211 results about "Image segmentation algorithm" patented technology

The invention provides a division method and system for a forest pest control area, and relates to the technical field of data processing, and the method comprises the steps: building a forest pest control database through collecting pest data and environment data of a control area; and training a pest risk prediction model based on historical data to generate a risk distribution map. And then, in combination with a GIS technology and a deep learning image segmentation algorithm, intelligent division is performed on the target area, and it is ensured that the unmanned aerial vehicle efficiently covers the prevention and control unit. According to the method, the optimal spraying route can be planned by adopting a path optimization algorithm based on the flight capability of the unmanned aerial vehicle, the pesticide carrying capacity and the environmental conditions, spraying parameters are dynamically adjusted by monitoring the wind speed, obstacle information and the like in real time, precise pesticide application is ensured, the pest control efficiency can be improved, repeated spraying and spraying blind areas are reduced, the pesticide use amount is reduced, and the pesticide application cost is reduced. Intelligent and precise forest pest control is achieved, and the method is suitable for large-scale unmanned aerial vehicle autonomous operation scenes.

The invention discloses a power transmission image defect detection and defect duplicate removal method and system based on a deep learning image segmentation algorithm, and belongs to the technical field of intelligent inspection of power equipment. According to the method, real-time tower identification and adaptive shooting are realized through a lightweight YOLO model deployed at the edge end of an unmanned aerial vehicle; pixel-level segmentation is carried out on the infrared image by using an MSAN-Net network, the network integrates a ResNet encoder, a cross-scale attention mechanism and a multi-level feature pyramid, and boundary learning is enhanced by using a composite loss function; based on a multi-view three-dimensional reconstruction technology, two-dimensional defects are mapped into space rays through feature point matching and pose estimation, and defect de-weighting is achieved through ray intersection judgment. Through the MSAN-Net network, the segmentation precision of the infrared component under a complex background is remarkably improved through an attention mechanism and multi-scale feature fusion, and the problem of repeated defect detection in multi-view inspection is effectively solved in combination with a three-dimensional space mapping method.

The invention discloses an intelligent track foreign matter detection system and method based on image recognition, and particularly relates to the technical field of image recognition. A real-time image is acquired, an area-of-interest is extracted in combination with an image segmentation algorithm, and potential foreign matters are identified; the space-time consistency of the detection result is verified by analyzing the space-time data of the track foreign matter so as to judge the real threat; extracting multi-dimensional features such as shape, texture, color and size from a real threat target, and outputting classification confidence to evaluate model classification stability; the detection accuracy is evaluated by combining the space-time consistency and the classification stability, the detection results are divided into different categories, the abnormal degree of subsequent detection is predicted for the incomplete accuracy detection result, and the image acquisition resolution or frame rate is dynamically adjusted to improve the detection definition. The problems of missing detection and frequent false alarm of small and medium-sized foreign matters in the prior art are solved, the accuracy and reliability of track foreign matter detection are greatly improved, and the running safety of a high-speed train is guaranteed.

The invention relates to the technical field of image discrimination, and further relates to a restoration supervision evaluation method based on mine ecological image discrimination, and the method comprises the steps: 1, carrying out the preprocessing of an obtained remote sensing multispectral image of a target mine region, and obtaining a preprocessed image; fusing the standard vegetation index, the bare soil area and the reflectivity ratio to obtain a vegetation recovery index; 2, dividing the preprocessed image into a plurality of ecological patches by using an image segmentation algorithm; calculating to obtain a slope stability index in combination with the crushing degree; 3, evaluating a hydrological recovery index corresponding to the water body area in the preprocessed image through the combination of short-wave infrared and near-infrared bands; and calculating a comprehensive restoration index of the target mine area in combination with the vegetation restoration index, the slope stability index and the hydrological restoration index. The method has the advantages of being high in adaptability, wide in monitoring range and the like, and the supervision efficiency and scientificity of mine repair engineering can be remarkably improved.

The invention discloses an interactive image segmentation algorithm based on an algorithm computer, and belongs to the technical field of computer image segmentation. The interactive image segmentation algorithm comprises the following steps: S1, image uploading and preprocessing, S2, construction of a bidirectional interactive propagation mechanism, S3, adaptive image segmentation, and S4, image detection after segmentation. The interactive image segmentation algorithm is combined with lightweight semantic propagation, adaptive segmentation and a user feedback closed loop, the user interaction frequency is remarkably reduced while the precision is ensured, and compared with a pure deep learning scheme, the interactive image segmentation algorithm is more suitable for small sample scenes and is suitable for scenes with double requirements on precision and efficiency, such as medical images and industrial quality inspection; the algorithm can quickly adapt to a new field, a new category or an unknown object, reduces dependence on a large amount of training data in a specific field, minimizes user interaction times and complexity on the premise of ensuring high segmentation precision, can improve user experience, and has the characteristics of extremely simple interaction, ultrahigh precision, superstrong robustness and real-time response.

The invention provides a concrete strength prediction method based on deep learning, and the method comprises the steps: obtaining a coarse aggregate surface image through employing a three-dimensional scanning method, recognizing the surface roughness of the coarse aggregate, and extracting the pore distribution characteristics of the surface of the coarse aggregate through combining with an image segmentation algorithm; carrying out feature extraction on the interface transition area by adopting wavelet transform to obtain local features of the interface transition area, and carrying out weighted calculation on the local features in combination with an adaptive weighted fusion algorithm to obtain enhanced local feature representation; fusing the marked microstructure parameters with the macroparameters of the whole concrete, constructing a concrete strength prediction model, and outputting a predicted value of the whole strength of the concrete; and predicting the weights of the regions corresponding to different intensities through an adaptive weighted fusion algorithm to obtain local features contributed to the target intensity.

The invention discloses a blasting fragmentation prediction method suitable for a complex terrain mine, and the method is based on high-precision muck pile three-dimensional reconstruction, is combined with an image segmentation algorithm, and enables the calculated overall fragmentation error to be small through the correction of a spatial distribution model. Meanwhile, the overall lumpiness distribution obtained through the method is closer to actual lumpiness distribution, and through boulder rate comparison evaluation, a scientific basis can be provided for mine blasting effect evaluation and production management, blasting parameters can be optimized, and production efficiency can be improved.

The invention discloses a medical image segmentation algorithm and system based on Mama, and a storage medium. The method comprises the following steps: processing medical image features through a multi-path convolution attention mixer MCAM; the VSS Block captures global information through a state space model to enhance the coding capability; the COC module extracts features through channel projection, vertical offset and horizontal offset; the up-sampling features and the features output by the MCAM are fused, the resolution of the image is gradually recovered through inverse operation, and the number of channels is adjusted to the proportion required by the segmentation task; inputting the feature maps after multiple complete coding and down-sampling into an up-sampling and visual state space module, and decoding the feature maps together with the corresponding feature maps of multiple coding cross offset connection operations; medical image segmentation is carried out, and key anatomical features and subtle pathological changes are accurately distinguished. By improving downsampling and U-Net jump connection based on Mama, the feature extraction capability and the capability of capturing complex semantic information are enhanced.

The invention discloses a plant extraction method and system based on image processing, and relates to the technical field of image processing. The method comprises the following steps: acquiring and standardizing a plant raw material image; extracting feature vectors of the images by using a neural network convolutional layer algorithm, comparing the feature vectors with a plant raw material standard feature database, and screening out qualified images of the plant raw materials; qualified images are processed through multispectral image analysis and an image segmentation algorithm, and an effective component spatial distribution diagram is obtained; constructing a partial least squares regression linear model, extracting spectral feature vectors of the effective components, inputting the spectral feature vectors into the model to obtain concentration values, and calculating quantitative data of the effective components; establishing an extraction process rule base, selecting a process parameter combination according to a spatial distribution diagram and quantitative data, and extracting to obtain a primary extracting solution and plant residues; and calculating an effective component residual error rate by matching and mapping to a qualified image coordinate, and if the effective component residual error rate is greater than a preset threshold, performing secondary extraction to obtain a secondary extracting solution. And quantitative basis is provided for extracting process parameters through the spatial distribution diagram of the effective components.

The invention discloses an agricultural insurance data acquisition and management system based on satellite remote sensing. The system comprises a data acquisition module, a preprocessing module, a verification module and a management module. The data acquisition module covers basic, universal and vector data acquisition according to data types; the preprocessing module improves the quality of collected data; the verification module verifies a cultivated land vector and insured user information, and data accuracy and reliability are enhanced; the management module builds two libraries, and constructs an agricultural insurance data large model by utilizing knowledge graph modeling. In the technical application, on the basis of satellite remote sensing and A I, large-user plot data is collected by means of an image segmentation algorithm, and the cost is reduced. A full-coverage model is constructed through abstract materialization, and business application is supported. Technical means such as satellite remote sensing, weight dispatch data superposition and AI image recognition are used for verifying data, manual operation is reduced, verification accuracy is guaranteed, data value is mined, and the agricultural insurance data management level is improved.

The invention discloses a coal mine image segmentation algorithm based on Swinin-UMama, and relates to the technical field of computer vision. According to the method, the design of double-branch dark light enhancement, self-adaptive multi-scale feature extraction of a Swindow-UMama framework and three-channel parallel processing (wavelet module, spatial pyramid and channel attention) is combined, and comprehensive optimization of a multi-loss function is supplemented, so that a coal mine image segmentation model is obtained; the provided coal mine image segmentation model shows excellent performance in an image segmentation task in a coal mine low-light environment, and the method not only significantly improves the segmentation accuracy and boundary definition, but also enhances the adaptability and robustness of the model in a complex environment. And powerful technical support is provided for coal mine safety monitoring and management.

The invention discloses a screen display content and background light collaborative optimization method, and relates to the technical field of screen display adjustment, and the method comprises the steps: collecting the image content currently displayed by a display screen, dividing the image content into a plurality of sub-regions through an image segmentation algorithm, and calculating the average brightness value of each sub-region; a background light intensity measurement value of each sub-region is obtained, a background light intensity distribution curve is formed through cubic spline interpolation, the distribution curve is inquired according to the center position of the sub-region, and the background light intensity is obtained; and constructing an adaptive mapping function based on the ratio of the average brightness value to the background light intensity of the sub-regions, dividing a dynamic adjustment interval, and dynamically adjusting the display parameters of each sub-region according to the adaptive mapping function to complete collaborative optimization of the screen display content and the background light. According to the invention, the visual performance of the display device under different ambient light is improved, the display effect is optimized in a targeted manner under different background light conditions, and the method has a wide application prospect.

The invention is suitable for the technical field of building model data processing, and provides an intelligent building texture repairing and beautifying method fusing a deep learning model, and the method comprises the steps: firstly, carrying out the recombination of the texture of a three-dimensional building model, and generating a to-be-processed wall surface and roof texture picture; a to-be-repaired area, needing to be repaired, of the building texture is extracted by combining low-rank matrix decomposition and an SAM image segmentation algorithm, and then whether an LAMA image repair model or a PatchMatch image repair algorithm is used for texture processing is determined according to the proportion of the to-be-repaired area in a whole texture image so as to achieve the maximum repair effect. And finally, carrying out texture distortion repair and material beautification by using an image diffusion model, a ControlNet control chart and cue words, and outputting beautified wall surface and roof textures. The problems of texture shielding, garland, noise, window distortion and the like can be solved at the same time, automatic detection and automatic texture repairing and beautifying of the texture shielding garland area are achieved, and the time consumption of texture editing processing in the building monotonized modeling process is remarkably shortened.

The invention provides an image processing system and device of an intelligent water affair inspection robot, relates to the technical field of water affair inspection, and solves the technical problems of poor light reflection restoration effect, difficulty in floating object feature extraction and relatively high water quality abnormity judgment misjudgment rate in the prior art. The system comprises an image processing module, and an acquisition module and an image analysis module which are connected with the image processing module, the acquisition module is used for acquiring an original image of a water body area acquired by the inspection robot; the image processing module is used for performing reflection restoration on a reflection area in the original image through pixel restoration to obtain a restored image; performing water body and surrounding environment segmentation on the restored image through an image segmentation algorithm to obtain a water body image; performing feature extraction on the water body image through edge detection to obtain water quality features; and the image analysis module is used for carrying out abnormity judgment on the water body area according to the water quality characteristics and generating an abnormal signal. The method is used in the water affair inspection process.

Systems and methods for detecting artificial intelligence generated images are provided. The system accepts an input image (e.g., a digital still image, or a frame from a digital video file or image stream) and subdivides the input image into a set of patches using a patch partitioning algorithm. The system then processes each patch and produces a feature embedding for each patch within a high dimension space. The system then utilizes these patches with further processing as input to machine learning models, which allows the system to achieve image, patch-level, and video-frame generated image classification and localization alongside identification of the generative model used to synthesize the image.

The invention, which belongs to the technical field of medical image processing and computer vision, discloses a multispectral imaging-assisted post-radiotherapy skin injury early detection system comprising a multispectral data acquisition module, a deep tissue feature extraction module, a three-dimensional lesion segmentation module and a space-time tracking evaluation module. The subcutaneous 2.5 cm depth tissue information is obtained through multispectral imaging at the wave band of 400-1350nm, blood perfusion, melanin concentration, collagen structure and other physiological parameters are inversed based on the radiation transfer theory, a three-dimensional medical image segmentation algorithm is adopted to achieve three-dimensional accurate segmentation of an injury area, a spatio-temporal evolution model is established to predict the injury development trend, and the damage development trend is predicted. The radioactive skin injury can be detected in the subclinical period, the detection time window is advanced by 5.2 days on average, the occurrence rate of severe dermatitis is reduced by 65%, and a basis is provided for clinical timely intervention.

The radar target scattering center region segmentation method based on local density clustering first carries out Frost filtering to the original radar image to realize global noise suppression of the image; secondly, the image segmentation is carried out to the Frost filtered image based on the level set method, and the suspected target region of interest is preliminarily obtained; then, the area filtering is adopted to extract the maximum connected domain, and the target region of interest is obtained; finally, the scattering center is detected and the region segmentation is carried out by using the local density clustering algorithm. The radar target scattering center region segmentation method based on local density clustering has high accuracy, and can effectively improve the technical problem that the SAR image is difficult to have high enough resolution in the actual situation, so that the traditional image segmentation algorithm is difficult to segment the region containing only one scattering center.

The invention relates to the technical field of alimentary canal diagnosis and treatment, in particular to an alimentary canal detection drug delivery system which comprises an alimentary canal sensing unit for collecting an image, a pH value, a temperature and pathological tissue pathological characteristic data in an alimentary canal cavity; the lesion positioning analysis unit identifies a lesion area through an image segmentation algorithm, constructs a lesion three-dimensional coordinate model according to physiological parameters, prejudges a lesion displacement trend in combination with an alimentary canal peristalsis rule, analyzes a lesion state in stages, and outputs lesion severity; the response type drug delivery module controls the micro drug delivery device to perform targeted release and adjust the drug release rate based on the lesion coordinates and the intracavity environment parameters; the data collaboration platform shares a detection result and a drug administration record through a real-time interaction interface; the curative effect feedback unit is used for collecting physiological index change and patient symptom improvement data after drug administration, judging the drug onset period and possible adverse reactions, and optimizing drug administration parameters and detection frequency. Therefore, the problems of low targeting precision, poor diagnosis and treatment efficiency and the like in the prior art are solved.

The invention relates to an interactive image annotation system and method based on facial posture and eye movement tracking and positioning, and solves the problems of poor annotation precision and low efficiency caused by the fact that two interactive technologies of facial posture detection and eye movement tracking are not organically combined with an automatic image segmentation algorithm in the existing image data annotation field. According to the invention, independent operation of two interaction modes of face posture detection and eye movement tracking is realized through modular design, a proper interaction mode is selected by judging the complexity of a scene, the subsequent processing time can be saved, and through secondary sliding mean filtering processing, the detection accuracy is improved. Errors caused by data noise of a depth camera or near-infrared eye movement tracking equipment based on iris reflection and micro movement of an operator are avoided, and the filtered stable coordinates are matched with a segmentation model of the interaction control module to carry out fine extraction on a target area. A traditional segmentation model and operator interaction information are organically combined in the interaction control module, and the manual labeling process is greatly simplified.

The invention discloses a multi-modal medical image segmentation method based on deep learning. The method comprises the following steps: 1, obtaining a multi-modal medical image and standardizing the size of the multi-modal medical image; 2, inputting modals 1-3 into an FCSA fusion module of a channel 2 of the segmentation network to obtain a fused feature map; 3, inputting the dominant mode 4 into a channel 1 of the segmentation network; 4, extracting feature information of different scales through five layers of improved Tok-KAN modules and down-sampling of a channel 1 and a channel 2 of the segmentation network; 5, in a decoding process, realizing feature recovery and decoding of the coding feature patterns of different scales output by each layer through a dense jump connection decoder; and 6, through up-sampling and Sigmoid activation, generating segmentation prediction from the output of the last feature extractor of the decoder. The problem that a multi-modal medical image segmentation algorithm is low in segmentation accuracy and poor in model interpretability is solved.

The invention relates to a postoperative complication prediction method and device based on a two-way map neural network, and the method comprises the steps: collecting ICG video images before and after cerebrovascular bypass surgery, and obtaining the clinical feature data of a patient; preprocessing the collected ICG video image, extracting a blood vessel network by adopting an image segmentation algorithm, and generating corresponding blood vessel graph structures before and after bridging; extracting blood vessel features, blood flow dynamic features and clinical features from the collected ICG video images and clinical feature data, and constructing a multi-modal high-dimensional feature set; and taking the multi-modal high-dimensional feature set and the corresponding vascular graph structures before and after bridging as input, and generating a complication prediction score through a pre-trained prediction model based on a two-way graph neural network. Compared with the prior art, the method has the advantages that the multi-modal feature information is fused, the graph neural network structure is optimized, the model performance is stable, the accuracy is high, and the method has important clinical application value.

The present invention discloses an intelligent track foreign object detection system and method based on image recognition, which specifically relates to the field of image recognition technology. The system obtains real-time images and extracts regions of interest in combination with image segmentation algorithms to identify potential foreign objects. The system analyzes the spatiotemporal data of track foreign objects to verify the spatiotemporal consistency of detection results to determine real threats. Multidimensional features such as shape, texture, color, and size are extracted for real threat targets, and classification confidence is output to evaluate model classification stability. The system evaluates detection accuracy by combining spatiotemporal consistency and classification stability, classifies detection results into different categories, and predicts the degree of abnormality of subsequent detection based on incomplete accuracy detection results. The system dynamically adjusts the image acquisition resolution or frame rate to improve detection clarity. The system solves the problems of missed detection of small and medium-sized foreign objects and frequent false alarms in the prior art, significantly improves the accuracy and reliability of track foreign object detection, and ensures the safety of high-speed train operation.

The invention discloses a curative effect evaluation system for treating liver cancer by combining HDRBT with thermal ablation in multi-modal radiomics, and relates to the field of liver cancer treatment, and the system comprises a multi-modal image data acquisition module which is used for collecting multi-modal image data; the three-dimensional visual model construction module is used for reconstructing a three-dimensional structure of liver tumors, peripheral blood vessels and bile ducts through an image segmentation algorithm, and generating a visual model by fusing radiomics characteristics; the combined treatment plan generation module is used for integrating the dosimetry parameters of the HDRBT and the process parameters of the thermal ablation, optimizing a catheter implanting path, the position of a thermal ablation electrode and a treatment sequence, and generating a combined treatment scheme; and the curative effect evaluation module is used for quantitatively analyzing tumor volume change, ablation boundary integrity and residual tumor activity through accurate registration of multi-modal images before and after treatment. According to the scheme, the problems of incomplete thermal ablation and high recurrence rate of hepatocellular carcinoma can be solved, and full-chain technical support is provided for clinical precise application.

The invention discloses an improved brain tumor image segmentation algorithm based on a generative adversarial network model. The problem of inaccurate segmentation of tumor edges and detail regions is solved. According to the method, a generative adversarial network is improved, and cross-scale jump links are introduced for a generator part of a model so as to better capture context information and extract more complete edge detail features; in a decoder stage, position information and a channel relation are captured in combination with a coordinate attention mechanism, so that the network is focused on tumor features, and irrelevant features are inhibited at the same time; and the objective function combining L1 loss and Dice loss is used to solve the problem of sample category imbalance, and the accuracy and robustness of the brain tumor MR image are significantly improved. Experimental results show that the improved network has a better segmentation effect than other typical brain tumor segmentation methods.

The application discloses an image segmentation method and system for double-layer liquid crystal screen display, the double-layer liquid crystal screen is a front liquid crystal screen close to an audience and a rear liquid crystal screen close to a backlight module, and the method comprises the following steps: constructing a convolutional neural network model, the convolutional neural network model is used for processing an input image to obtain a first image and a second image, and the second image is sent to the rear liquid crystal screen, wherein the first image is used for being sent to the front liquid crystal screen; through the first image, the second image is subjected to image reconstruction, the image reconstruction is used for improving the artifact problem of double-screen liquid crystal display and improving the image display quality; and the second image after reconstruction is sent to the rear liquid crystal screen; the application has great optimization effects on the artifact phenomenon and the image quality of the double-layer screen, and the calculation speed is greatly improved compared with an image segmentation algorithm based on viewpoint compensation.

Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an image segmentation algorithm (122). The image segmentation algorithm is configured for outputting one or more predetermined anatomical regions within initial magnetic resonance imaging data (124) descriptive of a predetermined field of view (109) of a subject (318). The medical system further comprises a computational system (104), wherein execution of the machine executable instructions causes the computational system to: receive (200) the initial magnetic resonance imaging data (124); receive (202) the image segmentation comprising the one or more anatomical regions within the magnetic resonance imaging data in response to inputting the initial magnetic resonance imaging data into the image segmentation algorithm; select (204) at least one of the one or more anatomical regions as a selected image portion (128) using a predetermined criterion; and reduce (206) image intensity within the selected image portion to provide intensity corrected magnetic resonance imaging data.

The present application provides a kind of geologic stratum lithology regional prediction method, system, terminal equipment and storage medium, it is related to computer system field.The system includes data management module, sample management module, model training module, model management module, lithology prediction module and the like.The present application utilizes geophysical, geochemical, remote sensing and other multi-source data, based on deep learning image segmentation algorithm, forms regional shallow surface stratum lithology identification model, carries out high-precision rapid stratum lithology prediction and regional segmentation to the bedrock exposed area and shallow cover area with lower exploration degree, supports geological mapping and mineral exploration work.

Disclosed is an image-based lost ball automatic classification method comprising: a learning image generation step of generating a brand learning image for learning a brand of a golf ball and a defect learning image for learning a defect; a golf ball brand learning step of training an image classification algorithm about the brand of the golf ball by using the brand learning image; a golf ball defect learning step of training an image segmentation algorithm about the defect in the golf ball by using the defect learning image; a target image generation step of generating at least eight target images from a target lost ball to be classified; a target lost ball brand classification step of classifying a brand of the target lost ball from a target image by using the trained image classification algorithm; and a target lost ball grade classification step of classifying a grade of the target lost ball from the target image by using the trained image segmentation algorithm.

The invention relates to a multispectral image optical performance detection method, system and device and a storage medium, and relates to the field of image optical performance detection. The method comprises the following steps: acquiring a multispectral image containing a plurality of cross targets by using a high-resolution camera; extracting and separating the multispectral image according to the plurality of cross targets to obtain a plurality of target multispectral images, and transmitting the plurality of target multispectral images to an image detection and analysis module; when the image detection and analysis module receives a target multispectral image, the target multispectral image is segmented by using an adaptive Niblack image segmentation algorithm to obtain a binary multispectral image; an improved double-iteration parallel skeleton extraction algorithm is adopted to extract a cross target skeleton of the binarized multispectral image; adopting a neighborhood analysis method to extract center coordinates of the cross target and storing center coordinate data; and calculating the optical performance index of the multispectral image according to the plurality of pieces of center coordinate data. The technical effect of the invention is that the optical performance of the multispectral image can be detected more efficiently.