2208results about "Filling planer surface with attributes" patented technology

The present disclosure generally describes user interfaces related to time. In accordance with embodiments, user interfaces for displaying and enabling an adjustment of a displayed time zone are described. In accordance with embodiments, user interfaces for initiating a measurement of time are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface using a character are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface that includes an indication of a current time are described. In accordance with embodiments, user interfaces for enabling configuration of a background for a user interface are described. In accordance with embodiments, user interfaces for enabling configuration of displayed applications on a user interface are described.

A method, apparatus, and non-transitory computer readable medium for image generation are described. Embodiments of the present disclosure obtain a content input and a style input via a user interface or from a database. The content input includes a target spatial layout and the style input includes a target style. A content encoder of an image processing apparatus encodes the content input to obtain a spatial layout mask representing the target spatial layout. A style encoder of the image processing apparatus encodes the style input to obtain a style embedding representing the target style. An image generation model of the image processing apparatus generates an image based on the spatial layout mask and the style embedding, where the image includes the target spatial layout and the target style.

The invention discloses a multi-modal semantic and physical law driven remote sensing image generation method, belongs to the technical field of computer vision and remote sensing image generation, and aims to solve the problems of insufficient cross-modal semantic alignment, low reliability of a generation result and insufficient physical mechanism fusion. The four-stage method comprises the following steps: firstly, rejecting low-quality samples from original data and unifying a spatial scale; then, extracting a multi-modal semantic vector by adopting a BLIP model and a CLIP model, and introducing a remote sensing physical rule to carry out vector optimization; then position coding and physical constraint conditions are embedded in the submerged space, and multi-source information joint modeling is achieved through a cross-modal encoder; and finally, by taking text description, physical priori knowledge and diffusion time steps as joint conditions, performing de-noising reasoning based on a Transform architecture, and completing back diffusion reconstruction by means of a trans-attention mechanism. According to the method, physical rationality and semantic consistency are improved, and a more reliable technical normal form is provided for remote sensing image generation in the fields of disaster monitoring, military simulation and the like.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate utilizing a segmentation neural network, an object mask for each object of a plurality of objects of a digital image. The disclosed systems detect a first user interaction with an object in the digital image displayed via a graphical user interface. The disclosed systems surface, via the graphical user interface, the object mask for the object in response to the first user interaction. The disclosed systems perform an object-aware modification of the digital image in response to a second user interaction with the object mask for the object.

The invention relates to the technical field of image processing, in particular to a glioma area marking method and system for a neuromedical image, and the method comprises the following steps: obtaining a brain nerve image, segmenting a left brain area and a right brain area, carrying out the gray correction, extracting a contour boundary, carrying out the calibration, repairing a broken contour, dividing grids, and recognizing a texture extension path. And matching the transparency level and performing visual rendering to obtain a visual marking result of the glioma region. According to the invention, gray correction is carried out through the symmetry of the brain, the accuracy and the anti-interference capability of boundary point identification are improved by combining a boundary screening mode driven by gray difference, an angle change continuity detection mechanism is adopted, the integrity and the path stability of a curvature structure are enhanced, and a rasterized texture path extraction mode is adopted. And realizing continuous expression of cross-regional texture features, and optimizing a visual hierarchy and detail comparison effect of the marked region in combination with a transparency setting scheme of gray offset matching.

The invention provides a feature-enhanced advertisement service scene image generation method. The method comprises the following steps: S1, training data set preparation: collecting and preprocessing advertisement and e-commerce picture data sets; s2, selecting and initializing a basic model: selecting an appropriate stable diffusion large model as the basic model, and carrying out initial testing on the basic model; s3, Lora model training: training a Lora model on the basis of the stable diffusion large model; s4, model integration: integrating the trained Lora model parameters into a stable diffusion large model to realize characteristic enhanced image generation; and S5, generating and optimizing an image. According to the method, a customized feature enhancement model is efficiently trained at low cost, and then the model is integrated to a diffusion model to realize an image generation method of an advertising service scene, so that the problems of deficiencies in feature extraction and personalized generation of a traditional method and the problems of low efficiency and large resource consumption in fine adjustment of a large model are solved.

The invention discloses a model training method, a garment version image generation method, equipment and a readable medium. The method comprises the following steps: acquiring training data of which the type comprises text data and / or image data, and encoding the training data to obtain a training condition encoding vector; determining an original token sequence and a token position code according to the initial data of the clothing version and a preset clothing version coding rule; according to each original token sequence and a preset time step, obtaining a noise-containing token sequence; inputting each training condition coding vector, the token position code, a preset time step and each noisy token sequence into an initial clothing pattern prediction model to obtain a noise reduction token sequence output by the initial clothing pattern prediction model for each training condition coding vector; the noise reduction token sequence can be used for generating a garment pattern image corresponding to the training data; and adjusting parameters of the initial clothing model prediction model until a convergent and fitted clothing model prediction model is obtained.

Artificial intelligence-based systems and methods are described for providing personalized skin product recommendations. Natural language data of a user is received using a conversation engine, such as a generative pretrained transformer AI model. The natural language data is input into a synthetic image generation model for generating a digital twin image depicting skin condition(s). A product recommendation model inputs the digital twin image and / or phenotype and / or demographic classifications of the user, and outputs a skin care product recommendation for the user. An image simulation model inputs the product recommendation, the digital twin image and phenotype and / or demographic classifications of the user to output a simulated image of the user having one or more graphical enhancements. The simulated image may be displayed to the user on display screen along with natural language data describing the simulated image as depicted skin as predicted to appear following treatment with the skin care product.

The invention discloses a pathological section virtual staining image generation method based on a diffusion model, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, passing through Hamp; e dyeing serves as a source image data set, and pathological section images subjected to IHC dyeing serve as a target image data set; preprocessing the source image, and converting the source image into a corresponding YUV color space; extracting a saliency map and a gradient map of the source image, and generating a weighted attention map; building a diffusion model by using a neural network, and inputting the preprocessed source image for fine tuning; repeatedly iterating until the loss function converges, and ending the training; and inputting a weighted attention map to perform reasoning of virtual dyeing. According to the method, weighted attention of a pathological section image is added in a reasoning part to serve as an image prompt input network, so that the cell structure of a finally generated image is controlled not to be changed, and meanwhile, color features of a target dyed image are reserved and emphasized; and the accuracy and the reconstruction effect of the generated virtual dyeing image are improved.

The invention discloses a shielding object moving and editing method and system based on a diffusion model, and belongs to the technical field of computer vision and image processing. Comprising the following steps: acquiring an original image and a visible area mask and performing preprocessing to generate a standard input image and a standard mask; processing the standard input image and the standard mask through a de-occlusion branch, guiding a diffusion process by using a cross attention mechanism and a self-attention mechanism, and generating a de-occlusion object image; and obtaining a target position, inputting the original image, the visible area mask and the target position into a moving branch for processing, introducing a potential space adjustment operation, and generating a shielding object moving image under the guidance of a local text condition. According to the method, the integrity and authenticity of the shielded object after editing can be kept, natural fusion of the target position and traceless reconstruction of the original position are realized, and the problems of insufficient content completion, unnatural fusion of the target position and residual artifacts of the original position in the prior art are solved.

The pattern generation method based on diffusion model fine tuning comprises the following steps: constructing a data set; the generation model comprises an edge detection model ControlNet and a stable diffusion model, the stable diffusion model comprises a text encoder CLIP, a diffusion model U-Net and an image decoder VAE, the CLIP text encoder converts an input natural language into word vector features and inputs the word vector features into a U-Net network in a submerged space, and the diffusion model U-Net and the image decoder VAE are connected with the edge detection model ControlNet and the diffusion model U-Net; the U-Net network is controlled to carry out iterative denoising on pure noise in a low dimension to generate a compressed image, and finally the compressed image is restored to an original pixel image through a decoder VAE, so that text content control image generation is realized; carrying out fine tuning training on the generative model by adopting a LoRA parameter efficient fine tuning technology; and intelligently generating a batik style pattern. The method has the advantages that the texture precision of the generated image can be improved, and the personalized style image can be generated.

The invention provides a semantic input-based artistic font generation method, which comprises the following steps of: acquiring text data required by artistic font generation, and performing word segmentation processing on a text by adopting a word segmentation method to obtain a keyword list; performing semantic extension on each keyword in the keyword list according to a pre-constructed HowNet knowledge base to obtain synonyms, hypernyms and hyponyms of the keywords; performing word frequency statistics and TF-IDF weight calculation on the expanded keyword list, and screening out a core semantic concept required by the keyword art font generation with the highest weight; calculating semantic similarity among the core semantic concepts, and constructing a semantic similarity matrix; taking the screened core semantic concepts as nodes and the semantic similarity as weights of edges, and constructing a semantic network atlas; k-means clustering analysis is carried out on the semantic concept vectors, concepts with high semantic similarity are clustered according to a clustering result, and a semantic vector representation model required by artistic font generation is constructed.

This document relates to providing adaptive teleconferencing experiences using generative image models. For example, the disclosed implementations can employ inpainting and / or image-to-image restyling modes of a generative image model to generate images for a teleconference. The images can be generated based on prompts relating to the teleconference. Users can be superimposed on the generated images, thus giving the appearance that the users are present in an environment generated by the generative image model.

A tile-based graphics processor performs first and second processing passes to generate a render output. The first processing pass generates and writes out information representative of a set of bounding boxes, and the second processing pass uses the bounding box information to determine which primitives to process for which rendering tiles.

The invention discloses a structured data self-learning method based on a graph neural network, and the method comprises the following steps: S1, analyzing structured data, extracting entity fields and relation fields, and constructing a structure candidate graph; s2, generating a node embedding feature matrix, and initializing and recording the adjacency relation of candidate edges; s3, constructing a graph neural network model, inputting node features and an adjacent matrix, and defining a task loss function; s4, evaluating the gradient contribution degree of edge connection by adopting a gradient sensitive sparse adjacency self-learning algorithm, and updating the graph structure representation; s5, introducing an embedded interpretability gradient backtracking mechanism, correcting an edge connection relation and enhancing interpretability; s6, training the graph neural network by using the corrected structure, and updating the node embedding and graph structure; and S7, outputting a final graph structure and an interpretability index, and generating a graph modeling visualization result. According to the method, efficient modeling and explanatory analysis of structured data are realized through a dynamic graph structure learning and gradient backtracking mechanism.

An image processing method, performed by a computer device, comprising: extracting sketch texture features at multiple scales from a sketch image; extracting image noise features at multiple scales from preset noise; determining color guide information corresponding to the sketch image; encoding, for each scale, a noise feature based on a sketch texture feature and the color guide information to obtain multi-scale image features; and performing multi-scale decoding on these image features to obtain a colored image comprising a sketch texture corresponding to the sketch image and a color based on the color guide information. A related training method and apparatus are also provided to develop models for this image processing technique.

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

The invention discloses a deep learning-based cold resistance evaluation system for medicago sativa L., and the system comprises a data generation module which is used for generating a phenotypic image and corresponding physiological data of medicago sativa L. under low-temperature stress through a diffusion model embedded with plant low-temperature response physical constraints; the evaluation model module is used for extracting cold resistance characteristics from the image and physiological data by adopting a causal-driven dual-channel adaptive network; and the decision module comprises a hierarchical model distillation unit and a federal reinforcement learning unit, and the hierarchical model distillation unit and the federal reinforcement learning unit realize joint training of model compression and decision strategy optimization through an edge-cloud collaborative architecture, output a cold-resistant decision and realize visualization through an augmented reality interface. The method can effectively solve the core problems of traditional medicago sativa cold resistance assessment in the aspects of data generation, model generalization, decision-making efficiency and the like.

The invention discloses a multi-platform-oriented intelligent interface adaptation and rendering optimization method and system, and belongs to the technical field of interface rendering. The method comprises the following steps: firstly, acquiring and analyzing interface description information, and extracting hierarchical structures and constraint relationships of interface elements to obtain abstract description features; performing adaptive layout mapping based on the environment characteristic parameters of the target platform, and dynamically adjusting space occupation and display priorities under the constraint of keeping the relative position relationship of interface elements unchanged to obtain a layout scheme after platform adaptation; determining resource consumption nodes according to interface element distribution characteristics, and determining a layered rendering strategy by analyzing visibility states and updating frequency characteristics; applying the layout scheme and the rendering strategy to a rendering execution process to generate output; and collecting performance feedback data to adaptively update a space occupation and rendering strategy. According to the method and the device, automatic adaptation and rendering performance optimization of the cross-platform interface are realized, and the multi-platform interface rendering efficiency and the user experience are improved.

When generating a sequence of render outputs using a graphics processor, the completion status of rendering tasks for different render outputs is tracked so that processing tasks for later render outputs in the sequence of outputs can be processed concurrently with processing tasks for earlier render outputs in the sequence of outputs whilst ensuring that any dependencies between the rendering tasks are enforced.

The invention discloses a remote sensing image semantic segmentation prediction method and system based on a vision-language pre-training model. The method comprises the following steps: acquiring a source domain image with a label and a target domain image without a label; adding a pseudo tag to the target domain image without the tag by using a teacher network; performing style conversion on the labeled source domain image to obtain a style migration image with a target domain image visual style; extracting text embedding features of the labeled source domain image by using a pre-trained vision-language pre-training model to obtain text embedding features of a semantic category; fusing the tagged source domain image, the style migration image and the text embedding features of the semantic category to generate an intermediate domain fusion image containing double-domain information and language priori knowledge; performing random mask processing on the intermediate domain fusion image; and inputting the multi-scale context features and the text embedding features of the student network extraction mask image into a vision-language decoder, and then carrying out semantic segmentation on a prediction result.

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

The invention relates to the technical field of image information processing, in particular to an image information processing method for short video production. According to the invention, each segment in a short video is analyzed and classified in real time by using a scene cognition algorithm; the scene cognition algorithm recognizes different scene types, and applies the most suitable image processing strategy for each scene; the definition of facial details and expressions is enhanced in a character close-up scene, the blurring and distortion of key points are reduced in a fast moving scene, and the optimal image quality is realized in each scene through dynamic processing; a digital twinning technology is introduced, a virtual model based on digital twinning is constructed, and virtual elements generated by AI are seamlessly integrated into an actual scene; physical characteristics of illumination, shadow, material reflection and the like in a real scene are simulated in real time by using a digital twin model, so that fusion between virtual content and a real background is more natural; the problem of image information processing in current short video production is solved.

The embodiment of the invention discloses a primitive distribution method and device, equipment and a storage medium, the method is applied to a graphics processor, and the method comprises the steps that at least one piece of primitive group data sent by a primitive distribution module is output to a corresponding partitioning device through a geometric processing pipeline; the primitive group data comprises multiple pieces of to-be-distributed data with a distribution sequence; distributing a plurality of pieces of data to be distributed in each piece of primitive group data based on the distribution sequence through each partitioning device, and generating a polygon list of each graph block; the polygon list comprises distributed data corresponding to the blocks; and for each image block, acquiring distributed data corresponding to the image block from each polygon list corresponding to the image block through a rasterization processing pipeline so as to perform rasterization processing on the image block.

Disclosed are systems and methods to track an object in a scene even when the object is partially or wholly obscured by an artifact, such as another object. As the tracked object moves through the scene, frames of video are processed and used to refine and tune a diffusion model that predicts an appearance of the tracked object in future frames of the video as well as the appearance of artifacts in the frames of the video. Frames of the video may then be enhanced to illustrate the tracked object as if the tracked object were visible through the artifact.

A method of generating digital piping data from a paper isometric image. The method includes receiving a 2-Dimensional (2D) isometric image corresponding to a piping structure. The 2D isometric image comprises one or more piping components corresponding to the piping structure. The method also includes identifying at least one a direction, an orientation, a name, and dimensions of each of the piping components in a 3D space. The method further includes identifying 3D coordinate information corresponding to a start point and an end point of each of the piping components. Furthermore, the method includes generating 3D geometry configuration data corresponding to each of the piping components based on the corresponding identified 3D coordinate information, and the corresponding at least one of the direction, an orientation, and a dimension in a predefined standard format.

The unmanned intelligent concrete vibrating system comprises the steps that a to-be-poured bin surface is subjected to high-angle shooting through a camera, a vibrating forbidding area is obtained through marking according to a high-angle shot picture, and meanwhile coordinates and elevation information of all points of the bin surface are obtained through the surveying and mapping function of a space positioning system; the computer system is used for planning a vibrating operation route according to the high-angle image and coordinates and elevation information of all points of the bin surface obtained through the surveying and mapping function of the space positioning system; and the unmanned aerial vehicle is used for carrying a vibrating rod to conduct vibrating according to the vibrating operation route, and whether vibrating is completed or not is judged according to the actual situation of concrete. The advantages that the unmanned aerial vehicle flies stably and is easy to control and has certain loading capacity can be fully utilized, the functions of vibration route design, intelligent vibration end point judgment and the like are combined, the unmanned aerial vehicle carries the vibration system to conduct vibration operation, traditional manual operation is replaced, and the vibration quality and efficiency of concrete are improved.

A technique for rendering is provided. The technique includes determining a first correspondence between a screen space and a shade space in a visibility pass; selecting a size for a shade space tile based on the correspondence between the screen space and the shade space; and shading the shade space tile based on the material texture correspondence.

The technology introduces 3D Moments, a new computational photography effect. As input a pair of near-duplicate photos is taken (FIG. 1A), i.e., photos of moving subjects from similar viewpoints, which may be very common in people's photo collections. As output, the system produces a video that smoothly interpolates the scene motion from the first photo to the second, while also producing camera motion with parallax that gives a heightened sense of 3D (FIG. 1B). To achieve this effect, the scene is represented as a pair of feature-based layered depth images augmented with scene flow (306). This representation enables motion interpolation along with independent control of the camera viewpoint. The system produces photorealistic space-time videos with motion parallax and scene dynamics (322), while plausibly recovering regions occluded in the original views. Experimentation demonstrating superior performance over baselines on public benchmarks and in-the-wild photos.

The invention provides a lace texture image diffusion generation method based on an ADM and a dynamic denoising filtering model, the model combines a learnable Gaussian blur layer and a high-pass filtering technology, and the Gaussian blur layer aims at adaptively adjusting the blur degree of the image according to different input images and reducing high-frequency noise in the lace texture image. According to the high-pass filtering technology, key areas such as the contour and the edge of a main pattern in the image are enhanced, so that main texture features of the image are highlighted. The module is added into a down-sampling layer of an ADM network, the phenomenon that lace texture images generated by an original model are too smooth is improved, and the texture layering sense of the images is enhanced.