315 results about "Model image" patented technology

The invention discloses a modal language model image editing technology fusing non-perpetual culture elements. The modal language model image editing technology comprises the steps that a model is selected and trained, an LLaMA model is selected as a basis, LoRA is introduced for adaptive fine adjustment, and in this way, the model is subjected to adaptive adjustment under the condition that original parameter freezing is kept; according to the method, the understanding and reasoning capabilities in instruction editing are enhanced by combining MLLM (such as LLaVA), the MLLM can perform collaborative learning across text and image modalities and extract deep semantic information, so that the model can not only process basic instructions, but also understand complex non-perpetual culture elements, and the method has great significance in improving the understanding of the model on the non-perpetual culture elements. An enhanced bidirectional interaction mechanism is designed, and the mechanism realizes deep interaction between image and text features through a cross attention mechanism, so that the image features can be used as query and key value pairs to perform bidirectional communication with the text features, and the expression of a model in a complex non-perpetual scene is improved.

The invention discloses a foggy day perception-based feature refinement ship instance segmentation method and equipment, and belongs to the field of instance segmentation of ship images in a foggy day scene. According to the method, YOLOv8s is used as a basic model, a joint learning framework is designed, and defogging and feature refinement enhanced perception strategies are combined. Through a state space model and a multi-scale channel aggregation gating strategy, modeling image features are refined, and foggy day long-distance dependence feature information captured by the model is enhanced. And through the adaptive defogging module, fog background interference features are reduced. Through multi-window branch feature extraction and a multi-angle similarity measurement strategy, the ability of the model to extract local detail information and global information is further enhanced, the focusing ability of a ship target area and the adaptability to a complex haze scene are improved, and the ship instance segmentation performance and generalization ability in a foggy day scene are effectively improved.

A virtual try-on method and apparatus, and a storage medium. The method comprises: acquiring a clothing image to be subjected to try-on and a first model image; on the basis of the clothing image to be subjected to try-on, determining a target clothing image; on the basis of the first model image, determining a try-on area image and a first key point, wherein a try-on area comprises an area of model clothing, a body area which is in contact with the model clothing, and a background area which is in contact with the model clothing; and on the basis of the target clothing image, the try-on area image and the first key point, generating a try-on effect image by means of a trained diffusion model.

The invention discloses an overhead transmission line mechanical construction three-map one-table intelligent generation method, which comprises the following steps of: fusing a digital orthoimage model, elevation data and a power transmission and transformation project model through a coordinate transformation matrix, and establishing a three-dimensional scene with a terrain curvature attribute; based on the terrain curvature and equipment coordinates of the three-dimensional scene, a multi-objective optimization algorithm is adopted to synchronously plan a composite path of a cableway, a track and a road, and a road network list map containing an obstacle avoidance topology is generated; recognizing a tower footing projection polygon in the three-dimensional scene, segmenting the types of surface attachments in combination with a digital orthoimage model image, and generating a surface feature list map with a water and soil conservation identifier; and establishing a mapping function of the terrain curvature and the environment water conservation rule, outputting equipment types and spatial poses according to the hoisting safety margin and the earth surface disturbance threshold value, and generating an equipment list map. According to the method, a unified space reference and multi-system dynamic cooperation mechanism is constructed, and the planning precision and construction compliance of three graphs and one table are remarkably improved.

The invention provides a method and a system for processing a cell image based on a DistSegNet model. The method for detecting the cells based on the DistSegNet model image processing technology comprises the following steps: preprocessing a collected cell image; performing cell segmentation on the cell image based on a DistSegNet model image processing technology to generate a cell nucleus region; based on the segmented cell nucleus region, generating a background region, a foreground region and an edge region by adopting morphological operation; calculating the shortest Euclidean distance from each background region pixel to the nearest foreground region pixel, and segmenting the cell image by using a watershed algorithm; performing result optimization on the segmentation map corresponding to each cell instance label to obtain a filtered segmentation label map; and extracting the outer contour of the intra-nucleus region and the outer contour of the surrounding region of each cell from the segmentation mark graph, and storing the outer contours to a corresponding result file. According to the invention, the accuracy and accuracy of a WSI-level cell detection result can be improved.

The invention discloses an anti-corrosion control system and method for a pipeline operation robot, and relates to the technical field of robot control, and the system is composed of a plurality of functional modules, and comprises a multi-sensor fusion module which obtains a pipeline image in real time through a camera; meanwhile, a sensor is used for dynamically collecting internal environment parameters of the pipeline; the three-dimensional modeling and positioning module is used for constructing a pipeline three-dimensional model based on an SLAM algorithm and the pipeline image and positioning the position of the pipeline where the robot is located in real time; the intelligent decision-making module is used for identifying a pipeline corrosion area and position by utilizing YOLOv5 based on the pipeline image and performing corrosion analysis on the corrosion area, and the corrosion analysis comprises corrosion thickness and form contour; measuring the corrosion thickness of the corrosion area by utilizing electromagnetic eddy current detection, and extracting the shape contour of the corrosion area by utilizing deep learning model image recognition; and triggering a self-adaptive control strategy according to the internal environment parameters of the pipeline and the pipeline position of the robot.

Disclosed herein are methods, systems, and computer-readable media for generating image captions for training a machine learning model. Current image generation models are hindered by the prevalence of improper or inaccurate captions, which leads to suboptimal training data. This results in less effective image generation models. Disclosed systems and methods involve obtaining a text-to-image dataset including one or more digital image-caption pairs. Systems and methods involve generating a recaptioned dataset by applying an image captioner model to images in the text-to-image dataset. An image captioner model can be trained with an improved image dataset, a first tuning stage, and a second tuning stage, for improved performance.

The invention provides an intelligent bill recognition method based on a deep neural network model, and relates to the technical field of intelligent bill recognition based on the deep neural network model.The intelligent bill recognition method comprises the steps that various bill data are collected, image features and text features are extracted respectively and preprocessed, and complete multi-modal data support is provided for subsequent modeling; secondly, modeling image features by using a convolutional neural network and modeling text features by using a deep neural network, splicing the image features and the text features into a joint feature vector, and deeply fusing information of an image and a text modal through a cross-modal attention mechanism to enhance multi-modal relevance; and finally, designing a dynamic weight adjustment mechanism, adaptively adjusting the optimization weights of the cross entropy loss of the classification task and the mean square error loss of the field analysis task based on the cross entropy loss of the classification task and the mean square error loss of the field analysis task, and balancing optimization conflicts in a multi-task scene.

The invention discloses an image processing method and device, a medium, electronic equipment and a program product. The method comprises the following steps: acquiring an article image comprising a first article and a model image comprising an interaction posture of a model and a second article; superposing the article image to the position of the first limb in the model image to replace the second article in the model image with the first article to obtain a first image; obtaining a first mask picture of the foreground area including the first article and the first limb; and according to the first mask image, performing local redrawing on the first limb in the first image to obtain a second image. Through superposition of the article image and the model image, the first article and the model are fused, and the real consistency of the generated content can be ensured. And according to the first mask image, local redrawing is carried out on the shielding conflict area in the first image, so that the natural interaction feeling of the model and the first article is improved.

The invention relates to the technical field of industrial detection, and provides a real-time monitoring method and device for slag in a boiler. A plurality of high-temperature-resistant cameras are installed around an observation opening in the bottom of a boiler in an annular array mode, the slag accumulation condition in the boiler is collected in real time through the high-temperature-resistant cameras, and a single-view-angle image is obtained; evaluating the visual field states of the high-temperature-resistant cameras according to the single-visual-angle images, and determining a plurality of to-be-modeled images from the plurality of high-temperature-resistant cameras; and generating slag point cloud data by using the at least one to-be-modeled image, and performing three-dimensional reconstruction on the slag point cloud data to obtain the slag pile volume. According to the method, the slag falling condition of the boiler bottom of the power plant boiler is monitored and recognized in real time, and the problems that the slag falling at the boiler bottom cannot be detected in real time depending on manpower, the detection accuracy is insufficient, the three-dimensional form of the slag falling at the boiler bottom cannot be synthesized by detecting the slag falling at the boiler bottom through sensor equipment, and practicability is poor are solved.

The invention relates to the technical field of computer vision and artificial intelligence, in particular to a visual state space model-based neural network optimization method for bridge diseases, which comprises the following steps of: constructing a neural network comprising a feature extraction backbone network and a task adaptation head network, modeling an image feature sequence by the backbone network based on a visual state space model, the task adaptive header network is used for capturing local details and long-distance global dependence and outputting disease categories, confidence coefficients and positions; a standardized data set is used for training a model, and dynamic optimization is completed by evaluating the importance of neurons and sorting and cutting redundant neurons; and preprocessing a to-be-detected image, inputting the preprocessed to-be-detected image into the optimization model, and outputting a precise disease identification result through feature extraction, multi-branch processing and non-maximum suppression. The method improves the disease feature extraction precision, achieves the light weight of the model, adapts to multi-scale disease detection, improves the practicability of an identification result, and is suitable for a bridge disease intelligent detection scene.

The invention provides a method and a system for processing a cell image based on a DistSeg model. The method for detecting the cells based on the DistSeg model image processing technology comprises the following steps: preprocessing a collected cell image; segmenting the cell image based on a DistSeg model image processing technology to generate a cell nucleus region; based on the segmented cell nucleus region, generating a background region, a foreground region and an edge region by adopting morphological operation; calculating the shortest Euclidean distance from each background region pixel to the nearest foreground region pixel, and segmenting the cell image by using a watershed algorithm; performing result optimization on the segmentation map corresponding to each cell instance label to obtain a filtered segmentation label map; and extracting the outer contour of the intra-nucleus region and the outer contour of the surrounding region of each cell from the segmentation mark graph, and storing the outer contours to a corresponding result file. According to the invention, the accuracy and accuracy of a WSI-level cell detection result can be improved.

Systems and methods for performing surface matching using three-dimensional (3D) and color information. An example method includes obtaining, by a 3D camera, a first 3D image of a field of view. The 3D image includes (i) a plurality of voxels, or 3D points, of the field of view and (ii) color information of the field of view. A processor normalizes the color information into a common color space, and determines scene histograms from the normalized color information. Each of the scene histograms is determined for a voxel of the plurality of voxels. The method further includes determining a color score from at least the scene histograms and model histograms, the model histograms being indicative of color information of a model image of a 3D object, and determining the presence of a surface of an object in the first 3D image from the color score.

To realize a service for easily generating an apparel image.SOLUTION: According to an embodiment of the present invention, an apparel image generating system has: a display unit for displaying a selected screen for which a user can select at least one of a model image, an item image, a background image, a face image and a pose image; and an image generating unit for using an image generation model to generate an apparel image from the image selected by the user.SELECTED DRAWING: Figure 9

The present teachings relate to a method comprising: receiving at a user container runtime environment, via a container registry, an image of an obfuscated containerized model of an asset; wherein the containerized model comprises an API, and wherein the containerized model has been generated in a supplier runtime environment located within a supplier network environment, interfacing, with a model execution logic, the API of the containerized model image, wherein the interface comprises a process data signal that is transmitted to the API of the model image, and a model result data signal that is transmitted from the API of the model image; wherein the process data signal comprises data related to the usage conditions of the asset, and the model result signal is indicative of the response of the asset to at least some of the usage conditions, wherein the user container runtime environment is located within a user network environment which is isolated from the supplier network environment. The present teachings also relate to a method for transmitting a model of an asset, a method for transmitting an augmented model, a computer software product and a computer network arrangement.

Disclosed in the embodiments of the present application are a model rendering method and a related apparatus. The method comprises: for an initial three-dimensional model to be processed, simplifying the model structure of the initial three-dimensional model on the basis of a topological structure feature of the initial three-dimensional model, so as to obtain a simplified three-dimensional model, wherein the number of meshes comprised in the simplified three-dimensional model is less than the number of meshes comprised in the initial three-dimensional model, and the difference between the simplified three-dimensional model and the initial three-dimensional model meets a preset similarity requirement; by means of lighting simulation processing, determining a lighting intensity corresponding to a vertex in the simplified three-dimensional model when the simplified three-dimensional model is in a virtual environment; and on the basis of the simplified three-dimensional model and the lighting intensity corresponding to the vertex in the simplified three-dimensional model, rendering the simplified three-dimensional model, so as to obtain a model image for representing the simplified three-dimensional model. The method can automatically reconstruct and render a three-dimensional model, thereby improving the reconstruction efficiency of the three-dimensional model and reducing labor costs.

Disclosed are example embodiments of systems and methods for virtual try-on of articles of clothing. An example method of virtual try-on of articles of clothing includes selecting a garment from a pre-existing database. The method also includes loading a photo of a source model wearing the selected garment. Additionally, the method includes generating a semantic segmentation of the model image. The method also includes extracting the selected garment from the photo of the model. Additionally, the method includes determining a correspondence between a target model and the source model by performing a feature point detection and description of the target model and the source model, and performing feature matching and correspondence validation. The method also includes performing garment warping and alignment of the extracted garment. Additionally, the method includes overlaying and rendering the garment.

The invention discloses a diffusion model image completion method based on boundary preserving perception, and belongs to the technical field of artificial intelligence and image generation. The method comprises the following steps: firstly, preprocessing an input image and a corresponding defect mask, and extracting gradient attenuation boundary features and a structure edge graph; a boundary sensing convolution module is introduced into the completion model and is used for boundary information modeling of a coding end; meanwhile, a gating mechanism is adopted in U-Net jump connection, the weight of a gradient attenuation mask is combined, and the boundary information transmission intensity is dynamically adjusted; furthermore, in the diffusion generation process, a region self-adaptive scheduling strategy is adopted, only high-step diffusion iteration is carried out on a defect region and a boundary neighborhood, and low-step or freezing operation is carried out on a non-defect region, so that balance between boundary consistency and calculation efficiency is realized. Experimental results show that the method is superior to an existing diffusion model image completion method in the aspects of structure retentivity, edge continuity and visual quality, and has wide application prospects.

Systems and techniques are described herein for segmenting images. For instance, a method for segmenting images is provided. The method may include encoding, using a machine-learning-model image encoder, an image to generate a plurality of image features; selecting a first image feature from among the plurality of image features; determining a first image point related to the first image feature; encoding, using a machine-learning-model prompt encoder, the first image point as a first encoded prompt; generating, using a machine-learning-model image decoder, a first mask based on the plurality of image features and the first encoded prompt, wherein the first mask is indicative of pixels of the image that are semantically similar to the first image point; and storing the first mask.

The invention discloses a model scheduling method, a storage cluster, a device and a medium, and relates to the technical field of computers. The method comprises the following steps: performing hierarchical processing on a model mirror image file in advance to obtain a base layer mirror image file and a model layer mirror image file, storing the base layer mirror image file in a cache space corresponding to a cloud platform, and storing the model layer mirror image file in a network sharing space. The mixed storage of the cache space and the network sharing space of the mirror image file is realized, and the redundant transmission and storage overhead is also reduced. In response to the current scheduling request, after the scheduling of the base layer mirror image file is completed for the first time, the base layer mirror image file does not need to be mounted for the non-first scheduling, so that the multiplexing of the base layer mirror image file is realized. And only the incremental model layer mirror image file is mounted for model iteration, so that the transmission overhead is reduced, the iteration requirement of each model is met, the network bandwidth consumption is reduced, and the deployment efficiency is improved.

The invention relates to the technical field of three-dimensional model image data processing, and discloses a three-dimensional model particularization splitting deduction and multi-mode feedback method, which comprises the following steps: carrying out lightweight three-dimensional model reconstruction according to an equipment process structure diagram, and generating a glTF format data packet with a process information embedding characteristic; establishing a virtual three-dimensional scene through a three.js engine, and instantiating a three-dimensional model object in a glTF format; non-uniform granulation processing is carried out on the equipment model by adopting a splitting and deducting mechanism, discrete motion tracks of parts are solved through a response algorithm, and an equipment dynamic decomposition path display effect is constructed; a calibration sensing link framework is established, an interaction response mechanism based on user behaviors is constructed, and a multi-mode visual feedback system of a large screen is achieved; according to the method, the technical bottlenecks of slow model loading, high interaction delay and the like in traditional industrial large screen display are effectively solved, and the dynamic interaction display effect and user perception of an industrial equipment structure are improved.

The invention relates to the technical field of computer vision and deep learning, and discloses a cascade diffusion model-based virtual fitting method, which comprises the following steps of: inputting a model image and a target clothing image into a multi-condition diffusion model, and outputting a posture control variable and a depth control variable; integrating the posture control variable and the depth control variable into a de-noising U network through a multi-control injection module, and generating a low-resolution try-on image of the model wearing the target garment; inputting the low-resolution fitting image into the super-resolution diffusion model, and performing feature extraction on the low-resolution fitting image; feature codes corresponding to feature extraction are transmitted to a control network space through a full connection layer, then the feature codes are integrated into a super-resolution diffusion model through a cross attention mechanism, the super-resolution diffusion model is guided to reconstruct a low-resolution try-on image, and a high-resolution try-on image is generated. According to the method, high-fidelity and high-resolution clothing try-on image generation is realized through a multi-condition control and super-resolution reconstruction technology.

The invention discloses an image generation method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring an initial model image; performing human body posture recognition and mask segmentation processing on the initial model image to obtain a mask image; generating a clothing text cue word according to the basic quality constraint parameter, the semantic enhancement parameter and the attribute combination generation parameter; performing image generation processing on the mask image through a generation model according to the clothing text cue word to obtain a target generated image; and carrying out anti-detection edge optimization processing on the target generation image to obtain a target model image. The image generation efficiency can be improved, and the method can be widely applied to the technical field of image processing.

A respiratory motion plane alignment system and method based on ultrasound images is disclosed. The system acquires three-dimensional ultrasound images of respiratory organs at at least two different respiratory phases, calculates the motion direction of the respiratory organs based on the three-dimensional ultrasound images, controls and adjusts an ultrasound direction adjustment mechanism to align the scanning plane of the ultrasound probe with the motion direction of the respiratory organs, acquires a three-dimensional model image of the respiratory organs and multiple frames of real-time two-dimensional ultrasound images, performs registration between the multiple frames of real-time two-dimensional ultrasound images of the respiratory organs, and / or registers the three-dimensional model image and the multiple frames of real-time two-dimensional ultrasound images, acquires three-dimensional position data of a target point during respiratory motion, determines the tracking path of the ultrasound probe based on the three-dimensional position data of the target point during respiratory motion, and controls the ultrasound probe to move along the tracking path, thereby achieving long-term stable observation or tracking of target points in the respiratory organs.

The invention discloses a black box antagonism deep learning model image classification detection system and method, and belongs to the technical field of artificial intelligence attack and defense. The method comprises the following steps: 1, constructing an original sample and an adversarial sample, obtaining a prediction classification result through a deep learning model, and taking the adversarial sample different from the prediction classification result of the original sample as an initial detection sample; 2, adding disturbance to the detection sample in an original sample direction and an orthogonal direction of the original sample direction; 3, carrying out image preprocessing and target defense processing on the disturbance detection sample, and identifying an effective detection sample; 4, respectively inputting the effective detection samples into the image preprocessing target defense model and the original model, and taking the detection samples which are misclassified by the target defense model and correctly classified by the original model as defense classification recognition results; compared with the prior art, the technical problem of identifying the antagonistic defense category in the black box antagonistic deep learning model in the prior art is solved.

The invention relates to the technical field of image restoration, in particular to a diffusion model image restoration method based on regional mask and dynamic exit, and mainly solves the technical problems of high calculation overhead and iteration step number redundancy of an existing diffusion model image restoration method. According to the method, on the basis of an extension architecture of encoding and decoding, a UNet denoising network and a ControlNet conditional constraint module, on the basis of retaining the core functions of an existing encoder, an existing decoder and an existing Unet denoising network, a space region mask generation sub-module, a back diffusion dynamic exit module and the ControlNet conditional constraint module are creatively integrated; and a triple optimization architecture of'constrain structure protection, space redundancy reduction and time step number reduction 'is formed, so that the structure consistency is met and efficient acceleration is realized during image restoration.

The invention belongs to the technical field of image processing, and particularly relates to a boundary intrusion target detection method based on machine vision, and the method comprises the steps: obtaining a boundary monitoring video frame sequence, extracting a grayscale image, and constructing a background model image based on a historical frame sequence; according to the gray scale change of the pixel point in the time window, a time sequence fluctuation index is obtained through the standard deviation of the time sequence gray scale set and the overturning frequency of the gray scale difference value; according to texture distribution characteristics in a pixel point neighborhood, obtaining a spatial discrete entropy through a gray level histogram; obtaining a direction chaos index through the vector sum modulus and the algebraic modulus sum of the motion gradient vector; and correcting the original motion response diagram according to the time sequence fluctuation index, the spatial discrete entropy and the direction chaos index to obtain an intrusion confidence coefficient, and carrying out connected region detection according to the intrusion confidence coefficient. According to the method, the environmental dynamic interference is effectively inhibited by fusing the time sequence oscillation, the space texture and the motion direction, and the accuracy of boundary intrusion detection is improved.

The invention relates to the technical field of image processing, in particular to an anesthesiology department airway intubation image recognition method based on artificial intelligence. The method comprises the following steps: acquiring an airway CT image of a patient at double view angles, constructing a multi-curvature airway model and generating a corresponding image; correcting the model image in combination with the inherent distortion parameter of the lens to obtain a distortion corrected image; secondly, the influence of different curvatures on distortion is quantified under a multi-curvature distortion correction image, and a curvature-direction-distortion correction factor mapping relation is generated; acquiring a current airway image in real-time intubation, extracting curvature features by performing pixel-level matching with the distortion correction image, associating the curvature features with the mapping relation, and determining a distortion correction index corresponding to the current airway image; and finally, performing double correction on the current airway image by combining the lens distortion parameter and the distortion correction index, and generating a high-credibility airway correction image for subsequent image recognition.

Provided are an image recognition program capable of accurately counting the population of nematodes that are detection targets even when a plurality of detection targets overlap each other, an image recognition device using the same, a detection target population counting method, and a model image creation device for image recognition learning to be used therefor.An image recognition program causes a control unit 11 and a storage unit 12 of an image recognition device 1 to function as detection target image acquisition means for acquiring a detection target image showing a plurality of nematodes 3, extraction means for extracting from the detection target image a detection target presence area that possibly includes an image of the nematodes 3, storage means for storing a plurality of pattern images including a single-body pattern image showing one of the nematodes 3 and a multiple-bodies pattern image corresponding to an image showing two or more of the nematodes 3 overlapping each other, and recognition means for recognizing the population of nematodes 3 included in the detection target presence area by detecting a degree of concordance between an image of the detection target presence area and each of the pattern images.

The invention discloses an RH refining furnace control method and related equipment, and relates to the technical field of steel smelting, the method comprises the following steps: obtaining in-furnace color image data of a target RH refining furnace; performing flame state recognition on the color image data in the furnace through a first preset model to obtain a flame detection result; performing material adding action recognition on the color image data in the furnace through a second preset model to obtain blanking event information; performing metallurgy stage feature extraction on the color image data in the furnace through a third preset model to obtain a refining state recognition result; and according to the flame detection result, the blanking event information and the refining state recognition result, interlocking control operation is triggered. Automatic judgment and interlocking control of key working conditions of the RH refining furnace are achieved through multi-model image recognition, the control precision and the system stability can be improved, and energy conservation, consumption reduction and refining quality improvement can be achieved.