24results about How to "Good details" patented technology

The invention discloses a real-time video quality optimization and enhancement method based on deep learning. The method comprises the following steps: S1, collecting a video stream and extracting continuous frames; s2, extracting motion area features by using an optical flow estimation algorithm, and generating a multi-modal feature matrix; s3, carrying out normalization processing; s4, outputting a de-noising optimization frame by using an adaptive multi-dimensional residual network and a frequency domain analysis technology; s5, generating a detail enhancement frame by using a progressive generative adversarial network; s6, constructing an inter-frame relation graph through locality sensitive hashing, correcting the frame by using a double-flow time sensing network, optimizing the frame sequence based on a dynamic time warping method, and outputting the corrected frame sequence; s7, inputting the corrected frame sequence into a dynamic feedback optimization module to generate an optimized frame sequence; and S8, recombining the optimized frame sequence into a continuous video, and transmitting the continuous video to a target platform. According to the method, the video definition and the time consistency are comprehensively optimized through methods of multi-modal feature fusion, dynamic optimization, progressive network generation and the like.

The invention relates to the field of 3D modeling, and particularly discloses a smart city 3D model construction method and system, and the method comprises the steps: carrying out the feature extraction of three-dimensional point cloud data of a to-be-modeled city region through employing a deep learning-based data processing technology, capturing the three-dimensional structure semantic features of the city region, and carrying out the feature extraction of the three-dimensional point cloud data of the to-be-modeled city region; meanwhile, in combination with natural language description about urban area characteristics input by a user, characteristic modulation optimization processing is carried out on the three-dimensional structure of the urban area, the detail expression of the three-dimensional model is further optimized, and therefore intelligent construction of the urban area three-dimensional model is achieved. Through the mode, the geometric information of the urban area and the urban characteristic language description information can be effectively integrated, so that the generated urban area three-dimensional model can reflect the urban internal structure and functional layout more accurately, and the application value of the model in urban planning and management is improved.

The invention relates to a television box intelligent interaction control unit image processing method and device. The method comprises the following steps: carrying out feature analysis on an input video stream of the television box to obtain an enhanced video stream; the method comprises the following steps: collecting visual and voice interaction information of a user based on an intelligent interaction control unit, and inputting the visual and voice interaction information into a double-branch Transform model for multi-modal information fusion to obtain a fusion feature vector; performing conjoint analysis to obtain a user interaction intention and an interaction response strategy; performing local contrast enhancement and motion compensation to obtain a high-quality video frame sequence; and performing scene adaptive color space mapping and dynamic color temperature adjustment on the high-quality video frame sequence according to the environment light information acquired in real time and the pre-stored color characteristics of the display device to obtain an output video stream. According to the implementation of the method, limited computing resources can be fully utilized, efficient video processing and natural man-machine interaction are achieved, and meanwhile the display effect is dynamically optimized according to environmental changes.

The invention discloses a metal surface defect detection and classification method, system, equipment and medium, and the method comprises the steps: processing images of different defect types through gray normalization, adaptive histogram equalization, a Gabor filter, a maximum entropy algorithm, morphological processing and an improved local binary pattern; carrying out defect position and size calculation on the processed image, and carrying out GUI (Graphical User Interface) measurement page design; the system, the equipment and the medium are used for realizing the metal surface defect detection and classification method. According to the method, the image processing effect is remarkable, the image contrast is improved, the defect boundary identification is enhanced, the accurate segmentation of the image and the detailed optimization of the structure are realized, the image features are accurately extracted, and efficient classification training is supported; the method has the advantages of improving image quality, enhancing defect detection precision and optimizing feature extraction and analysis, and can be widely applied to the field of high-precision image analysis such as metal defect detection, industrial quality control and computer vision.

The invention relates to the technical field of defect detection imaging, in particular to a method for multi-angle imaging and image fusion of an area-array camera, which can acquire a large amount of source image data in a short time by using the combination of the area-array camera and a light source, and improves the quality of a source image by preprocessing the source image data. The method comprises the following steps: firstly, creating a frame image, creating an image according to the frame image and a specific formula, extracting data of a specified row from each frame image, fusing the row data extracted from all frame images until the motion is finished, generating a preliminary fused image, and integrating information at different time points, so that features and changes in the motion process are more comprehensively described, and finally, the motion speed is improved. According to the method, the preliminary fusion image is decomposed through wavelet transform, the enhancement coefficient of the preliminary fusion image is obtained through convolution operation, the preliminary fusion image is enhanced through the enhancement coefficient, the fusion image is obtained, detail information in the image is highlighted, and the image is clearer and sharper.

The invention relates to the technical field of metal button surface defect real-time detection methods, in particular to a metal button surface defect real-time detection method based on dry deep learning, which comprises the following steps: acquiring a to-be-detected metal button image and preprocessing the to-be-detected metal button image to obtain a to-be-detected metal button image; and inputting the preprocessed image into a deep learning network model to extract a defect region, optimizing and verifying the extracted defect region, and finally generating a statistical result of defect detection. According to the method, dynamically regulated progressive boundary construction, adaptive gradient mapping, Gaussian fitting detail enhancement, multi-dimensional difference correction factors, sectional defect region generation and other algorithms are innovatively introduced in sequence, so that boundary recognition and detail optimization are progressive layer by layer. The innovative multi-stage optimization design enables the data output of each step to be connected with the subsequent steps, guarantees the continuity and consistency of defect detection, and avoids the problems of boundary error and information loss caused by algorithm independence in a traditional method.

The invention belongs to the technical field of stereoscopic vision, and discloses a stereoscopic matching method based on unsupervised online learning. The method is realized through a stereo matching module based on a multi-level cost body, a non-occlusion region confidence estimation module and a Mann-Kendall trend detection module. Compared with the prior art, the method has a better stereo matching effect. The model provided by the invention has the best result in both the initial stage and the later stage, and due to the good generalization of the model, the model can always keep a relatively accurate prediction result in a sheltered region. The model has a good prediction result in the occlusion area due to high generalization, and the tendency of parameter error update is relieved due to the fact that the self-adaptive adjustment strategy provided by the invention eliminates the influence of error loss of most of the occlusion areas, so that the network can maintain the optimal performance all the time.

The invention relates to the technical field of content recommendation, in particular to a user music recommendation method and system based on data analysis, and the method comprises the following steps: based on a music playing record of a user, extracting the playing frequency and frequency information of each piece of music, evaluating the preference degree of the user for each music type, analyzing the preference degree of the user for each music label, and recommending the user music. And obtaining a user preference analysis result. According to the method, the preference degrees of the users for different music types and labels are analyzed according to the playing frequency and frequency, the ages and genders of the users are considered, the fineness and adaptability of recommendation are optimized, user groups with similar music preferences can be identified more accurately through evaluation and classification of the similarity between the users, and the user experience is improved. A more accurate recommendation list is provided, the playing habits of the user in different time periods are analyzed, the recommendation opportunity is optimized, it is ensured that the user receives the recommendation in an acceptable time period, the participation degree of the user is improved, and the acceptance degree of the user to the recommended content is increased.

The invention belongs to the technical field of 3D modeling based on image processing, and particularly relates to a 3D modeling method based on animation design and a control system.The animation modeling subsystem completes automatic three-dimensional modeling by obtaining a front image of a single character to obtain a character three-dimensional model; the animation action driving subsystem firstly outputs final motion parameters according to the input motion data, and then adjusts a role three-dimensional model; finally, after the output motion parameters and the role three-dimensional model are obtained, frame-by-frame motion parameter adaptation is carried out, layer-by-layer driving skeleton operation is carried out, and preliminary role motion roaming is generated; and the rendering display module is used for rendering and displaying the initial role motion roaming to obtain final role motion roaming. According to the invention, rapid role three-dimensional modeling can be carried out according to a single front image, the motion parameters are obtained according to the motion data, and a plurality of role three-dimensional models and a plurality of motion parameters are randomly matched to directly and rapidly obtain three-dimensional animation roaming of a plurality of roles in different motion states.

This utility model discloses a 3D scanner with replaceable filters, comprising: an instrument body; a camera unit and a laser projection unit disposed within the instrument body, wherein the camera in the camera unit and the projection surface in the laser projection unit are both disposed on the measurement surface of the instrument body; and a supplementary lighting unit located around the camera and disposed on the measurement surface, wherein the filter unit in the supplementary lighting unit is detachably disposed at the front end of the light source assembly in the supplementary lighting unit. This utility model significantly improves the scanner's adaptability to different material surfaces and scanning quality through the design of replaceable filter units, optimizes image clarity and detail, and allows for flexible adjustment and rapid replacement of filters according to the characteristics of different materials.

The application discloses a low-contrast image joint enhancement and super-resolution reconstruction method, which comprises the following steps: firstly, performing multi-scale Gaussian filtering on the low-contrast image; secondly, expanding the imaging dynamic range of the quotient image by using an S function; thirdly, performing color correction on the expansion result to obtain a contrast-improved image; and finally, optimizing the resolution and detail information of the contrast-improved image by a blind super-resolution reconstruction model constrained by a total variation to obtain a detail information and resolution improvement result. The low-contrast image joint enhancement and super-resolution reconstruction can help to overcome the influence of image adverse factors, such as low contrast / resolution and fuzzy details, and lay a foundation for image feature extraction and image interpretation.

The present application relates to the field of automobile technology, and in particular to a vehicle driving method, a vehicle, and a computer-readable storage medium. The vehicle driving method includes: obtaining a power switching instruction, switching the driving mode of the vehicle to a target driving mode indicated by the power switching instruction; wherein the target driving mode is one of the preset driving modes of the vehicle; the preset driving mode includes a normal mode, a plurality of first modes, and / or a plurality of second modes, the driving power of the first mode is higher than the driving power of the normal mode, and the driving power of the second mode is lower than the driving power of the normal mode; in response to obtaining a power change request for carrying power demand, the vehicle is controlled to respond to the power demand according to the power response rule of the target driving mode. The use of this method can enrich the driving mode of the vehicle under driving state, which is conducive to optimizing the refinement of the power response efficiency strategy of the vehicle driving mode.

The present invention relates to the technical field of digital printers, and discloses a belt tension stabilization method, system and device for a dual-motor printer, the method comprising: setting the sampling time of the initial output torque and the actual output torque of the active roller servo motor; obtaining the actual output torque of the active roller servo motor at the sampling time; based on the sampling time, the initial output torque of the active roller servo motor and the actual output torque of the active roller servo motor at the sampling time, using a PID algorithm to control the power of an electronic load set on a driven roller generator, so that the output torque of the active roller servo motor is stabilized at the initial output torque. The present invention controls the power of the electronic load to offset the uneven force of the belt, thereby stabilizing the tension of the belt, ensuring the stability of the speed and torque of the servo motor of the active roller, and ensuring the stable progress of the printing process, and has the advantages of fast response and no loss.

The present invention discloses a method for clearing visible light OCT images based on a generative adversarial network. The method is as follows: 1. Acquisition of training samples. Use a visible light OCT device to obtain a V-domain image. Use a near-infrared light OCT device to obtain an N-domain image. 2. Preprocess the V-domain image and the N-domain image obtained in step 1. 3. Establish a training sample data set based on the preprocessed V-domain image and the N-domain image. 4. Construct and train an OCT image clearing model. 5. Input the visible light OCT image into the generator G1 to obtain an OCT image with improved quality. The new unsupervised generative adversarial network proposed in the present invention constructs an OCT image clearing model with the help of the idea of ​​dual learning, and can obtain better results without using paired images for training. In addition, the present invention adds a network that can edit image attributes, which enhances the ability of the generator to learn clear structures during dual training.

The invention discloses a multi-focus microscopic image fusion method based on a convolutional neural network. The convolutional neural network is trained to fuse microscopic images of a plurality of focal layers. A plurality of partially focused images in the same scene are given, and feature extraction is performed through a convolutional neural network to obtain significant image features. Afterwards, the convolution features of a plurality of input images are fused according to the element-by-element maximum fusion rule, and the fused features are reconstructed through two convolution layers, so that a fused image with rich information is generated. According to the method, the images of multiple focal planes of the pathological section under microscopic scanning are input and effectively fused, so that the detail representation of the microscopic image is improved, the fused image has good visibility in focal and non-focal areas, and the advantages of flexibility and generalization of deep learning are fully utilized. Parameters which need to be manually and continuously adjusted in a traditional method are replaced by a neural network structure, and convergence of an algorithm and reconstruction precision are theoretically supported.

The invention discloses a magnetic resonance high-resolution multi-parameter quantification method and system based on multi-scanning overlapping echoes, and relates to a magnetic resonance imaging method. Comprising the following steps: designing a high-resolution multi-parameter simultaneous quantitative imaging sequence based on multi-scanning overlapping echoes and determining sampling parameters of the high-resolution multi-parameter simultaneous quantitative imaging sequence; performing data acquisition on an imaging object by using the designed sequence and sampling parameters in a magnetic resonance instrument meeting the sequence and sampling parameters to obtain a magnetic resonance signal; after the magnetic resonance signal is processed, an image acquired by the sequence is obtained; designing a high-fidelity quantitative reconstruction method; the acquired magnetic resonance image is subjected to quantitative reconstruction by using the method, and a high-resolution multi-parameter magnetic resonance quantitative image is obtained. Magnetic resonance high-resolution multi-parameter simultaneous quantitative imaging can be realized, and the problem that reconstruction details are lost under the condition of low signal-to-noise ratio is solved. Experimental results show that the magnetic resonance quantitative parameter image generated through reconstruction is highly consistent with the result of a reference method, the imaging precision is high, and the method has wide application prospects.

The invention relates to a garment raised pattern manufacturing method which comprises the following steps: step A, pretreatment: preparing a garment fabric, a lining patch and an adhesive film sheet, pretreating the garment fabric, and compounding the lining patch and the adhesive film sheet to respectively obtain a treated garment fabric and a compound sheet; step B, positioning, namely pre-heating a dispensing machine, positioning and placing the composite sheet in a dispensing area, and then paving the composite sheet; step C, pattern manufacturing: operating a dispensing machine, and performing dispensing operation on the fixed composite sheet to enable sprayed glue points to form a coherent preset pattern so as to obtain a composite sheet with a convex pattern; e, combining: combining the patch with the convex pattern with the treated garment fabric to obtain the garment fabric with the convex pattern; the raised patterns on the garment fabric manufactured by the method are strong in three-dimensional effect, the pattern lines are smooth and coherent, the corners have good detail representation, the garment fabric has good use durability, the manual operation is reduced, and the implementation difficulty is simplified.

The invention discloses a method for approximately calculating microalgae radiation characteristics based on discrete dipoles, which mainly relates to the field of microalgae radiation and comprises the following steps: S1, selecting algae species; s2, analyzing structures and components of the microalgae, and determining optical constants of the components; s3, constructing a microalgae cell model according to the structure and the components; s4, calculating algae radiation characteristics of the microalgae model by using a discrete dipole approximation method; s5, comparing the calculation result with the actually measured radiation characteristic parameter to verify the accuracy of calculating the microalgae radiation characteristic based on the discrete dipole approximation method; the problem that in the prior art, the radiation characteristic parameters of the microalgae cannot be accurately obtained due to the experiment cost problem and the limitation of an existing calculation method is solved.

The application provides an image super-resolution processing method and device, electronic equipment and storage medium. The method comprises the following steps: receiving a user input image to be processed; inputting the image to be processed into a lightweight neural network model, extracting detailed features of the image to be processed by the lightweight neural network, and fusing the extracted detailed feature map and the image to be processed to obtain a super-resolution image corresponding to the image to be processed. The user can directly process image data online on the terminal device, and the lightweight neural network composed of a convolution layer, a plurality of structure blocks and an up-sampling layer can greatly improve the image processing speed, avoid time-consuming network transmission and achieve real-time image processing.

The invention discloses a three-dimensional reconstruction method based on a single image, including a training phase: the training samples include sampling points on a three-dimensional model and actual implicit function values ​​corresponding to the sampling points, and a single image corresponding to the three-dimensional model; a feature space is generated through a single input image, the sampling points of the three-dimensional model are used as query points, the implicit function values ​​of the query points are predicted, and the loss is calculated according to the predicted implicit function values ​​and the actual implicit function values ​​to complete the fitting of the implicit function field; a reconstruction phase: after the training phase is completed, a feature space is generated for the single input image, a number of points are sampled in the observation space, and these points constitute the query points; the implicit function values ​​of the query points are predicted; the query points and their predicted implicit function values ​​are input into a marching cubes algorithm to reconstruct the three-dimensional model. The method improves the problem of serious lack of prior conditions in single image reconstruction and can obtain fine details in the visible part.

The present application relates to the field of 3D modeling, and specifically discloses a method and system for constructing a 3D model of a smart city, which uses a data processing technology based on deep learning to extract features from the 3D point cloud data of the urban area to be modeled, captures the semantic features of the 3D structure of the urban area, and combines the natural language description of the characteristics of the urban area input by the user to perform feature modulation optimization processing on the 3D structure of the urban area, further optimizes the detailed performance of the 3D model, and thus realizes the intelligent construction of the 3D model of the urban area. In this way, the geometric information of the urban area and the language description information of the urban characteristics can be effectively integrated, so that the generated 3D model of the urban area can more accurately reflect the internal structure and functional layout of the city, and improve the application value of the model in urban planning and management.

The present invention relates to the field of target recognition technology, specifically a target recognition and positioning system based on image segmentation and its terminal device, the system includes an edge processing module, a feature recognition module, a regression analysis module, an attention weighting module, an abnormal analysis module, and a target positioning module. In the present invention, the recognition ability of the target in the image is enhanced by using texture, shape and color analysis, the accuracy and reliability of recognition are improved, the prediction of the target position is refined by probability analysis and regression technology, the positioning error is reduced, the positioning accuracy is improved, the dynamic weight adjustment highlights the important features of the target area, and the focus of target identification is optimized. The abnormal analysis calculates the deviation between the target and the predetermined coordinates, enhances the system's recognition and response capabilities to abnormal situations, improves the adaptability and safety of the system, and jointly improves the overall performance of the target recognition and positioning system and the practicality of application.

The present invention discloses a method for generating a small sample of infrared images of conveyor rollers and a fault simulation device thereof. The method uses a generative adversarial network model including a generator and a discriminator, and includes a simple task learning stage and a complex task learning stage. In the simple task learning stage, only infrared images of completely stuck rollers are used to learn their global temperature distribution and local high temperature area distribution, and the generative adversarial network model is trained in combination with the loss of global temperature distribution and local high temperature area distribution. In the complex task learning stage, infrared images of other preset stuck rollers are used to learn their global label distribution and local landmark area label distribution, and the generative adversarial network model is trained in combination with the loss of global label distribution and local landmark area label distribution. The trained generative adversarial network model is used to generate a small sample of infrared images of conveyor rollers. The present invention can generate high-quality infrared images of rollers, thereby improving the accuracy and efficiency of fault detection.

The invention relates to a GIS switch contact identification method and device based on a mixed wave band light source illumination technology. GIS switch contact images under different wave band light sources are collected, the collected images are processed and fused, the overall image quality and detail performance are optimized, the fused images are subjected to image identification processing, and a feature map is obtained; the state of the switch can be analyzed and judged according to the number and the positions of the identification lines on the feature map; if the identification lines do not exist in the feature map, the switch is in an opening state at the moment; if one identification line exists in the feature map, the switch is in a fault state at the moment; and if two identification lines exist in the feature map, determining the area proportion of the identification lines occupying the identification line region feature image, determining the position stroke of the identification lines relative to the moving contact, and determining whether the GIS switch is closed in place according to the area proportion and the position stroke. According to the method provided by the invention, the state of the GIS switch can be accurately identified.