path planning.

A multi-view input image covering multiple sampled views is received. A multi-view layered image stack is generated from the multi-view input image. A target view of a viewer to an image space depicted by the multi-view input image is determined based on user pose data. The target view is used to select user pose selected sampled views from among the multiple sampled views. Layered images for the user pose selected sampled views, along with alpha maps and beta scale maps for the user pose selected sampled views are encoded into a video signal to cause a recipient device of the video signal to generate a display image for rendering on the image display.

The invention relates to the technical field of image processing, and discloses a breast lump image processing and classification method and system, and the method comprises the steps: collecting breast lump image data, and carrying out the spatial registration and fusion of breast lump images; converting the two-dimensional slice image stack into a three-dimensional model, and detecting and marking a suspicious lump area according to the three-dimensional model; and separating the lump area from the background, and displaying the segmented lumps. According to the method, imaging data of two-dimensional ultrasound, CT or MRI and the like are converted into an intuitive three-dimensional model through three-dimensional reconstruction, a doctor can observe a lump and the relation between the lump and surrounding tissues from any angle, and more comprehensive space information is provided. The three-dimensional reconstruction can more accurately measure lump data, is beneficial to improving the benign and malignant judgment precision, and carries out refined reconstruction on the lump edge, so that a complex boundary which is difficult to identify in a two-dimensional image becomes clear, and is beneficial to evaluating the wettability of the lump and the invasion condition of surrounding blood vessels and lymph nodes.

The invention discloses a multi-channel millimeter wave SAR enhanced imaging method based on low-rank tensor chain decomposition, and the method comprises the following steps: carrying out the two-dimensional SAR imaging of multi-channel echoes, and carrying out the phase difference compensation of an image; stacking the images of all the channels into a three-dimensional tensor, and constructing a tensor image stack; extracting inherent low-rank characteristics in the SAR image tensor by using tensor chain decomposition; establishing a multi-channel joint enhanced imaging problem model with TTNN constraint by using TT nuclear norm minimization; a Ket augmentation method is introduced into the model, and image neighborhood local features are enhanced; and performing iterative solution based on an ADMM framework to obtain an enhanced imaging result. According to the method, the multi-channel millimeter wave SAR image is subjected to combined processing through a low-rank tensor chain decomposition method, image enhancement is achieved, a clear and complete imaging result is obtained, a good side lobe suppression effect is achieved, and the effects superior to those of other algorithms are achieved in the aspects of peak side lobe ratio, structural similarity and equivalent looks.

The invention relates to a full-reference light field image quality evaluation method from sparse viewpoints to dense viewpoints, and the method comprises the steps: obtaining a plurality of sub-aperture image arrays with different sizes, respectively selecting a plurality of sub-aperture images from the sub-aperture image arrays, carrying out the processing of the sub-aperture images, and obtaining the Y channel information of original and distorted viewpoint image stacks; feature extraction is carried out on original and distorted viewpoint image stack Y channel information, complete original image stack features and complete distorted image stack features are obtained, and comparison features are obtained; carrying out image attention information extraction on the complete original image stack features, the complete distorted image stack features and the comparison features; after the dimensions of the image attention information are rearranged, light field image features are extracted; and obtaining final feature vector information based on the light field image features, and obtaining a final quality score based on the final feature vector information. According to the invention, the quality evaluation quality of the full-reference light field image is improved.

The invention discloses an insect compound eye detection method, system and device and a storage medium, and relates to the field of computer vision and biological image processing, and the method comprises the steps: carrying out the preprocessing of an insect compound eye CT image; carrying out intensity thresholding processing on voxels in the CT image stack by utilizing a dynamic region self-adaptive thresholding strategy and a GPU (Graphic Processing Unit) multi-thread parallel computing method to obtain screened voxel data; decomposing a CT image stack into a plurality of sub-regions, and performing block processing on the screened voxel data by adopting a GPU multi-thread parallel computing architecture to construct three-dimensional point cloud data; performing coordinate transformation by utilizing a GPU parallel computing method to generate cross slice data of different depths; a CPU multi-core parallel computing method is used for conducting compound eye geometric structure analysis on the cross slice data, and a detection result of the insect compound eye structure is obtained; according to the insect compound eye detection method, system and device and the storage medium, efficient detection of large-scale insect compound eye CT images is achieved.

Systems and methods for delineating tumour boundaries using a three-dimensional (3D) image stack, such as photoacoustic image slices, comprising a plurality of images. Multiple maximum intensity projections (MIPs) are derived from the stack, and level set segmentation is performed on those MIPs, to derive a boundary that bounds a region of interest (i.e., a tumour boundary). Structural and functional information is then derived for the region of interest, to analyse the tumour tissue.

A video generation method and apparatus, and an electronic device, a computer-readable storage medium and a computer program product. The method comprises: acquiring a target trajectory map, and mapping the target trajectory map on the basis of a target rank decomposition matrix configured for a motion control network, so as to obtain a target motion feature, wherein the target rank decomposition matrix is used for controlling the target motion feature to constrain a motion trajectory of a video lens and / or a motion trajectory of a video display object during a video generation process; acquiring target reference content required during the video generation process, and extracting a first content feature of the target reference content; and acquiring a target noise image stack, and by means of a diffusion model, using the target motion feature and the first content feature as constraint conditions to perform denoising processing on the target noise image stack, so as to obtain a target video in which the motion trajectory of the video lens and / or the motion trajectory of the video display object is constrained.

The invention relates to the technical field of image processing, in particular to an implicit nerve-guided super-network blind light field image quality evaluation method, which comprises the following steps: acquiring a to-be-evaluated light field sub-aperture array image; extracting target Y channel information of the light field sub-aperture array image; the target Y channel information is input into a preset quality evaluation model, a perception quality evaluation result is output, and the quality evaluation model uses an implicit neural guidance network to extract high-dimensional features of the Y channel information and obtains a perception quality score according to the high-dimensional features. According to the method, through a representation form extracted from a light field sub-aperture image stack, high-dimensional feature representation is obtained by using an implicit neural guidance network. In addition, a multi-scale semantic feature extraction network is combined, a perception quality score is finally generated, visual characteristics of human eyes are fully considered, and the scoring accuracy and effect of non-reference light field image quality evaluation are remarkably improved.

A biological fluid analyser is disclosed. The biological fluid analyser is configured to obtain image data of one or more image planes of an image stack in a prepared biological fluid sample. The image data comprises first image data associated with a first image plane. To obtain the first image data comprises to obtain first primary image data of the first image plane. The first primary image data is associated with a first incident light setting. The first incident light setting has a first angular light distribution. To obtain the first image data comprises to obtain first secondary image data of the first image plane. The first secondary image data is associated with a second incident light setting. The second incident light setting has a second angular light distribution. The biological fluid analyser is configured to classify, based on the first primary image data and the first secondary image data, a cell in the prepared biological fluid sample for provision of a cell parameter associated with the cell.

Methods and systems are provided herein for generating a morphology-based recomposition based on 3-dimensional input image data including an image stack of 2-dimensional (2D) images, the morphology-based recomposition being a final image generated by performing one or more white top hat (WTH) transforms on each 2D image, generating two or more image layers based on two or more WTH transformed images, and scaling adjacent image layers based on one or more scaling factors, each scaling factor being based on an estimated point spread function (PSF), two structure element sizes, and standard deviations of the estimated PSF and image data.

Provided herein are methods, systems, and computer program products for image fusion. A first z-stack of images of a biological sample may be received. The first z-stack of images may correspond to a first field of view, which comprises a plurality of patches. A second z-stack of images of the biological sample may be received. The second z-stack may correspond to the first field of view. A focus map may be determined based on the second z-stack, the focus map indicating, for each of a plurality of patches of the first field of view, one of the images of the second z-stack bringing into focus that patch of the first field of view. The focus map may be applied to the first and second z-stacks to generate respective first and second fused images. The first fused image may be subtracted from the second fused image to produce a subtracted image.

The invention discloses a volume electron microscope microscopic image sequence registration method based on Gaussian filtering, and belongs to the technical field of volume electron microscope images. According to the method, the 3D registration problem is regarded as a challenge of high-frequency and low-frequency superposition: natural deformation is represented as progressive low-frequency change, and nonlinear distortion is represented as sudden and violent high-frequency change. To this end, an optical flow network is employed to calculate a deformation field between adjacent slices, and a one-dimensional Gaussian filter is extended to a three-dimensional image stack to integrate neighborhood deformation fields. In this way, high-frequency distortion can be smoothed on the premise that the integrity of a natural structure is not damaged, and therefore high-precision and rapid 3D registration is achieved. And practical verification shows that the registration precision and speed of the method are better than those of the existing method, and the method is suitable for high-resolution large-scale real data sets.

An image sensor structure including an image stack disposed over a device stack. The image stack includes a plurality of light detectors. A first optical filter stack is disposed over the image stack. The first optical filter stack includes a light guide layer. Light pipe cavities are disposed in the light guide layer. Each light pipe cavity is associated with a light detector. Each light pipe cavity has an aspect ratio that is greater than about 2.5 to about 1. A nanowell layer is disposed over the first optical filter stack. Nanowells are disposed in the nanowell layer. Each nanowell is associated with a light detector.

The invention provides a coastal zone ecosystem carbon reserve estimation method based on long time sequence remote sensing data, and the method comprises the steps: obtaining satellite remote sensing data, and constructing a high-quality time sequence remote sensing image stack according to the satellite remote sensing data; classifying the remote sensing images based on a random forest method to obtain coastal zone surface land utilization type data; on the basis of the coastal zone surface land utilization type data, coastal zone ecosystem carbon reserve calculation is carried out through an InVEST model, and a coastal zone ecosystem carbon reserve accounting result is obtained; and counting and analyzing the change trend of the coastal zone ecosystem carbon reserve in the aspect of time based on the coastal zone ecosystem carbon reserve accounting result. Technical support can be provided for a coastal zone ecosystem carbon income and expenditure quantitative calculation method.

The invention discloses a land type intelligent identification and updating method and system for land investigation, and relates to the technical field of land investigation and remote sensing land type identification, and the method comprises the steps: carrying out the unified coding and weight fusion of optical, radar, terrain and auxiliary features on a standardized land block image stack through a feature fusion and dimension reduction model; generating plot-level fusion feature representation; jointly inputting the land block level fusion feature representation and the pixel statistical features in the land block into a land class recognition model, outputting a land block level land class category and confidence through a double-layer fusion structure, and generating a multi-temporal land class recognition result; and calculating a change credibility score of the land parcel based on a multi-temporal land class identification result and a time sequence observation index, and determining whether the land parcel belongs to a stable state, a suspected change or a confirmed change by comparing an index trend, class stability and space consistency. According to the method, collaborative identification of macroscopic structure information and microcosmic detail information is realized, and the accuracy and the spatial integrity of regional land class classification are improved.

A method includes capturing images of a surface of a workpiece using an optical sensor. Each image respectively images a region of the surface. Each image is assigned a defined 6-DOF pose of the optical sensor relative to the workpiece and a defined focal plane position of the optical sensor. The captured images form an image stack. The method includes determining a sharpness value for each picture element of each image of the image stack to generate a sharpness map for each image. The sharpness maps of the images form a sharpness map stack. The method includes transforming the sharpness maps of the sharpness map stack into a defined reference system based on the respective assigned 6-DOF pose and focal plane position of the optical sensor in order to generate a sharpness cloud. The method includes generating a surface profile of the workpiece based on the sharpness cloud.

The invention relates to the technical field of rock physics, discloses a histogram matching-based coal digital core modeling method, and is suitable for digital core reconstruction of experimental rock physics research. Comprising the following steps: S10, collecting a rock core and preparing a sample; s20, sample microstructure CT scanning and physical property parameter testing; s30, preprocessing the CT image; s40, performing rock component CT image segmentation based on a histogram matching algorithm; and S50, digital rock core reconstruction based on CT image stacking. On the basis of digital image processing, a rock physical experiment and a digital rock core technology are combined, and the histogram matching-based coal digital rock core modeling method is provided. The method is low in cost and high in operability, the reconstructed rock core and the original rock core have high matching degree, and a high-precision model can be accurately provided for physical property characteristic finite element simulation of the coal digital rock core.

The invention relates to a method (100) for acquiring a stack of images (PIL) of a scene by means of at least one camera module of an apparatus, said method comprising the following steps: - acquiring (110) a plurality of images of the scene, at least two of the images being acquired with different focuses, and - storing (116) a stack of images comprising at least a portion of the acquired images; characterized in that the method additionally comprises, prior to the acquisition step (110), a step (102) of adjusting the number of images to be acquired according to at least one parameter relating to the apparatus and / or at least one parameter relating to the scene. The invention also relates to a computer program, a device, an apparatus and a vehicle implementing such a method.

The invention relates to a method (100) for acquiring a stack of images of a scene at different focusing distances, the method (100) furthermore comprising, for at least one of the focusing distances, an acquisition phase (120) comprising the following steps: - comparing (126) a luminosity value of at least part of the scene located at a depth of field associated with the focusing distance with at least one predetermined luminosity threshold value, - when the luminosity value does not satisfy the at least one luminosity threshold value, adjusting (128) the luminosity for acquiring an image of the scene at the focusing distance, by modifying at least one parameter of the device, and - acquiring (130) an image of the scene at the focusing distance. The invention also relates to a computer program, a device, a system and a vehicle implementing such a method.

The present invention relates to a method and apparatus for accelerating three-dimensional microscopy with structured illumination. In three-dimensional structured illumination microscopy (SIM), the focal plane of a sample is focused and each focal plane is sequentially illuminated with multiple phases with structured illumination light, and the sample light emitted by the sample is recorded in a corresponding individual image. A result image with improved resolution relative to the individual image is reconstructed from the individual images to produce a super-resolution image stack. The method is time-intensive and stresses the sample. By reconstructing a result image from individual images of two different focal planes by an approximate method, the result image represents a sample plane located between the focal planes, an image stack can be produced in a short time with less stress on the sample. The present invention also relates to fluorescence microscopy.

A method for enforcing smoothness constraints on surface meshes produced by a Graph Convolutional Neural Network (GCNN) including the steps of reading image data from a memory, the image data including two-dimensional image data representing a three-dimensional object or a three-dimensional image stack of the three-dimensional object, performing a GCNN mesh deformation step on the image data to obtain an approximation of a surface of the three-dimensional object, the surface represented by triangulated surface meshes, at least some vertices of the triangulated surface meshes having a different number of neighboring vertices compared to other vertices in a same triangulated surface mesh, and performing a deep active surface model (DASM) transformation step on the triangulated surface meshes to obtain a corrected representation of the surface of three-dimensional object to improve smoothness of the surface.

The application discloses a digital image coding method based on metabolomics mass spectrum data. The method groups the mass-to-charge ratios in a preset mass range in the obtained first liquid chromatography-tandem mass spectrometry data according to preset division conditions; generates a full metabolome profile image according to the first mass-to-charge ratios and the first scan indexes in each group after grouping, and cuts and stacks the image to obtain a first multi-channel image; and screens a first target block based on the pooling signal intensity and the image entropy corresponding to the first multi-channel image, and the second multi-channel image stacked by the first target block is used to train a deep learning model for classifying biological samples. Thus, the metabolomics mass spectrum data can be converted into an image thereof through the digital image coding mode, the image can accurately retain the mass spectrum structure of the mass spectrum data of the metabolite species and level in the analytical metabolomics, and the deep learning model trained thereby can comprehensively reflect the original state of the metabolomics in LC-MS.

The invention belongs to the technical field of computer vision and light field imaging, and discloses a large-area highlight detection and repair method and system. The method comprises the following steps: imaging by using a light field camera array to obtain a multi-view-angle light field image, and calculating an initial parallax; the method comprises the following steps: obtaining a multi-view highlight area mask through highlight detection, removing the highlight area mask to obtain a light field image without a highlight area, carrying out light field refocusing to obtain a non-highlight refocusing image stack, applying the highlight area mask obtained through central view angle detection to an initial parallax image, and constructing a reliable parallax set to realize parallax layering; according to parallax layer information of a non-highlight area pixel adjacent to a highlight pixel space in the highlight area, carrying out adaptation on a parallax layer of the highlight pixel; and constructing a hierarchical restoration strategy based on observation conditions of different viewpoints when no-highlight refocusing image of the highlight pixel in the corresponding parallax layer is superposed and averaged at the pixel position, thereby realizing restoration of the highlight area. According to the method, high-quality and large-area highlight repair can be realized.