662 results about "Candidate image" patented technology

The invention provides a single-view image three-dimensional modeling method and system based on comparative learning, and the method comprises the steps: starting from a single input image, generating a multi-view candidate image through a pre-training two-dimensional diffusion model in combination with score distillation sampling; performing positive and negative sample division on the candidate image pairs based on texture fidelity and structure coherence; constructing a perceptual contrast learning mechanism, calculating perceptual loss between the images, and optimizing texture consistency in a three-dimensional content generation process; proposing a number-aware triple loss function, and dynamically adjusting the contribution ratio of positive and negative samples in training; then, a super-resolution reconstruction module is embedded to perform detail enhancement on the image, and the quality of a contrast gradient signal is improved; and finally, performing joint training on the perception comparison loss and a neural radiation field or volume rendering module to generate a high-quality three-dimensional grid or volume rendering result. The problems that in an existing diffusion generation method, multi-view textures are inconsistent, structure reconstruction is not coherent, and the semantic distinguishing capacity is insufficient are effectively solved.

The invention discloses a graph-driven intelligent medical image retrieval method and system, and relates to the field of image retrieval, and the method comprises the steps: firstly, coding a text query input by a user through constructing a medical knowledge graph, querying to generate a graph-driven enhanced embedded vector containing an advanced medical concept, and querying a related graph concept set; then, performing preliminary vector similarity retrieval by using the enhanced vector, and quickly screening out Top-K preliminary candidate images; and finally, on the basis of querying a related map concept set, carrying out map concept secondary sorting on the preliminary candidate images, and accurately matching deep medical semantics of the images by calculating a concept coincidence degree. The mechanism of two-stage retrieval and map deep fusion ensures that the retrieval result is not only similar in vision, but also highly related in medical concept, so that the accuracy and intelligent level of medical image retrieval are remarkably improved.

The invention relates to the technical field of image retrieval, in particular to an image retrieval method based on big data mining, which comprises the following steps: collecting images and metadata, screening metadata fields, determining a candidate image set and obtaining a metadata screening result, according to the method, in the image retrieval process, image metadata screening, geographic information clustering, visual feature fusion, index construction and matching strategy optimization are combined, and the retrieval accuracy and response efficiency are improved. In the metadata screening link, data is screened by using information such as time, places and equipment types, so that interference of irrelevant data is reduced, and a candidate image set is more targeted. Geographic information and timestamps are combined for classification, and clustering analysis is performed by utilizing a geographic information system, so that spatial distribution information plays a greater role in a retrieval process, and a more accurate result can be obtained for query of a specific region.

The invention discloses a three-dimensional scene target blanking method and system based on 3DGS, and belongs to the field of computer vision processing. The method comprises the following steps: firstly, performing three-dimensional reconstruction through a multi-view image to generate an original Gaussian point cloud scene; after a scene is divided into foreground and background point clouds based on target text information, spatial expansion is performed on the foreground to generate a three-dimensional soft mask. And projecting the mask to a two-dimensional image space to generate a two-dimensional mask image of each view angle, and inputting the two-dimensional mask image and an original image into an image blanking model to generate a candidate blanking image set. After a user selects a reference image from an initial view angle, a candidate image most similar to a preorder view angle is selected as a final blanking result from a non-initial view angle, and fidelity enhancement is carried out. And finally, optimizing the original Gaussian point cloud parameters by using the blanking result of each view angle to obtain a target three-dimensional scene after blanking. Based on the invention, a user can carry out texture remodeling on the structure of the original three-dimensional scene, and the multi-view consistency of the three-dimensional scene is maintained.

The invention relates to the technical field of image processing, in particular to an image search precision optimization method and system based on the combination of rough arrangement and fine arrangement, and the method comprises the steps: a rough arrangement model stage: calculating the rough arrangement similarity of a query image and all images in a database, and sorting and screening out a preset number of candidate images according to the rough arrangement similarity; a model fine arrangement stage: performing multi-scale reconstruction on the query image to generate reconstructed images with different resolutions; extracting feature vectors of the reconstructed images through a fine arrangement model, performing expansion operation on the feature vectors, and calculating fine arrangement similarity between the query image and the candidate image; and a similarity weighting combination stage: performing normalization processing on the rough ranking similarity and the fine ranking similarity, dynamically adjusting the weight coefficients of the rough ranking similarity and the fine ranking similarity according to the difference of the rough ranking similarity, generating a final similarity through weighted average, and finally sequencing and outputting a retrieval result. And by dynamically combining the distance of rough arrangement and the distance of fine arrangement, the image retrieval precision is further improved.

Systems and methods for image search result filtering can include obtaining a search query, determining a plurality of candidate image search results, processing the search query with a generative model to determine a plurality of search result criteria, and refining the plurality of candidate image search results based on determining whether the candidate results satisfy the plurality of search results criteria. The systems and methods can perform a plurality of determinations based on the output of the generative model.

The invention provides an injection molding part surface flaw detection method and device based on double-model collaboration, and the method comprises the steps: carrying out the local abnormal reflection feature analysis of a multi-view optical image collected on the surface of an injection molding part based on a first detection model, and obtaining a candidate flaw region; performing spatial positioning in the multi-view optical image based on the candidate flaw area to obtain a multi-view candidate image segment; performing surface geometric continuity analysis on the multi-view candidate image segments based on a second detection model to obtain a three-dimensional surface geometric consistency result; carrying out authenticity discrimination on the candidate flaw area based on a three-dimensional surface geometric consistency result to obtain a real flaw area; wherein the first detection model is used for capturing an optical response model of local abnormal reflection characteristics; the second detection model is a stereoscopic vision model for analyzing the geometric continuity of the multi-view lower surface. According to the invention, the false alarm rate of surface defect detection of the injection molded part under a complex surface condition is reduced.

The invention discloses a complex text image retrieval method and system based on a multi-modal large model thinking chain. The method comprises the following steps: S1, carrying out adaptive semantic disassembly; s2, reconstruction and optimization are carried out; s3, constructing a matching probability matrix; elements of the matching probability matrix represent probability scores of matching degrees between the candidate images and the matching texts; combining the matched text with the candidate image, and sending the combined image to a pre-training visual language model pair by pair to obtain a corresponding matching score; then a binary discriminant scoring mechanism is used to convert the matching score into a probability score that the cue word is' YES '; and S4, according to the matching probability matrix, calculating the matching degree between each candidate image and the original proposition, and selecting the candidate image with the highest matching degree as an image retrieval result. The invention aims to extract and optimize semantic features from complex text description by using a self-adaptive local deconstruction and global optimization method and a binary discriminant scoring mechanism guided by cue words, so as to improve the accuracy and generalization ability of image retrieval.

The invention discloses a machine vision-based enzyme freeze-dried powder quality detection method, and belongs to the technical field of machine vision, and the method specifically comprises the following steps: in a closed optical cavity, synchronously collecting a multispectral polarization sequence of a sample through annular multispectral polarization illumination and microscopic imaging; performing short-time airflow pulse disturbance and continuous imaging on the sample, and constructing a polarization time sequence texture map by means of inter-frame polarization retention difference; according to Stokes parameters, calculating a deskewness graph and a specular reflection component graph, and carrying out joint normalization on the deskewness graph and the specular reflection component graph and the polarization time sequence texture graph to form a multi-channel feature stack; constructing a double-branch encoder, and performing comparative learning on the feature stack to generate an impurity prototype dictionary; using the dictionary to match the feature stack pixel by pixel, and tracking and inhibiting powder agglomeration false detection through a topological constraint connected domain; and recovering a height map by using an oblique incidence fringe phase shift method, performing pixel-level fusion with the candidate image, retaining targets with height abrupt change and consistent polarization characteristics, and outputting an impurity mask.

The invention provides a method for distinguishing cell activity behaviors in a culture medium under the assistance of a computer, and relates to the technical field of cell image processing, and the method comprises the following steps: obtaining a target microscopic image of a target cell at the current moment, extracting the static characteristics of the target cell in the target microscopic image, and if the recognition confidence is lower than a first threshold value, determining that the target cell is a cell activity behavior; if yes, calling a historical image sequence of an area where the target cell is located; at least N candidate images are obtained through screening in the historical image sequence, time sequence dynamic characteristics of the target cells are obtained based on the candidate images, and the time sequence dynamic characteristics comprise the motion trail, the motion speed, the morphological change related to the activity behavior and the periodic change condition presented by the target cells within a certain time period; and combining the time sequence dynamic characteristics with the static characteristics, and carrying out activity behavior matching to obtain activity behaviors of the target cells. According to the method, the robustness and the accuracy of cell behavior recognition in a complex culture environment are remarkably improved through static-dynamic feature fusion.

The invention provides a scheme detection method and device for a complex image-text mixed file, and the method comprises the steps: extracting standard review items and standard review contents in a standard manual, and carrying out the structural processing, and obtaining a standard review file; converting a to-be-detected file into a to-be-detected image, extracting a to-be-examined multi-scale candidate region image from the to-be-detected image by using an initial recognition model and a matching model obtained by OCR and pre-training, and extracting character information of the multi-scale candidate image to obtain character information of the candidate region; inputting the multi-scale candidate region image and the standard review file into a lightweight hybrid twin network obtained by pre-training for image feature matching, and outputting a matched feature image; inputting the matched feature image, the standard review file and the candidate area text information into a multi-modal large model obtained by pre-training for compliance analysis, and outputting a detection result; according to the method, the automation and intelligence degree of review of the complex image-text mixed file can be remarkably improved.

The embodiment of the invention discloses a picture display method, electronic equipment and a computer readable storage medium, in the method, the electronic equipment responds to a target operation used for uploading a picture, and target information related to interface content of an application program is obtained; according to the first picture number and the target information, a target candidate picture set of each target element is obtained from the picture set, the target elements are determined according to the target information, the target candidate picture set comprises at least one target candidate picture, and the total number of the target candidate pictures of each target element is smaller than or equal to the first picture number; the first picture number is the number of pictures capable of being displayed on a first screen interface of the picture selection interface; and displaying the target candidate picture of each target element on the first screen interface. Thus, the user can obtain the pictures of the multiple target elements on the first screen interface, the pictures of the target elements do not need to be obtained through page turning, and the convenience of selecting the pictures by the user is improved.

A method for generating an image editing dataset is provided. The method may include obtaining a candidate image for inputting an AI model from among at least one candidate image. The method may include determining an editing operation to be performed by the AI model on the candidate image from among at least one editing operation. The method may include determining a base prompt and a control prompt based on inputting of the candidate image and the editing operation into the AI model, wherein the base prompt comprises base instructions for detection of at least one object within the candidate image, and the control prompt comprises control instructions relevant to the editing operation to be performed by the AI model on the candidate image. The method may include generating the image editing dataset based on the base prompt and the control prompt.

A robustness verification device uses the similarity between feature amounts obtained by extracting a feature amount to identify a similar image having a predetermined rank of similarity with respect to an input image within a candidate image group; counts the rank of the similar image with respect to the input image in the candidate image group in a case where adversarial perturbation is applied to the image; calculates the rank of the similar image with respect to the input image in the candidate image group in a case where adversarial perturbation is not applied to the image; and a verifies whether or not the counted rank of the similar image counted is within a predetermined range that includes the calculated rank of the similar.

The invention provides a zero-sample multi-modal combined image retrieval method and device based on context awareness, and relates to the technical field of image retrieval, and the method comprises the following steps: obtaining a training sample set; the training sample set is input into a target CLIP model, target image embedding and target text embedding of the training sample set are obtained, and learnable language prompt embedding and visual prompt embedding are introduced into a text encoder and an image encoder of the target CLIP model respectively; training the mapping network model according to the target image embedding, the target text embedding and the target loss function to obtain a target mapping network model; obtaining a target image retrieval model according to the target CLIP model and the target mapping network model, and inputting the reference image and the text description into the target image retrieval model to obtain a target query feature; and outputting a retrieval result according to the similarity between the target query feature and the image embedding of each candidate image. According to the image retrieval method, a multi-modal combined image retrieval task can be completed.

The present invention discloses a mapping and positioning method based on multi-sensor fusion. This method uses three sensors, namely LocalSense, 2D lidar, and RGB camera. When constructing a map in real time, LocalSense is used to track the rough pose of the mobile robot; candidate areas are screened around the rough pose, and the pose is corrected through the result of 2D laser information matching; the cartographer algorithm is used to establish a two-dimensional grid map; and the RGB image of the current pose is segmented, and each piece of the image is compressed into a one-dimensional vector through a convolutional autoencoder and stored, corresponding to the current pose. In the positioning stage, candidate RGB images are screened by feature matching, and then candidate positioning areas are screened according to the poses corresponding to the candidate images. Laser information matching is performed in the candidate positioning areas to obtain the accurate pose. The method of the present invention does not depend on the content and features of the images, can be applied to different types of images and different application scenarios, and can better adapt to some scenarios with fewer corners and edges.

The invention relates to the technical field of automobile part detection, in particular to an automobile part detection method, which comprises the following steps of: dividing a region range of a to-be-detected surface of an automobile part to obtain a plurality of image acquisition regions, and setting acquisition points for the plurality of image acquisition regions; acquiring basic feature data of a plurality of image acquisition areas, setting corresponding acquisition environment light sources, and performing image acquisition by an image acquisition camera corresponding to the acquisition environment light sources to obtain a plurality of detection images; performing feature analysis comprehensive processing on the plurality of detection images to obtain a defect area candidate image, and performing defect feature enhancement processing on the defect area candidate image to obtain a defect positioning image; and inputting the defect positioning image into the pre-training model, and outputting a detection result according to the defect parameters. According to the invention, the acquisition effect of the detection image is guaranteed, the visibility of the detection image is improved, and the accuracy of defect identification is guaranteed while the detection efficiency is improved.

The invention relates to the technical field of visual position recognition, and particularly discloses a multi-modal visual position recognition reordering method and system based on guidance, and the method comprises the steps: obtaining a query image, and retrieving a plurality of candidate images based on a pre-trained visual basic model and the query image; constructing a composite multi-modal prompt object, wherein the composite multi-modal prompt object comprises an image pair formed by the query image and the current candidate image, and an instruction text used for guiding a multi-modal large language model to perform visual comparison; outputting a structured similarity judgment result, wherein the result comprises a quantitative similarity score; and sorting based on the similarity scores corresponding to all the candidate images, and determining the candidate image with the highest score as an optimal matching result. Through combination of guiding type prompt engineering and structured output, an intermediate text generation link is avoided fundamentally, and the calculation efficiency is improved while the fidelity of all original visual information is reserved.

The invention discloses a task execution method and device, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps that a task prompt word and an interface image are obtained, the task prompt word is used for describing a to-be-executed interaction task, and the interface image is divided into at least two image areas; acquiring image feature representations corresponding to the at least two image areas and text feature representations corresponding to the task cue words; identifying the relevance between the image feature representation and the text feature representation corresponding to the at least two image regions, and determining at least one candidate image region of which the relevance meets the requirement; and generating a task instruction based on the at least one candidate image area, wherein the task instruction is used for instructing execution of an interaction task on the first interface. In combination with the text feature representation corresponding to the task prompt word, redundant feature input is reduced, and the correlation between the candidate image area and the interaction task is improved, so that the accuracy of the task instruction and the execution efficiency of the interaction task are improved.

A method for generating an optimised domain-generalisable model for re-identification of a target in a set of candidate images. The method optimises a local feature embedding model for domain-specific feature representation at each client of a plurality of clients, then receives, at a central server, information on changes to the local feature embedding model at each respective client resulting from the optimising step, and then updates a global feature embedding model based on the changes to the local feature embedding model. The method further receives, at each client from the central server, information representative of the updates to the global feature embedding model, then maps, at each client, on to the respective local feature embedding model at least a portion of the received updates, and subsequently updates, at each client, the respective local feature embedding model based on the mapped updates. The steps are repeated until convergence criteria are met, wherein the global feature embedding model is the optimised domain-generalisable model for re-identification of a target in a set of candidate images.

The invention provides a data processing method and device, a product and a terminal, and belongs to the technical field of image processing. The method comprises the steps that according to feature vectors of all to-be-processed images in a to-be-processed image set, the to-be-processed images in the to-be-processed image set are filtered, a candidate image set is generated, and the similarity of any two images in the candidate image set is smaller than a similarity threshold value; images in the candidate image set are input into a license plate and face hybrid screening model, a to-be-labeled image set is output, and to-be-labeled images in the to-be-labeled image set comprise face images and / or license plate images. The invention aims to improve the efficiency of the screening process of the to-be-labeled image.

The invention discloses a vehicle violation manned intelligent detection method and system based on a CLIP and D-Fine cascade framework, and the method comprises the steps: collecting a real-time video frame image of a preset traffic monitoring network, processing the real-time video frame image, inputting a monitoring image into a CLIP-ILP model, employing the CLIP-ILP model as a filter, and combining with a Top-K screening strategy, and screening out candidate images; and inputting the candidate image into a constructed context enhanced D-FINE detection model, carrying out positioning and multi-class detection on a preset target, outputting a multi-class detection result, and executing verification of a spatial co-occurrence rule, a license plate position heuristic rule, a size consistency rule and a context consistency rule. And marking the result which does not pass the verification as suspicious or rejecting the result, and outputting the result which passes the verification as an illegal manned detection result to the target terminal. According to the method, the problem of balance between the recall rate and the precision in manned detection can be solved.

A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.

Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for retrieving image search results using embedding neural network models. In one aspect, an image search query is received. A respective pair numeric embedding for each of a plurality of image-landing page pairs is determined. Each pair numeric embedding is a numeric representation in an embedding space. An image search query embedding neural network processes features of the image search query and generates a query numeric embedding. The query numeric embedding is a numeric representation of the image search query in the same embedding space. A subset of the image-landing page pairs having pair numeric embeddings that are closest to the query numeric embedding of the image search query in the embedding space are identified as first candidate image search results.

The invention relates to an end-to-end pulsar candidate body recognition method and device and computer equipment. The method comprises the steps of obtaining an original pulsar candidate image corresponding to a pulsar candidate body signal, and performing enhancement processing on the original pulsar candidate image to obtain a pulsar candidate image; marking a sub-map in the pulsar candidate image, and constructing a target data set for identifying the pulsar signal type; constructing a positioning data set by marking the position information of the sub-map in the original pulsar candidate image; training a preset pulsar recognition model according to the positioning data set to obtain a positioning model for recognizing sub-graph position information; and obtaining a preset reasoning prompt, and training the positioning model according to the preset reasoning prompt and the target data set to obtain a pulsar recognition model for recognizing the pulsar signal type. By adopting the method, the pulsar candidate body recognition accuracy can be improved.

The invention discloses an industrial finished product microdefect detection method based on a generative adversarial network, and the method comprises the following steps: S1, collecting a high-resolution image of the surface of an industrial finished product, and constructing a data set of a normal image and a defect image; s2, extracting an image feature vector, splicing the image feature vector with a defect type vector, and inputting the spliced image feature vector and the defect type vector into a generator network containing a geometric structure transformation module to generate a defect candidate image; s3, inputting the defect candidate image into a discriminator network containing a topological structure analysis module, and outputting an authenticity discrimination result, a structure connectivity characteristic index and a defect type prediction label; s4, jointly training a generator and discriminator network; s5, inputting a to-be-detected image, and obtaining a defect candidate image and a prediction result; s6, generating a defect probability graph, a saliency graph and a binary mask, and positioning a defect area; and S7, outputting a microdefect detection and classification result in combination with the prediction label, the saliency map and the mask. According to the method, the detection precision and the classification capability of the micro-defects under the complex background are improved.

The invention discloses a combined image retrieval method and device, and the method comprises the steps: inputting text data and image data in combined query data into a first feature extraction network and a second feature extraction network respectively, and obtaining a first text feature and a first image feature; respectively inputting the first image feature and the first text feature into a first conditional fusion network and a second conditional fusion network for bidirectional fusion; outputting the first joint semantics and the second joint semantics; and respectively calculating the similarity between the candidate image semantics of each candidate image in the candidate image set and the first joint semantics and the second joint semantics, and carrying out weighted aggregation on the first similarity matrix and the second similarity matrix to obtain a fused similarity matrix so as to determine a target image corresponding to the combined query data. The technical problems that in an existing combined image retrieval technology, single-mode dependence is serious, semantic alignment is insufficient, and multi-mode fusion is insufficient can be solved.

An image retrieval method includes acquiring an image retrieval condition, the image retrieval condition comprising a reference image and modification text, the modification text being configured to indicate a modification expectation for the reference image; composing the reference image and the modification text, to obtain an image-text composition; acquiring a plurality of candidate images, and determining, for a candidate image, a first similarity between the candidate image and the image-text composition, and a second similarity between the candidate image and the modification text; and determining at least one target image satisfying the image retrieval condition from the plurality of candidate images with reference to the first similarity and the second similarity.

The embodiment of the invention relates to a method and device for generating video content, equipment and a storage medium. The method provided by the invention comprises the following steps: acquiring input content of a user through an interactive interface with a virtual object; in the interactive interface, first response content from the virtual object is presented, and the first response content comprises at least one candidate image generated based on the input content; video generation information is determined based on an interaction operation associated with the first response content, and the video generation information at least comprises a reference image used for generating a video; and providing second response content from the virtual object, the second response content including video content generated based on the video generation information. In this way, according to the embodiment of the invention, the at least one candidate image generated based on the input content can be presented for the user in the video generation process, so that the video generation quality can be improved based on the interaction operation between the user and the at least one candidate image.

A sample palm print image generation method includes generating a major palm print line according to a preset major palm print line distribution pattern, determining a palm print distribution region in which the major palm print line is located, generating a minor palm print line in the palm print distribution region based on a preset minor palm print line distribution pattern, generating a candidate palm print image based at least on the major palm print line and the minor palm print line, labeling the candidate palm print image with a candidate image label that represents a palm print distribution of the candidate palm print image including at least one of a major palm print line distribution or a minor palm print line distribution, and inputting the candidate palm print image to a palm print generation model to obtain a sample palm print image.