98 results about "Image contour" patented technology

The invention discloses an accurate segmentation method and system for an abdominal organ image contour, and relates to the field of medical image processing, and the method comprises the steps: obtaining a to-be-segmented abdominal medical image sequence, CT image data at least containing one phase, and a corresponding Hensi unit matrix; based on an abdominal organ positioning network, obtaining a region of interest containing a target small organ and a preliminary space prior probability graph; a local HU goodness of fit diagram is generated through the dissection focusing path, a boundary response intensity diagram is generated through the boundary enhancement path, and a refined feature diagram is output; inputting the refined feature map into a main body segmentation network; fusing with corresponding hierarchical features of the decoder network, performing up-sampling and reconstruction, and generating an initial segmentation probability graph of the target small organ; and generating a final segmentation result graph and a comprehensive confidence graph. According to the method, the technical problems that in the prior art, due to the fact that inherent physical attributes are not fully mined and utilized, boundary positioning is fuzzy, the image segmentation precision is insufficient, and the confidence coefficient is low are solved.

The invention relates to the technical field of rice phenotype extraction, and discloses a rice fine phenotype extraction method based on three-dimensional vision, a terminal and a storage medium. The method comprises the following steps: acquiring a multi-view image of a rice plant by using a depth camera, and performing camera attitude estimation on the multi-view enhanced image to obtain camera internal and external parameters corresponding to each view; constructing a neural radiation field network, training the neural radiation field network by taking the enhanced image, the contour mask and internal and external parameters of the camera as input, and learning color and density distribution of a scene by utilizing a volume rendering technology; extracting surface points based on a mixed threshold strategy by using the trained neural radiation field network, and generating a rice three-dimensional point cloud; inputting the rice three-dimensional point cloud into a point cloud instance segmentation network, and outputting an instance mask containing each organ of the rice and a segmentation result of a three-dimensional bounding box; and counting phenotypic parameters of the rice according to a segmentation result. According to the method, the fine instance segmentation of the high-density point cloud is realized while the reconstruction precision is ensured, and the phenotypic analysis of the spike fraction is supported.

The invention provides a curvature-enhanced large-displacement image variational optical flow method for motion estimation, relates to the field of image processing, and aims to describe the local structure complexity of an image by introducing an image contour curvature. On the basis of the curvature, limited self-adaptive weighted adjustment is carried out on the brightness invariant constraint and the gradient invariant constraint on the data item level of the opto-rheological model, so that the interference of unreliable matching in a complex structure region on optical flow estimation is inhibited; the robustness of optical flow estimation in illumination variation, complex texture and large displacement scenes is improved; and the numerical stability and convergence of the model in the multi-scale calculation process are ensured.

This invention provides a target contour extraction method, system, device, and storage medium, relating to the field of image processing technology. The target contour extraction method includes: acquiring a target image and processing it to obtain a region of interest (ROI); within the ROI, processing to obtain the gradient of pixels and the intensity and orientation fields of the gradients; extracting multiple starting points based on the intensity and orientation fields of the pixels; processing multiple contour points based on the starting points; and obtaining the target contour of the target image based on the contour points. This invention effectively avoids the problem in existing methods of failing to achieve good contour detection results without prior geometric constraints. It also considers three diagonally opposite pixels in the gradient's vertical direction, three pixels in the gradient's positive direction, and three pixels in the gradient's negative direction, resulting in a more accurate extracted image contour and greater flexibility and adaptability in use.

The application provides a curvature-enhanced large displacement image variational optical flow method for motion estimation, relates to the field of image processing, and aims to depict the complexity of local structures of an image by introducing the curvature of the image contour line, and to restrict the adaptive weighting adjustment of the brightness invariable constraint and the gradient invariable constraint on the basis of the curvature in the data item level of the variational model of the optical flow, so as to inhibit the interference of unreliable matching pairs on the optical flow estimation in the complex structure area, improve the robustness of the optical flow estimation under the scenes of illumination change, complex texture and large displacement, and guarantee the numerical stability and convergence of the model in the multi-scale calculation process.

This application relates to a method, apparatus, processing equipment, and readable storage medium for optimizing processing parameters. The method includes: acquiring a workpiece processing planning position point cloud and a workpiece image contour point cloud under different combinations of processing parameters; determining the processing quality evaluation results under different combinations of processing parameters based on the workpiece processing planning position point cloud and the workpiece image contour point cloud; and determining the optimized processing parameter combination for the workpiece processing process based on the processing quality evaluation results under different combinations of processing parameters and the different processing parameter combinations. This method can effectively adjust processing parameters during workpiece processing, improving upon the problem that traditional processing parameter adjustment relies on engineers' experience and repeated trials, which is time-consuming, labor-intensive, and difficult to guarantee processing efficiency. It can ensure both workpiece processing efficiency and the accuracy and adjustability of the processing process, thereby meeting higher requirements for CNC machining effects.

The invention relates to the technical field of foreign matter recognition based on hyperspectrum, in particular to a foreign matter recognition method and device based on spectral feature selection, equipment and a medium, and the method comprises the steps: firstly obtaining a main dimension conversion matrix and a plurality of first dimension conversion matrixes; converting each first spectral vector in the first spectral vector set according to the main dimension conversion matrix, and screening out a plurality of first risk spectral vectors from the first spectral vector set according to a conversion result; performing conversion on each first risk spectral vector through the plurality of first dimension conversion matrixes, and performing spectral characteristic matching according to a conversion result; and finally, extracting a first image contour from the target image according to spectral characteristics, and sending the first image contour to an identification model for foreign matter identification. The method is good in preprocessing effect, and the recognition efficiency and the recognition precision are improved.

A method for measuring volume of pomelo based on three view image contour fitting is provided, for measuring the volume of the pomelo based on images, includes: a number of image capturing devices capturing a number of surface images of a pomelo to be measured; calibrating a position of an image capturing device for capturing a top view image of the pomelo to be measured; performing enhanced extraction processing on the number of surface images, and obtaining a standard contour image; obtaining a size of each pomelo slice based on the standard contour image, and obtaining a B-spline curve by fitting a contour of the pomelo slice; and calculating volume of each pomelo slice according to the B-spline curve, and obtaining total volume of the pomelo to be measured by calculating a sum of the volume of all of the pomelo slices.

The invention discloses a fish growth state monitoring method and system based on underwater image recognition, and relates to the technical field of fish growth state monitoring, and the method comprises the following steps: setting a temperature sensing region around the optical axis direction of a camera device, and carrying out the real-time collection of the temperature change of a local water body in an underwater environment, acquiring temperature gradient data corresponding to the light propagation path, and generating temperature layered mapping according to the temperature gradient data; and on the basis of temperature hierarchical mapping, constructing a refraction track sequence on the light propagation path, and adjusting the refraction angle weight according to the temperature difference of each hierarchical region to form the light propagation path. According to the invention, through combination of temperature sensing and light field compensation, continuous tracking and space geometry reconstruction of a light propagation path are realized, and the image contour is kept clear and stable. Based on the dynamic smoothing processing of a time sequence stable chain and a form balance area, the fish body form is kept uniform in time and space, false growth transition is avoided, and the stability and accuracy of a monitoring result are improved.

The invention discloses an accurate attachment process optimization system based on computer vision, and the system comprises an image collection module which is used for obtaining a product and label image; the contour construction module is used for extracting edges and generating a contour point set; the pose recognition module is used for determining the spatial positions and angles of the product and the label based on the shape context and the Hungary algorithm; the coordinate mapping and compensation module is used for mapping the spatial pose to a tool coordinate system and generating a compensation amount; the track generation and motion control module is used for generating a smooth attaching track and driving an attaching mechanism to execute operation; the quality detection and distribution module is used for collecting the post-pasting image, judging the qualification and controlling the distribution of defective products; and the beat optimization module is used for dynamically adjusting the conveying speed and the camera triggering opportunity. According to the method, stable product and label pose recognition, rapid point pair matching and dynamic beat optimization based on the queuing theory can be realized under complex working conditions, so that the attachment precision and the production efficiency are both considered.

The application discloses a kind of vehicle identification methods based on multi-sensor data fusion, comprising the following steps: S1. synchronously collecting the video information of vehicle and radar point cloud information;S2. extract vehicle image feature information in video information, and 3D detection is carried out to radar point cloud information, and vehicle prediction frame information is obtained;S3. vehicle image feature information and vehicle prediction frame information are fused, and vehicle fusion result is obtained;S4. whether the maximum outer rectangle overlap of vehicle image contour projection and vehicle prediction frame contour in vehicle fusion result is greater than set value, if yes, vehicle fusion result is effective, and the outline of vehicle is determined;If no, vehicle fusion result is invalid.The application solves the problem that the recognition rate of traditional video monitoring scheme is not high and the monitoring range is small in low visibility environment, and improves the robustness of vehicle identification.

According to the high-throughput drug screening method and system based on large-view-field imaging and machine learning, data acquisition of high-throughput organs or tumor balls is achieved through a large-view-field biological imaging system, the data acquisition process is accelerated, and the integrity of original image information is guaranteed; a two-stage image processing means that a lightweight target detection model is connected with a semantic segmentation model is adopted, so that the calculation parameter quantity is reduced, heterogeneity interference is removed, and the image information integrity is ensured while the accurate acquisition of the individual image contour of the organoid or the tumor ball is realized; for organoid image acquisition, a Z-axis scanning strategy is combined with a screening strategy based on IOU and a definition function, so that information acquisition integrity and subsequent organoid activity prediction precision are ensured; in combination with a machine learning model based on an ensemble learning principle for a regression task, the activity prediction of the bright field image of the organoid or tumor sphere is realized, and the influence of a traditional method on the activity of a culture is avoided.

The application discloses a visual-based contact type measuring robot adaptive measuring point relative guiding method, which comprises a 2D visual-based product image contour matching method, realizes 4-degree-of-freedom measuring point guiding of plane translation, rotation and height scaling, and is suitable for contact type six-axis cooperative measuring robot measuring point guiding measurement of products with local features; the method comprises the following steps: teaching contact type six-axis cooperative measuring robot measuring points of a product to be measured; determining a teaching 2D measuring pose, obtaining a product multi-scale contour image template under the teaching 2D measuring pose and a multi-scale image of the current product, and performing contour matching of corresponding scale levels; based on the contour matching result, calculating a first pose deviation under the teaching 2D measuring pose, transforming the first pose deviation to all teaching point positions of the teaching 2D, performing pose transformation, performing iterative updating on all measuring point positions, and obtaining all updated first measuring point poses. The application better realizes adaptive contact type measuring point measurement of products.

The invention belongs to the field of flame detection, particularly relates to a flame detection method and system based on flame dynamic characteristics and electronic equipment, and aims to solve the problems of unstable detection effect and false alarm in the existing method. The method comprises the following steps: inputting a flame picture into a flame detection model to obtain a flame detection frame area; in the current frame, a flame detection frame area is cut out to serve as a first picture; cutting a flame detection frame area detected by a previous frame or a next frame corresponding to the frame as a second picture; respectively carrying out Gaussian blur processing on the grey-scale map of the first picture and the grey-scale map of the second picture to obtain a first image and a second image; respectively carrying out binary image processing on the first image and the second image based on the gray value threshold K; and judging whether flames exist in the field or not based on the image contour area after binarization image processing. And the final detection effect, the detection efficiency and the accuracy of the detection result are improved.

This invention relates to the field of computer vision technology and discloses a method for accurate pedestrian image segmentation based on contour attribute features. The method includes: acquiring an image containing pedestrians as the original image; binarizing the original image to obtain a binarized image; sequentially performing edge detection, local edge processing, and fusion on the binarized image to extract image contours, obtaining a contour image; extracting contour features from the contour image to obtain a contour feature image; training pedestrian and background contour classifiers, and recognizing pedestrian contours based on these classifiers to obtain a pedestrian mask; and separating pedestrians and background based on the pedestrian mask. The accurate pedestrian image segmentation method based on contour attribute features provided by this invention does not require a large amount of data; it only needs to collect a small amount of data in a fixed scene, reducing model overfitting, improving segmentation performance, and directly outputting the segmentation result without a complex decoding process.

The application discloses a binocular vision and laser radar-based bionic human eye target recognition system and method, which comprises the following steps: S1, collecting and registering synchronous monocular images and laser radar point clouds; S2, extracting image contours and point cloud attributes to generate feature maps and attribute sets; S3, jointly encoding to generate size-distance representations; S4, constructing an improved Ginzburg-Landau phase field energy model based on the representations and attribute sets, obtaining potential function parameters and anisotropy tensors; S5, introducing SE(2) sub-Riemann geodesic direction continuity constraints into a phase field gradient term to adjust the strength; S6, aligning and optimizing evolution of adjacent frame phase fields in time by using optical flow; S7, extracting zero isograms to generate contours and output parameters; S8, inputting the parameters and size-distance representations into a three-dimensional structure program generation module for matching and screening; S9, performing consistency checking and sorting to output a recognition result. The application fuses visual and point cloud features, and improves the recognition accuracy and stability in a complex scene.

The invention relates to the technical field of microbe counting, in particular to a digital clone accurate counting recognition system and method. The method comprises the following steps: injecting a bacterial liquid to be detected into a preset microcavity array, and constructing a plurality of partition units; placing the micro-chamber array in a preset constant-temperature culture environment for parallel culture, and acquiring a chamber image set of each partition unit; performing image segmentation on the cavity image set of each partition unit, and extracting an image contour corresponding to each partition unit; identifying a positive chamber based on the image contour to determine a predicted loading number of the monoclonal growth chamber; and evaluating the relative growth capability of each positive chamber according to the predicted loading number and the growth kinetic parameter set of each monoclonal growth chamber, and identifying a target dominant clone type corresponding to each positive chamber. The screening efficiency and accuracy of research and development of probiotics and other microorganisms are improved, and the stability, functionality and safety of microorganism products are remarkably improved.

The application discloses a method for fitting a circular arc based on feature points and restoring an image contour, which comprises the following steps: identifying each circular arc according to the feature point information of the contour of a target image, and determining the position information of each circular arc; determining the center and the radius of the circular arc according to the initial point, the intermediate point and the terminal point of the circular arc; determining the fitting direction of the circular arc according to the initial point, the intermediate point and the center of the circular arc by using the vector cross product; parameterizing the circular arc, and calculating the range of the corresponding angle of the circular arc according to the fitting direction of the circular arc; fitting the circular arc according to the center, the radius and the range of the corresponding angle of the circular arc; and connecting the remaining straight line feature points after fitting each circular arc, and restoring the contour of the target image. The application can improve the accuracy of the circular arc recognition, thereby improving the circular arc fitting capability, and completing the low data amount restoration of the image contour shape.

The application discloses a mine fire identification and alarm method based on image contour area features, which comprises the following steps: installing a camera in a mine, collecting and identifying a suspected fire image of a monitoring area in real time, and identifying and alarming a mine fire by monitoring irregular shape features of a fire flame burning image contour. The mine fire identification and alarm method fully considers the features of the fire flame burning image, is fast, accurate, simple and easy to identify, and can help a person in danger in the mine to gain valuable rescue and escape time.

A method for identifying a target region of a digital pathology slide, including: obtaining a scanned image of a pathology slide; inputting the scanned image of the pathology slide into a preset deep learning-based identification model; extracting a contour feature of the scanned image of the pathology slide by using an image contour feature extraction submodel, to obtain a contour image; segmenting the contour image by using an image segmentation submodel to obtain a plurality of sub-contour images; separately performing classification and identification on the plurality of sub-contour images by using an image classification submodel, to obtain a region category corresponding to each sub-contour image; and determining a target region image based on the region category of each sub-contour image. In addition, a system for identifying a target region of a digital pathology slide, a device, and a medium are further proposed.

The invention relates to the technical field of pipeline electromagnetic detection, in particular to a pipeline inner wall detection method and system based on a variable-diameter pipeline robot, and the method comprises the steps: synchronously collecting a pipeline inner wall image and a magnetic flux leakage signal through a camera and a magnetic flux leakage sensor carried by the variable-diameter pipeline robot, and preliminarily determining a first contour of a defect area through the magnetic flux leakage signal; then searching a most matched target contour in an image domain based on the contour, and performing adaptive filtering on the magnetic flux leakage signal by using image gradient information in the target contour; and iteratively optimizing edge detection parameters to enable the correlation between the second contour determined by the filtered magnetic flux leakage signal and the image contour to be optimal. According to the method, vision and magnetic flux leakage data are fused, the limitation of single sensor detection is overcome, the accuracy and robustness of defect contour recognition are remarkably improved, and more accurate and reliable detection of the defects of the inner wall of the pipeline is achieved.

The invention relates to the technical field of image processing, and discloses a complex background image contour matching method based on a physical contour feature point template, and the method comprises the steps: firstly converting physical contour data into contour image coordinate data according to an image pixel equivalent, and generating a template image; gradient information is extracted through an image pyramid model and a Scharr filter, candidate feature points are screened in combination with non-maximum suppression, and feature point uniform sampling is achieved through a grid statistical method; rotating and scaling the sampled feature point set based on preset parameters to generate a multi-angle and multi-scale template set; traversing the template by adopting a sliding window method and carrying out similarity calculation on the template and the to-be-matched image; and finally, obtaining a matched contour center point through center point offset compensation. The method improves the matching efficiency on the premise of guaranteeing the completeness of feature information, effectively resists complex background and illumination change interference, solves the problem of positioning offset caused by irregular contour rotation, and has the advantages of high robustness and high precision.

The application provides an automatic welding method, device and system for energy storage cabinet processing. The automatic welding method comprises the following steps: obtaining welding plan information of an energy storage cabinet body; generating a first welding instruction based on the welding plan information and sending the first welding instruction to a controller of a welding device to control the welding device to preliminarily weld the energy storage cabinet body, and obtaining two-dimensional image data of each welding area after the preliminary welding is completed, and extracting image contour features from the two-dimensional image data; judging an area that does not meet a target geometric feature threshold as a surface defect area; generating a second welding instruction according to the positions of the surface defect areas and corresponding function area identifiers, and performing repair welding on the surface defect areas of the energy storage cabinet body, and obtaining three-dimensional point cloud data of each welding area after the repair welding is completed and sending the three-dimensional point cloud data to a cloud server. Therefore, the application balances the overall product welding performance and cost by differentiating detection according to function areas, performing real-time repair welding closed loop and cloud three-dimensional analysis.

The invention discloses a cross-terminal hand back vein living body detection method based on an illumination response characteristic. The method comprises the following steps: S1, respectively collecting a multi-light-intensity vein image sequence of a target hand back and a to-be-detected hand back through a first terminal and a second terminal; s2, performing hand back contour extraction, direction adjustment and image normalization preprocessing on the two terminal images to ensure that the sizes and the orientations of the image contours are consistent; s3, extracting a hand back region of interest, and synchronously realizing region scaling and noise reduction by adopting mean filtering; s4 and S5 are combined with the camera response function and the inverse function of each terminal, illumination response offset values of pixel points in the region of interest are calculated, and a first offset feature map and a second offset feature map are generated respectively; and S6, selecting a matching template from the second offset feature map, performing sliding matching with the first offset feature map, calculating similarity through a Pearson's correlation coefficient, and comparing the similarity with a preset threshold to judge whether the to-be-detected hand back is a living body or not. According to the invention, cross-terminal hand back vein in-vivo detection with high adaptability and high accuracy is realized.

The application discloses an image recognition method and device, electronic equipment and readable storage medium, and belongs to the technical field of image processing. The method comprises the following steps: acquiring encoding data of a target image, wherein the encoding data of the target image comprises encoding data of a plurality of macroblocks in the target image, the consumed information amount of any macroblock in the plurality of macroblocks during encoding processing is proportional to the texture complexity of the any macroblock, and the texture complexity of the any macroblock is related to the pixel value of each pixel point in the any macroblock; acquiring a contour image corresponding to the target image based on the encoding data of each macroblock, wherein the contour image is used for reflecting the contour of an object in the target image; and performing image recognition processing on the contour image to obtain an image recognition result. Since the encoding data of the target image does not need to be decoded to obtain the target image, a large amount of computing resources can be saved, and the image recognition efficiency is improved.

An image processing method for extracting a target object from an image includes: obtaining, by using an image segmentation model, a first segmentation mask of the target object in a target region covering the image and a segmentation probability map representing a segmentation probability of each pixel in the target region belonging to the target object; detecting, by using a contour detection model, at least one image contour in the target region; determining, for the detected at least one image contour, whether a pixel inside each image contour belongs to the target object based on the segmentation probability map to obtain a second segmentation mask representing pixels belonging to the target object; and extracting the target object based on the first segmentation mask and the second segmentation mask.

The invention discloses a machine vision target shielding correction calculation method, and aims to solve the problems of image missing and deformation caused by non-uniform visibility, animal shielding and external factor shielding in actual working conditions due to the fact that a machine vision displacement monitoring system uses an open zone, and if a known target is circular, image contour detection is carried out in an imaging picture, so that the target is corrected. And judging whether a circle exists or not, and then carrying out defect judgment, arc segmentation, fitting complementation and circle calculation. The gross error caused by direct calculation of the centroid based on an actual imaging image in a traditional mode is solved, and correction calculation can be carried out in time when a target image has certain deficiency and non-uniformity. The system is applied to a machine vision displacement monitoring product of an active target (the target actively emits infrared light), and the system is applied to safety monitoring projects of infrastructures (bridges, tunnels, slopes, water conservancy dams, geological disaster landslide monitoring and the like).

The application provides a non-contact steel pipe end face size measurement method and system, on the basis of acquiring an image and identifying an image contour, traversing the identified contour points, calculating the distance between the contour points, judging the contour points with too far distance as burrs and eliminating the influence of the points, and performing elliptical fitting on the contour, and according to the fitting result of the ellipse, the inner and outer diameters and roundness and other parameters can be calculated. The application identifies the pipe end contour based on the original image of the pipe end, adopts the combination of image edge detection and graphological methods, calculates the position relationship of the contour points and the contour through a self-developed algorithm, accurately positions to the pipe end burrs and excludes the influence of the burrs on the measured pipe end size. The application not only solves the problems of insufficient precision and high maintenance cost of the manual measurement method, but also improves the sensitivity to the burrs and accurately obtains the effective pipe end size.

This invention relates to the field of medical image processing technology and discloses an image enhancement method for breast imaging diagnosis assistance. The method acquires a raw dataset of breast images, comprehensively covering key information across multiple dimensions, including overall image brightness, image zone texture variability, and image floor interference intensity. Then, a dynamic feature representation system is constructed based on this raw dataset, outputting targeted image optimization and control parameters. These control parameters are used to calculate a real-time image quality factor, and a preset baseline image quality norm and the actual image contour evolution rate are fused to accurately generate an image quality deviation coefficient. A bidirectional correction operation is performed based on the quality deviation coefficient, generating brightness modulation and texture enhancement amounts, thereby effectively enhancing the image. The processed image dataset is fed back to the dynamic feature representation system to iteratively optimize the image optimization and control parameters.