1863 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 relates to the technical field of data generation of sparse scenes, and discloses a data set generation method and system of a sparse scene, and the method comprises the steps: inputting an image which is actually collected by a target sparse scene into a target detection model, and obtaining a detection frame of a target foreground and a background; using a preset segmentation large model to take the detection frames of the target foreground and the background as cue words to perform model reasoning, respectively obtaining masks of the target foreground and the background, and obtaining a target foreground image and background area coordinate information based on the masks of the target foreground and the background; and integrating the target foreground image and the background area coordinate information by using an adaptive image fusion technology to generate a new target sparse scene image, and adding the newly generated target sparse scene image into the training data set of the target detection model. According to the method, only a small number of target objects and background images are needed, a large number of new high-quality images can be generated, the problem of scarcity of scene data is solved, and rich data resources are provided for subsequent model training.

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 embodiment of the invention provides an image processing method and device, a storage medium and computing equipment. Comprising the following steps: performing image segmentation processing on a target area in a source image to obtain a first mask image for the target area; performing superposition processing on the target area in the source image according to the first mask image to obtain a background image; wherein the background image comprises a source image after a first foreground image displayed in the target area is removed; inputting the background image and the first mask image into a pre-trained AIGC model to generate a target image; wherein the target image has the same background image, and a second foreground image replacing the first foreground image is displayed in the target area of the target image.

Synthesizing training data for training a change detection model includes receiving a patched image comprising a background image with a patch pasted into the background image. It further includes synthesizing a harmonized patched image at least in part by harmonizing the patched image using a machine learning model trained on the background image. It further includes providing as output a synthetic training sample usable to train a change detection model. The synthetic training sample includes a reference image, at least a portion of the harmonized patched image, and a corresponding mask.

To provide a food information providing device for encouraging customers to order and contributing to increase in an average customer spend in stores for providing food products such as conveyor belt sushi.SOLUTION: A food information providing device A as an example of the food information providing device, comprises a rail display part 1. Screens displayed on the rail display part 1 include a top screen 13, where the top screen 13 displays menu image data 130, menu name data 131, background image data 132, and character image data 133. Further, the plurality of menu image data 130 and menu name data 131 are displayed by scrolling in a fixed direction in set order. Furthermore, regarding the top screen 13, when the screen is touched with a finger and a right swipe operation or a left swipe operation is performed, the menu image data 130 having been displayed by the scrolling can be moved in a direction of the scrolling or in an opposite direction of the scrolling and displayed.SELECTED DRAWING: Figure 4

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 image processing, and provides an image rain removal method based on space-frequency double-domain perception, which comprises the following steps of: 1, constructing an image rain removal network based on space-frequency double-domain perception; 2, training the image rain removal network model in the step 1 until the model converges; and step 3, for any acquired rainy day scene image, reasoning by using the rain removal network trained in the step 2 to obtain a rain-free background image. Compared with the prior art, the rain-removed image generated by the method has fewer rain stripe residues and clearer texture features, and the problems of rain stripe residues, blurred background and the like in an existing image rain-removing method can be effectively solved.

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 present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital designs utilizing a diffusion neural network to preserve readability and design composition while modifying image content background images and design assets. In some embodiments, the disclosed systems access a text prompt defining visual attributes of a digital design. Furthermore, the disclosed systems generate a modified text prompt by replacing chromatic information within the text prompt. Additionally, the disclosed systems determine an adaptive strength for a diffusion neural network from the text prompt. Also, the disclosed systems generate a modified digital design utilizing the diffusion neural network to process the modified text prompt according to the adaptive strength.

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 discloses a two-stage hyperspectral image band selection and target detection method, which comprises the following steps of: based on a two-stage band selection and background reconstruction network of a transformer, realizing band selection through a first-stage training transformer and realizing background reconstruction through a second-stage training transformer; a transform position coding module and a multi-head self-attention mechanism are used for learning similar features and difference features among wave bands, a full connection layer network is used as a clustering device, a structural similarity index and an Euclidean distance are added to serve as a loss function training model, wave band clustering is achieved, and finally a local variance is used for estimating the noise level of an image of each wave band. Obtaining a band training subset; the band subset is subjected to constraint energy minimization detection and then sent to a background feature extraction network composed of a transformer and a discriminator, background pixels are arranged through position coding, network learning background features are enhanced through a multi-head self-attention mechanism, an improved loss function is used for training, and therefore reconstruction of a background image is achieved. The invention provides a two-stage wave band selection and background reconstruction network based on transformer so as to realize hyperspectral target detection. The network comprises a first-stage wave band selection work and a second-stage background spectrum learning work. And difference detection is carried out on the final background reconstruction image and the original image, so that a final task is realized, and the detection precision is improved.

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 relates to the technical field of image processing, and provides an image filling method and device, electronic equipment and a storage medium, and the method comprises the steps: encoding a text prompt word to obtain a text encoding vector, and encoding a masked background image to obtain an image encoding vector; splicing the image coding vector, the position mask and the random noise to obtain a fused noise vector; and step-by-step denoising is carried out on the fused noise vector, a text coding vector and a texture guide vector are added in the denoising process as guide conditions, a filling image is generated, and the texture guide vector is obtained by carrying out splicing, smooth transition and coding processing on texture features of the foreground image and texture features of the background image. According to the method, the filling image which is rich in details, natural in texture and highly fused with the background image can be generated through a step-by-step denoising process and combining the text coding vector and the texture guide vector as double guide conditions.

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 maritime scene image generation method and device. The method comprises the following steps: determining a real image of a maritime scene as a background image of an expected generation target; the target type of the target is generated, a mask image is created at the target position where the target is expected to be generated in the background image, and the mask image is matched with the target type; jointly inputting the mask image and the background image into a diffusion model, and converting image information into low-dimensional potential representation by an encoder of the diffusion model; performing forward diffusion and reverse diffusion on the potential representation in the hidden space, obtaining the state of the potential representation at any moment through noise adding processing in the forward diffusion process, performing denoising processing based on a U-Net network in the reverse diffusion process, and obtaining the potential representation of the target image through noise prediction; a target image of the offshore scene is generated based on the target image potential representation. According to the scheme, virtual-real combined marine target training samples are generated, the quality and diversity of the generated samples are improved, and the data acquisition cost is reduced.

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.