276 results about "Image properties" patented technology

A system for generating highly personalized image content with generative artificial intelligence (AI) that incorporates brainwave data collected from a Brain-Computer Interface (BCI) device worn by the user while they are immersed in their experiences. The system can identify a mood (i.e., cognitive, mental, and emotional states) felt by the user during that time. The system develops a prompt that describes the target scene and modulates the weights assigned to each parameter in the prompt based on the mood identified. The generative AI engine can then to apply the mood to the target scene by adjusting image attributes such as hue, saturation, and lighting. The outputted image can offer a visual representation not only of the objects that were in the user's environment, but also the user's general perception and experience of the occasion. The process is passive and requires no interaction from the user to define their emotional affect.

A method for training a face swap model includes concatenating an expression feature of a template image and an identity feature of a source face image to obtain a combined feature; performing encoding based on the source face image and the template image to obtain an encoding feature required for face swap; fusing the encoding feature and the combined feature to obtain a fused feature; performing decoding based on the fused feature to obtain a swapped face image; respectively predicting image attribute discrimination results of the swapped face image and a reference image by using a discriminator network of the face swap model; and calculating a difference between an expression feature of the swapped face image and the template image, calculating a difference between an identity feature of the swapped face image and the source face image, and updating the generator network and the discriminator network.

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 discloses an image credibility detection method, system and device and a storage medium, and relates to the field of image processing, and the method comprises the steps: extracting visual features from a to-be-detected target image, the visual features comprising color features, texture features and shape features; performing weighted fusion on the visual features and the metadata features to obtain a fusion feature vector; the metadata features represent image attribute information; and calling a credibility detection model, and detecting the credibility of the target image according to the fusion feature vector. Visual features and metadata features of a target image are extracted, weighted fusion is carried out, and then a trained credibility detection model is used for evaluation, so that the accuracy of image credibility evaluation is improved, a comprehensive evaluation means is provided, and the reliability and practicability of evaluation are enhanced. The method can effectively deal with various complex scenes, and provides a reliable evaluation means for various fields depending on image information.

Image analysis is a vital field since images can provide contextual, environmental, and emotional factors. Conventional methods are facing challenges in analyzing an image accurately when the image is having lesser data or if the image is having less resolution. Conventional machine learning architectures are computationally intensive when run on high power computing devices for training and inference. The present disclosure provides a robust deep learning model to inference in any given environmental condition. Initially, image data is generated using a pre-trained Generative Adversarial Network (GAN). The GAN receives a plurality of images of varying domain and generates image data. The image data is annotated and segmented to obtain a contextual label map. The contextual label map is given as input to a pre-trained transfer learning model to obtain a plurality of image attributes including number of objects and activity performed by each object.

The invention discloses a fine-grained costume image retrieval method and a fine-grained costume image retrieval device based on large language model common knowledge injection. According to the method, firstly, fine-grained visual features of an input image are extracted through an image encoder, and optimization is carried out in combination with a low-rank adapter, so that the representation capability of an image patch level is improved; thirdly, generating attribute-enhanced common knowledge context through a pre-trained large language model, and enriching image attribute representation, thereby helping the model to understand and infer unknown attribute information in an open scene; according to the method, a switchable mode prompt and interpolation mechanism is introduced, and it is guaranteed that agent embedding can be dynamically supplemented when attributes or texts are missing. In the retrieval process, fine-grained image content matching is carried out based on the relation between image features and attribute enhancement contexts through an attribute-guided cross-modal attention mechanism. According to the method, through multi-modal feature alignment and optimization, the accuracy and robustness of clothing image retrieval in an open world scene are improved.

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 invention relates to a method for evaluating the shape of an orthodontic splint, said method comprising the following steps: - a") acquisition of at least one image representing at least partially the splint in a service position in which it is worn by a patient, called "analysis image";- b") analysis of the analysis image using a deep learning device, preferably a neural network, trained using a training set, so as to determine a value for at least one tooth attribute of an "analysis tooth area" representing, at least partially, a tooth on said analysis image, the tooth attribute being relative to a spacing between the tooth represented by the analysis tooth area, and the groove represented on the analysis image, and / or for an image attribute of the analysis image, the image attribute being relative to a spacing between at least one tooth represented on the analysis image, and the groove represented on said analysis image.;

A method, a system, and computer program product for optical systems including a metasurface that supports quantitative phase imaging are provided. Optical system characteristics of an optical system are received. The optical system characteristics include an optical path, the optical system comprising a light source and an imaging system. Optical parameters of at least one metasurface are received. A location of the metasurface is selected within the optical path of the optical system to modulate an incident wavefront generated by the light source. An image acquired by the imaging system is processed, based on the optical parameters of the at least one metasurface and based on the location of the metasurface within the optical path, to determine one or more image properties.

The invention provides a remote sensing image scene classification method based on attribute guidance, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining a to-be-classified remote sensing image and a corresponding scene class text; constructing and training a scene classification network; processing the to-be-classified remote sensing image and the scene category text by using the trained scene classification network to obtain a scene classification result corresponding to the to-be-classified remote sensing image; according to the method, the attribute features of the remote sensing image are utilized, so that the language information can contain each part of the image with differentiated content, and the consistency between the language information and the image content is improved.

A method may include obtaining core image data regarding a geological region of interest. The method may further include obtaining well log data regarding the geological region of interest from one or more wells. The method may further include determining a sliding window that corresponds to a predetermined window size. The method may further include determining various quantitative image attributes using the core image data, the well log data, and the sliding window. The quantitative image attributes may be determined in a continuous manner by moving the sliding window along the core image data. The method may further include generating predicted rock data for the geological region of interest using the quantitative image attributes, a machine-learning algorithm, and a machine-learning model.

An electronic device may have a display and a camera. Control circuitry in the device can gather information on a user's point of gaze using a gaze tracking system and other sensors, can gather information on the real-world image such as information on content, motion, and other image attributes by analyzing the real-world image, can gather user vision information such as user acuity, contrast sensitivity, field of view, and geometrical distortions, can gather user input such as user preferences and user mode selection commands, and can gather other input. Based on the point-of-gaze information and / or other gathered information, the control circuitry can display the real-world image and supplemental information on the display. The supplemental information can include augmentations such as icons, text labels, and other computer-generated text and graphics overlaid on the real world image and can include enhanced image content such as magnified portions of the real-world image.

Techniques for generating a novel image using tokenized image representations are disclosed. In some embodiments, a method of generating the novel image includes generating, via a first machine learning model, a first sequence of coded representations of a first image having one or more features; generating, via a second machine learning model, a second sequence of coded representations of a sketch image having one or more edge features associated with the one or more features; predicting, via a third machine learning model, one or more subsequent coded representations based on the first sequence of coded representations and the second sequence of coded representations; and based on the subsequent coded representations, generating, via the third machine learning model, a first portion of a reconstructed image having one or more image attributes of the first image, and a second portion of the reconstructed image associated with the one or more edge features.

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 invention discloses an intelligent fire detection method based on data enhancement, and the method comprises the steps: collecting multi-modal time sequence signals used for representing a fire, carrying out the variational modal decomposition reconstruction of each modal time sequence signal based on Bhattacharyya distance, and obtaining a first reconstruction signal corresponding to each modal time sequence signal; performing wavelet packet threshold denoising on the first reconstruction signal to obtain a second reconstruction signal corresponding to each modal time sequence signal; performing short-time empirical mode decomposition on the second reconstruction signal to obtain a time-frequency data matrix corresponding to each mode time sequence signal; respectively adopting a signal-to-image coding form for the time-frequency data matrix corresponding to each modal time sequence signal to obtain a pixel matrix with an image property; and performing principal component analysis on each pixel matrix to perform dimensionality reduction to obtain a corresponding dimensionality-reduced matrix, and inputting the dimensionality-reduced matrix into the intelligent fire detection model to obtain a fire detection result.

The invention relates to a multi-mode-based family member relationship autonomous learning method and device. The method comprises the following steps: acquiring image data and voice data; the image data comprises a face image, and the voice data comprises voiceprint information and semantic information; acquiring attribute information corresponding to the face image based on the image data, predicting a family identity corresponding to the face image based on the face image in combination with the voiceprint information and the attribute information, and generating a family personnel knowledge graph; identity keywords related to family identities are monitored on the basis of continuously updated semantic information, and a family personnel knowledge graph is iteratively corrected on the basis of the identity keywords and continuously updated face images, attribute information and voiceprint information, so that autonomous learning of family personnel relationships is realized. According to the invention, through combination of various modes such as human faces and voiceprints and combination of a natural language processing technology, completely autonomous learning of family personnel relationships is realized, and naturalness and friendliness of man-machine interaction are improved.

The invention relates to the technical field of face search, and particularly discloses a multi-modal technology-based face search method, which comprises the following steps of: performing basic attribute marking on a face image in a face database by adopting a deep learning-based data processing technology to generate an attribute description text of the face image; semantic analysis and matching analysis are performed on the target face search text description input by the user and the generated face image attribute description text, so that face image search based on text description is realized. In this way, face search can be supported on the basis of semantic information of text description, so that face screening work can be quickly carried out, and the method has positive significance for a non-graph scene.

The invention relates to the technical field of artificial intelligence, in particular to a digital fabric physical attribute recognition method and system based on a pattern recognition technology, and the method comprises the steps: constructing a digital fabric picture library, determining the influence significance of each physical attribute parameter on the appearance of a fabric, performing corresponding sampling on each physical attribute parameter based on the influence significance to obtain a sample physical attribute of the digital fabric picture; determining an image area of the digital fabric picture, and endowing image attributes of the image area to the digital fabric picture; constructing a training sample based on the image attribute and the sample physical attribute of the digital fabric picture, and inputting the training sample into a neural network for training to obtain a physical attribute recognition model of the digital fabric; performing image data acquisition on the real fabric, preprocessing the acquired image data, and inputting the preprocessed image data into the physical attribute recognition model to obtain predicted physical attributes of the real fabric; according to the invention, the physical attribute parameters of the fabric can be accurately obtained.

A computer-implemented method for generating pairs of registered microscope images includes training a generative model to create generated microscope images from input feature vectors comprising feature variables. The training uses image data sets which respectively contain microscope images of microscopic objects but which differ in an imaging / image property. It is identified which of the feature variables are object feature variables, which define at least object positions of microscopic objects in generated microscope images, and which of the feature variables are imaging-property feature variables, which determine a depiction of the microscopic objects in generated microscope images depending on the imaging / image property. At least a pair of generated microscope images is created from feature vectors with corresponding object feature variables and differing imaging-property feature variables, so that the generated microscope images show objects with corresponding object positions, but with a difference in the imaging / image property.

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.

A method for training an image generation model includes receiving training data having multiple training images, image captions corresponding to the training images, and corresponding image features, each training image associated with one image caption and one or more image features. The method further includes performing a training process to condition the image generation model using the training images, the image captions, and the image features, resulting in a trained model that generates images conditioned to the image features. The image features include image properties that are extracted from pixels or regions of each training image and camera properties that are associated with each training image.

The present application discloses an image stitching method, device and equipment, which relates to the field of medical image processing technology. For images with no overlapping area or very small overlapping area, the stitching requirements can also be met, thereby improving the adaptability of image stitching scenarios. The method includes: obtaining the tag information associated with each segment of the image to be stitched, the tag information including image position information and image attribute information; by traversing and querying the tag information associated with each segment of the image, selecting the image attribute information that meets the stitching conditions as the stitching attribute information of each segment of the image; according to the stitching attribute information of each segment of the image, processing each segment of the image into each segment of the image containing the same image attribute information; stacking and stitching the images with the same image attribute information segment by segment according to the image position information to obtain the stitched image.

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.

An extended reality (XR) system is described herein. The XR system includes a display system mounted to the headset for displaying a display screen including computer-generated images thereon, and a controller that includes one or more processors programmed to execute an algorithm for operating in an optical character recognition (OCR) mode to display a text selection screen by generating an image distortion zone within the text selection screen associated with a non-viewable boundary and modifying received video images to adjust a viewable image attribute of a portion of the received video images displayed within the image distortion zone, and operating in the scrolling text mode to display a text display screen by generating machine-readable text OCR and modifying the generating machine-readable text using a lightweight language model for text correction.

The invention discloses an image acquisition method and electronic equipment, relates to the technical field of terminals, and can flexibly adjust the image attribute of an image so as to meet the requirements of flexible adjustment and personalized creation of a user. In the scheme, the electronic equipment can display the shooting interface, and the first preview image and the adjustment items of multiple image attributes such as saturation, color temperature or light and shadow are displayed to a user through the shooting interface, so that the user can adjust the image attributes through the adjustment operation on at least one adjustment item according to individual requirements. And adjusting the display of the preview image according to the corresponding image attribute.

An method of inspecting a tire carcass and either accepting the tire carcass as a candidate for retread or rejecting the tire carcass by identifying anomalies and or defects using an artificial intelligence deep learning image recognition software, whereby the system is improved by use of two dimensional image data augmented with depth data assigned to an image attribute of the a dimensional image for processing by, for example, a convolutional neural network.

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.