3557 results about "Image area" patented technology

The invention provides a multi-modal intelligent question-answering system based on a large model and a construction method and device, and the method comprises the steps: carrying out the region recognition of a PDF document, and obtaining a table region picture, a text region picture and / or an image region picture; the Word document is analyzed, paragraph content of the Word document is obtained, and the paragraph content is composed of the first text, table data and / or Word pictures; constructing a knowledge base; wherein the knowledge base comprises vector representation of document slices; the document slice is determined based on the table area picture, the character area picture and / or the image area picture, or the document slice is determined based on the paragraph content and the corresponding chapter information thereof. Visibly, according to the method, layout recognition can be carried out on the PDF document, catalog chapter recognition can be carried out on the Word document, the knowledge base is constructed based on the layout recognition, the semantic integrity and coherence of multi-modal text question and answer recall can be improved, and the answer accuracy is improved.

The invention relates to the technical field of multi-image fusion recognition, in particular to a data fusion method and system for CCD visual detection, and the method comprises the following steps: obtaining a horizontal pixel row calculation gradient construction trend sequence, repairing an edge fracture to generate an integrity index, extracting a gray value to detect feature mutation, and distributing fusion weights to establish a mapping relation. And executing image fusion and balancing the contrast to generate a fusion matrix result. According to the method, the fracture edge region is identified, interpolation compensation is executed, the structural similarity index of the local gray sequence in the image overlapping region and feature direction mutation detection are combined, accurate identification of the edge matching result is guided, and fusion weight factor mapping corresponding to signal-to-noise ratio distribution is introduced; according to the method, the distribution relation between the pixels in the region and the credible weight is effectively established, the edge transition among the multi-source images is more natural through Poisson constraint and contrast balance adjustment of the fusion region, and the structural fidelity and the judgment stability of the fusion image are remarkably enhanced.

The invention discloses a data compression transmission method and system applied to a ferry inspection image, and the method comprises the steps: collecting and obtaining the ferry inspection image in real time, recognizing a key inspection target region in the image, and carrying out the segmentation and partitioning of the image; compressing the key inspection target area based on lossless compression coding; the quantization step size is dynamically adjusted by comparing statistical variances of background pixels between continuous frames, and lossy compression coding is carried out on a background area; based on the boundary distance between the key inspection target area and the background area, adaptive compression coding is carried out on the transition area; constructing a hierarchical data packet; and constructing a data transmission optimization model, dynamically allocating data transmission links, and obtaining a transmission scheme with the highest total transmission. The method has the advantages that efficient data compression transmission is realized by accurately segmenting the image area and adopting a lossless, lossy and adaptive compression technology, the overall transmission efficiency is improved through intelligent transmission optimization, and the definition and real-time performance of the inspection image are ensured.

Disclosed herein are methods, systems, and computer-readable media for regenerating a region of an image with a machine learning model based on a text input. Disclosed embodiments involve accessing a digital input image. Disclosed embodiments involve generating a masked image by removing a masked region from the input image. Disclosed embodiments involve accessing a text input corresponding to an image enhancement prompt. Disclosed embodiments include providing at least one of the input image, the masked region, or the text input to a machine learning model configured to generate an enhanced image. Disclosed embodiments involve generating, with the machine learning model, the enhanced image based on at least one of the input image, the masked region, or the text input.

The invention relates to the technical field of thermal power production, artificial intelligence and time sequence analysis, in particular to a measuring point time sequence anomaly analysis method and system based on a multi-modal large model. Calculating a cross-modal feature similarity weight based on a self-attention mechanism to obtain a fusion feature vector; comparing the fusion feature vector with the normal mode feature representation through a self-supervised learning algorithm to realize anomaly detection, wherein a result comprises anomaly time, a fault image area and a text keyword; and a result is input into a multi-modal large model, cross-modal attention mechanism association data is utilized, fault reasoning and causal relationship analysis are carried out, a visual diagnosis report is generated, and then the accuracy of measuring point screening and time sequence anomaly analysis is improved.

The invention discloses a foam concrete image processing method based on sub-pixel edge reconstruction, and particularly relates to the field of foam concrete image processing.The method comprises the steps that an image of foam concrete is obtained, and a boundary response feature set containing edge direction information, edge gradient change information and local intensity comparison information is extracted; performing edge structure classification on the image region based on boundary response feature set combined threshold judgment; and aiming at the image region which is judged to be the local direction convergence mutation region, identifying a cross mutation point set by fitting a direction tensor convergence trend, and executing directionally-guided corrosion and expansion image processing. By constructing a structure judgment variable based on image boundary response characteristics and guiding a corrosion expansion path and a vein backtracking path to execute sub-pixel-level aperture boundary separation, image structure processing and contour reconstruction of a porous adhesion area are realized.

The invention relates to the field of image processing, and particularly provides a chip packaging defect identification method and system based on machine vision, and the method comprises the steps: determining a to-be-identified part in a chip packaging image, and extracting a corresponding related image region; detecting to obtain interested image features in each extracted related image region, and determining interested image implicit representation under each channel; for each defect evaluation channel, determining a reference defect implicit representation of the to-be-identified part under the corresponding defect evaluation channel; according to the reference defect implicit representation of the to-be-identified part under each defect evaluation channel in the plurality of defect evaluation channels, determining defect evaluation parameter implicit representations respectively corresponding to the plurality of defect evaluation parameters; and obtaining a to-be-identified part defect identification result corresponding to the to-be-identified part according to the defect evaluation parameter implicit representation. According to the invention, each interested image feature in the chip packaging image can be integrated and defect identification can be carried out so that identification precision can be enhanced.

The invention is suitable for the technical field of image data processing, and provides a printing deviation rectifying method of a roller printing machine and the roller printing machine, and the printing deviation rectifying method of the roller printing machine comprises the steps that multiple color layers and standard printing object surface images corresponding to the multiple color layers are obtained; then, a printed object surface image is collected in real time, and a surface area is extracted and compared with a standard printing image; the method comprises the following steps: calculating a pixel difference value between image areas, counting the number of pixels with the difference value exceeding a threshold value, and judging whether the difference value exceeds a set pixel number threshold value or not; and if yes, extracting feature points of the first image area and the second image area, and calculating displacement and scaling deviation correction parameters according to the distances between the feature points and the object contour. And finally, the printing parameters of the roller printing machine are adjusted to correct the deviation. According to the method, through intelligent and automatic means, the real-time performance of deviation monitoring and deviation correction precision are improved, and the defects in a traditional method are overcome.

A method may include obtaining a petrographic image. The method may further include determining various region proposals based on the petrographic image and a selective searching function. A respective region proposal among the region proposals may correspond to various pixels in the petrographic image according to a predetermined dimension. The method may further include determining color histogram data for the petrographic image. The method may further include determining input image data based on the petrographic image, the region proposals, and the color histogram data. The method may further include determining a rock object using the input image data and a machine-learning model.

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

The invention relates to the technical field of data processing, and provides a target detection method and device, a model training method and device, electronic equipment and a medium. The target detection method comprises the steps that a to-be-recognized image and a query text are acquired, and the query text is used for querying a target object corresponding to the query text in the to-be-recognized image; performing image recognition on the to-be-recognized image to obtain image description features and region detection visual features; performing regional multi-modal fusion processing on the image description features and the regional detection visual features to obtain regional multi-modal fusion features; performing feature fusion processing on text features obtained based on the query text and the regional multi-modal fusion features to obtain text regional fusion features corresponding to the query text; and a target detection result is obtained based on the text features and the text region fusion features, so that the fusion degree of text semantics and image region features is improved, and the accuracy and robustness of target detection in a complex scene are improved.

The invention relates to the technical field of medical cosmetology and computer vision, in particular to a 3D skin measurement method based on high-precision measurement of a face concave-convex degree region, and the method comprises the following steps: arranging a structured light camera to collect a face RGB image and depth data, and carrying out the filtering and denoising to generate a smooth point cloud; detecting an RGB image based on FaceMesh to obtain face key points, and constructing a multi-region segmentation algorithm; segmenting an RGB local area and converting the RGB local area into point cloud data; iDW interpolation is carried out to generate a dense point cloud, and a two-dimensional contour line is extracted through a Marking Square algorithm; and mapping the color of the matched color card to an image area to generate a contour map. According to the method, the face recognition model is constructed through the FaceMesh architecture, accurate positioning of multiple face types is adapted, two-dimensional contour mapping is adopted to three-dimensional contour mapping, data are intercepted, local processing is combined, the operation load is reduced by 60%, and efficiency is remarkably improved.

The invention relates to the technical field of image recognition, in particular to a bridge pier underwater structure image intelligent analysis method and system, and the method comprises the following steps: obtaining RGB channel values of all pixels of a bridge underwater structure image region, converting a main color temperature, generating a color temperature map, dividing the region in a vertical direction, extracting a main color temperature track, and carrying out the linear fitting. And comparing the residual error with a continuity threshold, and marking a color temperature continuous section. According to the method, a two-dimensional map is constructed through fusion of main color temperature and space coordinates, spatial distribution expression of a structural region is enhanced, a stable region is extracted by combining residual and continuity discrimination, interval judgment and RGB finishing are introduced into gray scale lifting, image brightness balance and color consistency are enhanced, a gray scale trend model is constructed through radial chain grouping, and light attenuation interference stripping is achieved. Feature point continuous frame tracking is combined with disturbance vector mapping, structure dynamic changes are captured, abnormal behaviors are recognized through cross analysis of tension differences and response terms, and efficient recognition and interference isolation under multi-dimensional feature fusion are achieved.

The invention discloses an image generation method, a model determination method and an image generation system, and relates to the technical fields of large model technology and image processing. The method comprises the steps that an initial object image to be processed and a mask image are determined, the image content of the initial object image comprises at least one to-be-displayed object located on a pure color background, and the mask image is used for representing an image area to which the to-be-displayed object is to be displayed in an initial scene image; using the image processing model to take the initial object image as visual reference information of the mask image, and predicting to obtain a predicted object image corresponding to the mask image; and superposing the predicted object image to the initial scene image to obtain a target scene image, the target scene image being used for representing a display result of the to-be-displayed object in the image area. The technical problem that the image generation effect is poor in the prior art is solved.

The invention relates to the technical field of display screens, in particular to a liquid crystal display screen backlight local dimming method and device, a terminal and a medium. Comprising the steps of obtaining brightness and color distribution data of a display picture; calculating a target brightness value of each backlight block through the dimming algorithm module based on a dynamic partition control strategy and a preset brightness mapping model; wherein the dynamic partition control strategy is used for dividing adjacent backlight blocks into dynamic dimming groups according to brightness characteristics of a picture area on the premise that the number of the backlight blocks is not increased; the backlight control device drives an LED backlight block of the backlight module according to the target brightness value; and monitoring a display effect in real time, and adjusting parameters of the brightness mapping model based on feedback. According to the invention, the problems of cost increase and related derivation caused by increase of the number of partitions in the prior art can be solved.

The invention provides a safety production behavior monitoring method and system based on AI video analysis. The method comprises the steps of collecting a real-time video data stream of a production area; inputting each frame of video image in the real-time video data stream into a target detection model for target detection to obtain a personnel target output by the target detection model and a target position coordinate of the personnel target in each frame of video image; cutting out a local image area of the personnel target in each frame of video image based on the target position coordinate, and performing feature recognition based on the local image area to obtain personnel features; performing comparison on the basis of the personnel characteristics and the personnel standard behavior characteristics to obtain personnel behavior states, and performing track association on the basis of the personnel behavior states corresponding to the continuous multi-frame video images to obtain personnel behavior tracks; and performing safety production behavior monitoring based on the personnel behavior state and the personnel behavior track, and generating an abnormal behavior early warning signal. According to the method and the device, the real-time performance and the accuracy of safety monitoring in a production scene are improved.

The invention provides a digital evaluation method and system for fair-faced concrete based on Mockup, and belongs to the field of building construction. According to the technical scheme, the method comprises the steps of collecting a surface image of a bare concrete Mock sample plate, extracting a reference Lab color value and a reference texture feature from the collected sample plate image, and constructing an evaluation reference model; collecting a surface image of the to-be-evaluated bare concrete member, and generating image data; performing defect area identification on the image data, extracting an effective image area, and extracting a Lab color value to be evaluated and a texture feature to be evaluated from the effective image area; generating a comparison result based on the evaluation reference model; and based on the comparison result, generating evaluation output information. The method has the beneficial effects that the color and texture double-feature model is constructed, a defect identification and shielding mechanism is introduced, and a structured comparison algorithm and an output system are adopted, so that the automation of the whole process from data acquisition, feature extraction, defect avoidance to intelligent comparison and evaluation output is realized.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention relates to the technical field of target detection, in particular to a search and rescue task-oriented unmanned aerial vehicle thermal imaging visual target detection method, which comprises the following steps of: acquiring multiple frames of thermal imaging images of an unmanned aerial vehicle, extracting regional thermal difference characteristics according to a window, marking a non-background region to generate a candidate set, and fitting and reconstructing a suspected thermal target contour. And analyzing the track and the thermal change rate, screening background interference, identifying jump abnormity, positioning the gravity center, and generating a target repositioning signal. According to the method, the heat value range and variance index sequence in the image area is constructed, the thermal anomaly area is judged in combination with the temperature baseline difference, background disturbance comparison is executed in combination with the direction vector of the coordinate trajectory in the multi-frame image and the thermal change parameter, the false detection probability caused by background noise is reduced, and the detection accuracy is improved. The target jump identification is carried out according to the inter-frame heat value and area change rate in linkage with the thermal isoline closure degree, the target discrimination accuracy in a shielding scene is improved, and the robustness of thermal target extraction in a complex search and rescue environment is integrally improved.

The invention relates to the field of machine learning, and discloses a printing quality detection method and device based on machine learning, and the method comprises the steps: obtaining the original image data of a printed matter under the conditions of multiple batches, heterogeneous illumination and multi-angle imaging, and constructing a printing image training set through combining an image region equalization strategy and a light interference shielding mechanism; performing image block-level fine-grained division on the printing image training set, performing multi-channel feature extraction on each image block by adopting a deep convolution model based on a residual attention mechanism, and generating a composite feature map through a feature coupling structure; based on the composite characteristic spectrum, constructing a defect positioning network, and identifying a potential defect response area; and in combination with the initial defect candidate set, introducing an image background disturbance simulation mechanism and a local artifact inversion model, establishing a defect error maintenance positive frame, and eliminating an interference area caused by non-printing errors. The method has the advantage of improving the accuracy of defect identification.

The invention discloses a sludge treatment automatic monitoring method based on computer vision, and relates to the technical field of sludge treatment automatic monitoring, and the method comprises the following steps: collecting sludge image information through computer vision, extracting an edge integrity index and a brightness distribution index, and constructing a fusion judgment function based on the time sequence change of the two indexes, determining whether a thin water film exists on the sludge surface or not according to an output result of the fusion judgment function; under the condition of determining that a thin water film exists on the sludge surface, acquiring a pixel highlight gradient polymerization rate, a texture missing fluctuation frequency and an image saturation nonlinear deviation value of a corresponding image region, and establishing a specular reflection image feature recognition matrix; by constructing a multi-dimensional image feature recognition and dynamic regulation and control mechanism, the problem of image misjudgment caused by thin water film mirror reflection in the sludge dewatering process is solved, and accurate recognition and automatic closed-loop control of the real water-containing state of the sludge are achieved.

The invention discloses a hierarchical encryption method, system, device and medium for a privacy image, and relates to the technical field of image processing and information security, and the method comprises the steps: segmenting a to-be-encrypted image divided according to a region threshold, and obtaining images with privacy of different importance degrees; according to the privacy importance degree of an image region, selecting encryption algorithms of different levels to encrypt a high-privacy-level image, a middle-privacy-level image and a low-privacy-level image, and performing main element product operation by using a chaos matrix and the encrypted high-privacy-level image, middle-privacy-level image and low-privacy-level image to obtain a high-privacy-level image, a middle-privacy-level image and a low-privacy-level image; and a final encrypted image is obtained. According to the image encryption method, selective algorithm encryption is carried out on each part of the to-be-encrypted image, high-strength encryption processing does not need to be carried out on the whole image, and the encryption method is numerous, so that the encryption strength and security can be remarkably improved while the encryption efficiency is improved.

The application discloses a method, electronic device, and storage medium for image processing, which includes: in response to determining that the trigger of an effect display function is detected, adding a virtual model to a collected image to be processed to obtain a presentation video frame; magnifying-displaying an image area of the virtual model in the presentation video frame, and processing the virtual model into a target three-dimensional effect model in response to determining that a magnification stop condition is reached; fusing the target three-dimensional effect model and a target object in the image to be processed to present a target video frame.

The invention discloses a table data recovery method based on an OCR model, and the method comprises the steps: S1, recognizing table types through employing a lightweight model, and enabling the table types to comprise a wired table and a wireless table; s2, respectively acquiring a logic coordinate and a physical coordinate of a detection frame of the wireless table and a cell physical coordinate of the wired table; s3, recovering a logical structure of the table according to the detected table coordinates, and determining a row-column relationship of the table; s4, performing character recognition on the image by using a text recognition model, obtaining the position and content of a character box, performing accurate matching of characters and cells by calculating the coordinate overlap ratio of the OCR recognition character box and the cell box, and determining the character content in each cell; and S5, for the cells which are not matched with the characters, cutting the cell areas, carrying out secondary OCR identification on the image areas of the cells, and supplementing missing character contents. According to the method, the automatic identification and analysis capability of the complex table data is improved.

The invention discloses a mathematical ancient book illustration identification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a mathematical ancient book illustration; dividing the mathematical ancient book illustration into a character area, a formula area and an image area, and recording respective position information of the three parts as first position information; carrying out OCR (Optical Character Recognition) on the character region, carrying out formula restoration on the formula region, and restoring the image region; and performing layout reconstruction on the math ancient book illustration according to the text identified by the OCR, the restored formula, the restored image and the first position information to obtain a math ancient book illustration identification result, thereby realizing effective identification and digitization of the math ancient book illustration.

The invention provides a textile industry broken yarn identification method and system based on a neural network, and the method comprises the steps: collecting yarn multi-modal data, including a surface image, a fracture sound wave signal and tension change data; preprocessing the data, and extracting an image ROI region, sound wave spectrum features and a tension mutation sequence; performing space-time alignment and feature extraction on the extracted content to obtain a joint feature vector; inputting the broken yarn into a trained broken yarn identification model, wherein the model can identify broken yarn features; judging whether broken yarns exist or not according to the output result and outputting an identification result; and updating the model through online incremental learning. According to the method, multiple types of data are combined, the adaptive preprocessing and feature extraction technology is used, environmental noise is inhibited, key features are focused, and the problem of false alarm of a traditional sensor is solved; multi-modal features are fused through space-time alignment and a self-attention mechanism, and bidirectional LSTM modeling is combined, so that a broken yarn dynamic rule is accurately captured, and the problems of missing detection and delay of manual inspection are avoided.

The invention relates to an image data desensitization method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the target object detection of a whole first image based on the size of an initial detection window through a target detection model, obtaining a rough first detection result, determining the target density of each image region based on the first detection result, and obtaining a desensitization result; adjusting the size of the initial detection window based on the target density to obtain a target detection window size corresponding to each image area, and accurately detecting each image area based on the target detection window size corresponding to each image area to obtain a more accurate second detection result corresponding to each image area. And performing fuzzy processing on the image area based on the second detection result. According to the method and the device, the detection window of the target object can be intelligently reduced or expanded by dynamically adjusting the size of the detection window, so that the detection precision is improved, the missing detection is reduced, and the fuzzification processing is more accurate.

The invention discloses a data labeling method and system based on user behaviors and attention tracking, and the method comprises the steps: synchronously collecting multi-source behavior signals of a mouse, a keyboard, eye movement and the like of a doctor in real time, combining identity and interface metadata, and carrying out the standardized normalization, abnormality elimination and short time sequence behavior unit division. And extracting individual behavior micro-modes by using unsupervised clustering, and constructing a behavior portrait library. Through multi-modal time sequence modeling and a self-adaptive space-time attention mechanism, behavior characteristics, an interface area and a report text are deeply fused, a multi-level correlation probability is output, and high-precision automatic tagging of content and an image area is realized.

The invention discloses a seal area character recognition method and device and a nonvolatile storage medium. The method comprises the following steps: determining a candidate seal image area in an image according to color information of pixel points in the image; point-by-point sliding convolution processing is carried out on the candidate seal image area through a multi-scale annular convolution kernel group, so that a target seal image area is determined in the candidate seal image area, and the multi-scale annular convolution kernel group comprises a plurality of convolution kernels which are of concentric ring structures and have different radius lengths; mapping the target seal image area into a rectangular expanded image, and identifying and extracting a character image to be identified in the rectangular expanded image; and performing identification processing on the character image to be identified to obtain a seal text corresponding to the target seal image area. The technical problem that the text image processing efficiency is low due to the fact that the text information of the seal area cannot be effectively recognized in the related technology is solved.