518 results about "Associated image" patented technology

An online concierge system trains a fine-tuned generative image model for distinct categories of items based on a generative image model that takes a textual query as input and outputs and an associated image. Training of the fine-tuned generative image model is additionally based on a small set of representative images associated with the various categories, as well as textual tokens associated with the categories. Once trained, the fine-tuned generative image model can be used to generate realistic representative images for items in a database of the online concierge system that are lacking associated images. The fine-tuned model permits the generation of different variants of an item, such as different quantities or amounts, different packaging or packing density, and the like.

A method for annotating images to create a corpus for training a multi-task computer vision machine learning model is presented. The method comprises receiving, at one or more annotation specialist models, a plurality of images to be annotated. Via operation of the one or more annotation specialist models, pre-filtered annotations are generated for the plurality of images. Via operation of a data filtering and enhancement module, the pre-filtered annotations are filtered in accordance with predefined noise criteria so as to output candidate annotations for the plurality of images. The method further comprises, for each of one or more candidate annotations, selectively (1) storing the candidate annotation into the corpus as a final annotation for its associated image, or (2) adding the candidate annotation to its associated image using the one or more annotation specialist models and the data filtering and enhancement module for subsequent iterative annotation and filtering.

A method of automated generation of contextually-relevant images for a music segment includes receiving at least one of basic metadata information and lyric information for the music segment, generating a first prompt for a computer-implemented machine-learning language model based on the at least one of the basic metadata information and the lyric information, receiving context information from the computer-implemented machine-learning language model in response to the first prompt, generating a second prompt for the computer-implemented machine-learning language model based on the context information, generating a third prompt by providing the second prompt as an input to the computer-implemented machine-learning language model, and generating an image descriptive of the music segment by providing the third prompt as an input to a computer-implemented machine-learning image generation model.

A technique for generating a digital photograph comprises blending two related images, each sampled according to a different illumination environment. The two related images are blended according to a blend surface function that includes a height discontinuity separating two different blend weight regions. Color consistency between the two related images is achieved by spatial color correction prior to blending. The technique enables a digital camera to generate a strobe image having an appearance of consistent color despite discordant strobe and ambient scene illumination.

The invention belongs to the technical field of image processing, and discloses a multi-image processing method based on multi-modal entity alignment, which comprises the following steps: acquiring a plurality of images; retrieving rich semantic information of the entity from an external knowledge base; selecting the most representative image for each entity in the multi-image scene by using semantic information; encoding the original input of each mode; performing first-layer fusion between a visual mode and a text mode by using cross-divergent attention, then performing second-layer interaction with a structured mode, and finally aligning an entity representation of an image by using contrast loss; and outputting multiple images fusing the text modality and the visual modality. According to the invention, hierarchical interaction fusion is utilized to enhance multi-modal interaction; enhancing entity text representation by integrating the external attribute value and the context information; and the most representative image is selected by using the semantic text, so that the influence of uncorrelated images is minimized.

The invention discloses a fire situation analysis method based on multi-dimensional data fusion, and particularly relates to the field of fire image analysis, and the method comprises the steps: analyzing the change of the direction retention rate between adjacent frames through extracting the main direction texture vectors of a building and a vegetation region, and recognizing object state change candidate segments; in the candidate area, combining main direction disturbance and image definition reduction to construct a spatial scoring graph, performing nonlinear amplification on the spatial scoring graph, and extracting a gradient increasing path to generate a structure damage main path set; then calculating a directional included angle between paths and a space coincidence rate, constructing a trend consistency aggregation channel graph, and extracting a spreading principal axis; and finally, superposing a temperature rise area in the thermal infrared image with a spreading principal axis, extracting a dual response area to generate a fire behavior boundary prediction layer, and realizing fire behavior spreading path prediction based on fire scene related image structure damage information analysis.

A system (100) comprising one or more processors (110) and one or more storage devices (120) is configured to obtain biology-related image-based input data (107) and generate a high-dimensional representation of the biology-related image-based input data (107) by a trained visual recognition machine-learning algorithm executed by the one or more processors (110). The high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to at least one of store the high-dimensional representation of the biology-related image-based input data (107) together with the biology-related image-based input data (107) by the one or more storage devices (120) or output biology-related language-based output data (109) corresponding to the high-dimensional representation.

The provided is a method and system for object detection based on user-defined categories. The method includes: a user inputting a natural language description and a related image, obtaining a detection target auxiliary input using an auxiliary characterization generation technique for a detection target based on a phrase boundary point modeling technique; calling a detection target characterization generation model based on a multimodal reconstruction and alignment network to obtain a plurality of text characterizations of the detection target; generating target reverse characterizations based on an image-adaptive target characterization matching estimation technique to meet custom requirements of the detection target; and optimizing a vision-language multimodal model based on feedback data of the detection target of the user under detection, and optimizing the vision-language multimodal model based on the feedback data during usage of custom object detection.

This disclosure relates to techniques for generating and utilizing custom stickers in a digital communication environment. A technique involves receiving a text-based message input during a chat session and using a generative language model (e.g., a Large Language Model, or LLM) to create a text prompt. This prompt is then used by a generative image model to produce a custom sticker. The generated sticker is sent to a client device where it is displayed in a sticker tray alongside other selectable stickers. Users can select and send these stickers directly within their chat interface, enriching communication with visually expressive and contextually relevant imagery.

The invention provides a structured light calibration method and system under three coordinates, and relates to the technical field of structured light imaging, and the method comprises the steps: 1, collecting calibration plate images of a plurality of angles, extracting sub-pixel angular point data, constructing a camera parameter description mode including radial and tangential distortion, and based on a pinhole imaging principle, carrying out the calibration of a plurality of angles; solving an internal parameter matrix and a distortion coefficient of the camera by establishing a linear equation set so as to obtain calibrated camera parameters; 2, projecting a composite coding pattern to the calibration plate based on the calibrated camera parameters, synchronously acquiring related images, decoding phase and coding data, establishing a corresponding relation between projector pixels and three-dimensional space points of the calibration plate by using the calibrated camera parameters, and solving an internal and external parameter matrix of the projector to obtain initial projector parameters; according to the invention, through multi-view image acquisition, composite coding projection and three-dimensional target matching, structured light collaborative calibration of camera and projector parameters is realized.

Methods and systems for interacting with users via a chatbot. A natural language query is received and processed by submitting a search query to a search engine. The search engine identifies relevant information including textual information and images for formulating a response. The identified information and query are submitted to a Large Language Model which generates a response displayed via the chatbot. The response may include textual information and relevant images. The system can extract text from images of documents and convert textual information into numerical vector representations for processing. Selectable options based on clustered relevant information can be provided to users for query refinement when appropriate. The chatbot interface enables natural language interactions while leveraging search capabilities and Artificial Intelligence to provide informative and helpful responses with both text and visual elements.

A georegistration (a.k.a. georectification) of an image captured by a camera in an aerial vehicle, such as a satellite, is based on identifying multiple features using descriptor sets, and sending to a ground station only the descriptors of the identified features and the associated locations in the captured image, without sending of the captured image itself, thus requiring a low communication bandwidth. Using a database of geosynchronized reference images, the ground station uses the received descriptors sets and the associated image locations to localize the features on a selected geosynchronized reference image from the database, and forms a mapping function that map any locations in the captured image to geographical coordinates on Earth. The mapping may be used to geosynchronize an additional feature identified in the aerial vehicle, or to geo synchronize a region that may be cropped from the captured image and sent to the ground station.

The invention relates to the technical field of image processing, in particular to an interpretable farmland image enhancement method based on physical perception and reinforcement learning, and the method comprises the steps: obtaining an original farmland image, carrying out the global perception analysis, extracting multi-scale features, and recognizing a degradation region and feature distribution; on the basis of the global perception analysis result, constructing a semantic enhancement blueprint for quantifying the physical attributes and optimization requirements of the degradation area; initializing a reinforcement learning agent according to the semantic enhancement blueprint, and selecting an image processing operation sequence through a physical constraint reward function; executing the image operation sequence, and terminating enhancement processing according to the quality evaluation index increment threshold and the physical consistency of the semantic enhancement blueprint; and outputting an interpretability report of the enhanced image and associated image operation sequence physical basis traceability. The objective of the invention is to solve the technical problems of lack of interpretability, insufficient environmental adaptability and rigid decision-making mechanism of a farmland image enhancement technology.

The invention relates to a multi-modal sentiment analysis method and system based on a thinking chain and background knowledge. The method comprises the following steps: constructing a sample set containing original texts, related images, aspect items and sentiment polarities thereof; constructing a multi-modal sentiment analysis model, and generating background knowledge information by utilizing a visual language large model and combining an input text and an image; the generated background knowledge is screened, optimized and fused; for each sample in the sample set, in combination with the text data and the emotion polarity, generating a thinking chain thinking process and adding the thinking chain thinking process into the sample set, and then finely adjusting the visual language large model through the sample set added with the thinking chain thinking process; finally, the fused background knowledge, the text data and the picture data are sequentially input into the trained visual language large model, aspects in the text data are extracted, and the emotion polarity corresponding to the aspects is predicted. The method and the system are beneficial to improving the accuracy of multi-modal emotion polarity analysis.

The invention discloses a tear total IGE detection method and system based on multi-modal data fusion. The method comprises the following steps: acquiring ocular surface images, physiological parameters and environmental allergen data, and acquiring historical medical data from a hospital information system; preprocessing and feature extraction are performed, patient symptom description is analyzed in combination with a natural language processing technology, and related image block features are recognized; adjusting a space-time attention weight based on frequency domain analysis and transfer learning, inputting a lightweight Transform model, and predicting a tear total IgE concentration and an allergy risk score; whether a trace tear sampling program is started or not is judged, and the actually measured concentration of total tear IgE is detected through a disposable micro-fluidic chip; and integrating all information by using a Bayesian adaptive filtering algorithm to determine the total tear IgE level. By implementing the method provided by the invention, the sampling amount is extremely small, the detection time is short, the environmental interference can be dynamically corrected, and the absolute concentration value is output, so that the dual requirements of clinical precise diagnosis and treatment and home monitoring are met.

Systems and methods for identifying novel objects in an image include detecting one or more objects in an image and generating one or more captions for the image. One or more predicted categories of the one or more objects detected in the image and the one or more captions are matched to identify, from the one or more predicted categories, a category of a novel object in the image. An image feature and a text description feature are generated using a description of the novel object. A relevant image is selected using a similarity score between the image feature and the text description feature. A model is updated using the relevant image and associated description of the novel object.

A technique for generating a digital photograph comprises blending two related images, each sampled according to a different illumination environment. The two related images are blended according to a blend surface function that includes a height discontinuity separating two different blend weight regions. Color consistency between the two related images is achieved by spatial color correction prior to blending. The technique enables a digital camera to generate a strobe image having an appearance of consistent color despite discordant strobe and ambient scene illumination.

Aspects of the invention include methods of obtaining associated images and sequence data of a single cell, such as a single cell of a cell sample. Embodiments of the method include combinatorily barcoding cells with a specific binding member / oligonucleotide barcode to produce combinatorily barcoded cells, e.g., obtained from an initial cell sample. The resulting combined barcoded cells are then partitioned to produce combined barcoded single cells each having a partition of a combined barcode. Image data and sequence data of the partitioned combined barcoded single cells are then obtained, and then the image data and sequence data sharing the common combined barcode are correlated to obtain correlated image and sequence data of the single cells of the cell sample. Compositions for practicing the methods of the invention are also provided.

The disclosure relates to systems and methods automatically extracting an image and related image components, computationally determining an understanding of the image, and generating mathematical vector embeddings via sentence encoders based on the computationally determined understanding. The mathematical vector embeddings may be used for semantic image retrieval that enables image searching based on a semantic understanding of input images and / or input text. The mathematical vector embeddings may be used for training and executing generative Artificial Intelligence (AI) models to create new content that includes retrieved images and / or generate new images.

The invention discloses an image enhancement method, electronic equipment and a storage medium. The method comprises the following steps: acquiring a current frame image; first differential data of the current frame image and the first reference frame image and motion detection results of multiple frames of associated images of the current frame image are synthesized, a plurality of target motion pixel points are searched from the current frame image, and the current frame image, the first reference frame image and the associated images are obtained through shooting of the same shooting device; the current frame image and the first reference frame image are obtained by shooting at different moments, the associated image is the current frame image or the number of frames differing from the current frame image is smaller than a preset frame number, and the target motion pixel point is a first motion pixel point determined based on first differential data and is a second motion pixel point determined based on a motion detection result; and performing enhancement processing on the plurality of target motion pixel points in the current frame image to obtain an enhanced image. Through the above mode, the target can be accurately enhanced so as to distinguish the target from the background.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

System and method for extracting human pose information from an image, comprising a feature extractor connected to a database, a convolutional neural network (CNN) with a plurality of CNN layers. Said system / method further comprising at least one of the following modules: a 2D body skeleton detector for determining 2D body skeleton information from the human-related image features; a body silhouette detector for determining body silhouette information from the human-related image features; a hand silhouette detector for determining hand silhouette detector from the human-related image features; a hand skeleton detector for determining hand skeleton from the human-related image features; a 3D body skeleton detector for determining 3D body skeleton from the human-related image features; and a facial keypoints detector for determining facial keypoints from the human-related image features.

The image processing apparatus obtains data of a plurality of captured images obtained by capturing an object from a plurality of directions and image capturing camera parameters, which are camera parameters corresponding to each of the plurality of captured images, generates a plurality of object images by extracting an image region corresponding to an image of the object from each of the plurality of captured images, generates object image camera parameters, which are camera parameters corresponding to each of the plurality of object images, based on the image capturing camera parameters and information indicating a position of an image region corresponding to the image of the object in the plurality of captured images, and obtains spatial information representing a space in which the object exists based on the plurality of object images and the object image camera parameters corresponding to each of the plurality of object images.

An image pickup apparatus includes an imaging unit configured to capture an object image formed by each of a first optical system and a second optical system arranged in parallel with the first optical system, the first optical system and the second optical system being included in a compound-eye optical system, and a processor configured to detect a defocus amount of each of the first optical system and the second optical system regarding the object image, and evaluate an imaging state regarding the object image by setting a first reference value that is used to determine an in-focus state of the first optical system and a second reference value that is used to determine an in-focus state of the second optical system according to the imaging state.

The invention discloses a user stand detection method and system for a target topic, electronic equipment and a storage medium, and the method comprises the steps: obtaining speech data of a user from a social media platform, the speech data comprising text speech data, dialogue speech data and image speech data; obtaining a target topic, and expanding background knowledge of the target topic by using the knowledge graph to obtain a topic related word set and a topic related image set; based on the topic related word set and the topic related image set, respectively carrying out site detection on the text speech data, the dialogue speech data and the image speech data of the user to obtain respective corresponding site label sets; and comprehensively analyzing the three site label sets, and determining a site detection result of the user for the target topic. According to the method, three kinds of modal data of texts, dialogues and pictures are comprehensively considered, and background knowledge of the target topic is expanded by utilizing the knowledge graph, so that the accuracy of comprehensive site detection is improved.

The invention discloses a historical record backtracking method based on LLM, and relates to the field of data retrieval and natural language processing, and the method comprises the steps: collecting various operation data of a user in a local computer, extracting text information in pictures and voices in combination with an OCR technology and an ASR technology, and storing the text information and related information in a database in a unified format; the method comprises the following steps of: processing collected multi-modal data information, firstly performing de-duplication and cleaning on the collected data, then converting a collected screenshot into a video, and finally performing vector embedding on a video image frame, an OCR (Optical Character Recognition) result and a voice transcription text and storing the video image frame, the OCR result and the voice transcription text into a vector database; optimizing the pre-trained LLM in a specific direction by adopting a fine tuning technology; according to the method, conversation with a historical timeline is realized, user input is captured and stored in a query queue, personalized response is generated through LLM interaction in combination with a memory module and an RAG technology, and answers and related image or audio links are displayed.

The invention provides a large-view-field high-resolution compound eye camera array system based on bionic human eye vision. The system is composed of a curved surface support and an industrial camera array arranged on the curved surface support. Reverse extension lines of optical axes of all the cameras intersect at the same point to form a concave surface structure similar to the retina, and the fixed view field overlapping rate of the adjacent cameras is kept. The industrial camera array is used for collecting a target scene image, large-view-field information is obtained by fusing and splicing all camera images, and a high-resolution image is obtained by conducting super-resolution reconstruction on a plurality of related images in a view field center interested area. The system provides three working modes, namely a low-resolution mode, a conventional-resolution mode and a super-resolution mode, which can be switched by adjusting camera parameters. Finally, the system can perceive the dynamic change of the edge of the field of view in a high-frame-rate and low-resolution mode, magnify the details in the center of the field of view in a super-resolution mode, realize efficient non-uniform imaging, and optimize the information acquisition and processing efficiency and the structure compactness.

A vehicle information delivery device includes an image processing unit configured to generate vehicle related images indicating the status of a vehicle moving unmanned in a predetermined target area based on camera images taken in the target area where unattended parking is performed, and an information sending unit configured to send delivery information including the vehicle related images to a display terminal visually recognized by a user of the vehicle.

The application provides a parking lot-based laser radar monitoring method, system, device and storage medium, wherein the method comprises the following steps: collecting point cloud data in a parking lot and dividing the point cloud data into a static point cloud set and a dynamic point cloud set, dividing target dynamic point cloud sets to be tracked from the static point cloud set and the dynamic point cloud set, obtaining a coincidence coefficient of a first projection area of a preset parking space and a second projection area of the target dynamic point cloud set, and updating the target dynamic point cloud set to a static point cloud when the target dynamic point cloud set is static in the range of a parking space point cloud and the coincidence coefficient meets a preset threshold, and determining that a collision is sent when the nearest distance between the second contour of the target dynamic point cloud set and the third contour of the static point cloud representing all vehicles parked on the parking space is 0. The application can solve the problem of car safety protection after parking, confirm the danger and capture the relevant image, and provide the greatest vehicle safety guarantee for the owner.

The invention relates to a monitoring video viewing method and device, computer equipment and a medium, and the method comprises the steps: determining a source camera of a target monitoring video needing to be viewed and corresponding viewing time information in response to a monitoring video viewing instruction; searching a camera to which an external monitoring video having a view overlapping area with the target monitoring video within the same time according to the viewing time information, and taking the camera as a part of neighbor cameras of the source camera; determining the optimal privacy metadata from the privacy metadata of the corresponding viewing time information of the partial neighbor cameras, wherein the privacy metadata comprises frame associated image blocks corresponding to the shielded areas of the privacy sensitive targets in the external monitoring videos of the corresponding neighbor cameras; and repairing a covered area of a corresponding privacy sensitive target in the target monitoring video according to the frame associated image block of the optimal privacy metadata, and then playing the target monitoring video. According to the invention, on the premise of default privacy protection, key details of the monitoring picture are intelligently restored and enhanced as required.