3067 results about "Reference image" patented technology

In a measurement processing apparatus for measuring a geometric feature of an object image: a measurement-reference-element setting unit automatically sets at least one first measurement reference element for use in measurement of the geometric feature of the object image, at at least one first position on the object image based on first image data representing the object image and position information indicating at least one second position of at least one second measurement reference element which is set on a measurement reference image corresponding to the object image; and a geometric-feature measurement unit measures the geometric feature of the object image based on the at least one first position of the at least one first measurement reference element.

Disclosed in the present application are a multi-modal data processing method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring a reference image and a reference text; extracting a reference visual feature of the reference image; by means of a multi-modal large language model, determining an embedding of the reference text, an embedding of a start mark of the reference visual feature, an embedding of the reference visual feature, and an embedding of an end mark of the reference visual feature; on the basis of the multi-modal large language model, splicing the embedding of the reference text, the embedding of the start mark, the embedding of the reference visual feature, and the embedding of the end mark into a target embedding sequence, performing attention processing on the basis of the embedding of the start mark, the embedding of the end mark, and an embedding selected by a sliding window in the target embedding sequence, and outputting a predicted sequence; and generating a predicted image and a predicted text on the basis of the predicted sequence.

The invention relates to the technical field of image processing, and discloses a packaging material printing quality detection method and system based on machine vision. The method comprises the following steps: acquiring a multispectral image sequence and three-dimensional shape data of a moving packaging and printing material under different illumination, and constructing a dynamic three-dimensional physical attribute field; generating a virtual reference image and a dynamic reference image, constructing a multi-modal reference image, carrying out space-time registration on the multi-modal reference image and the dynamic three-dimensional physical attribute field, and calculating the difference between the multi-modal reference image and the dynamic three-dimensional physical attribute field in different dimensions to generate a multi-dimensional difference quality field; each dimension difference is enhanced through local statistics, and the comprehensive defect confidence coefficient is calculated based on the enhanced dimension difference; and extracting a defect region based on the comprehensive defect confidence, generating a defect evolution sequence and a defect track, analyzing defect track characteristics, constructing a correlation model in combination with process parameter time sequence data of the printing equipment, and positioning a defect reason. According to the invention, high-precision, multi-dimensional and self-adaptive printing defect detection and traceability can be realized.

The invention relates to the technical field of engineering materials, in particular to an engineering material quality detection method and system based on image recognition, and the method comprises the steps: reference image acquisition, sampling point selection and marking, image acquisition, image comparison and positioning, secondary acquisition and anomaly analysis. Compared with the defects that a detection system in the prior art is rigid in process, poor in adaptability and difficult to cope with a complex and changeable engineering field environment, the scheme constructs a full-process automatic system from intelligent sampling, self-adaptive image acquisition, precise registration and semantic level difference detection to intelligent post-processing and analysis; the method has high intelligence, adaptivity and robustness, and can stably and efficiently complete quality detection tasks in a complex engineering environment.

The invention relates to the technical field of goods shelf detection, in particular to a self-detection method and device for goods shelf settlement, and provides the following scheme: obtaining a top view image through an image sensor arranged right above the top of a goods shelf, dividing the image into a plurality of grid units, and positioning a rectangular geometric shape by utilizing Hough transform; and screening a plurality of to-be-detected areas in combination with the edge features. For an area to be measured, homographic registration and ortho-rectification are carried out based on a reference image, a displacement field is obtained by adopting sub-pixel-level dense registration, and a geometric parallax component field corresponding to imaging parameters is obtained through robust estimation. And under the hypothesis of small deformation, inverting the parallax into a pixel normal distance, and carrying out weighted aggregation on the local region to obtain a local distance measurement result. And by iteratively combining adjacent grids, determining a settlement area boundary, and finally outputting a settlement detection result. Millimeter-level settlement quantification can be realized under a single-frame image, hardware transformation is avoided, and the method is suitable for automatic detection and long-term monitoring of multi-specification goods shelves.

The invention relates to the technical field of image processing, in particular to an unmanned aerial vehicle positioning method and system based on machine vision, and the method comprises the steps: obtaining a current frame image and a reference image in a real-time video stream of an unmanned aerial vehicle, generating an initial matching pair set, and calculating the structural consistency of each matching pair, the method comprises the steps of adaptively determining a screening threshold value of a current frame image based on structural consistency, determining a screening matching pair set by utilizing the screening threshold value, evaluating a positioning contribution weight of each matching pair of the screening matching pair set, executing weighted pose calculation based on the positioning contribution weights, and obtaining an instantaneous pose of an unmanned aerial vehicle in the current frame image. And inputting the instantaneous pose as an observation value into a time sequence filtering model, performing time sequence fusion in combination with a motion model of the unmanned aerial vehicle, and outputting the final pose estimation of the unmanned aerial vehicle in the current frame image so as to complete the accurate positioning of the unmanned aerial vehicle. The method improves the accuracy of unmanned aerial vehicle positioning.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a transformer fault diagnosis method and system based on image recognition, and the method comprises the steps: collecting a transformer multi-mode image sequence in real time, and carrying out the definition and part integrity evaluation and screening to form an initial image set; performing multi-scale space registration on the initial image set and a transformer normal state standard template to generate a reference image, and reversely deriving a displacement vector field based on pixel-level difference; carrying out smooth optimization and geometric reconstruction on the displacement vector field under the geometric constraint of the transformer structure, and generating a correction image with a real structure; fault feature enhancement is carried out in a gradient domain of the corrected image, a fault area is identified through matching of multichannel feature extraction and a transformer typical fault feature library, and a diagnosis report integrating fault types, confidence coefficients and geometric parameters is generated; according to the method, the problem of image geometric deformation caused by shooting condition differences is effectively solved, and the accuracy and reliability of fault identification are improved.

A method for detecting printing defects in a photolithography mask that will print on a wafer when using the photolithography mask in a specific photolithography system to print semiconductor structures on the wafer, the method comprising: acquiring a first aerial image of the photolithography mask using a mask inspection system; generating a second aerial image of the photolithography mask by applying a machine learning model (26) to the first aerial image, wherein the machine learning model is trained to map a first aerial image acquired by a mask inspection system to a second aerial image that emulates the application of the specific photolithography system to the photolithography mask; and detecting printing defects in the photolithography mask by comparing the second aerial image to a reference image.

The invention discloses a shielding complementing method based on a continuous streetscape panoramic image, and aims to solve the problem of information loss caused by shielding of dynamic objects (such as vehicles and pedestrians) in the existing streetscape image, and the method comprises the following steps: S1, recognizing and positioning a dynamic shielding object area in a continuous streetscape panoramic image sequence; s2, providing a continuous panoramic image-oriented feature extraction and matching algorithm (FDMPano), realizing robust feature retrieval and matching in a cross-view angle, and accurately positioning a reference image region which can be complemented; and S3, carrying out feature alignment and content mapping on the sheltered area based on a matching result, and generating an unsheltered panoramic image with consistent vision. According to the method, the internal relevance of continuous streetscape data is fully utilized, the occlusion area can be automatically completed with high quality, the integrity and availability of streetscape images are remarkably improved, and the method has wide application value in the fields of automatic driving, digital cities, virtual reality and the like.

The invention belongs to the technical field of computer vision three-dimensional scene repair, and provides an automatic driving three-dimensional scene repair method based on color point cloud prior guidance, which comprises the following steps: acquiring multi-source heterogeneous data; constructing an incomplete semantic two-dimensional Gaussian field according to the multi-source heterogeneous data, and generating an incomplete image sequence, an incomplete depth image sequence and an opacity image sequence; obtaining a color point cloud based on the instance segmentation image, the opacity image, the incomplete image and the incomplete depth image of the reference image; generating a texture pseudo view angle sequence based on the color point cloud; generating a repaired image sequence by taking the texture pseudo view angle sequence as a condition signal of the fine-tuning video diffusion model; generating a repair depth sequence based on the color point cloud; calculating a repair Gaussian loss function based on the repair image sequence and the repair depth sequence, and performing iterative repair optimization on the incomplete semantic two-dimensional Gaussian field to obtain a complete repair two-dimensional Gaussian field; the invention further discloses a computer device. And texture-geometry collaborative automatic driving scene three-dimensional repair is realized.

The invention discloses an optical diffusion plate microstructure defect detection method, electronic equipment and a storage medium. The method comprises the following steps: acquiring images of the same diffusion plate area under at least two different illumination angles and performing elastic registration so as to eliminate image dislocation caused by plate movement; decoupling and separating physical defect features stably existing under all illumination and shadow interference features changing along with illumination from the registered image; carrying out enhancement and dynamic up-sampling processing on the physical defect features, and generating a suspected defect candidate region set with a low confidence threshold; for each candidate region, reconstructing a virtual reference image when the region has no defect through a generative model by utilizing the peripheral texture of the candidate region, and judging authenticity and outputting a confidence coefficient by comparing residual errors; and determining a final alarm result according to whether the confidence exceeds an alarm threshold. According to the invention, artifacts and real physical defects caused by mechanical micro-vibration and micro-structure reflection can be effectively distinguished, and the false alarm rate is obviously reduced.

A viewpoint setting unit of an image data generation device sets a reference viewpoint on a basis of position and orientation information regarding a head-mounted display, and a reference image drawing unit generates a reference image in a field of view corresponding to the reference viewpoint. An additional data generation unit acquires color information regarding an occluded part not represented in the reference image from a different viewpoint as additional sampling data. A reprojection unit of the head-mounted display transforms the reference image into an image from a latest viewpoint and determines a pixel value of the hidden part using the additional sampling data.

A video decoding method includes: determining first motion information of a current block; refining the first motion information based on motion information of a reference picture of the current block to obtain second motion information of the current block; and determining a prediction value of the current block based on the second motion information.

A georegistration (a.k.a. georectification) of an image captured by a camera in an aerial vehicle, such as a satellite, is based on identifying multiple features using descriptor sets, and sending to a ground station only the descriptors of the identified features and the associated locations in the captured image, without sending of the captured image itself, thus requiring a low communication bandwidth. Using a database of geosynchronized reference images, the ground station uses the received descriptors sets and the associated image locations to localize the features on a selected geosynchronized reference image from the database, and forms a mapping function that map any locations in the captured image to geographical coordinates on Earth. The mapping may be used to geosynchronize an additional feature identified in the aerial vehicle, or to geo synchronize a region that may be cropped from the captured image and sent to the ground station.

The invention relates to a computer vision processing method and system for industrial defect real-time detection. The method comprises the following steps: extracting geometric features and textural features of predefined defect types, and generating a structured descriptor set; generating a synthetic defect image set based on the defect-free image set and the structured descriptor set; inputting the synthesized defect image set into a double-flow feature extraction network to obtain a fusion feature vector; generating a defect category threshold set based on the vector and the structured descriptor set; and inputting the to-be-detected image and the corresponding defect-free reference image into the double-flow feature extraction network, calculating defect probability distribution in combination with the structured descriptor set and the dynamic classifier, and outputting a defect category decision result based on the defect category threshold set. According to the method, the precision, robustness and adaptability of defect detection are improved by means of fusing the global features of the defect-free reference image and the local features of the defect image and expanding training samples by using the synthetic defect image set.

The invention provides a pulmonary nodule display method and device, electronic equipment and a storage medium, and the method comprises the steps: segmenting a three-dimensional reconstruction image of the chest of a target patient to obtain a preoperative segmentation image, the three-dimensional reconstruction image being obtained based on preoperative CT data, and the preoperative segmentation image comprising the position information of a pulmonary nodule; segmenting the video image of the intraoperative lung tissue of the target patient collected by the thoracoscope in real time to obtain an intraoperative segmented image; feature matching is conducted on the preoperative segmented image and the intra-operative segmented image, the lens pose of the thoracoscope is determined, the preoperative segmented image is mapped into a two-dimensional reference image based on the lens pose, and the two-dimensional reference image comprises the position information of the pulmonary nodule; and carrying out image registration on the two-dimensional reference image and the intra-operative segmented image, and carrying out pulmonary nodule marking on the intra-operative segmented image to obtain a video image for displaying pulmonary nodules in real time. The position of the pulmonary nodule in the operation is accurately displayed in real time in a non-invasive mode, and the operation efficiency is improved.

The invention discloses a stylized visual text editing method, a stylized visual text editing system, stylized visual text editing equipment and a storage medium, which are corresponding schemes, and the related schemes aim to solve the problem of style consistency existing in image text editing of an existing diffusion model, and the stylized visual text editing efficiency is improved by combining visual features of a font image and an input text image. The method comprises the following steps of: extracting style embedded information from a text image, and inputting the style embedded information as an enhanced style condition into a diffusion model to realize fine control on a diffusion process, so that the diffusion model can generate a text image with high readability and style consistency, and can realize maintenance of an original text style or style migration based on a reference image.

The invention belongs to the technical field of deep learning, particularly relates to a two-stage blind image defogging method based on prior guide diffusion, and aims to solve the problem of distortion of a traditional decontamination method in a complex scene. Comprising the steps that a double-stage blind image defogging model is constructed, and the double-stage blind image defogging model comprises a first stage and a second stage; wherein in the first stage, physical modeling is carried out based on an improved atmospheric scattering model, the improved atmospheric scattering model is an enhanced atmospheric scattering model in which a light absorption coefficient is introduced, and a transmission image, a fogless reference image and atmospheric light parameters are output through the first stage; in the second stage, generation optimization is carried out based on a diffusion model, the transmission image, the fog-free reference image and the atmospheric light parameters output in the first stage are used as physical priori to be fused into the generation process of the diffusion model, and image defogging is carried out. In the second stage, self-adaptive difference fusion convolution is set, a fog domain multi-source fusion attention mechanism is set, and a pixel level and wavelet domain double-effect color correction strategy is adopted.

The invention provides a film thickness deviation measuring technology and a film manufacturing technology, which can acquire information of film thickness deviation of a film in a non-contact manner through a simple and safe device structure. Provided is a film thickness deviation measurement method for a film, in which a film to be measured is used as an object to be measured, a preset background image can be captured by an imaging means, and a reference image can be acquired. The reference image is an image obtained by capturing a background image by means of an imaging means in a state in which an object to be measured is not sandwiched, or an image obtained by calculating and deriving a visual representation based on the imaging means of the background image in a state in which the object to be measured is not sandwiched. The measurement image is an image in which the object to be measured is sandwiched between the background image and the imaging means and the background image is captured by the imaging means, and the amount of displacement of the background image in the two images is calculated on the basis of the acquired reference image and the measurement image, thereby obtaining the film thickness deviation of the object to be measured.

A method for training a prediction model to generate a high-resolution image representing defects on a substrate from a low-resolution image of the substrate. The method includes inputting a first image and a reference image of defects on a substrate, which are representative of images captured using different image capture conditions, to a neural network. The neural network is executed to generate a predicted image in response to the first image. A loss function that is indicative of a difference between a defect distribution in the predicted image and a defect distribution in the reference image is calculated and the neural network is modified based on the loss function. The neural network may be trained until the loss function is minimized.

A system and method to generate a target image from a reference image are provided. The system may receive, via a LDM, a reference image and a text prompt. The system may extract, via a trained vision encoder in the LDM, a vision control signal from an object in the reference image. The vision control signal indicates an identity of the object. The system may extract, via trained text encoders in the LDM, text control signals associated with the text prompt. The system may generate, via cross attention summation of an output of a vision cross attention unit(s) associated with the vision control signal and an output of text cross attention units associated with the text control signals, spatial features indicative of the reference image and the text prompt. The system may output, via a decoder in communication with the LDM, a target image based on the generated spatial features.

The invention provides a three-dimensional digital human reconstruction method, and the method comprises the steps: estimating a parameterized human body model corresponding to an input single human body reference image based on the input single human body reference image, and obtaining a multi-view human body semantic segmentation image of the parameterized human body model according to a plurality of preset camera poses and rendering parameters; taking the multi-view human body semantic segmentation image as a condition signal, taking the single human body reference image as input, and generating a multi-view human body color image based on a pre-trained first video diffusion model; taking the multi-view human body color image as a condition signal, taking a human body normal image extracted from a single human body reference image as input, and generating a multi-view human body normal image based on a pre-trained second video diffusion model; and performing 3D Gaussian splashing based on the multi-view human body color image and the multi-view human body normal image so as to reconstruct a three-dimensional digital human body corresponding to the single human body reference image.

The invention discloses a non-contact monitoring device for pavement settlement. The non-contact monitoring device comprises monitoring equipment and a plurality of light reflecting units, the plurality of light reflecting units are arranged on monitoring points located in a monitoring area and datum points located in a stable area; the monitoring equipment comprises an image acquisition device which is used for shooting a reference point and each shooting position before monitoring and during monitoring to obtain an initial reference image and a target image; and the controller is used for acquiring the absolute angle of each shooting position when the initial reference image is shot, judging whether the monitoring point in the target image is shielded or not according to the initial reference image, and calculating the settlement amount of the monitoring point according to the initial reference image and the effective target image after the effective target image without shielding at the current shooting position is obtained. And switching to the next shooting position according to the absolute angle until the settlement amount of all the monitoring points is calculated. According to the invention, real-time monitoring of non-contact, submillimeter-level and full-road-section coverage of the pavement settlement condition under the normal traffic condition of vehicles is realized.

The invention relates to the technical field of computer graphics and deep learning, in particular to a simulation bait automatic coloring method and system based on a 3D model. The method comprises the following steps: acquiring an uncolored 3D model and a reference image, and generating standard data through analysis verification, curvature grid division and image compliance detection; performing color conversion, texture enhancement and multi-scale downsampling on the compliant image to construct an image pyramid; performing multi-level feature extraction and adversarial training optimization based on a pre-trained convolutional neural network and a generative adversarial network, and generating an enhanced color texture map; performing UV expansion, color mapping and normal mapping fusion in combination with the model topology, and constructing an intermediate model with physical rendering attributes; and batch color consistency verification is realized through color histogram comparison, adaptive threshold segmentation and iteration parameter adjustment, and a standard model group is generated. According to the invention, efficient and highly realistic automatic coloring of the simulated bait is realized, and color consistency and rendering quality in batch production are guaranteed.

The invention relates to the technical field of power grid image retrieval, and discloses a power distribution network inspection image rapid retrieval matching method and system, and the method comprises the steps: obtaining a to-be-retrieved inspection image of power distribution network equipment, and extracting the equipment structure features of the inspection image through a hierarchical convolutional network; quantifying a surface texture attenuation index of the power distribution network equipment through fractal dimension based on the equipment structure characteristics; performing mapping relation coupling on the surface texture attenuation index and a space coordinate of an equipment connecting piece to generate a dynamic feature coding sequence containing an equipment structure topological relation; performing time sequence consistency matching on the dynamic feature coding sequence and a pre-constructed reference image library, and aligning an equipment aging track through a dynamic time warping algorithm to generate a similarity sorting result; according to the method, the problems that effective features cannot be extracted during retrieval matching and the retrieval precision is low are solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a target data sample set construction and screening method, device, equipment and medium, and the method comprises the steps: obtaining a target type description, carrying out semantic extension to generate an extension description, generating a reference image sample based on an image generation model, real data units are screened through feature extraction and similarity comparison, and a target data sample set is constructed in combination with knowledge base verification. According to the method, by introducing semantic extension, reference image generation, cross-domain feature comparison and knowledge base consistency verification, real samples highly fitting target type semantics are automatically screened from massive original data, so that the manual annotation dependence is reduced, the illegal sample construction efficiency is improved, and the manual annotation time is shortened. And the training quality and the expansion capability of a subsequent detection model are enhanced.

Disclosed in the present invention are a multi-modal image registration method and system based on iterative optimization. The method comprises: acquiring several low-light images and processing same, in order to obtain a training set and a validation set; constructing a multi-modal image registration neural network model; training the multi-modal image registration neural network model by means of the training set, and using a loss function to calculate loss, in order to obtain a trained multi-modal image registration neural network model; using the validation set to perform iterative optimization on the trained multi-modal image registration neural network model, and determining whether an iteration termination condition is met, in order to obtain an iteratively optimized multi-modal image registration neural network model; and acquiring multi-modal images in a real scene, forming image pairs to be registered, and inputting said image pairs into the iteratively optimized multi-modal image registration neural network model for processing, in order to obtain registered and fused images. The method can improve the robustness of image pairs, which each consist of a reference image and an image to be matched, during a registration process.

The invention relates to the technical field of defect detection, and particularly discloses a high-precision defect detection system based on image difference and threshold processing, which comprises a registration image acquisition module, a fixed mask conversion module, an image difference detection module, a correction difference graph generation module and a defect set output module, the method comprises the following steps: firstly, completing affine registration by shape features, changing a fixed mask into a dynamic mask, and performing edge robustness in the mask to obtain a preprocessed image; local movement difference is carried out on the preprocessed image and the multiple reference images, fusion of a reference domain and a time domain is carried out to generate a difference response image, and candidate areas are extracted in combination with a dynamic mask; estimating and correcting a displacement field in the candidate range, synchronously mapping to obtain a correction difference graph and a correction mask graph, and determining a peripheral rejection area; according to the method, false alarm and missing detection are effectively reduced, and the method is suitable for online high-precision detection.

The invention discloses a non-reference image quality evaluation method and device based on multi-perception feature fusion and dynamic enhancement, and the method comprises the steps: obtaining the quality information of a multi-domain distorted image based on superpixel segmentation and Gaussian kernel filtering texture generation; quality related feature extraction is performed on multi-domain information through a semantic perception module and a distortion perception module, bidirectional modulation is performed on global visual features of an original distorted image through a cross attention mechanism, and dynamic fusion of multiple perception features is realized; through a parallel feature enhancement unit formed by local adaptive filtering of a visual self-attention block and a dynamic residual block, dynamic allocation of perception modes to different content areas is realized; generating a weighted quality score consistent with human visual perception through a weighted dual-path regression device; and outputting a predicted score consistent with the human score from the distorted image through the three sub-networks. According to the method, the problems of insufficient adaptability to complex content of a distorted image and low local distortion sensitivity are effectively solved.

The invention discloses a story-driven role and scene image generation method. The method comprises the steps that a natural language story text input by a user is received and preprocessed; through predefined role description structure constraints, enabling the language understanding and generation model to output a structured role description information structure under template constraints; generating a role image according to the structured role description information text, and extracting image features for consistency control; establishing a mapping table of structured role description information and image feature representation, and realizing the consistency of the appearance of roles in multiple scenes; automatically disassembling the complete story text into a plurality of scene nodes, and generating structured scene description information for each scene; and generating a complete story picture in combination with the scene description information and the role reference diagram. The invention provides a story-driven role and scene image generation method, which is used for automatically generating a story text to a role image and a scene image through semantic understanding, information description structured generation and image consistency management.