158 results about "Image reference" patented technology

The invention relates to the technical field of image segmentation, in particular to an image anaphora segmentation method based on autoregression vertex generation and language structure guidance, and the method comprises the steps: obtaining an input image and a corresponding anaphora query statement; acquiring multi-scale visual features of the input image by using a pre-trained visual encoder; obtaining language features of the anaphora query statement by using a pre-trained language encoder; constructing an image semantic scene graph based on the multi-scale visual features; constructing a query language dependency graph based on the language features; utilizing a graph alignment mechanism and a feature alignment mechanism to realize structured multi-modal fusion of the image semantic scene graph and the query language dependency graph; based on a structured multi-modal fusion result, generating a contour point sequence of the target instance by adopting a language-guided autoregression instance generation method; generating a segmentation mask of the target instance according to the contour point sequence; and outputting the segmentation mask as an image anaphora segmentation result, thereby realizing remarkable performance improvement.

A method includes incorporating information from multi-modal embeddings in an indexed point-cloud data structure with three-dimensional (3D) spatial information for a captured scene. The method also includes performing at least one of querying or retrieving 3D-point-cloud data based on a user input including at least one of natural language or image references. The method also includes enabling global scene understanding and local scene understanding using instance segmentation in conjunction with the multi-modal embeddings.

The invention aims to disclose an image anaphora segmentation method based on multilevel feature fusion and dual-channel information enhancement. The method comprises the following steps: obtaining and extracting visual features to be segmented and language features in an anaphora text; self-adaptively matching visual features of different levels by using language features, and performing spectrum enhancement on the visual features by using Fourier transform; through cross-modal fusion of visual and language features of different hierarchies, multi-level visual features with language perception are constructed; constructing visual language fusion features by using dual-channel information enhancement; and generating a segmentation mask. According to the method, the advantages of a large model, adaptive dynamic matching and feature enhancement are fused, automatic generation of the segmentation mask is realized, the efficiency and accuracy are improved, and the method has wide application and popularization values.

The invention discloses a feature extraction method and system based on heterogeneous multi-modal information fusion, and the method comprises the steps: constructing a spectrum reference system through synchronously collecting the data of an image sensor, a laser radar and an infrared spectrometer carried by an unmanned plane, achieving the unified calibration of image data, and generating a unified image reference through employing a feature field fusion technology; extracting multi-modal characteristic spectral lines, performing complementary analysis to confirm fusion characteristics, and establishing a dynamic weight table by matching working frequency bands; a separation factor is generated based on correlation analysis of a point cloud density field and a modal characteristic spectral line, a correction coefficient is generated in combination with characteristic fluctuation analysis, and a pure visual characteristic signal is identified; a mapping transformation kernel is constructed to realize standardized conversion of feature data, a migration coefficient is generated through spatial positioning analysis and feature intensity analysis, a regulation and control instruction is generated through multi-level feature processing, a weight table is adjusted based on the regulation and control instruction to form closed-loop optimization, a hierarchical feature extraction signal is generated, and deep fusion and intelligent extraction of features are realized.

The invention discloses an intelligent interactive teaching system and method for oral English emotion based on deep learning, and relates to the field of speech evaluation. The system comprises an environment detection and evaluation module, a threshold comparison module, an audio and video collection and processing module, an optimization module and a suggestion output module. Threshold value range comparison and image reference threshold value comparison are carried out on the spoken English intelligent interaction environment evaluation coefficient obtained in the environment detection and evaluation module, and threshold value comparison is carried out on the explosion loss judgment evaluation coefficient of the spoken English intelligent interaction audio obtained in the audio and video collection and processing module. According to the method, the intelligent oral English interaction audio data input by the user are evaluated to obtain the explosion judgment evaluation coefficient of the intelligent oral English interaction audio, and then the text, the audio and the visual oral English error correction suggestion are output according to the explosion judgment evaluation coefficient of the intelligent oral English interaction audio, so that the problem that in the prior art, the user experience is poor is solved. And the accuracy of judging the explosion in the context collaborative pronunciation change is insufficient.

The invention provides a video reference frame management method applying a real-time scene. The method comprises the following steps: updating a reference frame queue: (1) inputting an image Fn to be compressed; (2) generating a reconstructed image F'n from the to-be-compressed image Fn through a coding inverse process; (3) sending the reconstructed image F'n into an image reference frame queue and a traditional reference frame queue at the same time for respective update processing; comprising the following steps of: (1) inputting an image Fn to be compressed; (2) generating a scene ID of the image; (3) comparing the scene ID of the generated image with the scene ID of the scene reference frame queue; (4) judging whether the scene ID of the to-be-compressed image Fn is the same as the scene ID in the scene reference frame queue or not; (5) if yes, taking the scene reference frame images with the same scene ID as reference frames of the to-be-compressed image; and (6) if not, obtaining the reference frame from the traditional reference frame queue. According to the method, the video compression efficiency can be remarkably improved.

The invention belongs to the technical field of computer vision, and discloses an interactive semi-automatic anomaly detection labeling method, system and device and a medium, and the method comprises the steps: obtaining a to-be-detected image of an industrial product, manual click input representing an abnormal region position, and natural language description of a defect type; based on the defect-free image reference set, extracting position constraint features of each image patch of the to-be-detected image, and calculating pixel-level position rapid abnormal residual features; converting the natural language description into a discriminant language feature vector by using a pre-training language model and a discriminant encoder; processing the residual feature through a residual feature branch, and generating a first fusion feature through a cross-modal attention mechanism under the guidance of a language feature vector; processing the original pixel information by combining the pixel information branch with click input to generate a second fusion feature; and fusing the two features and decoding to obtain a pixel-level abnormal annotation mask, thereby realizing high-precision semi-automatic annotation of the abnormal region in the industrial product image.

Provided is an inspection device capable of obtaining accurate coordinates of a chip provided on a substrate. Specifically, this inspection device comprises: a stage on which a target substrate is placed; a camera that images the target substrate to obtain a captured image; and a controller that calculates a target measurement value of target coordinates of a chip with respect to a target origin of the target substrate on the basis of the distance between an image reference position and the chip in the direction in which pixels are arranged and a relative movement set value between the stage and the camera. The controller corrects the target measurement value by using a projective transformation matrix to calculate a target correction value. The controller calculates an alignment measurement value of the alignment coordinates with respect to an alignment origin of an alignment substrate on the basis of the distance between the image reference position and an alignment mark in the direction in which the pixels are arranged, in a state in which the alignment origin is aligned with the image reference position. The controller calculates a parameter of the projective transformation matrix on the basis of the alignment measurement value and a known alignment actual measurement value.

The invention relates to a remote sensing image anaphora segmentation method and system based on multi-mode prompt, and belongs to the technical field of computer vision and remote sensing intelligent interpretation. The method comprises the following steps: S1, data preprocessing: dividing a remote sensing image anaphora segmentation data set; s2, constructing a remote sensing image anaphora segmentation model based on multi-modal prompt, wherein the remote sensing image anaphora segmentation model comprises an image encoder, a space prior module, a text encoder, a fine-grained visual language interaction device and a multi-modal prompt encoder; s3, training a remote sensing image anaphora segmentation model based on multi-modal prompt: for the training of the remote sensing image anaphora segmentation model, performing optimization training on the model by using a loss function combining Focal loss, cross entropy loss and Dice loss; and S4, outputting a segmentation result: inputting a remote sensing image to be segmented into the trained model, and then inputting a model output result into a mask decoder to complete anaphora segmentation of the target object. According to the invention, the interpretation efficiency is improved.

The invention relates to the field of image data processing, and provides a steam boiler gas leakage detection method based on image processing, which comprises the following steps: setting an image acquisition unit, covering a predetermined leakage risk area together, acquiring corresponding reference images under different production working conditions, and establishing an image reference library covering a target area; the method comprises the following steps: dividing a boiler operation time axis into a plurality of production states, calling corresponding reference images according to different states, and carrying out difference, partition comparison and motion detection on the real-time acquired images to obtain suspected leakage cloud cluster areas in the corresponding states; arranging a condensate water indication patch, collecting a patch image, extracting through color, brightness and texture features to form a patch humidity change curve, carrying out multi-source information fusion on the patch humidity change curve, time sequence features and production time states, and outputting a corresponding steam leakage risk level; and generating alarm information according to the steam leakage risk level, and recording and archiving the alarm information in a monitoring and management system for energy consumption analysis and risk tracing.

To provide an inspection apparatus with which accurate coordinates of a chip provided on a board can be obtained.SOLUTION: An inspection apparatus includes: a stage on which a target board is placed; a camera for imaging the target board to obtain a captured image; and a controller which calculates a target measurement value of target coordinates of a chip with respect to a target origin of the target board, on the basis of a distance between an image reference position and the chip in a pixel arrangement direction and a relative movement set value between the stage and the camera. The controller corrects the target measurement value by a projective transformation matrix to calculate a target correction value. The controller calculates an alignment measurement value of alignment coordinates with respect to an alignment origin, on the basis of a distance between the image reference position and an alignment mark which is aligned with the alignment origin of an alignment board. The controller calculates a parameter of the projective transformation matrix on the basis of the alignment measurement value and a known actual alignment measurement value.SELECTED DRAWING: Figure 1

The invention relates to an underwater image enhancement method, system and device combined with attenuation channel correction, and the method comprises the following steps: carrying out the color correction of an image through employing an attenuation channel adaptive correction white balance technology, and recording the image as a first enhancement image I1; de-noising the first enhanced image I1 by using a fast non-local mean algorithm based on a local block, and recording the de-noised first enhanced image I1 as a second enhanced image I2; improving the contrast ratio of the first enhanced image I1 by adopting a self-adaptive stretching method based on the local characteristics of a histogram, and recording the first enhanced image I1 as a third enhanced image I3; and obtaining a final enhanced image by using a bilateral weight fusion strategy in combination with the complementary advantages of the second enhanced image I2 and the third enhanced image I3. The system and the equipment are used for executing the method. According to the method, qualitative and quantitative experiment results on different underwater image reference data sets show that the contrast of the image can be uniformly improved and details and textures of the image can be enhanced while the color of the image is corrected.

The invention discloses an RAG document splitting optimization method and system in the operator field, and belongs to the technical field of large model optimizing.The method comprises the steps that a document is uploaded, and source files and file information are stored through minio and paradeDB respectively; constructing a document loader, automatically selecting a corresponding loader according to a file type to carry out file processing, converting the file into a uniform Markdown text, analyzing a picture in the document, and converting the picture into a base64 format; constructing a picture processor, extracting a picture base64 character string in the text, processing a bitmap and a vector diagram, and converting the bitmap and the vector diagram into a Markdown picture reference format; constructing a document divider; vector conversion; and recalling the text. According to the method, the problem that picture extraction, conversion and processing are complex in the current RAG is solved, vector retrieval, full-text retrieval and mixed retrieval functions can be achieved through one database, and the operation and maintenance cost is reduced.

The embodiment of the invention provides a video image component prediction method and device, and a computer storage medium. The method can comprise the following steps: acquiring a reference value set of a first image component of a current block; determining a plurality of first image component reference values from a reference value set of the first image component; performing first filtering processing on the sample values of the pixel points corresponding to the plurality of first image component reference values to obtain a plurality of filtered first image reference sample values; determining to-be-predicted image component reference values corresponding to the plurality of filtered first image reference sample values; determining parameters of a component linear model according to the plurality of filtered first image reference sample values and the to-be-predicted image component reference value; according to the component linear model, mapping the reconstruction value of the first image component of the current block to obtain a mapping value; and determining a predicted value of the to-be-predicted image component of the current block according to the mapping value.

This application provides a video image component prediction method and apparatus, and a computer storage medium. The method may include: acquiring a reference value set of a first image component of the current block; determining multiple first image component reference values ​​from the reference value set of the first image component; performing a first filtering process on the sample values ​​of the pixels corresponding to the multiple first image component reference values ​​respectively to obtain multiple filtered first image reference sample values; determining the image component reference value to be predicted corresponding to the multiple filtered first image reference sample values; determining the parameters of a component linear model based on the multiple filtered first image reference sample values ​​and the image component reference value to be predicted; performing a mapping process on the reconstructed value of the first image component of the current block according to the component linear model to obtain a mapping value; and determining the predicted value of the image component to be predicted of the current block based on the mapping value.

Techniques for video processing including video coding, video decoding, and video transcoding are described. One example method includes performing a conversion between a video comprising a video picture and a bitstream of the video, the video picture comprising one or more slices. The video picture refers to a picture parameter set, and the picture parameter set conforms to a format rule that specifies that the picture parameter set includes a column width list of N slice columns, where N is an integer. The (N-1) th slice column is present in the video picture, and the width of the (N-1) th slice column is equal to the (N-1) th entry in the explicitly included slice column width list plus a number of codec tree blocks.

The invention discloses a video generation method and device, a model training method and device and a computer program product, and the video generation method comprises the steps: obtaining a target audio and a reference picture used for generating a video, and the reference picture comprises a sound production object; determining a global visual feature of each to-be-generated video frame corresponding to the audio clip according to the clip features of one or more audio clips corresponding to the target audio and the reference image; according to the pronunciation feature of each audio frame of the target audio and the lip feature of the sounding object in the reference picture, determining the lip feature of the sounding object in a to-be-generated video frame corresponding to the audio frame; and generating each video frame according to the lip feature and the global visual feature corresponding to the to-be-generated video frame. Through the scheme provided by the invention, the expression of characters in the generated video is more vivid and natural, the lip action and the audio can be accurately synchronized, and the visual experience of a user is improved.

The invention provides an unmanned aerial vehicle image anaphora detection method based on a mixed granularity expert, and belongs to the field of unmanned aerial vehicle image anaphora detection. The cross-modal sequence is input into multiple layers of stacked coding blocks, each layer of coding block comprises an MHSA layer, an MoG layer and an FFN layer which are connected in sequence, and after multi-layer iteration, a cross-modal scale comprehensive memory is output; the cross-modal scale comprehensive memory and the target query are input into a multi-layer stacked scale comprehensive decoder, each layer of decoder comprises an MHSA layer, an MHCA layer, an MoG layer and an FFN layer which are connected in sequence, and coarse query representation is output; the coarse query representation and the cross-modal scale comprehensive memory are input into a multi-layer stacked scale sensitive decoder, each layer of decoder comprises an MHSA layer, an MMCA layer and an FFN layer which are connected in sequence, after multi-layer iteration, the output fine-grained query representation enters a regression prediction head, and an anaphora detection result is obtained; the invention further provides a detection system. The problem that an existing method is low in detection precision in an aerial photography scene is solved.

The invention relates to an image generation method and device, electronic equipment and a storage medium. The method comprises the steps that a preset page comprising at least one kind of image reference information is displayed; in response to a reference image selection operation for first image reference information, determining at least one reference image corresponding to the first image reference information, the first image reference information being any one piece of image reference information in the at least one piece of image reference information; in response to the image generation operation, displaying at least one generated image, the at least one generated image being generated based on target reference information corresponding to each of a plurality of target reference images, the plurality of target reference images being reference images determined when the image generation operation is triggered, the plurality of target reference images correspond to one or more pieces of image reference information in the at least one piece of image reference information. According to the embodiment of the invention, a user can flexibly and conveniently select the image reference information and the corresponding reference image, so that the image generation effect is improved.

Arrangements are disclosed that enable reliable and accurate referencing of image distance, orientation, and / or color. According to exemplary embodiments, an image reference marker is arranged on or near an area to be imaged. A display shows a live view of the area with a superimposed alignment pattern, while a user positions the image capture device so that the image of the marker is aligned with the alignment pattern. Once satisfactory alignment is achieved, an image of the area can be captured manually or automatically.

Arrangements are disclosed which provide reliable and accurate image ranging, orientation, and / or color referencing. In exemplary implementations, an image reference marker is arranged at or proximate to an area to be imaged. A display shows a live view of the area with a matching pattern superimposed thereon as a user positions the image capture device to align the image of the marker with the matching pattern. Once satisfactory alignment is achieved, an image of the area can be captured manually or automatically.

A method includes incorporating information from multi-modal embeddings in an indexed point-cloud data structure with three-dimensional (3D) spatial information for a captured scene. The method also includes performing at least one of querying or retrieving 3D-point-cloud data based on a user input including at least one of natural language or image references. The method also includes enabling global scene understanding and local scene understanding using instance segmentation in conjunction with the multi-modal embeddings.

The invention provides a method and a device for modifying a picture through computing power of an intelligent computing center. The modification method comprises the following steps: S1, receiving a to-be-modified picture, a reference attached drawing and a modification intention description text; s2, the computing power of the intelligent computing center is called to execute the step S21, the step S22 and the step S23 in parallel, and the step S21 comprises the steps that semantic understanding is conducted on the modification intention description text to obtain a modification target subject; determining a target area; the step S22 comprises the following steps: respectively carrying out reverse coding on the modification intention description text and the reference drawing, and fusing the coding to obtain a target prompt code; the step S23 comprises the following steps: determining a pre-trained multi-modal large model; and S3, modifying the to-be-modified picture according to the target region and the target prompt code by adopting a multi-modal large model. According to the method, high-accuracy modification can be performed according to the modification intention of the user, the execution efficiency is improved, and the user experience is improved.

The invention discloses a compression ultrafast imaging device based on instantaneous image reference. The compression ultrafast imaging device is composed of a dynamic scene acquisition system, an instantaneous imaging system, a coding compression imaging system and a control and processing system. According to the method, dynamic scenes including coding compression imaging and instantaneous imaging are recorded simultaneously through two different imaging models, and collected bimodal data are processed and reconstructed by using a bimodal information fusion image reconstruction algorithm based on a non-training neural network, so that a high-fidelity dynamic scene is recovered. According to the method, the image quality of compression ultrafast imaging can be effectively improved, the defect of a traditional compression ultrafast imaging technology in the aspect of image fidelity is overcome, the temporal-spatial resolution of an imaging system is remarkably improved, and the method is suitable for fine observation of ultrafast dynamic phenomena and particularly has important application value in the fields of scientific research, industrial application and military affairs.

A bonding apparatus and method for correcting movement amount of bonding head are provided. The bonding apparatus performs: mark correction of imaging a reference mark using a position detection camera at every prescribed timing and correcting the amount of movement of a bonding head on the basis of the amount of positional deviation between the position of the imaged reference mark and a reference position of the position detection camera; and actual position correction of detecting the actual bonding position of a semiconductor element after bonding using the position detection camera at every timing when the cumulative value of the amounts of correction of the mark correction from the previous actual position correction exceeds a prescribed first threshold and correcting the amount of movement of the bonding head on the basis of the amount of positional deviation between the detected actual bonding position and a target bonding position.

The present application relates to a scene text detection method, device and computer equipment based on VMamba framework and channel-spatial attention mechanism. The method comprises: obtaining a scene image reference sample data set and an enhanced sample data set containing text; constructing a deformable VMamba feature encoder based on the VMamba framework using deformable convolution to obtain multi-scale text instance feature maps corresponding to each scene image; obtaining a balanced feature map using interpolation and maximum pooling methods; constructing a GASM module based on the channel-spatial attention mechanism to obtain pixel-by-pixel enhanced text instance features; constructing a Fourier feature decoder to obtain a reconstructed text instance contour; and using a loss function to train a scene text detection model with an enhanced sample data set as input and a text instance contour as output. The present method can realize scene text detection with a global receptive field, fast convergence and precise positioning.

The embodiment of the invention provides a video image component prediction method and device, and a computer storage medium. The method can comprise the following steps: acquiring a reference value set of a first image component of a current block; determining a plurality of first image component reference values from a reference value set of the first image component; performing first filtering processing on the sample values of the pixel points corresponding to the plurality of first image component reference values to obtain a plurality of filtered first image reference sample values; determining to-be-predicted image component reference values corresponding to the plurality of filtered first image reference sample values; determining parameters of a component linear model according to the plurality of filtered first image reference sample values and the to-be-predicted image component reference value; according to the component linear model, mapping the reconstruction value of the first image component of the current block to obtain a mapping value; and determining a predicted value of the to-be-predicted image component of the current block according to the mapping value.

The present invention relates to the field of semiconductors, and discloses a detection system and method for semiconductor workpiece production, comprising: an image acquisition module, which is used to be deployed on a production line to obtain image data before, during and after raw material preparation, establish corresponding standard image reference systems according to different products, and compare the currently acquired image data with the data in the standard image reference system in real time to determine whether there is an abnormality; a working condition acquisition module, which is used to obtain the operating parameters of each workpiece production equipment, and regularly receive and convert the equipment working condition parameters according to a preset acquisition cycle to determine whether there is an abnormality; through real-time image detection and equipment detection, after one party has detected a problem, the other party is quickly called for verification to improve the accuracy of the detection and prevent misjudgment of abnormalities. When the same problem occurs, it can be indexed in the database and quickly associated with a suitable solution, thereby alleviating computing pressure.

The invention discloses a test device for measuring thrust of a small engine, and relates to the technical field of visual inspection in the automobile industry. In order to solve the technical problems in the prior art that the detection precision of the conventional method on the hole shrinkage defect is low and the detection reliability is influenced, the technical scheme provided by the invention is as follows: the device comprises an image reference selection module for selecting a plurality of images with different angles as reference images; the image restoration module is used for carrying out denoising processing on the reference image; the perspective transformation module is used for setting an area where diameter shrinkage may occur according to a design drawing and processing technology information of the sheet metal part and realizing automatic alignment of the detection area; the extraction module is used for carrying out multi-scale extraction and frequency domain analysis on the image; and the defect decision-making module is used for carrying out final decision-making on the hole shrinkage defect based on the gradient intensity, the area and the shape characteristics of the hole shrinkage area. The device is suitable for detecting the hole shrinkage of the sheet metal part.

A video encoding method using a plurality of reference pictures includes: selecting whether or not a resilient picture referencing scheme is to be used for encoding video; writing a parameter indicating the selection into a header of an encoded video bitstream; and encoding a picture using inter-picture prediction using a result of the selection.