168 results about "Image properties" patented technology

The invention provides a method for editing facial image attributes based on a text of a diffusion model. The method realizes flexible facial editing with high quality and identity consistency. The method comprises the following steps: constructing a description control face diffusion model comprising a noise prediction network and a variational auto-encoder, wherein the noise prediction network comprises a plurality of text alignment face Transform modules and an optimization residual feature module; the method comprises the following steps: inputting an original image and target text description based on a pre-training model, optimizing a target embedding generated by the input target text description by a text alignment face Transform module to obtain an optimized embedding, and performing model fine tuning after optimization is completed; and performing linear interpolation on the target embedding and the optimized embedding to obtain an initial editing result, introducing ArcFace-Loss as identity loss, extracting face features of an original image and the initial editing result through a pre-training model, calculating feature similarity and minimizing the loss, and ensuring consistency of face identities after editing.

A method for assessing the shape of an orthodontic aligner, said method comprising the following steps:a″) acquisition of at least one image at least partially representing the aligner in a service position in which it is worn by a patient, called “analysis image”;b″) analysis of the analysis image by means of a deep learning device, preferably a neural network, trained by means of a learning base, so as to determine a value for at least one tooth attribute of an “analysis tooth zone” representing, at least partially, a tooth on said analysis image, the tooth attribute relating to a separation between the tooth represented by the analysis tooth zone, and the aligner represented on the analysis image, and / orfor an image attribute of the analysis image, the image attribute relating to a separation between at least one tooth represented on the analysis image, and the aligner represented on said analysis image.

The invention provides an underwater multi-color space image enhancement method based on depth information guidance, which is characterized in that a model of the method is based on a training model framework capable of utilizing physical priori knowledge and multi-color information, and comprises a depth information generation module, a multi-color space feature fusion module and a depth enhancement module, the relation between a depth image and an underwater scene is enhanced through attention estimation, scene reconstruction training and fusion of multi-color information, so that insensitivity of a single RGB color space to brightness and saturation image attributes is made up; the feature representation capability is improved, and the scene adaptability of the model is enhanced.

The application discloses a fine-grained clothing image retrieval method and device for injecting common sense knowledge of a large language model. The method first extracts fine-grained visual features of an input image through an image encoder and optimizes them in combination with a low-rank adapter to improve the representation ability at the image patch level. Then, a pre-trained large language model generates attribute-enhanced common sense knowledge context to enrich the image attribute representation, thereby helping the model understand and reason about unknown attribute information in an open scenario. The application introduces a switchable modal prompt and interpolation mechanism to ensure that the agent embedding can be dynamically supplemented when attributes or text are missing. During the retrieval process, through an attribute-guided cross-modal attention mechanism, fine-grained image content matching is performed based on the relationship between image features and attribute-enhanced context. Through multi-modal feature alignment and optimization, the application improves the accuracy and robustness of clothing image retrieval in an open world scenario.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates text and image attributions and provides for display in a digital document the image attribution of an image element and the text attribution of text in the digital document. In particular, the disclosed systems receive a prompt relative to a digital document, and in response, generates an answer to the prompt using a multimodal large language model. Furthermore, the disclosed systems generating an image attribution and a text attribution in response to a selection of at least a portion of the answer to the prompt. Specifically, the image attribution and the text attribution indicate portions of the digital document that provide support for the at least a portion of the answer. Moreover, the disclosed systems provide for display in the digital document of a client device, the image attribution and the text attribution.

The present application provides a method and device for thinning flowers and fruits based on image recognition. The method obtains a first image of a fruit tree to be processed captured by an image acquisition device. Based on the first image, the fruit tree type of the fruit tree to be processed is determined. According to the fruit tree type, a flower and fruit thinning model that matches the fruit tree type is determined. Based on the flower and fruit thinning model, a plurality of target information to be processed corresponding to the fruit tree to be processed and the attribute value of each target to be processed are determined. Among them, the target to be processed includes at least a flower bud image and a young fruit image. The attribute value represents the fruit setting rate of the target to be processed after processing. The first image, the second image of each target to be processed and the attribute value of each target to be processed are sent to a preset expert planting model to determine the processing risk value of the target to be processed. Based on the attribute value of each target to be processed and the corresponding processing risk value, the processing target is determined so that the flower and fruit thinning management terminal corresponding to the image acquisition device thins the flowers and fruits of the fruit tree to be processed according to the processing target.

A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a style kit including a first image generation input indicating a first image attribute, a second image generation input indicating a second image attribute, and a selectability parameter indicating that the second image generation input is selectable. A third image generation input is received from a user based on the selectability parameter, wherein the third image generation input indicates a third image attribute different from the second image attribute of the second image generation input. An image generation model generates a synthetic image based on the style kit, the first image generation input, and the third image generation input, wherein the synthetic image has the first image attribute and the third image attribute.

Described herein are systems, methods, and instrumentalities associated with estimating the motions of multiple 3D points in a scene and predicting a view of scene based on the estimated motions. The tasks may be accomplished using one or more machine-learning (ML) models. A first ML model may be used to predict motion-embedding features for a temporal state of a scene, based on motion-embedding features for previous states. A second ML model may be used to predict a motion field representing displacement or deformation of the multiple 3D points from a source time to a target time. Then, a third ML model may be used to predict respective image properties of the 3D points based on their updated locations at the target time and / or a viewing direction. An image of the scene at the target time may then be generated based on the predicted image properties of the 3D points.

A design device and a method for providing a 3D model for a design development of an object. The method includes specifying an object type to a trained artificial neural network. the artificial neural network is trained with a plurality of basic image data of objects of this object type; generating an image data set of the predetermined object type by the trained artificial neural network, the artificial neural network provides image data from a plurality of perspectives as an image data set; checking the generated image data set for the presence of a design selection criterion, if the design selection criterion is not met, image data are regenerated by a change in an image property criterion; generating a digital 3D model from the image data set.

The application discloses a garbage classification model training method, device and equipment. A set proportion of garbage images is selected from garbage images, and image properties of regions where each target is located in the selected garbage images are adjusted to obtain a first data set. The first data set includes garbage images whose image properties are adjusted and garbage images whose image properties are not adjusted in original garbage images. The garbage images have been labeled with categories of the targets and regions where the targets are located. Garbage images including multiple targets that overlap with each other in the first data set are copied, and the copied garbage images are added to the first data set to obtain a second data set. The copied garbage images are labeled with one of the multiple targets that overlap with each other. A target detection model is trained based on the second data set. The application not only improves the efficiency of garbage classification model training, but also further improves the accuracy and detection speed of garbage classification performed by the garbage classification model.

The present application relates to the technical field of image processing, and particularly relates to a scenic spot image processing method and device, equipment and a storage medium, the method comprising obtaining a to-be-processed scenic spot image, determining a target region in the to-be-processed scenic spot image, removing the target region in the to-be-processed scenic spot image to obtain a removed image; obtaining image attribute information of the to-be-processed scenic spot image, obtaining a similar image corresponding to the removed image according to the image attribute information; determining a target region in the similar image, completing the removed image according to the target region in the similar image to obtain a completed image; inputting the completed image and the image attribute information into a preset image inpainting neural network model to perform image inpainting on the completed image, and obtaining an inpainted image of the to-be-processed scenic spot image, which realizes the elimination of sundries and inpainting of the to-be-processed scenic spot image, and meets the image processing requirements of users.

A system and method performed by an electronic device for generating a high-resolution image from an input low-resolution image are provided. The method includes determining one or more feature vectors for each of attributes associated with an input low-resolution image based on one or more image attributes, determining a degradation representation map of the input low-resolution image based on at least one of the input low-resolution image and the one or more feature vectors of the one or more image attributes, generating one or more personalization parameters based on at least one of the input low-resolution image and a user input, obtaining a first output image based on degradation information, a first process, and the one or more personalization parameters, obtaining a second output image based on the degradation information, a second process, and the one or more personalization parameters, and generating the high-resolution image by upsampling at least one of the first output image and the second output image.

The application provides an image encryption method and device, equipment, storage medium and program product, and relates to the technical field of privacy calculation. The method obtains an original image, generates a chaotic sequence corresponding to the original image, generates a secure thumbnail image and a chaotic compressed ciphertext image that reserve visual features of the original image based on the chaotic sequence and the original image, and embeds the chaotic compressed ciphertext image in the least significant bit of the secure thumbnail image to obtain a visually perceptible image. The method solves the contradiction between security and recognizability of the traditional encryption method through the fusion of multi-level encryption and visual perception technology, significantly improves the security and user recognizability of image transmission, reduces the possibility of identification by attackers, and ensures that users can quickly identify image attributes through visual preview.

An image processing method and apparatus, an electronic device, and a storage medium are provided, the image processing method includes: processing an image to be processed to obtain a panoramic complementary image of a target pixel ratio according to an image attribute of the image to be processed; determining a plurality of target patch maps on a bounding box according to the panoramic complementary image; wherein a displayed content on the bounding box corresponds to the panoramic complementary image; and determining a panoramic surround image based on the plurality of target patch maps.

Embodiments of the present application disclose an image generation method and device, computer equipment and a storage medium; an image generation instruction can be received, the image generation instruction including image attribute information of a target image to be generated, environment information is obtained, the environment information is collected through a display device displaying the target image, data mapping is performed on the environment information through a preset image generation model, a content image corresponding to the environment information is generated, style features are extracted from at least one preset style reference image, content features are extracted from the content image, and the target image is obtained based on the fusion of the content features and the style features; in the embodiments of the present application, the image generation model can map the environment information to obtain the content image, the content features in the content image can be fused with the style features in the style reference image, and finally the target image is generated, so that a wallpaper image with rich content and various styles can be generated, and the visual experience of a user is improved.

The embodiment of the present disclosure discloses an object information determination method, device, equipment and storage medium, the method comprises: acquiring a panoramic image and image attribute information of the panoramic image; determining pixel point matrix information of the panoramic image according to the image attribute information of the panoramic image, the pixel point matrix information includes depth direction information of each pixel point in the panoramic image relative to the image capture position point; identifying the panoramic image, determining a target object in the panoramic image and object region information of the target object; and determining region attribute information of the target object according to the pixel point matrix information and the object region information. By using the method, the region attribute information of the object in the real scene can be effectively determined through the analysis of the panoramic image, the determined region attribute information of the scene object can be used as data support to participate in the actual business operation, and the cost investment of introducing the region attribute information from the data information provider is reduced.

The invention discloses a multi-level dynamic layer management method and system, and belongs to the technical field of computer graphic processing and data management, and the method comprises the following steps: obtaining layer data from a plurality of external data sources, converting the layer data into a uniform format in the system, and importing the layer data into the system; constructing a multi-level tree structure system for the imported layers according to a preset rule or a user-defined mode so as to present a hierarchical relationship between the layers; marking the basic graphic image attribute of each layer, the position of each layer in the hierarchical system, the association relationship between each layer and other layers, and the business-related attribute; responding to various dynamic adjustment operations in real time, and updating the layer state according to the layer attribute and the association relationship; a visual and convenient operation interface is provided, and a multi-level graph layer structure tree is displayed; and different layer operation authorities are allocated for different user roles. The method is widely applicable to layer management systems in multiple fields, and the overall efficiency and practical value of layer management can be improved.

The invention relates to the technical field of gastric cancer image recognition, and discloses a gastric cancer early lesion image recognition method based on deep learning. The method comprises the steps of obtaining a to-be-analyzed gastric cancer medical image set, and screening an adaptive deep learning model group according to image attributes and recognition requirements; efficiency indexes such as model memory occupancy, single-frame processing delay and throughput rate are continuously tracked, priorities are evaluated for candidate models according to tracking data, and a model activation sequence list is formed. Selecting models one by one according to the list, and checking whether all the assigned images can be completed within a specified time limit or not: if the checking is passed, allocating computing resources to initialize a single model copy, and if the checking is not passed, deducing the number of the required model copies. Checking available computing resources, if the available computing resources are sufficient, starting corresponding copies, and if the available computing resources are insufficient, recovering low-priority activated model resources and then checking again; finally, whether all the active models are located at the top of the list or not is verified, if yes, scheduling is terminated, and if not, remaining models of the list continue to be processed.

A network model training method, an image attribute recognition method, an image attribute recognition device (500) and an electronic device (600). The network model training method comprises: acquiring an image sample set (101); constructing a basic model and a target loss function corresponding to the basic model (102); training the basic model according to the image sample set and the loss function until the basic model converges; and taking the converged basic model as a recognition model for recognizing image attributes (105).

To simulate illumination and imaging properties of an optical production system when illuminating and imaging an object by use of an optical measurement system of a metrology system, a pupil stop of the optical measurement system is initially provided for the purpose of specifying at least one measurement illumination setting created by use of the pupil stop. Measurement aerial images Imeas are recorded in an image plane of an imaging optics unit of the optical measurement system for different displacement positions of the object perpendicular to an object plane (xy) for the at least one measurement illumination setting. A complex mask transfer function M is reconstructed from the recorded measurement aerial images Imeas. A 3-D aerial image Isim of the optical production system is determined from the reconstructed mask transfer function M and a specified illumination setting σtarget of the optical production system as the result of the simulation method. The reconstruction includes the fact that the optical production system to be simulated comprises a production illumination setting BPy1, BPy2) to be simulated, with the latter varying in the object displacement direction (y). This yields an improved simulation method.

The application discloses a data processing method, device and computer readable storage medium. The method comprises: obtaining a label word vector corresponding to each category label in a category label set, obtaining an image attribute initial feature corresponding to an image, and an image semantic initial feature corresponding to the image; performing feature fusion on the label word vector matrix and the image attribute initial feature to obtain an image attribute feature, and performing self-attention enhancement on the image attribute feature to obtain an image attribute enhanced feature; performing feature fusion on the image attribute feature and the image semantic initial feature to obtain an image semantic enhanced feature; and determining a target category label of the image in the category label set according to the label word vector matrix, the image semantic enhanced feature and the image attribute enhanced feature. The application can improve the recognition accuracy of the target category label of the image. The embodiments of the application can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving.

A prompt generating apparatus includes a user prompt acquiring unit, a system prompt selecting unit, an input prompt generating unit, and a generated image acquiring unit. The user prompt acquiring unit is configured to acquire a user prompt inputted by a user. The system prompt selecting unit is configured to select a system prompt preset for an image usage specified by the user among system prompt presets that include image property setting values that have been selected of plural image property items for specific image usages. The input prompt generating unit is configured to generate an input prompt on the basis of the user prompt and the image property setting values in the selected system prompt preset. The generated image acquiring unit is configured to acquire a generated image corresponding to the input prompt using an image generation model.

The application provides an image rendering method, device and equipment, which can be applied to the technical field of image processing. The method comprises the following steps: performing feature fusion on a plurality of initial images to obtain image fusion features, wherein the plurality of initial images represent a target object based on different initial shooting angles; generating a voxel grid representing the target object according to the image fusion features, wherein a center point of the voxel grid is used as an anchor point feature; performing attention fusion on a specified pose of the plurality of initial images, the image fusion features and the anchor point feature based on an attention mechanism to obtain a target feature; performing image attribute detection on the target feature to obtain a target attribute; and performing image rendering by using the target attribute to obtain a target image representing the target object based on a target shooting angle.

The application discloses a test system and method for an unmanned device controller, which comprises an image acquisition device configured to acquire first image data, and send the first image data to a video injection core board, wherein the first image data comprises at least first image attributes and first image content; an image storage device configured to send second image data to the video injection core board, wherein the second image data comprises second image content; and the video injection core board configured to analyze the first image attributes to obtain analyzed first image attributes, replace the first image content with the second image content to obtain target image data, and send the target image data to the unmanned device controller, wherein the target image data comprises at least the analyzed first image attributes and the second image content; and the unmanned device controller configured to output a control instruction based on the received target image data, thereby improving the test accuracy.

The application belongs to the technical field of image processing, and discloses a cloud networking video AI image enhancement method, system, device, medium and terminal. The cloud networking video AI image enhancement method comprises the following steps: performing image analysis on a road material database composed of roads, vehicles and geological structures, combing image attributes, and classifying and collating, cooperating with an image enhancement algorithm, and obtaining a clear picture through material replacement, picture repair, contrast enhancement and intelligent deployment. The application enhances the definition of standard definition and below images, so that it meets the requirements of high-definition video. The application changes the original unclear image into a high-definition image through the algorithm of material replacement. Since the highway video is relatively single compared with the public security monitoring image content, the picture improvement effect is relatively obvious, thereby improving the accuracy of the intelligent analysis and event detection functions in the later stage, and the application effect is obvious. The application reduces the construction cost of the monitoring device. The application has good compatibility.

This invention provides a determination device, determination method, program, and recording medium that improve the accuracy of determining whether a person's eyes are open or closed based on an image. [Solution] The determination device includes an image acquisition unit that acquires a person's face image sent from an imaging device connected via a network, an attribute acquisition unit that acquires attribute information of the person based on the face image acquired by the image acquisition unit, and a determination unit that determines whether the person's eyes are open or closed based on the attribute information of the person acquired by the attribute acquisition unit and the face image.

A method for analyzing an image, called “analysis image”, of a dental arch of a patient. The method in which the analysis image is submitted to a neural network, in order to determine at least one value of an image attribute relating to the analysis image. The analysis image is a photograph or an image taken from a film. The image attribute relates to a position or an orientation or a calibration of an acquisition apparatus used to acquire the analysis image or a combination thereof, or a quality of the analysis image, and in particular relating to the brightness, to the contrast or to the sharpness of the analysis image, or a combination thereof.

The application discloses a kind of X-ray flat panel detector timing synchronization and correction method and system, image is acquired by flat panel detector;By timing control module, the main synchronization pulse is sent to the synchronization interface of flat panel detector on one hand, the working frequency of flat panel detector is controlled;On the other hand, the exposure window pulse returned by flat panel detector is frequency-division;After frequency-division, the secondary synchronization pulse is given to high voltage generator interface to control X-ray beam exposure;Image property is marked according to the pulse controlled by timing control module and the setting of frequency-division in the image acquired by flat panel detector in the above step 1;According to the strategy set, dark-field image multi-frame average or recursive average is carried out, and real-time offset correction data is generated.The offset correction data of the application is updated in real time, and does not need to be generated in advance, which greatly simplifies the generation mode of other correction files, solves the contradiction between the real-time application and the image quality of the equipment, and also provides a solution for variable frequency acquisition.

The invention provides a mammary gland ultrasonic focus interpretable diagnosis system, a storage medium and electronic equipment, and relates to the technical field of medical image artificial intelligence auxiliary diagnosis. According to the method, image attribute feature tags are mapped to corresponding nodes of a knowledge graph. Then, the graph neural network model carries out multi-hop reasoning on a graph structure through a message passing mechanism; according to the method, not only can information from related attribute nodes be aggregated, but also the reasoning result of the graph neural network model can be calibrated and explained by utilizing clinical expert rules extracted from the knowledge graph, so that a clear and traceable reasoning path is attached to each prediction of the graph neural network model. The technical problem that image information is separated from the knowledge reasoning process in an existing knowledge graph fusion ultrasonic focus diagnosis method is solved, the visual perception ability of the image and the symbol reasoning ability of the knowledge graph are combined, and therefore an intelligent diagnosis system which is accurate in prediction and clear in explanation is provided.

The invention discloses an underwater image quality intelligent evaluation method, relates to the technical field of computers, solves the technical problem that underwater image quality evaluation is not comprehensive enough, and is characterized in that firstly, an attribute feature map strongly related to five dimensions including brightness, color cast, definition, noisy points and naturalness is extracted; then carrying out statistical analysis on the attribute feature maps, and establishing reliable feature representation for each attribute; and finally, fusing and regressing the features through a constructed attribute feature fusion model and a proposed combined loss function to obtain a final quality score, thereby establishing an objective non-reference evaluation index. Compared with an existing method, the method has the advantages that image attributes considered by the method are more comprehensive and do not depend on any additional reference, the unprocessed underwater image and the enhanced underwater image can be effectively represented at the same time, and better effectiveness and robustness are achieved.