187 results about "Image database" patented technology

The invention discloses a power equipment state monitoring method based on image recognition, which belongs to the field of power equipment monitoring, and comprises the following steps: firstly, acquiring surface images of power equipment according to a preset interval through an image acquisition module, and constructing a time sequence image database; performing alignment and standardization preprocessing on the image to generate a standard time sequence image sequence; inputting the sequence into a differential convolutional neural network, and extracting the feature variation of the defect region to obtain a defect evolution feature sequence; the sequence is analyzed based on a defect evolution dynamic model, the evolution rate of the defect is quantified, and the residual safe life of the equipment is predicted; and finally, automatically generating a graded early warning and maintenance decision according to a life prediction result. Therefore, the problem that the dynamic evolution rule of the gradual change defect of the equipment is difficult to capture and quantify in the prior art is solved, the transformation from post-event monitoring to predictive maintenance is realized, and the accuracy of state monitoring and the perspectiveness of operation and maintenance decision are remarkably improved.

Provided is a system and method for digital content generation and recommendation. A first plurality of stories are collected from various content sources over a network. A second plurality of stories are selected from the first plurality of stories such that a degree of similarity between any two stories of the second plurality of stories is below a determined threshold. A summary and a headline are generated for a first story of the second plurality of stories. A set of images are selected, from an image database, for the first story based on the generated summary and headline. A multimedia item including the summary, the headline, and a first image of the set of images is created. The multimedia item is presented on a lock screen interface of a user device, displaying the generated summary, the generated headline, and the first image on the lock screen interface.

The application discloses an ethylene cracking furnace tube infrared intelligent analysis system, which comprises, which are connected in sequence, an image acquisition module, a temperature monitoring module, a coking diagnosis module and a health management module; the image acquisition module acquires infrared images of cracking furnace tubes and burners by using an infrared thermal imaging online monitoring system, and establishes a cracking furnace tube infrared image database; the temperature monitoring module extracts and records the outer surface temperature of the ethylene cracking furnace tube from the processed infrared images of the ethylene cracking furnace tube by using an infrared image processing and recognition method; the coking diagnosis module combines the technologies of artificial intelligence and data mining, establishes a coking degree diagnosis and prediction model of the ethylene cracking furnace tube, and realizes early warning and targeted tracking of the coking abnormal conditions; the health management module carries out health evaluation of the cracking furnace tube according to the results obtained by the coking degree diagnosis and prediction model of the ethylene cracking furnace tube, and provides maintenance decision support. The application can realize integrated management of cracking furnace tube detection, diagnosis and maintenance.

A computer architecture for interactive image based database searching includes a first server that acquires category information indicating a category of a vehicle owned by a user. The first server also acquires part information for identifying a vehicle part located at a position specified by a user operation within an image of a vehicle displayed on a terminal device of the user. A second server is in communication with the first server and searches for products by accessing a storage storing thereon product information on vehicle part products and by using the acquired category information and the acquired part information as search criteria. The system transmits search result information indicating results of the search to the terminal device.

The invention discloses a full-process visual monitoring and intelligent management method for a glass bracket production line, and relates to the technical field of industrial quality control, and the method comprises the steps: carrying out the continuous image collection of all stations of the glass bracket production line through an image collection unit, and constructing a full-process image data sequence; detecting the size of the glass bracket by using the qualified glass bracket size image database, and judging whether the size is abnormal or not; further performing defect detection on the glass bracket with the normal size, and determining whether an abnormal defect exists or not through pixel value difference analysis; and finally, summarizing the glass brackets with abnormal sizes or defects and related information thereof, and generating a visual quality report, so as to quickly position problems and optimize the production process. The comprehensive monitoring and intelligent management of the glass bracket production process are realized, and the production efficiency and the product quality are improved.

The invention provides an intelligent analysis method and device for population around a nuclear energy project factory site, and the method comprises the steps: constructing a factory site peripheral region image database through integrating a satellite map, GIS data and an unmanned aerial vehicle aerial image; automatically distinguishing buildings from non-buildings by using an image recognition algorithm, and intelligently classifying the buildings according to civil, office, commercial, industrial, cultural and other types; the population density or empirical parameters of various buildings are combined to calculate the total population number of single buildings and regions, and future population prediction based on the annual growth rate is supported. The system is further provided with a visual interaction interface, and parameter input, data storage, result query and report automatic generation are supported. According to the method, the automation, precision and high-efficiency level of demographic statistics around the nuclear power plant site is improved, the manpower and time cost is reduced, and scientific, dynamic and sharable data support is provided for nuclear power project site selection decision.

The invention belongs to the technical field of automatic driving testing, and particularly relates to an automatic driving system long-tail image generation method based on heterogeneous data fusion. Comprising the following steps: S1, extracting data input information for heterogeneous data fusion; s2, selecting a pre-training diffusion model as a heterogeneous data fusion backbone network, disassembling a reasoning diffusion process of the heterogeneous data fusion backbone network, and constructing a reasoning pipeline of a first stage; s3, refining and enhancing intermediate fusion features of the diffusion model; s4, developing a reasoning pipeline of a second stage; s5, screening and de-weighting the long-tail image samples, and establishing a high-quality long-tail image database; according to the method, a long-tail image database can be established, the method is used for high-efficiency and high-coverage test requirements of an automatic driving perception system, and industrialization of an automatic driving automobile is accelerated.

In a method, performed in a data processing system, an object marker is authenticated. The method comprises receiving object marker image data of the object marker. From an original marker image database, original marker image data of an original marker are retrieved. Marker comparison parameters based on a comparison of the object marker image data to the retrieved original marker image data are determined. The marker comparison parameters are input to a plurality of input nodes of an artificial neural network, ANN. The ANN has been trained to discriminate between object marker image data of the original marker, and a copy or clone of the original marker. The ANN provides output data, representative of the extent of authenticity of the object marker, at its output node. An authenticity signal based on the output data of the ANN is output.

This invention discloses a standardized real-time monitoring and error correction method and system for martial arts routine teaching, relating to the field of image recognition technology. The method includes: S1. Acquiring information on students' learning stages and training objectives, retrieving corresponding standard movement image models and image feature parameters from a multi-dimensional martial arts standard movement image database, and dynamically adjusting the weights of evaluation indicators based on image recognition; S2. Acquiring movement image data through a multi-modal image acquisition array, and after image preprocessing, using a two-stage temporal alignment algorithm based on image semantic attention to achieve accurate temporal alignment between the student's movement image sequence and the standard movement image model; S3. Calculating movement deviations based on image feature matching, extracting error precursor image features using a deep learning image recognition model, and triggering early warnings; S4. Outputting multi-modal error correction feedback based on image overlay, pushing a group common error image analysis report to the teacher's end, generating and dynamically adjusting personalized correction schemes based on the deviation diagnosis results of image recognition, forming a closed-loop error correction.

The application relates to the technical field of blind guiding, and particularly discloses an intelligent blind guiding instrument, which comprises a host computer internally provided with an MCU processor and a power module; a head-mounted device composed of a ring frame and a camera, a microphone and a loudspeaker mounted on the ring frame; and a blind guiding stick in Bluetooth connection with the MCU processor, wherein the MCU processor is in communication connection with a background server or a cloud server and loads an image database; the MCU processor is used for receiving image information sent by the camera and identifying and judging the image category, so as to control the blind guiding instrument to enter a voice navigation mode or a thing recognition mode; the MCU processor controls the blind guiding instrument to enter a voice control mode when detecting that the microphone sends predetermined voice content, so as to start or stop the blind guiding instrument or play predetermined audio. The above-mentioned intelligent blind guiding instrument combines the blind guiding stick with the head-mounted device, collects the images around the blind guiding stick to position a position to be guided, improves the navigation accuracy, has multiple functions, realizes one machine with multiple functions, and reduces the blind guiding cost of users.

The invention discloses a construction path planning optimization method combining YOLO v8 and an A-Star algorithm, and the method comprises the following steps: obtaining the image information of a construction target object and an obstacle, respectively defining a primary target object and a secondary target object, and building an image database through combining the initial position of a construction machine; converting the whole construction site image into a network map, and giving a cost weight according to the obstacle type to generate a cost map; different paths are planned by using an A-Star algorithm, and the minimum cost path is screened out by adjusting the cost weights of objects in the trap paths; real-time field image information is acquired, a target object is identified and ranged in real time by using a YOLO v8 technology, and a shortest obstacle avoidance path is found out; and dynamically optimizing the construction path comprehensively by referring to the static optimal path diagram and the dynamic real-time construction drawing. Dynamic obstacles in the construction process are recognized and measured in real time through YOLO v8, path planning is conducted in combination with an A-Star algorithm, selection of trap paths is avoided, and the accuracy and adaptability of path selection are remarkably improved.

The application discloses a small sample multi-element scene image recognition method based on pre-training cross-modal retrieval, and steps of the method comprise: 1) establishing an image database, including images of multiple general scene categories and images of several target scene categories, each scene category comprising multiple images, and each image being provided with a corresponding scene category label; 2) extracting features of each scene category image in the image database by using a scene recognition model to obtain a feature database corresponding to each scene category; and generating a feature vector according to features of the same image in the feature database; 3) for an image A to be classified, extracting features of the image A by using the scene recognition model and generating a feature vector; and 4) the scene recognition model performs similarity calculation on the feature vector of the image A and feature vectors in each of the feature databases respectively; and taking a scene category of an image corresponding to a feature vector with the highest similarity as a scene category of the image A.

The invention relates to the technical field of image processing, in particular to a retinopathy recognition method, system and device based on artificial intelligence and a medium, and the method comprises the steps: taking the eye of a detected pregnant woman as a target object, setting a preset position, scanning in the depth direction, obtaining point fault information, carrying out three-dimensional reconstruction, and generating a second image. Generating a third image, performing correction processing, presetting a straight line in the corrected image, obtaining pixel values corresponding to the positions where the first pixel and the second pixel are located, and obtaining the thickness data of each layer; taking the thickness data as a pixel value; setting the value of each layer of pixel to be equal to the thickness data of the layer; generating a fourth image; and establishing an image database, adding a disease marker to the reference image, training the image estimation model by using the reference image added with the disease marker, and outputting a disease corresponding to the fourth image. The method improves the prediction efficiency and accuracy of the eye fundus image.

The present application belongs to the field of terahertz imaging and image processing, and particularly relates to a terahertz image data expansion method based on a space-frequency degradation model. The method mainly comprises the following steps: pre-screening and pre-processing a high-resolution image database to establish a high-resolution image library; simulating a terahertz Gaussian beam to perform fuzzy degradation processing on the high-resolution image using a Gaussian fuzzy kernel; simulating terahertz wave source fluctuation and sensor noise to add Gaussian noise; simulating low resolution of terahertz imaging, and reducing the image resolution using a bicubic downsampling method; simulating mutual interference of multiple terahertz waves during imaging, and using a scheme of adding a frequency domain mask to change the frequency domain features; simulating the case that low image contrast is caused by low terahertz wave source power, and using a gray scale compression method to compress the gray scale histogram of the image to obtain a final low-resolution image data set. The present application can effectively expand the super-resolution training set of terahertz images, thereby improving the training effect of the terahertz image super-resolution reconstruction task based on the deep learning method.

The application relates to a test environment building method and device, computer equipment and a storage medium. The method comprises the following steps: receiving a test environment allocation request of a client, wherein the test environment allocation request comprises a virtual image identifier; in response to the test environment allocation request, determining target virtual image information corresponding to the virtual image identifier from an image database; and finally, sending the target virtual image information to the client, so that the client builds a test environment based on the target virtual image information. The method can be used to build a test environment using a virtual image prepared in advance when a user needs to build a test environment, thereby reducing the time required by the user to build the test environment and lowering the test cost. Meanwhile, the prepared virtual image is stored in the image database and cannot be destroyed, so that the virtual image can be reused, thereby greatly facilitating the management of the test environment.

The present application relates to the technical field of pavement crack detection, and particularly relates to a lightweight pavement crack detection method, wherein a lightweight feature extraction module SandGlass and a coordinated attention mechanism are integrated into an encoding-decoding structure to construct a lightweight pixel-level crack detection network, a pavement image database is used to train the network model, so that the network model can realize the same detection effect as a larger network model under less computing power, the coordinated attention mechanism module suitable for mobile terminals is introduced to reduce the influence of noise and improve the accuracy of crack detection, and finally the encoding-decoding structure is improved to fuse feature maps of different levels, so that the final prediction result is closer to the actual pavement condition. Further, the improved method can be effectively applied to mobile devices to realize real-time and effective end-to-end detection.

The invention relates to a power model security detection method, device and equipment, a medium and a program product. The method comprises the following steps: adding different types of basic trigger patterns to each image in an image database to obtain a plurality of candidate trigger images, calling a target power model to identify each candidate trigger image, and determining the probability that the target power model is attacked; calling a target power model to identify images in the first image set, and determining abnormal sample proportion information in the first image set; and adding different types of disturbances to the images in the second image set, calling the target power model to identify each disturbance sample image, and determining anti-interference quantization information of the target power model, and finally, fusing the attacked probability of the target power model, the abnormal sample proportion information and the anti-interference quantitative information to obtain a security detection result of the target power model. By adopting the method, the accurate detection of the potential safety hazard of the target power model can be realized.

Embodiments of the present application provide a virtual soft measurement method and system for machine vision training based on digital twinning. The method comprises: obtaining an image with target object edge features from an image database by a vision twin main core, and dynamically updating a virtual model of an industrial environment in a model database; the vision twin main core collects customized training data for an image processing model in the dynamically updated virtual model according to customized demand configuration information, and sends the customized training data to an artificial intelligence training end; and the embedded end re-mounts the image processing model trained by the artificial intelligence training end, to realize dynamic virtual soft measurement of the industrial environment. Embodiments of the present application can generate usable training data close to a real scene in a virtual model according to different industrial environment needs, improve the efficiency and reliability of machine vision construction, and the embedded end introduced in the method also reduces the data processing and data transmission burden of the vision twin main core.

The application provides a remote sensing target retrieval method, device and equipment based on a multi-path similarity graph, and relates to the technical field of remote sensing. The method comprises: acquiring a fine-grained category description text corresponding to a remote sensing target to be detected, and an optical image database to be retrieved; wherein the optical image database comprises a plurality of optical remote sensing images; and inputting the fine-grained category description text and the plurality of optical remote sensing images into a pre-trained remote sensing target fine-grained retrieval model, and determining a retrieval result corresponding to the remote sensing target from the plurality of optical remote sensing images through the remote sensing target fine-grained retrieval model. The remote sensing target fine-grained retrieval model is trained based on fine-grained category description text samples corresponding to a plurality of remote sensing target samples and a plurality of optical remote sensing image samples in an optical remote sensing image data sample library. The technical solution of the application can realize fine-grained retrieval of remote sensing targets, thereby improving the accuracy of the remote sensing target retrieval result.

In a method to improve backwards compatibility when decoding high-dynamic range images coded in a wide color gamut (WCG) space which may not be compatible with legacy color spaces, hue and / or saturation values of images in an image database are computed for both a legacy color space (say, YCbCr-gamma) and a preferred WCG color space (say, IPT-PQ). Based on a cost function, a reshaped color space is computed so that the distance between the hue values in the legacy color space and rotated hue values in the preferred color space is minimized. HDR images are coded in the reshaped color space. Legacy devices can still decode standard dynamic range images assuming they are coded in the legacy color space, while updated devices can use color reshaping information to decode HDR images in the preferred color space at full dynamic range.

The application discloses a multi-view clustering method and system based on self-step learning and view weighting, and belongs to the technical field of multi-view data processing. The method comprises the following steps: normalizing a multi-view data set, splicing the multi-view data, initializing each view clustering kernel and distribution matrix by using a kmeans algorithm, and calculating each view weight; each view sample weight matrix, each view clustering kernel and distribution matrix are iteratively updated in sequence through a target function; and when an iteration end condition is met, a final clustering kernel and distribution matrix are output. The clustering system comprises an acquisition module, a preprocessing module, a construction module, an optimization module and a clustering output module. The self-step learning model is used to sequentially learn clustering data and finally obtain a clustering result. Through view weighting, the model can selectively learn information of different views, thereby effectively improving clustering accuracy. The application can be applied to retrieval of an image database, a text database and the like.

The invention relates to an instrument panel indicator light detection method and system, a storage medium and an intelligent terminal, and relates to the field of instrument panel function detection. Obtaining an image before indication when the indication command is received; when the image before indication is a standard non-indication image, executing an indication command and acquiring an image in indication; searching a corresponding standard image from an image database based on the indication command; outputting a target abnormal result when the indicated image is inconsistent with the standard image; when the indicated image is consistent with the standard image, searching a corresponding anti-indication command from an anti-operation database based on the indication command; executing an anti-indication command and acquiring an indicated image; outputting a target normal result when the indicated image is consistent with the standard non-indicated image; and outputting a target abnormal result when the post-indication image is inconsistent with the standard non-indication image. The method has the effects of comprehensively analyzing the reaction of the whole indication lamp in the indication process and improving the comprehensiveness of indication lamp detection.

The application provides a food freshness evaluation method and system based on image processing. Wherein, multi-view images of food are acquired, the images are transmitted to a central processing center, personalized image acquisition parameters are set, and a food image database is formed; based on the food image database, high-precision dynamic monitoring is performed on the color and texture changes of food in different time periods to generate a preliminary evaluation result; according to the preliminary evaluation result, the freshness of food is predicted and modeled in combination with the type, storage condition and historical sales data of food, and a final freshness evaluation report is obtained; the inventory strategy is adjusted in real time, the food distribution plan is optimized, and an optimized management process is generated by using the freshness evaluation report. The technical scheme provided by the application improves the accuracy of food freshness evaluation.

The application discloses a kind of no reference underwater image quality evaluation methods based on multi-color space joint modeling.The method comprises the following steps: firstly, construct underwater image quality evaluation model, the model includes RGB feature extraction branch, luminance feature extraction branch and chrominance feature extraction branch, and introduce residual structure and attention mechanism in model to enhance the expression ability to quality degradation feature;Second, obtain the underwater image database containing subjective quality score, train the database, iteratively optimize model parameters by loss function, obtain the trained underwater image quality evaluation model;Finally, input the underwater image to be evaluated into the trained model, and output the corresponding quality score result.The method can effectively depict the luminance attenuation, structural degradation and color distortion features in underwater image, improve the accuracy and stability of quality evaluation, and is suitable for underwater image enhancement effect evaluation and quality monitoring in actual marine application.

The present disclosure relates to an image processing method. The method may include: receiving an input image to be compressed; extracting features of the input image; encoding the extracted features of the input image into a latent feature, zinput, determining, from a background image database which contains one or more background images, a background image that has a latent feature, zbackground, best-matching with zinput; calculating a delta feature, Δz, which is the difference between zinput and zbackground, in accordance with the following formula: Δz=zinput−zbackground; and compressing Δz to obtain a bitstream, Ydifferences. The method may further include: sending Ydifferences and an identifier (IDentifier—ID) of zbackground to a server; retrieving zbackground based on the ID of zbackground; decompressing Ydifferences based on zbackground to retrieve Δz; calculating a reconstructed latent feature, zreconstructed, in accordance with the following formula: zreconstructed=Δz+zbackground; and decoding zreconstructed to obtain a reconstructed input image. An image processing system and a computer program are further disclosed.

The application discloses an image segmentation method based on a microscopic hyperspectral imaging technology, which comprises the following steps: collecting images of a sample by a microscopic hyperspectral imaging system, constructing a microscopic hyperspectral image database, and marking the images by a doctor; pre-processing original hyperspectral data collected; inputting the pre-processed data into a feature extraction backbone network to obtain multi-scale feature images, wherein the backbone network is composed of four stages, each stage comprises an image merging layer, a spectral fusion feature extraction module and a spatial fusion feature extraction module; inputting the multi-scale feature images obtained by the backbone network into an FPN decoding network to output a segmentation prediction result image. The application effectively utilizes rich spectral spatial information of hyperspectral data, makes up for the deficiency of a convolutional network in global context information feature extraction, and improves the segmentation accuracy.

The application relates to the technical field of medical image database, and discloses a standardized cervical cancer pathological image database construction method, which comprises the following steps: step 1, specimen number is hashed to obtain pseudo-anonymous specimen number, unique slice identification and cell-level unique identification are generated; step 2, a unified reference system is established, the reference layer pixel spacing, the down-sampling multiple and the coordinate mapping are determined; step 3, the image is converted into an optical density space and normalized according to a target staining base to obtain a standardized image; step 4, cross-modal anchor point lists are established by manually comparing cytological and histological images; step 5, a hierarchical label set is generated by synthesizing multi-level unique labels according to the severity full sequence; step 6, the intersection-over-union index and the kappa consistency value are calculated according to the combination key, and a review to-do list is generated; step 7, point review and rule revision are carried out, and a data dictionary, a consistency report and an anchor point snapshot are output. The application realizes the standardized database construction and traceable quality control of cervical cancer cytological and histological pathological images.

The invention belongs to the field of ATE test, and particularly relates to a calibration robot spring needle module installation model automatic identification updating method, which comprises the steps of integrating an industrial camera on a calibration robot to acquire images of spring needle modules at all installation positions, presetting a standard image database through an upper computer and executing feature matching to identify an actual installation model, the identification result and the pin mapping information are compared and dynamically updated, and finally the pressing block is driven to be automatically matched and calibrated. The calibration robot autonomously moves to each module mounting position and performs image acquisition, so that the physical world configuration state is directly perceived, and the real model of the spring needle module mounted at each position can be accurately judged without manual intervention by utilizing a feature level comparison mechanism of a pre-stored standard image and an on-site real shot image.

This invention discloses a method, apparatus, and electronic device for target recognition. The method involves receiving a trigger command from a user terminal, the trigger command indicating that the user terminal is online and carrying a target number corresponding to the user terminal; determining the sector where the user terminal is located in response to the target number being in the number database of the radio frequency unit; acquiring a user facial image and / or user facial video within the sector at the current time, both the user facial image and the video including user facial information; saving the user facial image and / or the user facial video in response to the similarity between the user facial information and facial information in the radio frequency unit's image database being greater than or equal to a set value; and transmitting the user facial image and / or the user facial video. This method fully utilizes the computing power of the RU device to achieve target number detection and user facial recognition.

The invention relates to a method for enriching a database of medical images intended for a clinical study, comprising the steps of: - retrieving (100) a medical image to be analyzed; - identifying (103) a type of organ in the medical image to be analyzed by implementing an artificial intelligence model generated by training on a database of reference medical images whose predefined parameter values ​​are fixed; - selecting predefined parameters of the image to be analyzed according to the type of organ identified; - identifying (106) the values ​​of the predefined parameters of the medical image to be analyzed by implementing the artificial intelligence model; - storing (108) the identified parameter values ​​in metadata associated with the image to be analyzed. Figure to be published with the abstract: Fig. 1