20 results about "GrabCut" patented technology

The invention discloses a method and system for extracting and classifying blue and white porcelain patterns, and the method comprises the steps: obtaining a two-dimensional plane image of the blue and white porcelain patterns, and obtaining a source image; carrying out background separation on the source image by using a GrabCut algorithm to obtain an ornamentation image; judging whether the ornamentation image is adhered or not, and if so, removing background interference by marking the foreground and the background of the ornamentation image; s3, sending the ornamentation image output in the step S3 into a pre-trained deep learning network model to obtain a classification corresponding to the ornamentation image; and storing the ornamentation image and the classification corresponding to the ornamentation image in a visual mode. Based on an image processing algorithm and a deep learning technology, automatic extraction and efficient classification of blue and white porcelain ornaments are realized. Through a friendly user interaction interface, the room can efficiently complete loading, processing, classification and result storage of the ornamentation, and the problems that in the prior art, the ornamentation extraction precision is insufficient, the classification efficiency is low, and operation is complex are solved.

The application discloses a kind of extremely high resolution original drawing image layering segmentation method, equipment, medium and product, it is related to image segmentation field, the method includes obtaining extremely high resolution to be segmented original drawing image;Using SAM model to the original drawing image to be segmented is preliminarily segmented, and obtains preliminary segmentation result;Multiple initial mask regions are contained in preliminary segmentation result;Using GrabCut algorithm, the preliminary segmentation result is globally optimized, edge optimization and residual hole completion, and obtains the segmentation result after optimization;Using Poisson equation, the edge of the segmentation result after optimization is reconstructed, and obtains final segmentation result.The application can be segmented according to the needs of oneself to different granularity size.In the segmentation process, the application realizes maximum degree of automation using SAM model and GrabCut algorithm, in addition to this, the application optimizes the edge of last segmentation result, greatly improves the efficiency and accuracy of image segmentation.

The present invention discloses a method for contactlessly measuring the volume of ice masonry on a conveyor belt of a production line, and belongs to the field of image recognition technology. The method solves the problem that the conventional ice masonry volume measurement method in the prior art cannot perform contactless volume measurement on ice masonry with translucent and reflective characteristics on a conveyor belt of a production line; the present invention collects images of ice masonry to obtain an original image; pre-processes the original image based on multi-scale fusion filtering, and weightedly fuses the obtained image details into the original image to obtain the final image processing result; pre-sets the segmentation area of ​​the Grabcut algorithm, and uses the Grabcut algorithm to perform image segmentation on the final image processing result to obtain a segmented ice masonry image; performs minimum circumscribed rectangle fitting on the segmented ice masonry image to obtain the ice masonry volume. The present invention effectively realizes the fully automatic segmentation and contactless volume measurement of ice masonry images, and can be applied to ice masonry volume measurement.

The invention discloses a photovoltaic panel surface defect detection method based on improved YOLOv5s, and the method comprises the steps: collecting a surface image of a photovoltaic panel through a visible light camera carried by an unmanned plane, and segmenting a defect region through a GrabCut algorithm; according to the method, adaptive anchor frame calculation is performed on an input image, a Mosaic data enhancement technology is applied, a diversity training sample is generated, and a lightweight network Ghost Net is introduced to replace a backbone network of original YOLOv5s so as to reduce model complexity, a CIoU loss function of the original YOLOv5s is replaced by a SIoU loss function, a model convergence speed is accelerated, detection and positioning precision is improved, and a classification task and a regression task are separated. The average precision, the accuracy rate, the recall rate, the parameter quantity and the like are used as evaluation indexes, an ablation experiment and a contrast experiment are performed on the data set, and high-precision real-time effective detection on the surface defect image of the aerial photovoltaic panel is realized.

The invention provides a hardware corrosion detection and quantification method in an electric power inspection image, and relates to the technical field of electric power inspection image processing, and the method comprises the following steps: collecting an unmanned aerial vehicle inspection power transmission line aerial image; positioning hardware equipment in the image by using the improved YOLOv5s; separating the hardware fitting from the background by using a GrabCut segmentation algorithm and a morphological optimization algorithm; extracting and separating color images of three channels in a YCrCb color space, and obtaining a grayscale image of a Cr channel; according to the threshold segmentation of the Cr channel, obtaining a hardware corrosion binary image, and according to the binary image, judging whether the hardware is corroded or not; and counting the number of pixel points in the corrosion area and the number of pixel points in the hardware fitting area, and judging the corrosion grade of the hardware fitting by calculating the area ratio of the two areas. According to the method, deep learning and traditional image processing are combined, hardware identification, corrosion area detection and corrosion degree quantification in the inspection image are realized, and the defects of low working efficiency and high subjectivity caused by manual visual inspection of corrosion detection of power transmission line hardware equipment are overcome.

The present invention proposes an intelligent perception method for the plane displacement field of a bridge structure, which belongs to the field of monitoring, including: setting a target at a key position of the bridge to be tested, scanning the bridge using a camera fixed on an automatic rotating pan-tilt head, projecting the scanned image to the same plane through perspective transformation and splicing to obtain a two-dimensional panoramic image; eliminating camera disturbance, using the GrabCut algorithm to extract the image of the bridge structure to be tested, and performing grid discretization; calibrating the camera using the target to eliminate distortion, and calculating the pixel-displacement conversion coefficient at the same time, using a template matching algorithm improved by quadratic surface fitting to obtain the displacement of key nodes; using triangle shape functions and rectangle shape functions to transmit the displacement information of key nodes, so as to obtain the plane displacement field of the bridge structure to be tested. This method realizes accurate, fast and non-contact acquisition of the plane displacement field of the bridge structure. The present invention has the advantages of convenience and accuracy.

The present invention relates to the technical field of image recognition, and discloses a monitoring method for determining whether a target contacts a transmission line, including the following steps: Step 1, collect real-time images of the area where the transmission line is located in real time as background pictures, update the real-time images into background pictures by using a background update strategy, and separate the foreground and background of the background pictures by using an improved Grabcut algorithm; Step 2, determine whether there is a target intrusion in the real-time images; Step 3, predict the operation trend of the intruding target and the detection area demarcated on the transmission line, and feedback and transmit the prediction result to an information feedback system. If the prediction result exceeds a given threshold, it is an abnormal situation, and the information feedback system issues an alarm reminder. By using the improved Grabcut algorithm to separate the image background and foreground, the present invention obtains an image containing only the target for recognition, inputs it into an image recognition system for calculation, reduces the calculation amount, and improves the picture quality.

The present invention discloses a method and system for monitoring and processing steel structure deformation based on BIM, which is used in the field of steel structure safety monitoring. The method includes the following steps: obtaining a digital image obtained by scanning a steel structure with a laser scanner, and establishing a BIM model based on the digital image; using the GrabCut algorithm to process the digital image to detect the surface deformation of the steel structure and obtain the deformed area; using three-dimensional reconstruction technology to obtain the three-dimensional information data of the deformed area; comparing the three-dimensional information data of the deformed area with the three-dimensional information data in the BIM model to detect the precise position and deformation degree of the steel structure deformation; using an improved CNN model to evaluate the steel structure deformation based on the three-dimensional information; and formulating a steel structure deformation treatment plan. Through machine learning and deep learning technologies, the present invention can automatically detect deformations in digital images and three-dimensional information, reducing the workload of manual detection.

The present application belongs to the technical field of image segmentation, and particularly relates to a real-time image segmentation method for directional solidification dendrite growth morphology, which comprises the following steps: in a real-time image, calculating the periodic response intensity at a pixel point according to the modulus operation result of the distance from the pixel point to the nearest grain along the vertical direction of the local dominant direction vector of the pixel point and the equivalent dendrite spacing in the image space, determining the periodic term according to the periodic response intensity at all pixel points, reconstructing the energy function of the GrabCut algorithm based on the periodic term, minimizing the reconstructed energy function to realize the optimal segmentation of the real-time image, and obtaining the segmentation result of the real-time image. The present application significantly improves the resolution capability for the densely arranged and texture consistent dendrite structure, and solves the fundamental problem of "grain boundary adhesion".

The invention discloses a hierarchical segmentation method and device for an original image with extremely high resolution, a medium and a product, and relates to the field of image segmentation, and the method comprises the steps: obtaining a to-be-segmented original image with extremely high resolution; performing preliminary segmentation on the to-be-segmented original picture image by using the SAM model to obtain a preliminary segmentation result; the initial segmentation result contains a plurality of initial mask regions; performing global optimization, edge optimization and residual hole completion on the preliminary segmentation result by using a GrabCut algorithm to obtain an optimized segmentation result; and reconstructing the edge of the optimized segmentation result by using a Poisson equation to obtain a final segmentation result. According to the method, the image can be segmented into different particle sizes according to own requirements. In the segmentation process, automation is achieved to a great extent through the SAM model and the GrabCut algorithm, in addition, the final segmentation result edge is optimized, and the efficiency and accuracy of image segmentation are greatly improved.

The invention discloses a remote sensing image farmland area determination method based on improved GrabCut and semantic segmentation. The method comprises the steps of obtaining a satellite remote sensing image of a target area, sequentially performing radiometric calibration, atmospheric correction and geometric correction on the satellite remote sensing image, extracting a farmland area based on an improved GrabCut algorithm, and obtaining a satellite farmland segmentation mask image. Images of agricultural areas in multiple scenes are collected through the unmanned aerial vehicle, image enhancement is carried out, and a multi-scene farmland image data set is constructed. And training the improved U-Net + + model by using the multi-scene farmland training image set to obtain an unmanned aerial vehicle farmland segmentation mask image. Meanwhile, based on a collaborative application mechanism of a satellite and an unmanned aerial vehicle, selection of two remote sensing image calculation methods is realized. Finally, the area of the farmland in each remote sensing image is obtained through the obtained spatial resolution of the remote sensing images, the precision of the result obtained through the method is higher than that of a traditional calculation method, the calculation efficiency is improved, and higher universality is achieved.

The invention belongs to the technical field of image segmentation, and particularly relates to a real-time image segmentation method for a dendritic crystal growth form of a directional solidification furnace, which comprises the following steps of: in a real-time image, according to a modular operation result of a distance from a pixel point to a nearest crystal grain along a vertical direction of a local dominant direction vector of the pixel point and an equivalent dendritic crystal spacing in an image space, calculating the dendritic crystal growth form of the directional solidification furnace; calculating the periodic response intensity at the pixel points, and determining a periodic term according to the periodic response intensity at all the pixel points; reconstructing an energy function of the GrabCut algorithm based on the periodic term; and by minimizing the reconstructed energy function, optimal segmentation of the real-time image is realized, and a segmentation result of the real-time image is obtained. According to the method, the distinguishing capability of densely arranged dendritic crystal structures with consistent textures is remarkably improved, and the fundamental problem of grain boundary adhesion is solved.

The present application relates to a kind of based on deep learning fusion multi-source image's lettuce phenotype parameter estimation method, including the following steps: obtaining RGB image and Depth image, manually measure the phenotype parameter of each lettuce sample, form image dataset;Using GrabCut algorithm carries out lettuce image foreground segmentation, using Z-Score method to the normalization of lettuce image data;Construction and training lettuce phenotype parameter estimation deep learning multi-source data fusion model.The beneficial effects of the present application are: the method of the present application adopts deep learning technology, fuses visible light image and depth image features, utilizes lettuce phenotype parameter estimation deep learning multi-source data fusion model to accurately estimate lettuce phenotype parameter by multi-source image information;As can be seen from the experimental results, the present application can successfully fuse two-dimensional RGB image and Depth image, excellent performance, and has important application value for high-throughput growth monitoring and yield estimation of protected vegetable.

Disclosed are a semi-automatic image data labeling method, an electronic apparatus and a non-transitory computer-readable storage medium. The semi-automatic image data labeling method may include: displaying a to-be-labeled image, the to-be-labeled image comprising a selected area and an unselected area; acquiring a coordinate point of the unselected area and a first range value; executing a grabcut algorithm based on the coordinate point of the unselected area and the first range value acquired, and obtaining a binarized image divided by the grabcut algorithm; executing an edge tracking algorithm on the binarized image to acquire current edge coordinates; updating a local coordinate set based on the current edge coordinates acquired; updating the selected area of the to-be-labeled image based on the local coordinate set acquired.

The invention discloses an ancient topographic map place name identification method and system, and relates to the field of computer vision. And a two-stage strategy of character area detection and character recognition is designed, so that the end-to-end accuracy of place name recognition is effectively improved. According to the method, through processing of first image cutting and then detection in a first stage, adverse effects caused by too large scale difference between images and characters are reduced, accurate positioning of character areas is realized, and reasoning time is shortened; a space attention cross-dimension reconstruction module is utilized in a first-stage algorithm, and space, channel and frequency domain features are combined, so that the feature extraction capability of the model is improved, the space redundancy is reduced, and the adaptability of the model to variable fonts, variable directions, variant characters and the like in an ancient map is enhanced; in the second stage, in the preprocessing process, background features are weakened by adopting grey-scale map conversion, filtering and GrabCut algorithms, so that character features are highlighted; the original branch and the newly-added branch based on the image recovery module are used for cooperatively recognizing the characters, and the accuracy of low-quality character image recognition is improved.

This invention discloses a method for food image segmentation and removal based on instance segmentation and spatial prior clustering, comprising three steps: Step 1, inferring from the photo using the YOLO instance segmentation model, extracting the segmentation mask, and cropping it into a black background image; Step 2, extracting the foreground from the image using the GrabCut algorithm, converting it to a transparent background, and generating an RGBA format image; Step 3, performing spatial partitioning on the transparent background image based on Euclidean distance transformation, using the outer perimeter as the plate prior and the center as the food prior, performing K-means clustering in the Lab color space, automatically identifying the plate and food clusters based on the spatial enrichment ratio of each cluster at the perimeter and center, and removing plate pixels based on external reachability connectivity verification, outputting a transparent background image containing only the food. This invention eliminates the need for manual region labeling or setting color thresholds, can adaptively handle plates of different colors, shapes, and sizes, is suitable for diverse dining scenarios, and supports tasks such as food recognition and nutritional estimation.

To provide an apparatus for use with an output device regarding urine and feces of a subject that are emitted in a toilet bowl.SOLUTION: At least one computer processor is configured to: receive one or more urine-related parameters; determine that a subject is suffering from dehydration on the basis of at least some of the one or more urine-related parameters; receive one or more feces images; determine one or more feces-related parameters by performing computer vision analyses on the one or more feces images, the one or more feces-related parameters including at least one of texture or color of the feces, the computer vision analyses including at least one of detection of an area of interest, execution of form change, and execution of image segmentation using GrabCut; and perform output to an output device according to at least partially the above-mentioned processing.SELECTED DRAWING: Figure 1

The application discloses a farmland area measurement method based on improved GrabCut and semantic segmentation of remote sensing images, and comprises the following steps: acquiring satellite remote sensing images of a target area; performing radiation calibration, atmospheric correction and geometric correction on the satellite remote sensing images in sequence; extracting a farmland area based on an improved GrabCut algorithm to obtain a satellite farmland segmentation mask image; collecting images of agricultural areas in multiple scenes through a UAV, and performing image enhancement to further construct a multi-scene farmland image dataset; training an improved U-Net++ model using the multi-scene farmland training image set to obtain a UAV farmland segmentation mask image; simultaneously based on a cooperative application mechanism of satellites and UAVs, selecting two kinds of remote sensing image calculation methods; and finally obtaining the area of farmland in each remote sensing image through the spatial resolution of the obtained remote sensing images. The result obtained by the application has higher accuracy than that of a traditional calculation method, the calculation efficiency is improved, and the application has higher universality.

An embodiment of the present invention discloses a method, device and system for measuring the length, width and height of an object in an industrial assembly line. Ultrasonic ranging is performed on the object on the conveyor belt of the assembly line to obtain the height information of the object; an image of the object is collected, and the object image is segmented according to the obtained object image information by using the GrabCut algorithm, and the length and width of the object in the image are obtained; the object is modeled by using the principle of similar triangles to obtain the length and width information of the object. It can realize the intelligent detection of the size of storage packages, with high detection efficiency and good accuracy.

The present invention belongs to the field of image processing and artificial intelligence, and discloses a high-precision automatic segmentation method for pathological slice cells based on convolutional neural networks and Dbscan. The method uses pathological slices and manually annotated cell masks to train a U-Net convolutional neural network model improved for pathological slice cell features, and obtains a preliminary semantic segmentation result with low accuracy; then, through the GrabCut post-processing module, the cell under-segmentation condition is compensated, the segmentation edge is optimized, and a high-precision semantic segmentation mask is obtained; then, according to the slice cell mask pixel value density in the semantic segmentation result, the Dbscan algorithm clustering radius and clustering density are adjusted, and cluster instance division is used to obtain a high-precision instance segmentation result. The present invention can perform high-precision and high-efficiency semantic segmentation and instance segmentation on multiple types of pathological slice cells.