258 results about "Image contour" patented technology

The invention discloses an underwater image enhancement method and computer equipment, and the method mainly comprises the steps: correcting the color of an underwater original image through employing a self-adaptive color compensation algorithm, enhancing the details through employing a Multiply Contour Bougie (MCB) morphological detail enhancement algorithm, and achieving the global enhancement through employing a self-adaptive gamma correction method. And finally, fusing the detail and global enhancement result through a weighted stationary wavelet visual perception fusion strategy to obtain a final underwater image. The minimum color loss (MCL) algorithm designed by the invention can solve the problem of color channel information loss, so that the mean value of each color channel is similar to the histogram distribution; the image contour can be restored based on an MCB morphological detail enhancement algorithm, and details are highlighted; according to the weighted stationary wavelet visual perception fusion strategy, image information of different scales can be integrated, the definition and the layering sense are improved, the defects of a traditional method in detail and noise processing are overcome, and a high-quality image is obtained.

The invention discloses an image contour recognition method and device for a cell cluster, and relates to the technical field of biological image processing, and the method comprises the steps: converting an input cell cluster image into a binary image; carrying out expansion operation and distance transformation on the image after morphological operation, and carrying out subtraction operation on the two obtained images to obtain a third image containing an unknown region; detecting connected regions in the accurate foreground region, and marking each independent connected region, background region and unknown region to obtain a marked image; performing region segmentation on the original image to obtain segmented marked regions, generating a corresponding binary mask image, and extracting the contour of each marked region by using the binary mask image; identifying and filtering the contour of each marked area to obtain an effective contour; the size of each effective profile is converted into an equivalent sphere diameter. According to the invention, accurate segmentation of adjacent or adhesion areas in the image can be efficiently realized, and accurate counting and size measurement of cell clusters can be obtained.

The invention discloses a joint dilator of an orthopedic knee arthroscope and a control method thereof, and relates to the technical field of medical instruments, the method comprises the following steps: acquiring a geometric center of a preset distraction range and a rotating fulcrum of the dilator, and constructing a preset Y axis according to the geometric center and the rotating fulcrum; when the spreader performs initial rotation by taking a preset Y axis as a rotation axis, the graph acquisition piece acquires an actual image contour in real time; comparing the actual image contour with the target image contour in real time; when the coincidence degree between the actual image contour and the target image contour is a second coincidence degree, the position of the preset Y axis is confirmed again according to the coincident contour between the actual image contour and the target image contour in the first coincidence degree, and an actual Y axis is set; the angle control device is combined with the actual Y axis to control the dilator, through the arrangement, it can be ensured that the dilated range of the dilator coincides with the range corresponding to the actual injury characteristic, and then the wound area of a patient is reduced.

The invention discloses an intelligent cabin environment sensing and control method based on a vehicle sensor, and relates to the technical field of intelligent automobiles, and the method comprises the following steps: in the operation process of an intelligent cabin, an infrared thermal sensing system collects thermal imaging data in an automobile in real time, extracts multi-dimensional feature data based on the obtained thermal imaging data, and sends the multi-dimensional feature data to the intelligent cabin; and forming an infrared thermal image characteristic data parameter set. According to the method, the key features of the infrared thermogram are extracted, and the machine learning model is combined, so that accurate identification and dynamic suppression of the thermo-optical interference state are realized. According to the method, sampling and filtering strategies can be adaptively adjusted in complex environments such as strong light reflection, a real thermal image contour is reconstructed, a non-human body heat source is effectively eliminated, the recognition accuracy and the operation stability of an infrared thermal sensing system are remarkably improved, the misjudgment rate is reduced, and the safety and the reliability of an intelligent cabin are enhanced.

The invention provides a non-contact steel pipe end face size measurement method and system, and the method comprises the steps: traversing recognized contour points on the basis of obtaining an image and recognizing the contour of the image, calculating the distance between the contour points, judging the contour points which are too far away from each other as burrs, eliminating the influence of the points, carrying out the ellipse fitting of the contour, and obtaining the size of the end face of a steel pipe. And parameters such as the inner diameter, the outer diameter and the roundness can be calculated according to an ellipse fitting result. According to the invention, based on the original image of the pipe end, the contour of the pipe end is identified by combining image edge detection and a graphic method, the position relation between contour points and the contour is calculated through a self-research algorithm, pipe end burrs are accurately positioned, and the influence of the burrs on the measured pipe end size is eliminated. The problems that a manual measurement method is insufficient in precision and high in maintenance cost are solved, the burr sensitivity is improved, and the effective pipe end size is accurately obtained.

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.

This technology provides systems and methods for tracking facial movements and reconstructing facial expressions by learning skin deformation patterns and facial features. Frontal view images of a user making a variety of facial expressions are acquired to create a data training set for use in a machine-learning process. Head-mounted or neck-mounted wearable devices are equipped with one or more camera(s) or acoustic device(s) in communication with a data processing system. The cameras capture images of contours of the users face from either the cheekbone or the chin profile of the user. The acoustic devices transmit and receive signals to calculate a representation of the skin deformation. A data processing system uses the images, the profile of the contours, or skin deformation to track facial movement or to reconstruct facial expressions of the user based on the data training set.

The invention relates to a robot drawing method, device and equipment and a storage medium, and is applied to the field of robot control. Contour extraction processing and skeleton extraction processing are carried out on the original image, a contour image is generated, the contour image comprises a plurality of contour segments, and each contour segment comprises a plurality of contour points; a Douglas-Peucker algorithm is adopted to simplify the number of contour points of each contour segment in the contour image, and a contour optimization image is generated; converting the image coordinate system of the contour optimization image into an actual coordinate system of preset paper according to the size of the preset paper; according to a contour screening threshold value, screening an optimized contour section in the contour optimization image, and according to the optimized contour section, generating a contour secondary optimization image; and controlling the robot to finish drawing in the actual coordinate system according to the outline secondary optimization image and preset drawing parameters. The method has the technical effects that the drawing precision is guaranteed while the drawing efficiency of the robot is improved.

The invention relates to the technical field of image contour extraction, and provides an image edge contour extraction method and device, electronic equipment and a storage medium. Edge detection and region division are carried out on a target image to obtain a region matrix, scanning and contour point screening are carried out on each sub-region to obtain a contour point coordinate chain table of each sub-region, and single-region adjacent point screening is carried out on contour points of each sub-region in combination with a first distance threshold to obtain an initial contour track chain table set; performing cross-region contour fitting point extraction in combination with the boundary of each sub-region to obtain a to-be-verified point data set, and performing cross-region adjacent point screening on the to-be-verified point data set according to a second distance threshold and the region arrangement data to obtain a fitting point combination set, and carrying out cross-region merging on the initial contour track chain table set to obtain an edge contour track chain table set. Through multi-core parallel scanning, multi-distance screening and cross-block fitting technologies, the precision and efficiency of image edge contour extraction are improved.

The proposed technique introduces embodiments of a computer-implemented method for interpreting image delineations as vector objects and topological extraction from segmentation by visual computational methods applied to sections (slices) of seismic volumes, in order to aid geological interpretation and sampling of parameters originating from the fracture network and its topology. Embodiments of a developed method integrates a software / application that allows the loading and generation of statistical data related to the fracture network while maintaining georeferencing and scale of the two-dimensional input data. In addition, a fracture segmentation method is shown that uses pyramid image smoothing (decomposition into hierarchical levels of resolution) in order to reduce the amount of details and aid the identification of main faults or fractures. The segmentation after this smoothing is based on adaptive thresholding segmentation.

The invention discloses a contour extraction method based on edge detection, and belongs to the technical field of image processing, and the method comprises the following steps: obtaining a plurality of images to be subjected to contour extraction, and real labeled contours corresponding to the images to be subjected to contour extraction; performing noise level quantization and edge ambiguity quantization analysis on each image to be subjected to contour extraction, and calculating to obtain an image complexity level of each image to be subjected to contour extraction; performing complexity level classification on each image to be subjected to contour extraction, and constructing a complexity classification image data set; constructing a contour extraction model based on edge detection; a trained contour extraction model is obtained; and obtaining a new image to be subjected to contour extraction and an image complexity level, inputting the image to be subjected to contour extraction into the trained contour extraction model for contour prediction, and obtaining a predicted image contour of the image as a contour extraction result. The problem that it is difficult to accurately extract the object contour in a complex image scene is solved.

The invention provides a monocular vision pose tracking method, which comprises the following steps: according to a camera imaging principle, establishing a constraint relation between three-dimensional coordinates of the center of a ball target and coordinates of pixel points on an imaging contour, optimally solving the three-dimensional coordinates of the center of the ball target, realizing spatial positioning of a single ball target, and forming a tracking target by a plurality of ball targets. After the center position of each ball target is captured in real time, the six-degree-of-freedom space pose of the tracking ball target is obtained according to singular value decomposition, and for positioning errors caused by ball target contour extraction, ball target diameter deviation and the like, a calibration method based on feature points is provided to calibrate internal parameters of a system, and compared with a traditional PnP method, the method has the advantage that the positioning accuracy is improved. The positioning process does not need to pre-specify the corresponding relation between the feature points and the image points, does not depend on the pose relation of other feature points, does not relate to other auxiliary constraints, and is easy to realize automatically.

Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.

The invention relates to the technical field of image processing, in particular to a method and a system for extracting an algae microscopic image contour. The method comprises the following steps: acquiring a multi-view image flow of algae cells under a microscope, and carrying out frame-level scheduling and semantic splicing to obtain a multi-angle fusion image unit; constructing a topological contour tensor graph for the multi-angle fusion image unit; performing multi-scale positive and negative contour comparison training on the topological contour tensor diagram to obtain a contour embedded spectrum; holographic algae sample matching back-pushing is carried out on the contour embedded spectrum, and a candidate matching spectrum set is obtained; analyzing an edge feature contribution degree according to the candidate matching spectrum set, and carrying out dynamic fusion on the edge feature contribution degree and the multi-angle fusion image unit to generate an edge saliency heat map; screening independent algae samples by utilizing an edge significance heat map; and carrying out data modeling on the independent algae samples, and synchronizing a data modeling result to an algae sample library. According to the invention, the image contour extraction precision and efficiency can be improved.

The invention relates to the technical field of image recognition, in particular to an automatic detection grabbing system of an earphone cover assembly line, which comprises an earphone cover image acquisition module for capturing an earphone cover image; according to the invention, by calculating the edge sharpness integral of the earphone cover image and extracting the contour coordinate sequence, the image contour positioning precision is improved, and the ambiguity problem of earphone cover boundary identification is solved; main harmonic morphological characteristics are obtained based on low-order descriptor coefficient energy distribution, the expression mode of the morphological characteristics of the earphone cover is optimized, the accuracy degree of matching of the form of the earphone cover and a standard template is further improved, and the appearance misjudgment probability is reduced; in addition, morphological opening operation is carried out by applying multi-scale structure elements, the structure scale entropy is calculated to reflect change characteristics of fine textures on the surface of the earphone cover, finer texture particle size distribution is established, the texture consistency deviation degree is measured and calculated, and the error of surface texture detection is reduced.

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.

The invention discloses a data enhancement method and device based on a multi-scale feature adaptive focusing network, and relates to the technical field of image generation. The method comprises the following steps: carrying out adversarial training on a multi-scale feature adaptive focusing generative adversarial network; the generative adversarial network comprises a generator network and a discriminator network; the discriminator network comprises a multi-scale feature extraction module, a dynamic channel attention sub-module and a space attention sub-module, and the feature extraction capability of image contours, textures and details is enhanced; in the training process, multi-scale feature confrontation optimization is achieved through multi-scale Wasserstein distance and dynamic weight updating, the capturing capacity of a discriminator and a generator for details of different scales is enhanced, and the convergence of the model is ensured; according to the method, the required weight parameter can be obtained after the model is trained to be converged, and the high-quality image generation task is completed by using the weight parameter, so that the problems of low quality, lack of diversity and the like of the generated image in a small sample scene can be solved.

The embodiment of the invention provides an image definition judgment method and device, equipment and a medium, and the method comprises the steps: determining gradient information and Laplacian operator information of an image, the gradient information being used for representing the definition of the image, and the Laplacian operator information being used for representing the definition of the image; determining a definition weight coefficient according to a preset image contour detection algorithm; determining the target definition of the image according to the gradient information, the Laplacian operator information and the definition weight coefficient; when the target definition is greater than a preset threshold value, determining that the image is a clear image; and synthesizing the gradient information, the Laplacian operator information and the definition weight coefficient, calculating the target definition, and comparing the target definition with a preset threshold to judge whether the image is clear or not. According to the method, the edge detection precision is improved, and the reliability and adaptability of definition evaluation under a complex background are remarkably enhanced through multi-dimensional information fusion and weight adjustment.

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 invention provides a system and a method for drawing a ventricular contour and calculating ejection fraction by medical color Doppler ultrasound equipment. The system comprises an image acquisition and caching module for acquiring a plurality of frames of B-mode ventricular images; a background noise and ventricle distinguishing module obtains background noise and ventricle structure images; the contour boundary determination module obtains ventricle contour boundary points; the contour coordinate category distinguishing module performs category distinguishing on the ventricular contour boundary points and determines the position of the valve according to a category distinguishing result; a valve boundary point determination module determines a valve boundary point; the calculation module is used for calculating ventricular volume and ejection fraction; the display module is used for displaying the target ventricular image, the ventricular volume and the ejection fraction; according to the method, the requirement for noise can be effectively met, the ventricular contour can be accurately determined, the accuracy and efficiency of judging the cardio-pulmonary function can be guaranteed, and meanwhile the real-time state can be known more visually and efficiently through visual image superposition and numerical value display.

This invention discloses a mine fire identification and alarm method based on the number of image contour corners. This method uses a camera installed underground to capture and identify images of suspected fires in the monitored area in real time. It also uses the irregular morphology of burning flames to identify and alarm mine fires. This method fully considers the characteristics of fire combustion images, resulting in rapid, accurate, and simple identification, which can buy valuable time for rescue and escape for those in distress underground.

The invention relates to path-based contour extraction for metrology. Embodiments described herein relate to a process for image contour extraction. A system may include a memory storing computer executable components and a processor executing the computer executable components. These computer executable components may include: an identification component that identifies a contour line applied between regions of a pixelated image; and a subdivision component that subdivides the contour line into a set of barrier lines based on analysis of contour line pixels of the contour line. In one or more embodiments, the parsing may be based on the number of adjacent pixels following the contour pixels.

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.