12results about How to "Good segmentation result" patented technology

The invention relates to the technical field of machine vision, in particular to an image segmentation method and system for liquid crystal screen display. The method comprises the following steps: acquiring basic data of the liquid crystal screen; performing screen response characteristic analysis according to the basic data of the liquid crystal screen to obtain screen response characteristic data; performing multi-scale image acquisition according to the basic data of the liquid crystal screen to generate multi-scale image data; performing characteristic classification on the liquid crystal display according to the screen response characteristic data, and performing edge compensation analysis on the multi-scale image data to obtain edge characteristic compensation data; acquiring real-time data of the touch screen; and performing touch interference identification analysis on the real-time data of the touch screen according to the edge feature compensation data to generate touch interference mask data. By analyzing the screen response characteristics and performing edge compensation analysis, the phenomenon of edge blurring caused by liquid crystal response time limitation is reduced, and the accuracy of edge detection is improved.

The embodiment of the invention discloses an MRI (Magnetic Resonance Imaging) image segmentation method, device and equipment based on a Mask RCNN (Regional Convolutional Neural Network) algorithm and a storage medium. The method comprises the following steps: extracting features of a preprocessed MRI image to obtain a feature map; the RPN is used for generating a final Proposa box, and the RPN is used for generating a final Proposa box; the ROI is selected to be subjected to pooling operation, feature fusion and feature coding in sequence, and deep target features and a feature coding graph are obtained; performing classification and frame regression on the targets in the ROI, and determining a target frame and a classification label of each target; determining a prediction segmentation mask of each target through the segmentation network; carrying out post-treatment; non-maximum suppression processing is carried out in a classification mode, and a final target frame, a classification label and a post-processed mask image are determined. According to the scheme, focuses with different scales and different shapes, sizes and positions can be processed through the Mask R-CNN algorithm, compared with a traditional MRI image segmentation technology, higher accuracy and generalization ability are achieved, the two tasks of target detection and segmentation are achieved at the same time, and doctors are helped to provide more accurate diagnosis and treatment.

The invention discloses a temporal bone CT automatic segmentation method based on a convolutional neural network, and relates to the technical field of medical image processing, and the method comprises the steps: S1, data standardization; s2, a coarse segmentation stage; s3, a fine segmentation stage; and S4, a post-processing step. According to the temporal bone CT automatic segmentation method based on the convolutional neural network, through the four main steps of data standardization, coarse segmentation, fine segmentation and post-processing, rapid, accurate and automatic segmentation of the inner ear area in the temporal bone CT image can be achieved, the segmentation efficiency and accuracy are remarkably improved, and compared with the prior art, the method has the advantages that the segmentation efficiency is improved, and the segmentation efficiency is improved. According to the temporal bone CT key anatomical structure segmentation method, rapid and accurate segmentation of the temporal bone CT key anatomical structure is achieved through the two-stage convolutional neural network, the temporal bone CT key anatomical structure segmentation method has the advantages of being high in segmentation speed, precision and generalization ability and the like, important support is provided for clinical diagnosis, and the temporal bone CT key anatomical structure segmentation method has high clinical application value.

The present invention discloses a liver blood vessel segmentation method, device, equipment and medium based on weak supervised learning, and the method includes: training two liver blood vessel segmentation network models with differences through noisy labels; using the two models to segment respectively to obtain predicted labels; assigning weights to predicted labels and noisy labels through a label optimization module to obtain weighted optimized labels; using the optimized labels to iteratively train the two models respectively to obtain two iterative models; using the two iterative models to continue segmenting to obtain new predicted labels; when the segmentation effect of the iterative model is improved, continue to call the label optimization module to obtain new optimized labels, and continue to iterate the model until convergence; segment the input CT image through the converged model to obtain the segmentation result. The present invention can effectively reduce the adverse effects of noisy labels on the segmentation results, thereby reducing the manpower, material resources and time consumed by label annotation.

The present invention relates to a technical solution of a guidewire segmentation method, device and medium based on pixel topological coupling, including: topological expansion model processing and attention focus segmentation model processing; the topological expansion model processing includes: inputting a segmented first blood vessel image, performing guidewire expansion training through a first U-Net neural network, and obtaining a corresponding expansion map; the attention focus segmentation model processing includes: inputting the expansion map and the original image into a second U-Net neural network with a loss function for attention focus segmentation prediction, and obtaining a segmented second blood vessel image; taking the second blood vessel image as input, iteratively executing the topological expansion model processing and the attention focus segmentation model processing until the optimal segmentation result is obtained. The beneficial effects of the present invention are: realizing the combination of pixel segmentation and topological segmentation processing, and obtaining a better segmentation result.

The application provides a noise label dynamic correction semi-supervised segmentation framework fusing confidence learning, which can efficiently utilize a small amount of high-quality labeled data and a large amount of low-quality noise data to realize accurate and robust three-dimensional medical image segmentation. In the early training stage, the framework mainly uses high-quality data for full supervision learning to guide the model to learn reliable features and segmentation priori; in the middle and late training stage, low-quality noise labels are gradually introduced, noise region recognition is realized through three-dimensional multi-view slice confidence learning, and an uncertainty-guided dynamic soft correction strategy is adopted to dynamically weight and fuse the teacher model prediction results and the original noise labels, so that the noise labels are effectively utilized while avoiding the interference of false labeling, and finally the segmentation accuracy and generalization ability of the model are improved.

This invention relates to a hyperspectral camouflage target detection method based on spatial-spectral feature fusion. It combines deep learning and spatial-spectral feature extraction to establish a 3D network model suitable for spectral feature learning and target spatial localization. Anomaly detection and spatial-spectral constraints are incorporated into the model to improve detection accuracy. Simultaneously, a false positive / false negative removal training method based on spatial receptive domain is proposed to further adjust the network and improve its training effect. The RX-3DSSRF algorithm effectively compensates for the problems of missed detections and false alarms in the traditional RXD algorithm, achieving good segmentation results on low-altitude hyperspectral camouflage target detection data. By removing false positive features of the target and false negative features of the background, the final optimized target detection result is obtained.

The application provides an InSAR deformation anomaly extraction method and system based on multi-modal fusion and vector optimization, relates to the technical field of artificial intelligence and geological disasters, and comprises the following steps: acquiring regional multi-source remote sensing data, calculating surface deformation phase data, terrain relief, slope, vegetation index, water index and soil index, and resampling to obtain multi-source remote sensing full-band data; manually interpreting the InSAR surface deformation anomaly range of the region based on expert knowledge to obtain vector data and binary mask data of the InSAR surface deformation anomaly range; constructing a double-branch multi-modal feature fusion network, extracting InSAR surface deformation anomaly information based on the binary mask data and the multi-source remote sensing full-band data; constructing a dynamic vector optimization network, and optimizing the boundary of the InSAR surface deformation anomaly information based on the multi-source remote sensing full-band data to dynamically convert into a vector result. The application realizes seamless connection from remote sensing images to GIS vectors.

The invention relates to a placenta image segmentation algorithm based on a learnable visual center mechanism and a multilayer perceptron. Automatic segmentation of a placenta can be realized by using fetal magnetic resonance imaging data. According to the algorithm, an encoder-decoder architecture is used, and a marked multilayer perceptron module is introduced to capture a long-distance remote dependency relationship and a local corner region of global features, so that complementation of global and local feature information is realized, and the defects of traditional convolution operation in the aspect are overcome; in addition, the model also uses a learnable visual center mechanism, all front superficial layer features are regulated and controlled by aggregating local key regions of an input image and utilizing context information of an interested region obtained by deep layer features, and feature fusion between levels from top to bottom is realized. Experimental results show that the provided algorithm achieves a good effect on a placenta image segmentation task.

The invention discloses an RGB-T semantic segmentation method and system based on multi-attention guidance and hierarchical fusion, and relates to the technical field of image processing. The system is composed of a double-flow encoder, a discriminative local texture perception unit, a semantic-driven cross-modal fusion unit, a semantic enhancement unit and a multi-scale layered refinement decoder, and efficient fusion and analysis of multi-modal features in a complex traffic scene are achieved. According to the discriminative local texture perception method, saliency features are learned through multi-attention guidance and a self-adaptive gating mechanism, accurate modeling of shallow texture information is focused, and the distinguishing ability of a region of interest and a target edge is improved; according to the semantic-driven cross-modal feature fusion method, efficient aggregation of global contexts is realized through high-level semantic guidance and cross-modal feature interaction, and feature complementarity is enhanced, so that the semantic-driven cross-modal feature fusion method has significant advantages in analysis of small targets, long-distance targets and boundary regions. The decoder adopts a progressive fusion mode, an additional edge detection module does not need to be added, and the overall segmentation precision is improved.

The present invention relates to the field of computer vision, adopts a deep learning framework, and specifically relates to a pedestrian re-identification method based on weakly supervised human collaborative segmentation, comprising the following steps: 1) sending all images of the same pedestrian as a batch into a weakly supervised human collaborative segmentation network, training the network to obtain pseudo-pixel-level labels corresponding to the pedestrian images; 2) constructing a multi-task network framework including a backbone network, a pedestrian re-identification subnetwork and a human segmentation subnetwork, sending pedestrian images and pseudo-pixel-level labels into the multi-task network, obtaining image features, foreground features and human body part features; 3) alternately training the human collaborative segmentation network and the multi-task network to obtain the optimal pseudo-pixel-level labels and multi-task network model, sending pedestrian images in a test set into the multi-task network, using only foreground features and human body part features to calculate the distance between images, and performing pedestrian feature matching. The present invention is more robust to occluded scenes and more suitable for complex scenes in reality.

The present invention provides an image segmentation method, device and storage medium. The method comprises: obtaining first image data; segmenting the first image data through a hole gated attention model to obtain a segmentation result, wherein the hole gated attention model comprises two parts: an encoding part and a decoding part; wherein the encoding part comprises a common encoding unit and an attention encoding unit; the decoding part comprises an attention decoding unit and a common decoding unit; the common encoding unit, the attention encoding unit, the attention decoding unit and the common decoding unit are connected in sequence. By segmenting the first image data through the hole gated attention model, the amount of parameters and the amount of calculation in image segmentation are greatly reduced, and a better segmentation result is obtained.