1483 results about "Background image" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

A data processing system implements receiving, via a user interface of a client device, images for generating a collage image; generating captions for the images; constructing a first prompt by appending the captions to a first instruction string including instructions to a generative language model to extract a theme from the captions; providing the first prompt to the generative language model and receiving the theme therefrom; constructing a second prompt by appending the theme to a second instruction string including instructions to a text-to-image model to use the theme to create a background image with placeholders; providing the second prompt to the text-to-image model and receiving the background image therefrom; identifying the placeholders in the background image; creating the collage image by fitting the images into the identified placeholders; providing the collage image to the client device; and causing the user interface to display the collage image.

The invention provides a low-visibility environment pedestrian detection method based on an improved YOLOv8n model. A double-branch fusion attention network is adopted in a backbone network of an original YOLOv8n model, and a CBFuse module and a CBLinear module are introduced for feature fusion between different branches; a feature aggregation and calibration pyramid network is introduced, multi-scale feature fusion is carried out through an up-sampling module, a down-sampling module and a feature aggregation and calibration module, and the feature aggregation and calibration module carries out feature calibration and enhancement through a local attention mechanism, a global attention mechanism and a pixel attention mechanism; an adaptive task alignment detection head is introduced to execute a dynamic convolution mechanism, a task decomposition mechanism and a dynamic feature alignment mechanism; an improved YOLOv8n model is formed based on the improvement and serves as a foggy day pedestrian detection network model; according to the method, the detection accuracy and stability of the network in processing shielded and background complex images can be enhanced, and the boundary and detail features of a fuzzy target can be extracted more accurately in low-visibility environments such as foggy days and the like.

The invention discloses an adaptive rendering method and system for characters and images in AI digital human virtual and real scene fusion, and the method comprises the following steps: S1, collecting Chinese text and image data, and extracting semantic and regional features; s2, constructing a Shenchang differential equation converter model, and inputting text and image features to generate fusion representation; s3, extracting an attention weight matrix, and determining the spatial arrangement position of the Chinese text in the image; s4, optimizing the structural parameter vector by adopting a luminous insect colony optimization algorithm to obtain a fusion model; s5, generating an image-text rendering instruction set based on the preliminary arrangement and optimization model; s6, in combination with the digital human action state and background image information, attitude mapping and illumination adjustment are completed to generate a rendering frame; and S7, inputting the rendering frame into a rendering engine, and outputting the video content fused by the Chinese characters and the images. According to the method, accurate fusion of Chinese semantics and images is realized, and the naturalness and interactivity of AI digital human image-text presentation are remarkably improved.

The invention provides an industrial defect image generation system and method based on deep learning, and relates to the technical field of artificial intelligence. A defect form adaptive module, a physical attribute modulation module and a multi-mechanism fusion module are integrated in a defect image generation module; dynamically selecting a feature extraction unit according to the defect type label based on a pre-constructed generative network model, and generating a defect feature map; generating an affine transformation parameter based on the physical attribute vector, and performing channel-by-channel linear modulation on a defect feature map of a middle layer of the generative network model, so that a multi-scale defect feature map finally generated by the generative network model contains specified defect type features and physical attribute features; and fusing the multi-scale defect feature image and the defect-free background image to obtain a fused defect image. The problems that in the prior art, industrial defect image generation is insufficient in sense of reality, poor in controllability and poor in fusion effect are solved, the method can be used for data enhancement of industrial visual inspection, and downstream model performance is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an image generation method, device and equipment based on theme information and a medium. The method comprises the steps that input information is analyzed to generate theme information and copywriting information, a cue word set is generated, and a figure image set and a background image set are generated; fitting the segmented figure image with the background image to form a head image candidate set, and selecting a head image matching template frame to generate a basic image; decomposing the copywriting to generate a sub-module initial picture set, and adding a gradual change effect to form a sub-module picture set; the adjusted sub-module pictures are obtained based on size adjustment, and a pre-synthesized image is generated through splicing; and identifying the blank area to draw a title text to obtain a final image. Through theme analysis, template matching, image splicing, modular copywriting processing and blank drawing, poster generation efficiency is improved, layout flexibility is enhanced, and visual unification is realized.

The invention discloses a rainfall field image extraction method based on double-domain collaboration and progressive feature decoupling. The method comprises the following steps: acquiring a rain image; respectively inputting the rain image into the rain layer branch and the background branch, carrying out corresponding image block embedding operation, and generating an initial rain layer feature and an initial background feature; performing feature extraction on the initial rain layer features through a learnable wavelet transform algorithm to obtain rain layer features, and performing feature extraction on the initial background features through frequency domain separation convolution to obtain background features; performing multi-scale progressive coupling feature extraction on the rain stripe features and the background features to obtain processed rain layer features and processed background features; and performing multi-scale up-sampling on the processed rain layer features and the processed background features to reconstruct a rain layer image and a rain-free background image, and performing weighted fusion on the rain layer image and the rain-free background image through learnable residual gating to obtain a reconstructed rain image and a rainfall field image. According to the invention, accurate separation of the rainfall field can be realized.

A unified virtual background image is generated for multiple participants of a video conference to create an immersive conference experience based on its use within video streams of those multiple participants. Generative artificial intelligence software associated with a conferencing system obtains input associated with a video conference. The generative artificial intelligence software generates a virtual background image based on the input. The virtual background image is then for use within multiple participant video streams during the video conference

Methods, systems, and non-transitory computer readable storage media are disclosed for generating digital images with a diffusion-based generative neural network conditioned on background-extracted lighting features. The disclosed system determines, in response to a request to generate a digital image, a target background image for inserting a foreground object into the target background image. The disclosed system generates, from the target background image and utilizing a lighting conditioning neural network, a lighting feature representation indicating one or more lighting parameters of the target background image. Additionally, the disclosed system generates, utilizing a diffusion-based generative neural network conditioned on the lighting feature representation, the digital image including the foreground object inserted into the target background image based on a composite image comprising the foreground object and the target background image with a foreground mask corresponding to the foreground object.

The invention belongs to the technical field of image processing, and discloses a maskless image synthesis method and system based on a diffusion model, and the method comprises the steps: inputting a background image and a noise image containing a target object into a pre-trained potential diffusion model, and enabling the potential diffusion model to execute a reverse denoising process under the guidance of a condition vector, the target object is synthesized in a self-adaptive mode, and a synthesized image is obtained; wherein the condition vector acquisition mode comprises the following steps: encoding a reference image containing a target object into an object embedding vector, and generating a group of learnable background cue words according to the object embedding vector; splicing with the background prompt word; and performing spatial mapping on the spliced feature representation to obtain a condition vector. The method can overcome the defects that in an existing image synthesis technology, the process is tedious, the efficiency is low, a large amount of manual intervention is needed, the synthesis result lacks the sense of reality and harmony, and especially the foreground object cannot be adaptively adjusted according to the background environment.

A method for training an image generation model, a method for generating a digital human image, and related apparatuses are provided, relating to the fields of artificial intelligence, big model, big data and other technologies. The method for training an image generation model includes: obtaining N target facial images of a target face, wherein N is an integer greater than 1; inputting the N target facial images and at least one target background image into a preset image generation model to obtain a target digital human image after the target face is fused with each target background image; and training the preset image generation model based on a degree of difference between a first facial feature in the target digital human image and a second facial feature of the target face in the target facial images to obtain a target image generation model.

The invention discloses an irregular small target identification method under a non-high-definition complex background image. A multi-scale feature pyramid is constructed through bidirectional feature fusion, so that the feature expression ability of a small target is enhanced; applying a space-channel attention module to adaptively highlight target features and suppress complex background interference; by introducing a composite loss function including class balance focus loss and enhanced bounding box regression loss, model training is optimized to deal with class imbalance and improve the positioning precision of an irregular target. A self-adaptive multi-scale detection head is adopted, and dynamic feature fusion and scale perception branches are utilized to realize accurate detection of targets with different sizes; according to the method, the problems of low recognition precision and poor adaptability caused by weak features, background interference and irregular shapes of irregular small targets in low-resolution and complex background images are effectively solved, and the monitoring performance in actual applications such as unmanned aerial vehicle aerial photography and remote monitoring is remarkably improved.

The invention provides an AIGC digital human-based MOOC video generation method and device, and relates to the technical field of artificial intelligence content generation. The method comprises the following steps: inputting a target action sequence into a target digital human model, driving the target digital human model to execute a corresponding target action according to the target action sequence, and obtaining a target digital human teaching video; constructing a target virtual teaching scene by adopting a virtual engine, and embedding the target digital human model into the target virtual teaching scene to obtain a target teaching digital human model; determining a video background image and decoration and voice parameters of a target digital human model according to the target courseware by adopting a multi-mode AI model; and based on a preset template and the video parameters, according to the target digital person teaching video, the target teaching digital person model, the video background image, the decoration of the target digital person model and the voice parameters, generating a MOOC video. The method can improve immersion and practical operation effect, realizes teaching according to materials, reduces manpower and time cost, and is suitable for large-scale course production.

The invention provides a method and a system for transmitting a photographic image of a security camera, relates to the technical field of image transmission, and effectively divides an image frame into a differential image block and a background image block by introducing a partition mechanism based on inter-frame pixel difference, thereby remarkably improving the detection efficiency of a moving target and the pertinence of data processing. Through embedding marks such as a direction vector and a motion timestamp in a differential image block set, priority coding of a dynamic target image area is realized, and further in combination with a periodic compression strategy of a background image frame, redundant data is compressed to the greatest extent, and the overall data transmission load is effectively reduced. Meanwhile, an image fragment priority model is jointly constructed through brightness saliency and texture complexity, and structure entropy and direction gradient analysis is assisted, so that redundant backup generation of key content is realized, and data integrity under a weak network condition is greatly improved.

The invention belongs to the technical field of image processing, and particularly relates to a bridge pier intelligent inspection method, which comprises the following steps of: performing Gaussian filtering on an inspection image through Gaussian filters with different scales, and subtracting an original inspection image from obtained background images with different scales to obtain intermediate images with different scales; calculating the enhancement degree of the gradient features of the pixel points in the intermediate images under different scales compared with the gradient features of the pixel points in the original inspection image; calculating the retention degree of the multiple LBP features of the pixel points in the intermediate image under different scales compared with the original inspection image; and according to the enhancement degree and the retention degree, calculating the weights of the gray values of the pixel points in the intermediate image under different scales, and carrying out weighted summation on the gray values of the pixel points in the intermediate image under different scales so as to obtain a uniform light image. According to the invention, the finally obtained uniform light image achieves better balance in detail and texture aspects.

The invention provides a hyperspectral anomaly detection method based on double-stage attention guidance and a state space model, which relates to the technical field of hyperspectral image processing and comprises the steps of scene background modeling based on an auto-encoding network, generation of a reconstructed background image and a reconstructed residual image. Carrying out target signal enhancement on the original hyperspectral data based on the reconstructed residual image to obtain attention enhancement data, carrying out target feature depth extraction by adopting a state space model based on the attention enhancement data, generating an abnormal semantic feature image, and fusing a background guide feature image and the abnormal semantic feature image to obtain a hyperspectral image; and an abnormal probability graph is generated through adaptive gating fusion, and collaborative optimization is carried out based on a multi-task loss function. According to the method, the internal contradiction of a single network architecture is fundamentally solved, the prior guiding capability of the reconstruction method and the strong feature representation capability of the state space model are fully combined, and the accuracy and reliability of hyperspectral anomaly detection are remarkably improved.

A method, apparatus, non-transitory computer readable medium, and system for image generation include obtaining a foreground image and a background image. The foreground image depicts an object and the background image depicts a scene. The foreground image is encoded, using an image encoder of an image generation model, to obtain a foreground embedding. The foreground embedding preserves the identity of the object. A composite image is generated, using the image generation model, based on the background image and the foreground embedding. The composite image depicts the object from the foreground image within the scene from the background image.

The invention provides an image sample generation method in an expressway scene based on image-text double control, and relates to the technical field of artificial intelligence such as automatic driving, large language models, hybrid expert models and image generation. The method comprises the following steps: determining a background image according to a historical image sample in an expressway scene, and adding a mask on the background image to obtain a mask addition image; obtaining a text feature corresponding to the text description of the target road element; generating an image feature corresponding to a reference image of a preset road element related to the target road element; determining corresponding feature weights of the text features and the image features through a hybrid expert model, and performing weighted calculation according to the corresponding feature weights to obtain comprehensive features; and inputting the comprehensive feature and the mask addition graph as input information into a preset diffusion model to obtain an incremental image sample of the image with the target road element added at the position of the mask in the mask addition graph. By applying the method, the sample quantity can be effectively increased.

The invention relates to the technical field of image classification, and discloses a hyperspectral image domain generalization classification method, system and device and a medium. The method comprises the following steps: acquiring a plurality of hyperspectral images of different ground feature types; obtaining a mean value and a variance of each hyperspectral image in a channel dimension, performing random disruption, and determining a spectral variation parameter of the hyperspectral image according to the mean value and the variance before and after random disruption so as to generate a corresponding spectral variation image; separating the center and the background of each hyperspectral image to obtain respective center image and background image, randomly disorganizing the center image and the background image, and generating a corresponding spatial variation image by combining the disorganized center image and background image of each hyperspectral image; a hyperspectral image classification model is obtained by training an enhanced hyperspectral image obtained by fusing a plurality of corresponding spectral variation images and spatial variation images, so that ground feature classification is performed on a to-be-classified hyperspectral image, and the domain generalization performance of the hyperspectral image classification model is improved.

The invention relates to the field of drawing recognition, in particular to a PDF drawing data extraction method and system based on intelligent recognition. Comprising the following steps: reading an internal structure of a PDF engineering drawing to obtain a native text stream, a vector path and a grating image; identifying the native text flow through a shunt preprocessing framework to form structured text data; rendering the vector path and the grating image to obtain a background image; analyzing the structured text data by utilizing the intelligent recognition model through the character recognition and extraction sub-model, obtaining drawing metadata and recording the position, and obtaining a character recognition result; analyzing the background image through a graphic element recognition and classification sub-model, recognizing and classifying component elements, and obtaining a graphic recognition result; and performing fusion according to the visual space corresponding relation to form a drawing analysis result. According to the method, the adaptive capacity of engineering drawings with various sources and different qualities is improved through the shunting preprocessing framework and the intelligent identification model.

The invention provides a stone gradation automatic detection method based on image processing, belongs to the technical field of image processing, and designs a two-dimensional convolution kernel capable of enhancing features according to the features that small-particle stones are bright in center, dark in edge and approximate to a circle by acquiring a gray image and a background image of a stone field through a video stream. Convolution operation is carried out on the image to improve the contrast ratio of the stone and the background, then an accurate binarized image is obtained through double-threshold segmentation and background difference processing, in order to further separate the adhered particles, the particle center is positioned by adopting distance transformation, and effective segmentation is carried out by combining a watershed algorithm. And according to the extracted particle contour, geometric parameters are calculated and the mass is estimated, so that a stone grading curve for evaluating the filling quality is automatically generated. According to the invention, rapid and non-contact automatic analysis of rockfill material grading is realized.

The invention provides a video cover generation method. The method comprises the following steps: determining a foreground entity of a video cover based on an input video; selecting a target video frame comprising a foreground entity from the video and determining features of the foreground entity in the target video frame; text information of the video cover is generated, and features of the text information are determined; determining a background image of the video cover; and generating the video cover based on the target video frame, the features of the foreground entity, the character information, the features of the character information and the background image of the video cover. The invention further provides a video cover generation device, electronic equipment, a computer readable storage medium and a computer program product.

Apparatuses, systems, and techniques to infer synthesized images including digital representations of objects blended realistically with appropriate background images. In at least one embodiment, background image data and a description of semantic features for a type of object are fused together to infer such a synthesized image using one or more neural networks.

An image processing method for extracting a subject from an image including the subject using a background image includes: generating a background removed image obtained by removing a background region not including the subject in the image from the image; compressing the background removed image; transmitting the background removed image compressed via a network; receiving the background removed image compressed via the network; decoding the background removed image compressed and received; generating a restored image by synthesizing the background removed image decoded and the background image; and comparing the restored image with the background image to extract the subject.

The invention relates to the technical field of ocean structure monitoring, in particular to a method for monitoring the scouring degree of a pile foundation of an offshore wind power station through bubble tracing, which comprises the following steps of: 1, constructing a bubble feature data acquiring an underwater image, manually segmenting and selecting key bubbles, extracting contour features of the key bubbles and expressing the contour features by vectors, a Zernike moment is used for grouping according to shapes, and a bubble feature database is constructed; and 2, identifying underwater bubbles: obtaining an underwater background image, constructing a dynamic background, obtaining an image containing suspended particles, extracting a foreground main body through a background subtraction method, comparing contour features of the foreground main body with a bubble feature database, and screening out the bubbles. By introducing an innovative method combining image recognition, feature modeling and physical inversion, the key problems of bubble misjudgment, track loss, inaccurate flow velocity and the like in a traditional bubble tracing technology are effectively solved, and high-precision, low-cost and automatic monitoring of the scouring process of the offshore wind power pile foundation is achieved.

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

In one aspect, a computerized method of automatically generating suggestions for a graphic design using a one click virtual button using an artificial-intelligence (AI) assistant for generating one or more contextually relevant graphics designs includes the step of receiving a digital design in a digital design tool. The digital design tool comprises the one click virtual button using the AI assistant for generating the one or more contextually relevant graphic designs. The method includes the step of detecting an event trigger for the one click virtual button. The method includes the step of with the AI assistant. The AI assistants operations includes determining and suggesting a set of alternate background images for the digital design, determining and suggesting a set of alternate font types, determining and suggesting a set of color replacements, determining and suggesting a set of alternate layout suggestions of multiple elements in the scene, determining and suggesting a set of icon replacements, determining and suggesting a set of graphs and chart replacements, determining and suggesting a set of resizes of the digital design, and determining and suggesting a set of alternate collage configuration. The method includes the step of enabling a user to selection at least of the as a user selection. The method includes the step of integrating the user selection into an updated version of the digital design. The method includes the step of displaying the updated version of the digital design in the digital design tool.

The invention belongs to the technical field of image processing, and particularly relates to a casting surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a gray-scale image and a brightness background image of a vortex casting to be detected, and calculating the illumination deviation of each position in the image; segmenting the grey-scale map of the vortex casting to be detected by using an Otsu method to obtain a binary image; constructing a circular structural element according to the width of the optical interference region and the resolution of the imaging equipment; performing corrosion operation on the binary image based on the structural elements to generate a structural saliency image; obtaining a defect saliency value by using a product of a gray value and an illumination deviation in the structure saliency map; and calculating a segmentation threshold value based on the statistical characteristics of the defect-free sample, and identifying the position of which the defect saliency value is greater than the segmentation threshold value as a defect point. Through physical constraint and illumination feature fusion, the problem that a dark vortex structure and a real defect are difficult to distinguish is effectively solved, and the detection precision is improved.

The invention discloses a background spectrum accurate acquisition method for infrared remote measurement in a complex scene, and the method comprises the steps: carrying out the division of sky and ground object backgrounds for an input background image, and selecting different radiation spectrum generation modes based on a division result; if yes, sky radiation spectrum extraction is carried out based on an MODTRAN radiation transfer model; if the background is judged to be a single ground object background, performing background spectrum extraction by adopting ELM-EMD (Extreme Learning Mode-Empirical Mode Decomposition) which is extracted in real time based on a measurement spectrum and changes slowly at low frequency; and if the background is not single, fusion of the two spectrum extraction modes is adopted. The method has the advantages that the background classification method with the visible image as the reference is adopted, the difference of infrared radiation characteristics of the sky background and the ground feature background is analyzed, different spectrum extraction modes are selected according to the difference, and therefore accurate obtaining of the target smoke plume transmittance spectrum is achieved.

The invention discloses a cutting fluid solid particle concentration measurement method and system, and relates to the technical field of particle concentration measurement, a time sliding window mode is used for carrying out fusion processing on an image sequence, synchronous acquisition of temperature and illumination intensity is combined, a dynamic temperature illumination normalized background image is constructed, and the concentration of solid particles in a cutting fluid is measured. The problems of brightness drift, image flicker and the like caused by environmental fluctuation are effectively eliminated, and the stability and adaptability of background modeling are remarkably improved. Through a disturbance identification and characteristic parameter extraction module, multi-dimensional characteristic extraction is carried out on non-particle disturbance such as bubbles, oil films and light reflection possibly occurring in the image, a disturbance characteristic set Rdr is formed, and the system can accurately identify and distinguish non-real particle signals. And through a dynamic difference denoising analysis module, a disturbance feature set Rdr is combined to carry out adaptive denoising on a difference image, a real particle structure is reserved, a non-particle interference region is eliminated, and a purified image with an enhanced edge is output, so that cleaner and more accurate basic data is provided for subsequent particle identification.