362 results about "Key images" patented technology

The invention discloses an electronic component surface defect analysis system based on an attention mechanism, which comprises an image acquisition module, a data processing module, an attention mechanism module, a surface defect analysis module, a system maintenance and feedback updating module and a report generation and user interface module, the data processing module is used for preprocessing the collected image, the attention mechanism module is used for highlighting key area features in the image, the surface defect analysis module is used for detecting and analyzing device defects, and the system maintenance and feedback updating module is used for system maintenance and updating. And the report generation and user interface module is used for generating a detection report and providing an interactive interface. According to the electronic component surface defect analysis system based on the attention mechanism, a key defect concentration algorithm based on the attention mechanism is provided to extract key image defects; and providing a surface defect analysis algorithm based on Fourier transform to detect and analyze the surface defects of the device.

The invention discloses a method and device for generating medical image question and answer thinking chain data in multiple stages. According to the method, by introducing a multi-stage model collaborative reasoning mechanism, extraction of key image content, enhanced generation of image description and construction of a thinking chain are completed in sequence, so that the accuracy, pertinence and interpretability in a medical image question and answer task are improved; the problems that an existing model is inaccurate in image detail recognition, opaque in reasoning process, generalized in question and answer content and the like are solved.

The invention provides a video generation method and device, and relates to the technical field of computers, in particular to the technical fields of large models, deep learning, image processing, voice recognition and the like. According to the specific implementation scheme, semantic partitioning and position marking are conducted on a to-be-processed material, and a semantic text block set comprising at least one semantic text block is obtained; based on the key event information, classifying semantic text blocks in the semantic text block set to obtain a key text block set and a non-key text block set; generating a key image sequence based on the key text block set, the to-be-processed material and a video material library; generating a non-key image sequence based on the non-key text block set and a video material library; the target video is generated based on the key image sequence and the non-key image sequence, and the video generation effect is improved.

The invention discloses a valve state monitoring and safety interlocking method based on machine vision, and belongs to the technical field of industrial process control and safety. The method comprises the following steps: 1, calibrating a camera and establishing a model; step 2, image acquisition and preprocessing; step 3, key image feature extraction and state decision; fourthly, opening degree calculation and state judgment are carried out; 5, safety interlocking is triggered; and 6, feeding back data. The valve state monitoring and safety interlocking method is achieved through the valve state monitoring and safety interlocking system. According to the invention, the millimeter-level or even submillimeter-level displacement and angle real-time accurate measurement of the opening degree of the valve can be realized, the state monitoring of the valve is realized, and the stability and reliability in the production process are improved; state judgment is carried out by comparing the real-time opening value with the instruction opening from the control system, a safety interlocking signal is generated and transmitted to the safety interlocking system, and finally physical safety action is triggered, so that a set of complete safety protection system is realized, and accidents are avoided.

The invention provides a DICOM (Digital Imaging and Communications in Medicine) file sequence processing method, a medical image analysis method, system and device, a computer storage medium and a computer program product, the processing method comprises the following steps: loading a DICOM file sequence corresponding to a series of image sets obtained by tomography, and arranging according to a scanning sequence to obtain an initial image file queue; reading DICOM files from a specified position to form a key image file queue with a fixed length; and reading sequence-level DICOM metadata and image-level DICOM metadata for representing key features from the key image file queue, and arranging the sequence-level DICOM metadata and the image-level DICOM metadata according to a preset rule to obtain a sequence DICOM metadata queue with fixed length and fixed information items of each position. By adopting the scheme provided by the invention, the obtained metadata queue with the fixed dimension provides a data basis for subsequent deep learning network input, and improvement of the adaptability with the deep learning network and improvement of the analysis accuracy are facilitated.

The invention relates to a music recommendation method, system and device based on user emotion monitoring, and relates to the technical field of music recommendation, and the method comprises the steps: monitoring a music playing instruction in response to the emotion of a user, and collecting a face image sequence of the user; key face image extraction is carried out, a key face image sequence and a non-key image sequence are obtained, and user basic emotion recognition is carried out according to the key face image sequence; performing ambient light analysis according to the non-key image sequence, and performing user emotion correction to obtain a corrected emotion level; and performing music recommendation according to the corrected emotion level. According to the method and the device, the technical problem that the music recommendation result cannot really match the user demand due to the fact that the emotional state of the user cannot be quickly and accurately recognized in the traditional method can be solved; the efficiency, accuracy and reliability of user emotion recognition can be remarkably improved, then music meeting the requirements of a user can be accurately recommended according to the emotion state of the user, and the user experience is improved.

The invention relates to an image processing method, device, equipment and system for a double-layer metasurface. The method comprises the following steps: during decryption, using a bottom-layer metasurface unit in a double-layer metasurface and adopting electromagnetic waves of a first frequency to respectively restore a plurality of first password book images acquired from different terminals to obtain a plurality of second password book images; using the bottom layer metasurface unit and the covering layer metasurface unit in the double-layer metasurface and adopting electromagnetic waves of a second frequency to restore the obtained first key image to obtain a second key image; and determining a second original image according to the plurality of second password book images and the second key image. According to the technical scheme provided by the invention, the second password book image and the second key image are respectively determined by adopting two different frequencies. The cracking difficulty is increased, and the safety is improved.

A computer-implemented method for generating a three-dimensional (3D) digital model from one or more two-dimensional images is described. The method includes: obtaining, though an application programming interface (API), a series of two-dimensional (2D) images of a scene taken by an image capturing device; extracting, by a processing device, key images from the series of 2D images, wherein each of the key images depicts one or more components of a building structure in the scene; determining, by the processing device, and based on the extracted key images, a respective position and a respective direction of the image capturing device relative to each of the one or more components of the building structure; and processing, using a 3D image generation neural network, the extracted key images and the positions and directions of the image capturing device to generate metadata comprising a three-dimensional (3D) digital model of the building structure.

The invention belongs to the technical field of industrial safety monitoring, and provides a thermal power plant field operation safety identification alarm system, which comprises image acquisition equipment, network transmission equipment, an image identification unit and monitoring terminal equipment, the image acquisition equipment is used for acquiring video data of field operation; the image recognition unit is used for converting the video data into to-be-input image data and extracting key image data in the to-be-input image data; inputting the key image data into a target detection model to obtain an image recognition result; if the image recognition result is that the personnel violation behavior and / or the environment hidden danger state are included, determining an abnormity type, occurrence time, an occurrence place and image evidence; generating alarm information, and sending the alarm information to a monitoring terminal device; and the monitoring terminal equipment is used for displaying the alarm information and forwarding the alarm information to the mobile terminal equipment of the remote management personnel. According to the system provided by the invention, the identification precision, efficiency and convenience of the field operation safety of the thermal power plant are improved.

The invention relates to the technical field of image processing, in particular to a cultivated land monitoring method and system based on an unmanned aerial vehicle. The method comprises the following steps: obtaining adjacent key image frames of unmanned aerial vehicle video data, and splicing the adjacent key image frames to obtain a panoramic image; performing panoramic segmentation on the panoramic image and obtaining masks reflecting different types of cultivated land through an SEEM model; and extracting the contour of the mask, converting the extracted contour into coordinates in a geographic coordinate system, and mapping each coordinate point with the land category to which the coordinate point belongs. According to the application, farmland monitoring can be carried out by using the aerial image of the unmanned aerial vehicle, the farmland is monitored in real time, meanwhile, image processing is carried out by using the panoramic image splicing technology and the SEEM model, a wider view can be obtained, the limitation of a single image is eliminated, and refined management of the farmland is realized.

The invention discloses an image processing method for automatic welding operation of power transmission tower parts, and belongs to the technical field of tower production. The processing method comprises the steps that an initial image shot in the automatic welding process is obtained, channel expansion is conducted on the initial image, and a first-order feature matrix is obtained; processing the first-order feature matrix by using a plurality of feature generation modules to obtain a second-order feature matrix; and inputting the second-order feature matrix into an image reconstruction module, and outputting to obtain an improved image. According to the method, a semi-dense internal cross feature extraction mechanism is creatively designed in the first subunit, and other noise interference can be better identified and removed while key image information such as a welding seam is reserved. And the second subunit performs effective modeling on the importance of each image region through an internal view transformation calibration mechanism, and integrates the first result and the second result, so that the feature generation module has more accurate and more stable learning ability for various information.

The invention relates to a video abstract generation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a plurality of frames of video key images corresponding to key part features and key motion features in video data; respectively inputting the video key images into a plurality of pre-trained first network models to obtain corresponding prediction results; simultaneously extracting spatio-temporal information from multiple frames of video key images by using a first network model, and converting each frame of video key image into two-dimensional image features related to a time sequence; and combining the plurality of prediction results to obtain a video abstract of the video data. Through the stage recognition method and device, the problem of low accuracy of stage recognition in related technologies is solved, complementary information of key images of different frames of videos is provided by using two-dimensional image features and spatio-temporal information related to a time sequence, and then the accuracy of stage recognition is improved.

The invention belongs to the technical field of underwater image processing and crack detection, and particularly relates to a hydropower station dam underwater crack image positioning method, which comprises the following steps of: acquiring image data of a dam surface crack and acquiring a key image; the method comprises the following steps: preprocessing an acquired underwater image, detecting a crack region of the preprocessed image by adopting a crack target detection network based on YOLOv9, and outputting boundary frame coordinates and confidence of a crack; a post-processing algorithm is used to optimize a detection result, and classification is carried out according to fracture morphological characteristics after multi-frame fusion; storing crack detection and positioning data to a database, and displaying the spatial distribution and change trend of cracks; and establishing an early warning model based on the crack propagation rate and the morphological change, and if early warning is triggered, generating a risk assessment report. The method overcomes the defects that traditional manual inspection is low in efficiency, is greatly interfered by water quality and is difficult to cover hidden areas, and the automation level and reliability of health monitoring of the dam structure are remarkably improved.

The invention relates to the technical field of parking positioning, in particular to an automatic parking repositioning method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: when a parking enable signal is received, acquiring a vehicle look-around image set and positioning data; convolution of different Gaussian kernels is carried out on key images in the vehicle all-round view image set, difference calculation is carried out, and key points in the images are generated; based on a Newton iteration method, screening the key points to obtain feature points; based on the Euclidean distance, the feature points are matched, and matched feature point pairs are determined; determining a camera pose change value according to the matched feature point pair, the camera internal reference and the camera external reference; based on the Kalman filtering algorithm, the camera pose change value and the positioning data are fused, the current pose is determined, and the precision of automatic parking repositioning is improved.

The invention discloses a fruit and vegetable quality nondestructive testing imaging control system which comprises the following components: S1, a multispectral light source module which captures spectral information inside and on the surface of the multispectral light source module; s2, a precision imaging module generates multispectral image data; s3, extracting key image features related to fruit and vegetable quality by an image data preprocessing module; s4, the quality analysis module adjusts algorithm parameters in real time and generates an analysis data set corresponding to the fruit and vegetable quality indexes; s5, a spectrum enhancement and adaptation module, which optimizes the sensitivity and resolution of the multispectral signal; s6, a distributed detection control module is used for controlling system parameters to be adjusted in real time, and the detection efficiency and detection are optimized; and S7, a data management and output module which generates a fruit and vegetable quality evaluation report through a computer system. The method has the advantages of being high in detection precision, high in adaptability, good in nondestructive property and high in detection efficiency.

The invention belongs to the technical field of medical image processing, and particularly relates to a medical image processing method and system based on multi-modal fusion, and the method comprises the steps: obtaining a CT image and a corresponding digital pathological image, and carrying out the pairing of the CT image and the corresponding digital pathological image; respectively inputting the paired CT image and digital pathological image into a CT branch and a pathological branch; the CT branches extract macroscopic morphology and internal density distribution to obtain a CT feature map, and the pathological branches extract microscopic details to obtain a pathological feature map; the CT feature maps and the pathological feature maps from different layers are fused in a layer-by-layer cascade and feature splicing mode, and a comprehensive feature map is obtained. The method can still stably extract and identify key image features and modes in the face of image noise, different imaging devices or image visual differences among individual samples, and has high image analysis robustness.

The invention relates to a point cloud and finite element model generating system and method based on computer vision, the system comprises a video collecting and processing module, a three-dimensional point cloud establishing module and a finite element model generating module, and the method comprises the following steps: collecting and processing video data of a target structure to obtain a key picture and corresponding spatio-temporal information; performing three-dimensional point cloud reconstruction on the target object by adopting a multi-view angle geometric reconstruction technology based on deep learning based on view angle differences among the plurality of key pictures and corresponding spatio-temporal information, and preprocessing point cloud data; the method comprises the following steps: preprocessing point cloud data, extracting effective structure space geometric information from the preprocessed point cloud data, cutting the point cloud into a plurality of thin layers by adopting a mode of tomography nodes, establishing in a layered manner to obtain finite element components, and integrating the finite element components to obtain a finite element model. Compared with the prior art, the method has the advantages that the video data can be efficiently and accurately converted into the finite element model required by structural analysis, and tedious operation of manual modeling is avoided.

A medical image processing apparatus includes: an image acquisition unit that acquires a plurality of medical images; an image classification unit that classifies the medical images into at least one of a plurality of categories; an image display unit that displays, on a screen, at least one of the plurality of medical images based on a result of the classification as an automatically selected image; an input receiving unit that receives an input from a user to select an image requiring reselection as a key image from among the displayed automatically selected images as a selected image; and a display control unit that switches and displays, as a switching image, a non-automatically selected image other than the automatically selected image among the plurality of medical images classified into each category, instead of displaying the selected image.

The invention provides a semantic communication method and device based on a generative AI large model, equipment and a storage medium, and relates to the technical field of communication. Semantic segmentation is carried out on a to-be-transmitted image to obtain a key image area and a non-key image area, an undistorted transmission strategy is adopted for the key image area, and a text generation model is used for the non-key image area to generate a concise description text; and the receiving end reconstructs the non-key image region according to the description text of the non-key image region, and generates a reconstructed image comprising the original pixel data of the key image region and the synthetic pixel data of the non-key image region. The technical problem that in the prior art, accuracy and reliability of semantic communication are limited is solved, bandwidth resources are relieved, transmission efficiency is improved, and efficient transmission with high fidelity of the key image area and complete semantics of the non-key image area is achieved.

The utility model discloses a display module and a household appliance display panel with double-layer diaphragms matched for display, and belongs to the technical field of display panels, the display module comprises a first diaphragm and a second diaphragm, the first diaphragm is located on the lower layer in the light source projection direction, the second diaphragm is located on the upper layer in the light source projection direction, and after the first diaphragm and the second diaphragm are matched with a light source, the first diaphragm and the second diaphragm are matched. The display module only displays the background image-text and the function key image-text. According to the utility model, the two diaphragms, the two groups of light guide plates and the double-layer diaphragm are matched to realize the display of images and texts, the upper light guide plate and the diaphragm realize the slight brightness and constant brightness of background light, and the lower light guide plate realizes the highlight of the images and texts corresponding to the area when the touch key area is operated, so that the operation of a user is more convenient. The background light sources and the touch key light sources on the upper layer and the lower layer are independently controlled, and the light guide plate is arranged in a double-layer mode, so that background images and texts and function key images and texts do not affect each other, and display of the background images and texts and the function key images and texts is attractive and striking.

The invention discloses a multi-sensor fusion equipment state estimation method and device, and relates to the technical field of state estimation. In multi-sensor fusion equipment state estimation, point cloud data and image data are subjected to deep fusion in a data level, if a certain point cloud voxel lacks historical observation in a global visual map, prior space information provided in a point cloud map is utilized to obtain an adjacent point set of the point cloud voxel, and the adjacent point set is subjected to image fusion in the global visual map. According to the method, a candidate point set which may be incrementally updated to a global visual map is formed, then the candidate point set is projected to current image data, and key image blocks and corresponding point cloud voxels in the current image data are determined according to angular point response values of projection results, so that the point cloud voxels of the key image blocks in the current image data are determined for the situation of lack of historical observation. And forming an incidence relation between the point cloud voxels with the effective key information and the image blocks so as to update the global visual map. The correlation success rate of the image and the point cloud is remarkably improved, and the state estimation precision is improved.

The invention relates to the technical field of image monitoring, in particular to an intelligent visual monitoring method and system in an extremely cold environment, and the method comprises the following steps: based on an extremely cold monitoring environment, collecting environment temperature data through a temperature sensor, measuring the wind speed and the snowfall density, and obtaining low-temperature environment state data; according to the invention, environment state data is acquired in real time, a battery charging and discharging strategy is automatically adjusted to adapt to low-temperature influence, a heating element is automatically adjusted to prevent the equipment from frosting and freezing, normal operation of functions of the monitoring equipment is ensured, and light sensitivity and exposure parameters of an image sensor are adjusted, so that the monitoring efficiency is improved. The image quality is improved, the influence of an extremely cold environment on the image quality is eliminated, the monitored data is more accurate and reliable in vision, the compression ratio is dynamically adjusted, key image data is preferentially transmitted, and the efficiency and the real-time performance of data transmission are enhanced.

The invention relates to an abnormal behavior recognition method and system in combination with laboratory video data analysis, and relates to the field of video recognition, comprising: performing key image extraction on a plurality of part monitoring image sequences to obtain a plurality of key part monitoring image sequences, performing key timestamp extraction on a monitoring timestamp sequence to obtain a plurality of key part monitoring image sequences; obtaining a plurality of key monitoring timestamp sequences; performing action recognition according to the plurality of key part monitoring image sequences to obtain a plurality of behavior recognition results, and calculating according to the plurality of key monitoring timestamp sequences to obtain a plurality of change speeds; according to the multiple behavior recognition results and the multiple change speeds, the abnormal behavior probability is obtained through calculation, and a recognition result is obtained through judgment. Through the method, the problems that potential dangerous behaviors cannot be quickly and accurately identified in a dynamic and complex laboratory environment, response is slow and identification accuracy is low in a traditional method can be solved; the efficiency and accuracy of abnormal behavior recognition can be remarkably improved, and potential safety hazards are effectively prevented.

A system can be configured to: (i) obtain a set of input images depicting one or more bodily structures of a patient; (ii) determine whether one or more key structures are represented within the set of input images by utilizing a key structure detection module; (iii) determine one or more key images of the one or more images of the set of input images by utilizing a key image localization module; (iv) determine key structure segmentation by utilizing a key structure segmentation module; (v) determine one or more patient condition metrics using the key structure segmentation; and (vi) generate a report associated with the patient based upon the one or more patient condition metrics, or generate an entry at one or more practitioner worklists based upon the one or more patient condition metrics.

Disclosed herein are methods and systems for using artificial intelligence modeling techniques to generate a path for an ego. In an embodiment, a method comprises retrieving image data of a space around an ego, the image data captured by a camera of the ego; predicting by executing an artificial intelligence model, an occupancy attribute of a plurality of voxels corresponding to the space around the ego; generating a 3D model corresponding to the space around the ego and each voxel's occupancy attribute; upon receiving a destination, localizing, by the processor, the ego by identifying a current location of the ego using a key image feature within the image data corresponding to the 3D model without receiving a location of the ego from a location tracking sensor; and generating a path for the ego to travel from the current location to the destination.

The invention discloses a nasopharyngeal carcinoma necrosis prediction method and device based on feature fusion and a medium, and the method comprises the steps: obtaining multi-modal medical data of a target patient at a plurality of preset time points, and extracting medical features in different modals for each time point, so as to form a medical time sequence feature matrix in different modals; inputting the medical time sequence feature matrixes in the different modes into a gated twin-tower Transform network, and performing feature fusion on the medical time sequence feature matrixes in the different modes through a preset inter-channel-time step cross attention mechanism to obtain key image fusion features and nasopharyngeal carcinoma necrosis risk prediction results; and if the nasopharyngeal carcinoma necrosis risk prediction result is greater than a preset risk threshold, inputting the key image fusion features and the original three-dimensional image data into a 3D nnU-Net network to generate a nasopharyngeal carcinoma necrosis prediction map of the target patient. The prediction accuracy of nasopharyngeal carcinoma necrosis can be improved.

The invention discloses a complex motion perception method based on human visual inspiration, and the method comprises the steps: firstly capturing an athlete image in a motion process, recognizing a key image region through evaluating the semantic and visual importance of an image region, and then dynamically processing the region features of the key image region. And then, for the dynamically processed region features, a low-rank active learning technology is adopted, an objective function is optimized, and an optimized image region is obtained. And finally, classifying the optimized image area by using a support vector machine (SVM) to obtain a motion sensing result. According to the method, the extracted key region can be ensured to have visual saliency while better calculation efficiency is ensured, and the performance in a complex motion environment is remarkably improved.

The invention discloses a medical image classification and automatic medical report generation method based on self-attention and sparse attention mechanisms. The method combines a convolutional neural network (CNN), a self-attention mechanism and a sparse attention mechanism, can effectively extract local and global features of a medical image, focuses on a key image region, improves the accuracy of medical image classification, and automatically generates a corresponding medical report. The method comprises the following steps: firstly, preprocessing an input medical image through a convolutional neural network to extract local features; then, calculating a global dependency relationship between the image blocks by utilizing a self-attention mechanism, and enhancing the capture of the overall information of the image; thirdly, screening out image blocks important for a classification task through a sparse attention mechanism, and reducing redundant information; finally, according to the method, a medical diagnosis report is generated according to the key image blocks, and an interpretable classification result is provided. The method is suitable for various medical image diagnosis scenes, the workload of doctors is relieved, and the diagnosis efficiency is improved.

The invention relates to the technical field of remote sensing image processing, in particular to a homomorphic filtering-CLAHE remote sensing image enhancement method based on integration strategy multi-target particle swarm optimization, which comprises the following steps: acquiring remote sensing image data, and preprocessing the remote sensing image data; converting the preprocessed remote sensing image into an HSV color space, and extracting a V component in the HSV color space; constructing a frequency domain-spatial domain hybrid enhancement framework of homomorphic filtering and contrast-limited adaptive histogram equalization, and performing enhancement processing on a V-channel component by using the framework; constructing an integrated strategy multi-target particle swarm optimization algorithm; constructing a four-target fitness function including structural similarity, average gradient, information entropy and gray variance, and guiding particles to search in the direction of optimizing a plurality of key image quality indexes at the same time by using the function so as to realize optimal selection of remote sensing image enhancement parameters; the method can effectively enhance the definition and structural integrity of the terrain texture in the remote sensing image of the complex mountainous area.

A system can be configured to: (i) obtain a set of input images images depicting one or more bodily structures of a patient; (ii) determine whether one or more key structures are represented within the set of input images by utilizing a key structure detection module; (iii) determine one or more key images of the one or more images of the set of input images by utilizing a key image localization module; (iv) determine key structure segmentation by utilizing a key structure segmentation module; (v) determine one or more patient condition metrics using the key structure segmentation; and (vi) generate a report associated with the patient based upon the one or more patient condition metrics, or generate an entry at one or more practitioner worklists based upon the one or more patient condition metrics.