25593 results about "Image pair" patented technology

The invention discloses a decision-making method and model for off-line reinforcement learning and continuous on-line fine tuning, and the method comprises the steps: obtaining original text-image pair data and historical track data, and obtaining an enhanced state sequence through feature matching verification and standardization processing; performing sequence prediction by using a multi-level attention mechanism, and obtaining pre-training model parameters through knowledge distillation and task adaptability enhancement; on the basis of stability evaluation and plasticity control, online knowledge migration is carried out on the model, and optimized model parameters are obtained by combining experience playback optimization; and finally, performing multi-dimensional performance evaluation and resource optimization. According to the method, through multi-modal feature fusion, dynamic knowledge migration and an adaptive optimization mechanism, the problems of insufficient feature representation, disastrous forgetting, low optimization efficiency and the like are effectively solved, and the learning efficiency and generalization ability of the model are improved.

Using a pre-trained and fixed Vision Transformer (ViT) model as an external semantic prior, a generator is trained given only a single structure / appearance image pair as input. Given two input images, a source structure image and a target appearance image, a new image is generated by the generator in which the structure of the source image is preserved, while the visual appearance of the target image is transferred in a semantically aware manner, so that objects in the structure image are “painted” with the visual appearance of semantically related objects in the appearance image. A self-supervised, pre-trained ViT model, such as a DINO-VIT model, is leveraged as an external semantic prior, allowing for training of the generator only on a single input image pair, without any additional information (e.g., segmentation / correspondences), and without adversarial training. The method may generate high quality results in high resolution (e.g., HD).

Disclosed in the present invention is a salient contour matching-based method for target measurement in a severe imaging environment. The method specifically comprises: (1) acquiring a binocular image of a target; (2) establishing a global-local joint constraint-based background light estimation model, and removing a scattering effect of a medium in an imaging environment to obtain a restored left eye image and a restored right eye image; (3) learning an original image, and on the basis of a residual between a network reconstructed image and the original image, obtaining target localization prediction maps of the left eye image and the right eye image; and (4) respectively extracting contour lines of the target in the left eye image and the right eye image, constructing feature matching descriptors of contour points, performing stereo matching on the two sets of contour lines by minimizing matching cost, and performing three-dimensional reconstruction on the contour lines in light of calibrated intrinsic and extrinsic parameters to complete the measurement of a key size. According to the present invention, the key sizes of different targets in a severe environment can be accurately measured, thereby providing an effective solution for the problem of measuring the sizes of targets in a severe environment.

The invention discloses a stainless steel surface defect detection method based on machine vision, and relates to the field of material defect detection.The stainless steel surface defect detection method comprises the steps that stainless steel surface images are collected, the collected multispectral surface images are subjected to image fusion, and a multispectral composite image is generated; performing detail enhancement on the low-resolution image through a super-resolution reconstruction algorithm to obtain a high-resolution image set; positioning a defect area position from the high-resolution image set through a defect detection algorithm, and obtaining a defect position data set; extracting a historical image defect feature set and a real-time image defect feature set from the defect position data set, and constructing a defect classification model according to the historical image defect feature set; inputting the real-time image defect feature set into a defect classification model, and performing defect classification and evaluation to obtain a defect classification data set; and generating and distributing a defect detection report. By combining the multispectral imaging algorithm, the super-resolution reconstruction algorithm and the defect classification model, the automation, precision and efficiency of stainless steel surface defect detection are improved.

The invention provides an industrial part defect detection method, which comprises the following steps: acquiring an input industrial part burr defect image, extracting the shape, size and distribution position characteristics of burrs in the image to obtain a burr characteristic pattern, segmenting a burr defect region according to the edge roughness and color difference in the burr characteristic pattern, and generating a binary burr defect mask; performing morphological operation on the binarized burr defect mask, eliminating isolated noisy points through expansion and corrosion, connecting adjacent burr areas, segmenting the processed burr mask, and extracting independent burr defect areas according to the three-dimensional contour and surface texture features of burrs; performing image enhancement processing on the preliminary verification code character matrix, adjusting the color contrast of characters to highlight the thickness of character strokes, and generating a verification code image; and carrying out compression and format conversion on the generated verification code image to obtain a final industrial part burr defect verification code which is used for defect detection of workpiece parts.

A multi-scale cascaded hourglass depth map completion method guided by RGB image is presented. [Solution] The multi-scale cascade hourglass depth map completion method includes step S1 of obtaining a sparse depth map to be completed and a corresponding RGB color guide image, performing channel-dimensional pre-processing on the sparse depth map and the RGB color guide image and connecting them in series to obtain a target RGB_D image to be processed, step S2 of inputting the target RGB_D image to be processed into a trained depth map completion network model including an early fusion encoder, a sparse depth map teaching module, a multi-scale hourglass completion module and an optimization reinforcement module, and step S3 of obtaining the output of the optimization reinforcement module as a completed dense depth map.

The invention discloses a rolled metal surface defect automatic labeling method based on a multi-task self-adaptive model, which comprises the following steps: S1, collecting a rolled metal surface image, preprocessing and enhancing the rolled metal surface image, performing data labeling, and constructing a rolled metal surface defect data set; s2, constructing a multi-task adaptive model, and training the multi-task adaptive model by using the data set to obtain an automatic defect labeling model; and S3, inputting a to-be-labeled metal surface image into the defect automatic labeling model, and outputting a surface defect labeling result by executing the target detection task. According to the method, a multi-task learning framework is combined, joint training of target detection and instance segmentation tasks of metal surface defects in the rolling technology is achieved, the target detection task is focused in the reasoning stage, the labeling efficiency is improved, the labor cost is remarkably reduced, and the labeling efficiency is improved. The method can be widely applied to industrial defect detection and data automatic labeling scenes of rolling process metal, and is suitable for defect image automatic labeling tasks in a complex industrial environment.

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention provides a smoke and fire detection method and system based on deep reinforcement learning and electronic equipment, and relates to the technical field of fire fighting. The method comprises the following steps: acquiring thermal imaging data of a forest area through a thermal imager, obtaining temperature distribution conditions of different positions, and determining position coordinates, the number and specific temperature values of temperature abnormal points for an area with abnormal temperature rise; if the temperature value of the temperature abnormal point exceeds a preset temperature threshold value, judging that the temperature abnormal point is a suspected fire point, and collecting a visible light video image of the area through a visible light camera according to the position of the suspected fire point; smog detection processing is carried out on the collected visible light video image, whether smog exists in the image is judged by analyzing the color, texture, motion and other characteristics of the image, and the concentration, morphological characteristics and diffusion speed of the smog are obtained; the meteorological parameters such as the wind speed and the wind direction of a fire occurrence area are obtained through the meteorological sensor, the meteorological parameters are input into the fire spreading model, and the spreading direction and the spreading range of the fire under the current wind field condition are simulated.

The invention relates to the technical field of wire disaster prevention analysis, and discloses an overhead transmission line icing monitoring and disaster prevention method and system. The method comprises the following steps: acquiring meteorological parameters, stress data and icing images through distributed sensing nodes; performing time-space synchronization processing on the data; calculating icing state information; evaluating an icing risk level; selectively starting the deicing device; and storing the data and extracting a law to optimize a disaster prevention plan. According to the invention, accurate monitoring of the icing state, scientific assessment of risks, intelligent selection of deicing measures and continuous optimization of disaster prevention strategies are realized, so that the ability of the power grid to deal with icing disasters is significantly improved, and economic losses and potential safety hazards are reduced.

Disclosed herein are system, apparatus, device, method and / or computer program product embodiments, and / or combinations and sub-combinations thereof for blending multiple images of a financial instrument (e.g., check) into a single blended image to mitigate potential image errors. The method generates a confidence score for each of the plurality of images of the financial instrument, selects, based on the confidence score, a number of images of the financial instrument, ranks the number of images, arranging them from a highest to lowest confidence score, and assigns a weighting value, such as transparency, to each image of the selected number of images of the financial instrument, based on a respective ranking. The method builds a blended image by blending pixel content of common pixels, as modified by a respective weighting value, from the number of images and communicates the blended image to a remote deposit server.

A method for mapping a three-dimensional (3D) point cloud model based on deep learning includes: extracting features of image data by using a convolutional neural network; extracting features of point cloud data by using a PointNet point cloud processing network, and constructing a 3D model of the point cloud data by using a triangular mesh; aligning the 3D model and an image spatially and temporally; performing projection mapping on the aligned 3D model and image, and superimposing location information in the point cloud data onto texture information of the image to obtain a first fused image; fusing the features of the point cloud data and the features of the image data to obtain a second fused image with a fused feature; and superimposing the first fused image onto the second fused image, and obtaining a superimposition weight by using the convolutional neural network, to generate a mapped 3D model.

The invention relates to the technical field of chip testing, in particular to a chip testing method and a chip testing system. The method comprises the following steps: acquiring a thermal imaging image of a working state of a chip; global color temperature offset optimization is carried out on the thermal imaging image of the working state of the chip, and spatial heat distribution mining is carried out, so that a chip heat distribution diagram is constructed; performing multi-moment heat distribution tracking processing on the chip heat distribution diagram, performing dynamic heat distribution evolution fitting, and constructing a dynamic heat distribution trend evolution diagram; acquiring a high-definition image of a to-be-tested chip; performing chip assembly visual monitoring on the high-definition image of the to-be-tested chip, performing heat distribution trend rendering based on the dynamic heat distribution trend evolution graph, and constructing a chip assembly heat distribution trend rendering model. According to the invention, through limit working condition test analysis, the evaluation efficiency and accuracy of the chip performance are improved.

The invention provides a toll vehicle type identification method and system based on expressway passing images, and the method comprises the steps: obtaining an image set of passing vehicles, carrying out the multi-scale brightness compensation processing of a vehicle side contour image, generating a side feature enhancement image, carrying out the contour sharpening processing of a vehicle top contour image, and obtaining a vehicle top contour image; and generating a top feature enhanced image. And performing dual-channel feature fusion on the side feature enhanced image and the top feature enhanced image, generating a vehicle multi-dimensional fusion feature map, inputting the vehicle multi-dimensional fusion feature map into a pre-trained vehicle type matching model, and obtaining a vehicle type feature matching result output by the vehicle type matching model. And performing similarity comparison according to a vehicle model feature matching result and a pre-stored vehicle model feature library, determining a charging vehicle model category corresponding to the passing vehicle, and generating a vehicle model identification mark. According to the method, the robustness of vehicle type recognition in a complex illumination environment can be improved, and meanwhile, the accuracy of vehicle type classification and the charging and passing efficiency are improved by fusing dual-view feature enhancement and a multi-dimensional feature matching mechanism.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention relates to an image processing technology, and discloses an efficient panoramic image splicing method and system based on multi-view fusion, and the method comprises the steps: collecting a plurality of images from different views; performing multi-scale feature extraction on each image, generating a feature descriptor for each extracted feature point, and matching a corresponding feature point pair; performing multi-view geometric constraint screening on the feature point pairs; according to the screened feature point pairs, estimating a homography matrix between adjacent images, and carrying out global optimization on the homography matrix; aligning all the images into the same coordinate system; determining an overlapping region between adjacent images; and according to the pixel information in the overlapping areas, fusing the overlapping areas by adopting a self-adaptive weighted fusion algorithm so as to splice the plurality of images into a panoramic image. The invention further discloses a control device and a computer readable storage medium. The invention aims to improve the efficiency and accuracy of generating the multi-view fused panoramic image.

The invention relates to the technical field of intelligent vending, solves the problem that in the prior art, commodity identification cannot be accurately carried out in a multi-target scene, and provides a multi-target commodity identification method, device and system based on multi-modal data processing. The method comprises the following steps: acquiring multiple frames of real-time images in a commodity transaction scene; performing preprocessing and label information extraction on the real-time image, and determining character information corresponding to the target image and the commodity label; performing instance segmentation on the target image, and determining commodity position information; performing feature extraction on the target image, and determining commodity image feature information; according to pre-collected multi-source privatized data in an intelligent vending scene, performing fine adjustment and optimization processing on the open-source multi-modal visual language model to obtain a multi-modal large model; and inputting the commodity image feature information and the text information into the multi-modal large model for information fusion, and determining a commodity target identification result. According to the invention, commodity identification can be accurately carried out in a multi-target scene.

A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment. Upon receiving a user request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live stream of image data of a field of view of at least one camera, wherein the live stream includes imagery of at least a portion of the financial instrument. The method continues by extracting in real-time, based on the formation of byte array objects from the live stream of image data, data fields from a ranked sequence of imagery to be processed by an optical character recognition (OCR) program resident on the client device. The OCR process extracts one or more data fields from of the financial instrument that are communicated to a remote deposit server to complete the electronic deposit.

The invention relates to the field of image recognition, in particular to an automatic solid waste sorting method and device applying machine vision. The method comprises the following steps that monitoring images of to-be-sorted areas in multiple directions are obtained; performing spatial geometric view angle deviation correction on the monitoring images of the to-be-sorted areas in the multiple directions, performing spatial panoramic image optimization, and constructing a panoramic image of the sorting area; performing frame-by-frame solid waste visual positioning scanning on the panoramic image of the sorting area, and performing three-dimensional morphological structure analysis so as to generate a three-dimensional morphological structure representation of each solid waste; performing solid type classification decision according to the three-dimensional morphological structure representation of each solid waste, and performing bounding box coding mapping to obtain a coding mapping bounding box of each solid waste; and performing equipment action sequence simulation on the coding mapping bounding box of each solid waste, and constructing a fine tuning action sequence. Efficient solid waste recognition is achieved, and the sorting efficiency is improved.

The invention discloses a power transmission line inspection image denoising method and system, and the method comprises the following steps: S1, obtaining an original image of an insulator, adding noise to the original image, and constructing an insulator noise image data set; s2, improvement is carried out based on an SADNet denoising network model, a Mix module, a CSM module and a CCAM module are introduced, and an SADNet-S denoising network model is constructed; s3, training the SADNet-S denoising network model by using the insulator noise image data set to obtain an optimal training weight; and S4, loading the optimal training weight into the SADNet-S denoising network model, and inputting the noise image of the insulator to be detected into the SADNet-S denoising network model to obtain a denoised insulator image. The invention provides an improved SADNet-S denoising network model based on multi-scale feature extraction, dynamic adaptive filtering and an attention mechanism, and by adopting the SADNet-S denoising network model, the strong generalization and robustness of the SADNet-S denoising network model are brought into full play, so that efficient image denoising is realized, and the target detection precision is remarkably improved.

The invention discloses a quartzite surface defect detection method, system and device, and relates to the technical field of image processing, and the method comprises the following steps: fixing a to-be-detected quartzite on a detection platform, and carrying out preprocessing; constructing a double-path polarization imaging light path; polarization parameter optimization is carried out based on the optical anisotropy characteristic of quartz stone, so that a polarization state difference is generated between a defect area and a normal area; under the condition of polarization parameter optimization, synchronously acquiring a first polarization image and a second polarization image through a double-path polarization imaging light path; performing polarization difference calculation on the first polarization image and the second polarization image to generate a polarization difference image; and carrying out contrast enhancement processing on the polarization difference image, identifying a defect area, carrying out defect classification, and outputting a quartz stone surface defect detection result. By optimizing polarization imaging parameter configuration and combining image processing, high-precision detection and intelligent classification of quartz stone surface defects are achieved, and the automation level and reliability of detection are improved.

Embodiments of the present invention provide a server, a display device, and a digital human processing method. The method comprises: receiving image data; inputting the image data into a shared encoder to obtain a first depth encoding feature; inputting the first depth encoding feature into a segmentation decoder to obtain a segmentation mask; inputting the segmentation mask and the depth encoding feature into a guided flow architecture module to obtain a refined feature; inputting the depth encoding feature and the refined feature into an image matting decoder to obtain a face image having undergone image matting; performing image quality enhancement processing on said face image to obtain a face enhanced image; determining face keypoint data; and generating standard data. According to the embodiments of the present invention, a segmentation mask is obtained by means of a segmentation decoder, and the segmentation mask provides, for an image matting decoder, semantic information obtained by means of segmentation, such that the accuracy of an image matting result is higher, the portrait edge segmentation is cleaner, the quality and precision of standard data are improved, and human assistance information is not needed.

The present invention provides systems, methods, and devices for electroporation-based therapies (EBTs). Embodiments provide patient-specific treatment protocols derived by the numerical modeling of 3D reconstructions of target tissue from images taken of the tissue, and optionally accounting for one or more of physical constraints or dynamic tissue properties. The present invention further relates to systems, methods, and devices for delivering bipolar electric pulses for irreversible electroporation exhibiting reduced or no damage to tissue typically associated with an EBT-induced excessive charge delivered to the tissue.

The invention discloses a bridge disease detection method and system based on an improved multi-mode visual language model, and the method comprises the steps: obtaining a historical bridge original image which comprises a bridge cantilever casting section, a cast-in-place section, a web, a diaphragm plate, a closure section and a concrete box girder; constructing an improved multi-modal visual language target detection model; based on the bridge original image, training the improved multi-modal visual language target detection model to obtain a trained improved multi-modal visual language target detection model as a bridge disease detection model; obtaining a to-be-detected bridge image, and dividing the to-be-detected bridge image into a plurality of overlapped image blocks based on an SAHI sliding window mechanism; detecting each image block in a local region based on the bridge disease detection model to obtain a region detection result; and on the basis of the detection result of each region, generating a global detection result in a splicing and fusion mode.

The invention discloses a soil fertility intelligent assessment method and system based on image recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining land image information of a to-be-assessed region, the land image information comprising a multispectral image, a thermal imaging image and an RGB image of the to-be-assessed region; carrying out image preprocessing on the land image information, and carrying out vegetation coverage and health assessment according to the multispectral image: speculating soil characteristics according to the RGB image: speculating the humidity change of a soil surface layer according to the temperature distribution of a thermal imaging image, and identifying a drought region and a region with sufficient moisture; integrating the high-resolution texture features of the RGB image, the spectral information of the multispectral image and the speculation result of the temperature distribution of the thermal imaging image to generate a comprehensive analysis result; and classifying soil types and evaluating fertility grades according to a comprehensive analysis result. The problem that in the prior art, in intelligent soil fertility evaluation, workers need to carry out on-site sampling examination is solved, and the technical effect of improving the soil fertility evaluation efficiency is achieved.

The invention provides an engineering construction defect automatic detection and classification method based on deep learning, and the method comprises the steps: obtaining a welding seam surface image through the shooting of an unmanned plane, and carrying out the denoising and illumination normalization processing of the welding seam surface image, and obtaining a standardized image; welding seam surface texture features are extracted from the standardized image, a convolutional neural network is adopted to analyze the spatial distribution characteristics of textures, and vectorization processing is carried out to obtain texture feature vectors; segmenting a weld surface corresponding to abnormal region distribution by adopting a region growing algorithm, and analyzing pore and weld discontinuity in combination with the texture feature vector to obtain a defect candidate region; performing threshold division on the sizes and the numbers of the defects according to the defect types and the feature vectors of the candidate regions to obtain a severity grading result of each type of defects; and severity features are extracted from a grading result, and a Bayesian network is adopted to fuse texture feature vectors and defect type labels to obtain a welding quality evaluation score.

The invention discloses an engineering drawing label identification method and system based on multi-modal information extraction, and the method comprises the steps: obtaining an engineering drawing image, carrying out the preprocessing of the image, and setting a data structure Schema for controlling an extraction target and a relation mode; detecting a drawing label area in the drawing through the trained drawing label detection model to obtain a bounding box coordinate of each drawing label, mapping the detected bounding box coordinates back to a coordinate system of the original image, and cutting out a corresponding drawing label image from the original image; performing text recognition on the cut-out signature image, and extracting text content and corresponding textbox coordinate information; and inputting the clipped signature image and the text recognition result into a trained multi-modal information extraction model, performing information extraction according to a set Schema, and outputting a structured extraction result. According to the method, the extraction target can be flexibly defined, unified extraction of the two-tuples and the three-tuples is supported, and the extraction accuracy is high.

The invention discloses an MURA defect detection method based on background reconstruction and related equipment, and relates to the field of image processing. The method comprises the following steps: acquiring an initial image displayed by a display panel; performing discrete cosine transform processing on the initial image to extract low-frequency background components; denoising the low-frequency background components based on a singular value decomposition algorithm to obtain main background features; performing fitting reconstruction on the main background features through a polynomial surface fitting algorithm to obtain a background reconstruction image; performing contrast enhancement processing on the background reconstruction image to obtain a background enhancement image; subtracting the background enhancement image from the initial image to obtain a difference image; determining an MURA defect area to be detected according to the difference image; and performing identification processing on the MURA defect to-be-detected area through a preset MURA defect detection model to obtain an MURA defect detection result of the display panel. According to the technical scheme, the MURA defect detection precision can be improved.

The invention discloses a multi-modal entity and relation extraction method and system based on cross-modal alignment and fusion, and the method comprises the steps: carrying out the processing and coding of an input text and an image, and obtaining a plurality of types of image and text features; performing feature alignment on the fine-grained and coarse-grained text features and the pixel-level image representation by taking the semantic representation of the image as an anchor point, and mapping the image and the text features to the same semantic space; performing multi-granularity feature fusion through text-guided dynamic gating aggregation, visual prefix cross-modal fusion and cross-modal image-text matching, modeling association between noun phrases and image objects in a text while increasing feature complementarity, and obtaining multi-granularity multi-modal feature representation; fusing multi-granularity multi-modal features through entity guidance attention gating, and gathering visual information related to a text entity to obtain final multi-modal fusion representation; according to the multi-modal fusion representation, task prediction of multi-modal named entity recognition and multi-modal relation extraction is carried out.

The invention discloses an unmanned aerial vehicle multi-modal feature fusion target tracking method and system based on natural language description, belongs to the technical field of computer vision and image processing, and solves the problem that in the prior art, when the quality of an image collected by an unmanned aerial vehicle is poor or image features are not obvious, the target tracking capability and the long-time tracking capability are poor. Natural language description is carried out on a traffic accident scene in an image of an unmanned aerial vehicle visual angle, and a language prompt is obtained; constructing a scene-context feature pyramid network to perform context information enhancement processing on the image of the view angle of the unmanned aerial vehicle to obtain a feature-enhanced image; respectively carrying out visual coding and language coding on the enhanced image and language prompt to obtain visual features and language feature vectors, and carrying out visual-language bimodal feature local alignment; and fully fusing the obtained aligned new language features with the visual features to obtain multi-modal features for target tracking. The method is used for multi-modal feature fusion target tracking of the unmanned aerial vehicle.