305 results about "Image texture" patented technology

The invention discloses a roadside guardrail deformation detection method based on image and point cloud fusion and related equipment, and the method comprises the steps: collecting a guardrail region image through a high-definition camera carried by an unmanned plane, and synchronously collecting the point cloud data of a guardrail region through a laser radar; then, a joint calibration and iterative nearest algorithm is used for carrying out space-time registration on the guardrail area image and the point cloud data which are synchronously collected, and a multi-modal data set is generated, so that the problem of space-time dislocation caused by sensor movement is solved; an improved double-branch deep learning model is adopted to respectively extract image texture features and point cloud geometric features from a multi-modal data set, and the two features are fused to overcome a single-modal defect; and extracting the contour of the guardrail beam plate from the fusion feature map, aligning the contour of the guardrail beam plate with the reference model, and carrying out quantitative calculation to quantify the deformation degree of the guardrail and realize quantitative detection of the deformation of the guardrail.

The invention relates to the technical field of roadway surrounding rock danger identification, and discloses a roadway surrounding rock danger identification model construction method, which comprises the steps of collecting multi-modal data, and generating preprocessed data through synchronous calibration and denoising; extracting a seismic wave frequency domain and image texture features, and generating a multi-modal feature matrix; in combination with a geological prior clustering mining abnormal mode, generating a labeled sample data set; generating a danger identification model based on a transfer learning and feature fusion training network; and the edge deployment model performs real-time reasoning, and generates an early warning result through an adaptive algorithm. According to the method, the frequency domain features of the seismic fluctuation signals and the depth texture features of the surrounding rock images are fused, the multi-modal feature matrix is constructed, abnormal mode mining is carried out in combination with geological prior knowledge, and early weak abnormal signals such as hidden fault slippage or asymmetric microfracture extension which are difficult to find by a single monitoring means can be effectively recognized.

The invention discloses a water heavy metal concentration simulation and prediction method, which comprises the following steps: integrating original monitoring data, hydrodynamic data, total suspended solids, image remote sensing data and human activity data, and generating a multi-source cleaning sequence data packet; executing cross-modal adsorption capacity estimation by using image remote sensing data in the data packet, inferring particle chemical composition and adsorption isotherm parameters from image textures, and generating a capacity feature packet containing an adsorption capacity upper bound; time-varying travel time is calculated based on the hydrodynamic data and the human activity data, causal alignment is performed on the capacity feature packet and the upstream signal, and a travel time alignment feature packet is generated; and in combination with metal fingerprint parameters, applying an adsorption capacity upper bound as a physical constraint on a form distribution constraint head, explicitly decoupling and predicting the form, and generating a prediction result packet. According to the method, the hydrodynamic physical mechanism and the particle adsorption chemical mechanism are deeply coupled, and the prediction precision and the physical consistency of the model under the unsteady state condition are improved.

The invention relates to the technical field of industrial mechanical arm control, in particular to a mechanical arm execution control method for industrial production, which comprises the following steps: collecting parameters in real time; generating a geometric risk index; generating a mechanical risk index; extracting image features; abnormal mechanical arm judgment; determining an adjusting mechanical arm; and generating an adjustment instruction. According to the method, the inclination angle, the plane offset distance, the contact image features, the tightening torque and the force value of the tail end of the mechanical arm are monitored in real time in a multi-dimensional mode, a dual-risk judgment model based on geometric offset and mechanical loading is constructed, image texture fluctuation and posture change are further combined, the continuously abnormal mechanical arm is accurately positioned, and the safety of the mechanical arm is improved. And geometric and mechanical risk indexes are evaluated again after adjustment, so that the aluminum shell positioning and bolt assembling precision is guaranteed, and the problems that the production efficiency is reduced and the product defect rate is increased due to assembling quality fluctuation caused by mechanical arm positioning errors and inaccurate force control are effectively solved.

The invention provides an underwater image recognition method based on deep learning, and relates to the technical field of information, and the method comprises the steps: preprocessing an original image, recognizing image texture interference caused by the density of suspended particles in an interfered region, separating a noise signal, and obtaining a first processed image; performing deep analysis on the multi-scale feature response values and the key feature points in combination with an underwater target recognition task, and judging the category and position information of the target object to obtain a preliminary recognition result; comparing the preliminary identification result with an actual marine environment condition to obtain a matching degree between the identification result and an expected target feature, and if the matching degree is lower than a preset matching threshold value, adjusting a scale weight parameter and a feature point screening threshold value to obtain corrected identification data; and analyzing changes caused by environmental condition fluctuation according to the corrected identification data, generating target identification output, and determining the accurate position and category information of the underwater target through confidence weighted fusion and coordinate precision correction processing of the identification data.

The invention relates to the technical field of multi-unmanned-aerial-vehicle cooperative operation and three-dimensional modeling, in particular to a multi-unmanned-aerial-vehicle cooperative three-dimensional rapid modeling method for a highway accident scene, and the method comprises the steps: generating a three-dimensional grid map of an accident area through the scanning of a millimeter-wave radar by a main control unmanned aerial vehicle; subareas are divided according to a load balancing strategy and are distributed to slave unmanned aerial vehicles, the slave unmanned aerial vehicles traverse grids along a snake-shaped track, laser radar point clouds and five-view-angle images are synchronously collected, data are bound through double time stamps and space coordinates, the point clouds are preprocessed through edge computing nodes, and the point clouds are stored in a database; the master control unmanned aerial vehicle evaluates quality based on density standard deviation and overlapping matching degree and instructs to reacquire, performs high-precision Poisson reconstruction on an accident core area, performs voxelization processing on a peripheral area, maps image textures, optimizes vehicle deformation details, simplifies a model and retains key element precision, and finally performs data processing. License plate coordinates, a scattered object thermodynamic diagram and an emergency lane occupation state are automatically marked, a visual model is generated, and rapid and accurate restoration of an accident scene is realized.

The invention provides a scanning electron microscope image edge detection method, which comprises the following steps: carrying out anisotropic diffusion filtering on a scanning electron microscope image to suppress noise and reserve edges to obtain a filtered image; an original scale gradient and a down-sampling scale gradient of the filtered image are calculated, a plurality of pixel regions in the original scale gradient and the down-sampling scale gradient are fused based on a plurality of adaptive weights to obtain a gradient map, the adaptive weights are determined based on the complexity of the texture of the filtered image, and each pixel region corresponds to one adaptive weight; multi-dimensional features are extracted from the gradient map, the multi-dimensional features are input into a machine learning model for threshold prediction, a continuous pixel-level threshold map is generated based on a predicted threshold, and a gradient threshold included in the continuous pixel-level threshold map is a critical value for distinguishing different types of pixels in the gradient map; and comparing the gradient map with the continuous pixel-level threshold map to obtain a target edge pixel, and generating an edge detection result map based on the target edge pixel.

The invention relates to the technical field of semantic understanding, in particular to a three-dimensional scene semantic understanding method and system based on multi-modal deep learning, and the method comprises the following steps: collecting a point cloud image and a depth map in an automatic driving scene, carrying out the normalization standardization and deletion filling, extracting texture geometric space features, and carrying out the fusion through an attention mechanism; a multi-time-step state vector is introduced to calculate change features, a spatial relation between road participation objects is modeled, a dynamic instance graph structure is constructed, semantic tags are reasoned, and fusion features are compared to generate a three-dimensional scene semantic understanding result. According to the method, the fusion quality is guaranteed through multi-source data normalization standardization, the semantic complementarity is enhanced through collaborative extraction of image texture and point cloud geometric features, the dynamic scene perception ability is improved through state vector modeling, the object interaction semantic relation is described through a spatial relation graph, and the recognition accuracy and consistency are improved through a semantic label reasoning mechanism. And the integrity and robustness of three-dimensional semantic understanding are integrally enhanced.

The invention relates to the technical field of agricultural microbial monitoring, in particular to a microbial action monitoring and analysis system which comprises a pollution detection module, a distribution recognition module, a state analysis module, a period extraction module and a node traceability module. According to the method, a pollution identification basis is constructed based on a multi-channel conductance deviation trend and a synchronization feature, flora distribution information is established by introducing a feature grouping mode of image texture, gray level and structural density, and consistency judgment is performed by combining direction deviation of three types of response data of conductance, gas release concentration and pH; the continuity and the staged response precision of microbial function state recognition are enhanced, and meanwhile, a linked list structure index path is constructed according to sampling time, position and channel sequence, so that a continuous mapping process can be formed by a front-back sequence and behavior transition relation of a state period; the structure expression capability and the rhythm construction efficiency of the multivariable monitoring data in the microbial action stage analysis are improved.

The invention relates to a low-illumination image enhancement method based on multi-mode classification and brightness feedback, and belongs to the field of image processing. The method comprises the following steps of: firstly, training a network, constructing a multi-modal illumination prior feature of an image, inputting the multi-modal feature and a brightness score of the image into an illumination perception classification network, obtaining a local probability value of image brightness, adaptively selecting a local or global enhancement processing method, and generating a fusion weight; in the local enhancement processing, dark area details are enhanced through a multi-scale parallel and double-attention mechanism, in the global enhancement, the brightness is improved by using a symmetric coding-decoding structure and a residual attention block, and linear fusion is performed after the brightness is enhanced; then, the brightness evaluation value of the image is fed back through the lightweight brightness estimation network, and the brightness of the image is enhanced. And optimizing the training network according to the joint loss function of image processing. The method provided by the invention can enhance image texture details and improve image definition under a non-uniform illumination condition and an extremely low illumination condition.

The invention provides an ovarian cancer accurate diagnosis and personalized treatment method based on data integration, and the method comprises the steps: employing a feature extraction algorithm to generate a multi-dimensional feature vector set containing a gene expression level, an image texture parameter, a symptom score and a pathological classification according to a structured data set; performing dimension reduction processing on the multi-dimensional feature vector set through a feature fusion module to obtain a low-dimensional feature representation vector; performing grouping and risk prediction on the patient data by adopting a classification algorithm according to the low-dimensional feature representation vector, and generating a patient subgroup classification and disease risk layering result; according to the patient subgroup classification and the disease risk layering result, a scoring model is adopted to generate a diagnosis scoring result and a personalized treatment recommendation scheme, and a diagnosis report is output.

The present application discloses a video encoding method and apparatus, a device, and a readable storage medium. The method comprises: acquiring the current encoding bit rate of a video encoder; acquiring a frame sequence to be encoded of a target video source, and starting to encode an initial video frame in said frame sequence by means of the video encoder on the basis of the encoding bit rate; determining a first image texture value of a first video frame currently prepared to be encoded in said frame sequence, and determining a second image texture value of a second video frame immediately preceding the first video frame, the first video frame being any video frame following the initial video frame; when the first image texture value is greater than the second image texture value, determining an image texture ratio on the basis of the first image texture value and the second image texture value; and adjusting the encoding bit rate of the video encoder on the basis of the image texture ratio to obtain a target encoding bit rate, and encoding the first video frame on the basis of the target encoding bit rate. In this way, the bit rate of the video encoder is adjusted at the frame level, ensuring the quality of encoded video frames.

The invention relates to the technical field of image modeling, in particular to a three-dimensional modeling method and system for medical image data, and the method comprises the steps: carrying out the preprocessing of the obtained two-dimensional or three-dimensional medical image data, extracting a blood vessel region based on the image texture and gray features, and generating a preliminary blood vessel structure segmentation image; in combination with a blood flow simulation or real acquisition means, obtaining dynamic feature data from the vascular structure segmentation map, and mapping the dynamic feature data to a corresponding vascular structure position to form a vascular distribution map with blood flow constraint; and inputting the blood vessel distribution diagram into a three-dimensional modeling module, expanding and complementing small blood vessel branches by utilizing blood flow constraint, dynamically correcting the form of a main blood vessel, and generating a three-dimensional blood vessel structure model with real fluid physiological characteristics. The method can be widely applied to medical research and clinical aid decision-making scenes such as preoperative simulation, individualized intervention path planning and blood flow reconstruction analysis, and has good popularization and application prospects and engineering practical value.

The invention discloses a component multi-dimensional information measurement method and system based on multi-source information fusion, and the method comprises the steps: 1, obtaining a fluorescence speckle image and a texture image before deformation, and decomposing the two images into low-frequency features and high-frequency features through discrete wavelet transform; 2, fusing low-frequency features of the fluorescence speckle image and the texture image before deformation; fusing the high-frequency features of the fluorescent speckle image and the texture image before deformation; 3, reconstructing the fused low-frequency features and high-frequency features through inverse wavelet transform to obtain a fused image before deformation; 4, acquiring a fused image after deformation; 5, the three-dimensional shape of the component is obtained through an FPP method; then calculating a sub-pixel-level displacement field, and constructing a three-dimensional displacement field according to the three-dimensional morphology and the sub-pixel-level displacement field; and extracting a partial derivative of the three-dimensional displacement field to the pixel coordinate, and calculating the Green strain tensor. According to the invention, the interference of environmental factors on displacement and strain measurement is effectively overcome, and the measurement precision and robustness are improved.

The invention relates to the technical field of remote sensing geological information processing, and discloses a mineral geological exploration system based on remote sensing image texture analysis, which comprises a time sequence base line library construction unit, an ecological proxy texture channel processing module, a remote sensing image texture analysis unit and a remote sensing image texture analysis unit, the system comprises a normalization vegetation index texture purification module for purifying a normalization vegetation index texture of a current image according to a time sequence statistical baseline so as to generate an ecological proxy texture anomaly graph, a texture channel construction processing module for generating a texture anomaly graph, and a collaborative verification engine module for performing collaborative verification on the texture anomaly graph before executing spatial coupling judgment. According to the method, the information quality of two channels is evaluated according to image information entropy, verification logic is dynamically selected, earth surface covering information regarded as interference in traditional exploration is converted into an independent verification dimension, and an evidence chain of internal cross verification is constructed through double constraints of space and time and self-adaptive evaluation of the information quality.

The embodiment of the invention provides a CEST image super-resolution reconstruction method, medium and equipment, and the method comprises the steps: obtaining an original image and a reference image of a target object, the original image being a molecular function image collected through a chemical exchange saturation transfer imaging technology, and the reference image being an anatomical structure image collected through a magnetic resonance imaging technology; through a high-frequency, intermediate-frequency and low-frequency fusion enhancement network, extraction and fusion enhancement of corresponding high-frequency, intermediate-frequency and low-frequency characteristics are carried out on an original image and a reference image. According to the method, anatomical edge details of a reference image are captured through a high-frequency fusion enhancement network to enhance image edge details, image texture features are captured through an intermediate-frequency fusion enhancement network to enhance image texture details, image background details are enhanced through a low-frequency fusion enhancement network, and then images with enhanced frequency band information are fused. A reconstructed image with high resolution and molecular function signals is obtained, and the reconstruction precision of super-resolution reconstruction is improved.

The invention discloses a tire surface defect detection method based on multi-scale image sharpening, and the method comprises the steps: collecting a visual detection image of a tire surface, and carrying out the preprocessing of the visual detection image to generate a standardized visual detection image; carrying out multi-scale decomposition and sharpening processing, and carrying out fusion reconstruction to obtain a sharpened enhanced image; texture interference suppression and abrupt change region detail enhancement are carried out to obtain an interference suppression image; executing a TR-MUSIC algorithm to generate a global abnormal spatial spectrogram; constructing an improved U-KAN network model, and generating a defect segmentation mask; extracting defect area characteristic parameters, and outputting a tire surface defect detection result. According to the invention, through combination of multi-scale image sharpening enhancement, the TR-MUSIC algorithm and the improved U-KAN network model, high-precision automatic detection of weak and small defects on the tire surface under a complex texture background is realized.

The embodiment of the invention discloses an image quality evaluation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a to-be-processed image, and converting the to-be-processed image into a target color space to obtain a to-be-used image; wherein the to-be-processed image is an image acquired based on fusion of a plurality of sensors; determining a brightness mean value corresponding to the to-be-used image, and converting the to-be-used image into a frequency domain space to obtain frequency domain data corresponding to the to-be-used image; determining image texture attributes according to the frequency domain data; determining a target evaluation result of the to-be-processed image according to the image texture attribute and the brightness mean value; wherein the target evaluation result is used for representing the credibility of the image collected by the image sensor in the current environment. Through the technical scheme of the embodiment of the invention, image quality evaluation for non-single indexes can be realized, and the image quality evaluation efficiency and accuracy are improved.

The invention relates to a luggage material identification method and system based on multi-modal fusion knowledge distillation. The method comprises the steps of obtaining image data and point cloud data of a to-be-detected target surface; constructing a multi-modal teacher model to obtain image texture features and point cloud geometric features; obtaining a teacher object query; outputting a material category score and a bounding box position coordinate; constructing a lightweight student model, and generating student object query, material category prediction and bounding box position prediction; characteristic distillation loss is designed for characteristic distillation, and a total loss joint training lightweight student model is constructed; actually operating the trained lightweight student model in a luggage detection scene of an airport luggage turntable; calculating a stacking score and mapping the stacking score into a stacking label; according to the method, the semantic gap between the perception recognition module and the downstream planning strategy module is effectively linked, and the contradiction between the insufficient precision of traditional single-mode perception and the high cost of multi-mode deployment is solved.

The invention provides a self-service zooming method of a camera system based on depth-guided image quality evaluation. The method comprises three core links of multi-scale depth perception, depth-guided image quality evaluation and joint-driven intelligent focusing control. By fusing lightweight feature coding and a multi-scale optimization strategy, accurate depth information is generated, and a spatial perception basis is provided for subsequent processing; the depth features and the image texture features are combined, a focusing fuzzy evaluation model is constructed, and reliable feedback of the imaging quality is achieved; a key focusing area is identified based on depth and quality information, a target focal plane is decided, and a lens is driven through an intelligent control algorithm to complete rapid and accurate focusing. Through collaborative fusion of depth perception and image quality evaluation, intelligent zoom control of the camera system in a dynamic scene is realized, the imaging consistency and focusing precision are effectively improved, the method is suitable for multiple camera devices such as smart phones, security monitoring and automatic driving, and the imaging experience of users is improved.

The invention discloses an extremely shallow water blue-green laser sounding dynamic refraction vision correction method, which belongs to the technical field of marine surveying and mapping and underwater detection, is used for laser refraction correction, and comprises the following steps: synchronously acquiring binocular images and inertial measurement unit attitude data, and generating a three-dimensional point cloud; according to a light spot area in the laser emission angle index point cloud, fitting a micro-tangent plane to obtain a water surface original normal vector; calculating a confidence coefficient weight according to the image texture definition, and iteratively calculating an optimal estimation normal vector based on the original normal vector and the weight; calculating a real incident angle according to the optimal estimation normal vector and the laser emitting direction; calculating the direction vector of the underwater refracted light according to the Snell's law; and calculating the underwater three-dimensional coordinates of the target point by combining the propagation distance of the laser in the air and the water and the emission origin coordinates. According to the method, the visual confidence is introduced, so that the problem of data failure when the traditional visual sounding encounters water surface reflection, glare or broken waves is solved, and the robustness under the complex sea condition is remarkably improved.

The invention relates to the technical field of plate automatic production, in particular to a plate directional two-dimensional edge positioning method, and aims to solve the problems that a camera shooting and simple edge detection method cannot adapt to high-speed online measurement, image distortion influences precision, complex image textures and colors and the like, and edge positioning robustness is poor. According to the technical scheme, the method comprises the following steps: acquiring an original image of a moving plate and an image of a calibration plate through an image acquisition unit; based on a pre-generated calibration file, performing global distortion correction on the original plate image through a coordinate calibration module, establishing a mapping relation between image sub-pixel coordinates and physical coordinates, and obtaining a corrected plate image; and processing the corrected plate image, extracting a plate area through an edge positioning module, taking at least two edges of the plate in different directions as target edges, and performing directional two-dimensional edge positioning on the target edges in at least one direction under shadow interference.

The invention provides a graphics processor, a texture loading method, a texture processing unit, equipment and a medium, and relates to the technical field of image texture loading. The method comprises the following steps: uniformly distributing texture loading instructions to a texture processing unit for execution, wherein the texture loading instructions comprise texture cache loading instructions; analyzing and executing the texture loading instruction through the texture processing unit; wherein the texture state information of the texture loading instruction is obtained through hardware analysis of the texture processing unit, the texture loading instruction completes address calculation and data reading in an independent texture loading pipeline, and the texture loading pipeline is started in the texture processing unit. According to the technical scheme, the scheduling logic of the texture loading instruction can be simplified, the compatibility of dynamic resources is improved, meanwhile, the hardware utilization rate is increased, and the reliability and execution efficiency of texture data access are improved.

The invention discloses a digital twin video holographic display method and system based on a real geographic space, and the method comprises the steps: reading a real-time image frame at a current moment from real-time video data, and obtaining a real-time image frame according to the visibility and shielding relation between the real-time image frame and a three-dimensional live-action model; the method comprises the following steps: dividing an image texture of a real-time image frame into a texture mapping overlapping region and a non-overlapping region, for the texture mapping overlapping region, combining a plurality of historical image frames as a texture source, and adopting a multi-source texture fusion algorithm based on real geographic space consistency to reconstruct a texture map of the texture mapping overlapping region; dynamically mapping the texture map to the surface of the three-dimensional live-action model to obtain video-model data after texture mapping; and transmitting video-model data into a holographic display system, and displaying a holographic image according to the position difference of the eyes of the observer in the space. According to the invention, accurate space fusion is carried out on the three-dimensional real scene model and the real-time video data, and real, real-time and spatially synchronized three-dimensional holographic presentation of a physical scene is realized.

The invention relates to a grassland obstacle visual detection and identification method for a mowing robot, and belongs to the technical field of image identification. According to the method, firstly, a traditional neural network is used for carrying out obstacle recognition and elimination on a grassland image during mowing operation to obtain a preliminary screening image, and then the possibility that different sub-regions in the preliminary screening image contain unobvious obstacles is represented in a multi-dimensional mode based on image texture differences, edge trend differences and color distribution differences between the unobvious obstacles and a grassland background. Therefore, the parameters of the lightweight neural network are adaptively and accurately adjusted, so that the adjusted network is prevented from losing weak and sparse characteristic signals of an unobvious obstacle when facing a preliminary screening image possibly containing the unobvious obstacle, and the unobvious obstacle recognition with both recognition efficiency and recognition accuracy is realized. Finally, when the grassland obstacles in the mowing operation are recognized through the lightweight neural network, the obstacle recognition accuracy is remarkably improved.

The invention discloses a tactile image domain migration method and device based on a multi-scale generative adversarial network, and the method comprises the steps: constructing the multi-scale generative adversarial network, and achieving the domain migration between a simulation tactile image and a real tactile image. The generator takes U-Net as a trunk, introduces a multi-scale stacking module, a multi-stage attention gate mechanism and a channel-space attention module, and improves the reconstruction capability of simulation image textures, illumination and contact areas. And the discriminator adopts a multi-scale discrimination structure to realize the discrimination of image authenticity and detail consistency. Through joint training of joint adversarial loss, loop consistency loss, contact area consistency loss, illumination balance loss and frequency domain loss functions, it is ensured that an output image is consistent with a real image in visual and semantic levels. The method can be widely applied to a robot vision-touch fusion perception task, and the migration performance and robustness of the perception model are remarkably improved under the condition of non-paired data.

The invention discloses an underwater polarization image restoration method based on Transform and depth estimation, and belongs to the technical field of underwater image processing. The method comprises the following steps: acquiring an underwater polarization image data set, and dividing the underwater polarization image data set into a training set and a test set; training an underwater polarization image restoration network based on Transform and depth estimation, the network comprising an encoder and a decoder, the encoder comprising a U-Net module, a multi-scale content guidance attention module, a depth estimation network and a multi-scale convergence attention module; the decoder comprises two feature fusion modules which are connected in series and guide attention based on multi-scale content; the trained underwater polarization image restoration network based on Transform and depth estimation is tested based on the test set; and carrying out image restoration by using the tested underwater polarization image restoration network based on Transform and depth estimation. According to the method, the restoration effect of the underwater image can be remarkably improved, the image texture information is enhanced, and the definition and detail performance of the image are improved.

The invention discloses an underwater image enhancement method and system based on three-input multi-scale fusion. The method comprises the following steps: carrying out visibility recovery on an image to be processed; performing contrast enhancement through a nonlinear mapping function; carrying out enhancement processing on the contour of the high response area; respectively calculating a Laplacian contrast weight map, a saliency weight map and a saturation weight map of the three processed images; linearly combining all types of weight maps to obtain an aggregation weight map, and normalizing the aggregation weight to obtain a normalized weight map; decomposing the three processed images into a Laplacian pyramid, decomposing the normalized weight map into a Gaussian pyramid, and performing fusion through upward sampling and addition reconstruction; carrying out smoothing processing and edge-preserving sharpening processing on the fused image to obtain an image after underwater image enhancement and restoration; according to the method, the color cast problem of the underwater image is solved, the image contrast is effectively improved, and the texture details of the image are improved.

The invention relates to the field of computer vision and artificial intelligence, in particular to an image event multi-mode semantic segmentation method, device and equipment, which can be applied to scenes such as automatic driving, robot perception and intelligent traffic. Time slices are divided through a fixed time window, event information is accumulated, and asynchronous event streams are converted into T * H * W voxel tensors; a selective state scanning mechanism of a Mama framework is used for replacing a self-attention mechanism of a traditional Transform, the calculation complexity is reduced while modeling global feature dependence is achieved, and the problems of video memory and delay in a Transform high-resolution scene are solved; besides, image textures and event edges are aligned through cross-space interaction, an event dynamic time sequence is captured through cross-time interaction, and modal inherent characteristics are retained through residual connection, so that feature degradation caused by excessive fusion is effectively avoided; finally, the image segmentation precision is effectively improved, and the processing efficiency and the model robustness are improved at the same time.

The invention discloses a heart CT image segmentation method and system based on ultrasonic guidance, and relates to the field of image processing, and the method comprises an uploading module which is used for uploading a heart CT original image and an ultrasonic image of a corresponding part, and synchronously building the spatial position correlation mapping of the heart CT original image and the ultrasonic image; the preparation module is used for performing denoising and gray normalization processing on the heart CT original image and the ultrasonic image respectively, and completing spatial alignment of the two types of images based on spatial position correlation mapping; according to the method, spatial position correlation mapping of the heart CT and the ultrasonic image is established, denoising and gray level normalization processing of the two types of images are combined, the image quality and the spatial alignment precision are effectively improved, CT segmentation interested areas are positioned by means of heart structure features extracted by the ultrasonic image, interference of irrelevant areas is avoided, and the accuracy of CT segmentation is improved. And during segmentation, image texture, gray gradient and structure edge features are fused, and accurate pixel-level segmentation of the heart target anatomical structure is realized.