172 results about "Pixel classification" patented technology

In order to solve the problems that day lily plants are densely shielded and categories are difficult to distinguish, the invention provides a field dense day lily pixel classification and picking information acquisition method. The method comprises the steps of designing a double-branch decoder fusing local and global information, constructing a progressive stepped feature mining module (PLFM) and a multi-scale hierarchical category enhancement module (PCEN), optimizing hyper-parameters through a PALA algorithm, and constructing an adaptive double-branch loss enhancement and category enhancement network (DLCE-Net) to improve semantic segmentation performance. And designing a picking point positioning algorithm (MW-GCA) of Mini-Window guided angular point analysis based on a segmentation result, screening angular point coordinates through a U-shaped window, obtaining an attitude line segment, and realizing picking point pixel coordinate and angle estimation. The method can reduce the labor loss and accelerate the engineering landing of the automatic crop picking technology.

The invention relates to a hyperspectral image classification method and device based on a state space model. The hyperspectral image classification method based on the state space model comprises the following steps: sequentially carrying out feature extraction and serialization processing on hyperspectral image data to obtain a shallow feature projection vector; performing global-local feature extraction on the shallow feature projection vector by adopting a neural network based on a state space model to obtain a fused feature projection vector; and carrying out pixel-by-pixel classification and dimension rearrangement on the hyperspectral image data in sequence to generate a classification result of the hyperspectral image data. According to the hyperspectral image classification method based on the state space model, long-range dependence modeling is achieved through the neural network based on the state space model with linear complexity, the calculation complexity is effectively reduced, and through feature fusion and residual error connection, the classification accuracy of the hyperspectral image is improved. And the perception capability of the neural network on different scale space-spectrum structures in the hyperspectral image is effectively enhanced.

The invention relates to the field of image processing, and provides a seed multi-modal microscopic image acquisition method, device and equipment and a storage medium, and the method comprises the steps: carrying out the region segmentation of an obtained hyperspectral image of a plurality of seeds, and obtaining a single-seed region image; performing data extraction on the single seed area image to obtain spectral data of each seed; the spectral data comprises a seed identifier and a reflectivity value of each wave band; segmenting and extracting the acquired scanning images of the multiple seeds to obtain single seed scanning slices; performing three-dimensional reconstruction on the binary mask image to obtain seed three-dimensional physical structure information; the binary mask image is obtained by performing pixel classification processing on the single seed scanning slice. The method provides a basis for comprehensively and deeply analyzing chemical components and internal structures of the seeds, and improves the efficiency of seed detection and analysis.

The invention relates to a modeling method, in particular to an unmanned aerial vehicle spectral image BRDF rapid acquisition and adaptive generalization modeling method. The method comprises the following steps: 1, determining a research area, obtaining multi-angle BRDF data of the research area, and obtaining illumination change data at the same time; 2, preprocessing the acquired multi-angle BRDF data in combination with illumination change data to obtain preprocessed multi-angle images, splicing the multi-angle images to generate a panorama of the research area, and acquiring a digital surface model graph at the same time; 3, carrying out pixel-by-pixel classification on the research area based on the panorama to obtain a ground feature category map; calculating the gradient and the slope direction of the research area pixel by pixel based on the digital surface model graph; 4, correcting a preset BRDF model by using the acquired illumination change data, gradient and slope direction; and 5, self-adaptive generalization modeling is carried out. According to the method, high-efficiency BRDF acquisition can be realized, and the influence of regional change, space environment and topographic factors on BRDF modeling precision can be eliminated.

The invention relates to a road extraction method and system based on semantic segmentation, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: inputting a to-be-recognized remote sensing road image into a trained semantic segmentation model, and obtaining a first road segmentation result; extracting a road according to the first road segmentation result; wherein the semantic segmentation model performs reverse model parameter updating according to mixing loss during training; the mixed loss comprises pixel classification loss and connectivity loss; the connectivity loss is determined according to the predicted road center line and the real road center line; the predicted road center line is obtained by performing road center line extraction on a second road segmentation result, and the second road segmentation result is obtained by performing semantic segmentation on a sample remote sensing road image through a semantic segmentation model; the real road center line is obtained by performing road center line extraction on a real road segmentation result corresponding to the second road segmentation result. The method can improve the connectivity and integrity of the extracted road.

The invention provides a low-gray-scale image imaging defect visual detection method and system for an OLED display screen. The method comprises the following steps: aiming at original image data of a display screen, extracting a gray scale picture with a pixel brightness mean value lower than a preset brightness threshold value as a first image, carrying out color characteristic analysis on the first image, and generating a second image marked with a chromaticity feature vector; identifying the chromaticity feature vector according to the second image, generating a feature vector matrix for representing chromaticity distribution of each pixel in the second image, and performing color jump analysis through the feature vector matrix to obtain a third image; extracting a jump region in the third image, and setting marks for distinguishing different dispersion distribution for chromaticity distribution dispersion distribution according to the jump region to obtain a fourth image; and identifying dispersion distribution according to the fourth image, combining with a chromaticity abnormal pixel region in the hopping region, performing pixel classification in the hopping region, and setting defect marks for pixels with imaging defects represented by a classification result to obtain a fifth image.

The invention discloses a vehicle-mounted track inspection system detection method, and relates to the technical field of tracks, an inspection starting point is set in a to-be-inspected line, a complete track image of the to-be-inspected line is pre-collected, a plurality of fastener images are generated through cutting with a fastener as the minimum unit, and track historical data are obtained; the track historical data are deployed on a cloud service side and a local inspection side at the same time; a vehicle-mounted track inspection system collects track real-time images, then the track real-time images are matched with an inspection starting point and cut into a plurality of fastener real-time images, region division is conducted on each fastener real-time image, track historical data of a local inspection side are called to conduct pixel grading comparison on different regions, and abnormal region images are screened; and incremental data is generated and transmitted to the cloud service side, track historical data is called to carry out refined detection on the abnormal region image, and a disease detection result is output. According to the invention, the problems of insufficient local detection performance, data accumulation and detection delay under high-speed operation of the electric passenger car are solved, and real-time track disease inspection is realized.

The invention discloses a submarine image splicing method based on suture line optimization, and the method comprises the steps: carrying out the dual-stage feature extraction of a target submarine image based on an improved ResNet50 network, and obtaining a multi-level feature map containing global structure information and local detail features; respectively carrying out global coarse registration and local fine correction on the second feature map and the first feature map based on a deformable convolutional network to obtain a sub-pixel level aligned deformed image; and carrying out cross-scale feature interaction and dynamic mask optimization on the deformed images based on a cross-image self-attention mechanism, dividing suture line weights between the spliced images, and carrying out pixel classification according to the weights to obtain seamless spliced images. According to the method, semantic fusion is guided through deformable convolution modeling elastic deformation and a self-attention mechanism, sub-pixel-level alignment and seamless splicing are achieved, and the problems of geometric dislocation and suture line artifacts caused by refraction distortion, scattering noise and dynamic deformation in large-parallax submarine image splicing are solved.

Systems and methods for capturing and processing image data captured by an optical microscope, such as an Open Top Light Sheet (OTLS) microscope are disclosed. Image data can be processed by techniques including flat fielding, image depth correction, and edge correction, and pixel classification and image segmentation techniques can be applied to more accurately identify lipid droplets and fibrous structures for assessment of, for example, steatosis and fibrosis in human liver biopsy tissue samples. Systems and methods are also related to capturing low resolution images of a sample, identifying regions of interest, and then capturing high resolution image of the regions of interest to reduce memory used for storage, increase computation speed, and reduce computational power.

The invention discloses an ultrasonic phased array weld defect intelligent identification method based on adaptive filtering, and belongs to industrial nondestructive detection image identification methods, the technical scheme is that the method comprises the following steps: sampling and sorting pipeline weld PAUT detection images; dividing the collected image samples into a training set and a test set; pixel classification and filtering noise reduction are carried out on the training set images through a hybrid adaptive filter; performing abnormal signal labeling on the preprocessed training set image and the unprocessed test set image respectively; training a target recognition network through the preprocessed training set; and collecting a new fan-shaped scanning image of the pipeline weld defect as network input, and identifying the weld defect. The ultrasonic phased array weld defect intelligent identification method based on self-adaptive filtering has the beneficial effect that the ultrasonic phased array weld defect intelligent identification method based on self-adaptive filtering is provided.

The embodiment of the invention discloses a water body information extraction method based on a remote sensing image, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a green light wave band value, a red light wave band value and a near-infrared wave band value of a target pixel in a to-be-processed remote sensing image; based on the green light wave band value, the red light wave band value and the near-infrared wave band value, calculating a water and land pixel measurement variable of the target pixel through a preset water body index formula; comparing the land and water pixel metric variable obtained by calculation with a preset threshold value, and classifying the target pixel as a water pixel or a non-water pixel based on a comparison result; the accuracy of land and water pixel measurement can be further improved, the phenomenon that the water body area is confused with other interference areas such as roads, building roofs and bridges is reduced, and water body recognition is more accurate.

The invention discloses a semantic segmentation method and device based on a lightweight coding-decoding network, equipment and a medium. The method comprises the following steps: acquiring image data; preprocessing the acquired image data; inputting the preprocessed image into an encoder for low-resolution feature extraction; the extracted low-resolution features are input into a decoder for feature detail recovery, and a feature map with the same resolution as the original image is obtained; and inputting the feature map with the same resolution as the original image into a semantic segmentation classifier to obtain pixel-by-pixel classification prediction. Therefore, the method can achieve higher class precision and more detailed object contour prediction, thereby achieving better balance between speed and precision.

The invention belongs to the technical field of machine learning, and particularly discloses a plateau wetland landscape component extraction method and system and a program product, and the method comprises the steps: designing a spectrum-space double-branch interaction model, extracting the spectral features of pixels at a spectrum branch through a pixel dimension raising module and a ViT architecture Transformer encoder, and carrying out the extraction of the components of the plateau wetland landscape component; extracting neighborhood spatial features of pixels through a multi-scale feature extraction module and feature enhancement in a spatial branch, then integrating the features of the two branches through a cross attention fusion module, outputting pixel classification results, and determining each plateau wetland landscape component region in the remote sensing image according to each pixel classification result. The high-efficiency and reliable extraction of the plateau wetland landscape components can be realized. The plateau wetland landscape component classification method can effectively relieve the problem of salt and pepper noise existing in a traditional pixel-by-pixel classification method, guarantees the light weight of the network while improving the plateau wetland landscape component classification precision, and provides more accurate technical support for long-time-sequence plateau wetland landscape component extraction.

The invention discloses a two-stage parameter adaptive optimization honeycomb lung focus segmentation method, a storage medium and equipment, and relates to the technical field of honeycomb lung image processing methods. The method comprises the following steps: image preprocessing: acquiring a honeycomb lung CT image, preprocessing the CT image, and optimizing a preprocessing process of the CT image by adopting a mean shift algorithm; coarse segmentation: performing lung coarse segmentation on the preprocessed image by using a classification method based on an SNIC algorithm and a random forest and a superpixel classification method; and fine segmentation: performing fine segmentation on the coarse segmentation result and the original CT image based on an adaptive optimization network SRU-Net. The method can improve the perception capability of the model for focus boundary details, and improve the segmentation precision and stability.

Identifying hazard trees is described. An example method includes receiving multiple images for a geographic area that includes multiple electrical assets of a power distribution infrastructure. Pixels of the multiple images are classified as hazard tree pixels or as non-hazard tree pixels using one or more convolutional neural networks. Multiple polygons are generated based on the hazard tree pixels, a polygon corresponding to one or more hazard trees in the geographic area. A height of a polygon and a distance from the polygon to an electrical asset is determined. Based on the height and the distance, the one or more hazard trees corresponding polygon are determined to represent a potential hazard to the electrical asset. A notification of the potential hazard to the electrical asset is generated and provided.

A technique for a UAV includes: acquiring an aerial image of an area below a UAV that includes one or more instances of an object; analyzing the aerial image with an image classifier to classify select pixels of the aerial image as being keypoint pixels associated with keypoints of the object; grouping the keypoint pixels into one or more groups each associated with one of the instances of the object, wherein first keypoint pixels of the keypoint pixels are grouped into a first group of the one or more groups associated with a first instance of the one or more instances of the object; generating an estimate of a relative position of the UAV to the first instance of the object based at least upon a machine vision analysis of the first keypoint pixels; and navigating the UAV into alignment with the first instance based upon the estimate.

The present invention discloses a remote sensing change interpretation deep network with joint distribution sampling and feature decoupling, which belongs to the field of remote sensing image processing technology. In view of the problem that mixed feature extraction easily leads to blurred semantic boundaries, which in turn causes inaccurate classification of remote sensing change pixels, the mixed features are decoupled into change and invariant features through joint distribution sampling to complete the remote sensing change interpretation. In the training stage, the posterior distribution of the decoupled features is used to train the change prior generator through label calibration learning; the posterior distribution and feature separator are combined to achieve feature decoupling, and the features are further aggregated through prototype learning; an over-expectation push-pull loss regularization term is proposed, which increases the distance between classes by improving the predicted expectation to push and pull the positive and negative sample features to the farther end. In the testing stage, remote sensing image change detection is completed through modules such as feature separator and change detection head without the support of posterior distribution. Experiments have shown that this paper has achieved significant results in both qualitative and quantitative indicators.

The invention discloses a focus segmentation model based on a mask-guided multi-scale feature fusion technology and a training method thereof, and relates to the field of focus segmentation. By introducing a mask-guided multi-scale feature fusion mechanism, the joint modeling capability of the model on global context information and local detail features is effectively enhanced. The encoder extracts multi-scale features by using the Swin Transform, down-samples original guide masks through the mask guide module and adapts the original guide masks to feature spaces of all levels, feature fusion of target perception is achieved, and the characterization capacity of a complex focus structure is remarkably improved. In the decoder, a pixel decoder uniformly upsamples fusion features into a high-resolution feature map, and a Transform decoder is combined with a set prediction normal form to realize accurate positioning and segmentation of a focus instance through layer-by-layer interaction of a learnable query vector and the high-resolution features. According to the structure, redundancy prediction and post-processing dependence caused by pixel-by-pixel classification in a traditional method is avoided, and the recognition sensitivity and segmentation precision of small focuses are improved.

Detection and differentiation of gynecologic lesions in colposcopy The present invention relates to a computer-implemented method capable of automatically detecting gynecologic lesions in image / videos data, by classifying pixels as lesion or non-lesion, using a convolutional image feature extraction step followed by a classification step and indexing such lesions in the set of one or more classes.

The application discloses a magnetic flux leakage field scanning steel bar corrosion image recognition system and method, and belongs to the technical field of nondestructive testing and computer vision technology. The system rapidly acquires a two-dimensional distribution image of a magnetic flux leakage field through a magnetic sensor array, adopts a semantic segmentation network based on an encoder-decoder architecture to perform pixel-by-pixel classification on the image, and divides healthy, mild, moderate and severe corrosion areas. Spatial attention and channel attention mechanisms are introduced to identify severe corrosion features such as magnetic flux leakage field gradient mutation and polarity reversal, and to enhance detection accuracy. A domain self-adaptive strategy of adversarial training is adopted to reduce feature distribution differences under different detection scenarios and to improve generalization ability. A three-dimensional reconstruction is performed on the corrosion spatial distribution model by combining reinforcement information through coordinate registration and spatial interpolation. A repair priority list and a bill of quantities are automatically generated. The application has high detection efficiency, accurate recognition accuracy, strong adaptability and visual intuition, and provides an intelligent technical means for concrete structure health monitoring.

The present invention relates to a computer-implemented method, capable of automatically detecting clinically relevant colonic pleomorphic lesions and blood or hematic traces in colon capsule endoscopy images, by classifying pixels as colonic pleomorphic lesions or blood or hematic traces, using a convolutional image feature extraction step followed by a classification and indexing step of such findings into a set of one or more classes.

The invention relates to the technical field of liquid crystal screen detection with a defective pixel classification function, and discloses a liquid crystal screen detection method and device with the defective pixel classification function, and the device comprises a classification base, a conveying belt arranged at the top of the classification base, a second conveying belt arranged at the top of the classification base, and a third conveying belt arranged at the top of the classification base. The surface of the classification base is fixedly connected with a support, a cleaning and detecting device is arranged above the classification base, a classification device is arranged above the classification base, and a collecting device is arranged on the surface of the classification base. When the conveying belt is started, the liquid crystal display above the conveying belt can be driven to move, when the liquid crystal display passes through the position below the cleaning soft brush, the cleaning soft brush can clean dust above the liquid crystal display, and it is avoided that the accuracy of machine detection is affected by the dust; and dust cleaned by the cleaning soft brush can enter a dust collection box through a dust collection cover through a dust collection pipe, and collection work is carried out.

The invention discloses an auxiliary positioning method and system for anesthesia puncture, and the method comprises the steps: determining the number of adaptive blocks according to the variable coefficients of a target image gray histogram and an LBP histogram of a collected ultrasonic image, and achieving the intelligent response to the image content. And the problem of information loss or excessive processing possibly caused by traditional fixed partitioning is avoided. And meanwhile, a gradient operator is utilized to calculate a pixel point gradient magnitude, and a target pixel point is verified in combination with eight-neighborhood analysis, so that the pixel classification accuracy is improved, and texture regions and edge details in the image can be effectively distinguished. And finally, the enhancement degree is determined based on the confidence and gradient magnitude of the pixel points, and enhancement factors are obtained by combining local display comparative analysis, so that the problem of excessive enhancement or insufficient enhancement possibly brought by global unified enhancement is avoided, and the efficiency and effect of image processing are improved. And a diagnosis basis with higher quality is provided for doctors.

The application discloses a satellite remote sensing fire point real-time monitoring method based on space-time feature fusion, acquires historical remote sensing image data of geosynchronous satellite corresponding to a required prediction moment, constructs space information, band information and time information contained by each pixel point in each historical remote sensing image, and expands the number of fire points according to a copy method, combines a "rumination" method, completes the establishment of a data set, then takes the reconstructed pixel points of each historical remote sensing image in the data set as input, takes the actual fire point condition of each historical remote sensing image as output, trains a monitoring network model, finally constructs space information, band information and time information contained by each pixel point in a remote sensing image of a prediction moment of a required prediction date, inputs the reconstructed pixel points into the trained monitoring network model for feature extraction, and outputs final pixel classification results, generates a fire point map after visualization, and thus completes fire point monitoring.

Disclosed is a loop filter method based on an adaptive pixel classification criterion. The loop filter method based on an adaptive pixel classification criterion in an image decoding device includes a step of classifying a restored sample according to an absolute classification criterion or a relative classification criterion, a step of obtaining offset information according to a result of classifying the restored sample, a step of adding the offset value to the restored sample with reference to the obtained offset information, and a step of outputting the restored sample to which the offset value is added. Thus, errors of the restored sample can be corrected.

Embodiments of this disclosure relate to text thinning networks. Systems and methods for text segmentation are described. Embodiments of the inventive concept are configured to: receive an image comprising a foreground text portion and a background portion; classify each pixel of the image as foreground text or background using a neural network, the neural network using a key vector representing features of the foreground text portion to thin the segmentation prediction, wherein the key vector is based on the segmentation prediction; and identify the foreground text portion based on the classification.

The invention discloses a real-time polyp segmentation system based on a multi-domain hierarchical attention network, and relates to the field of medical image processing and computer vision, and the system comprises a dynamic region guide block which is used for partitioning an input image and selecting key region features through a two-stage routing attention mechanism; the potential entropy quantization channel space attention module is used for carrying out channel and space information entropy quantization calculation on the feature map and highlighting fine organization differences; the spatial frequency fusion module is used for performing pixel-level fusion on the spatial domain features and frequency domain features extracted through fast Fourier convolution, and capturing a periodic mode and global structure information; and the patch extension layer and the linear projection are used for layer-by-layer up-sampling and executing quadruple up-sampling in the final stage to recover to the input resolution, and pixel-level segmentation prediction is generated. The boundary precision and the pixel classification performance are both improved, and the real-time performance of polyp segmentation and the clinical application reliability are remarkably improved.

Disclosed herein are system, method, and computer program product embodiments for detecting lane segments in an image for creating high-definition (HD) maps. A neural network can be used to classify a pixel of an image of a location as belonging to a driving lane. If the pixel belongs to a first boundary line of the driving lane, it is labeled accordingly. Based on the labeled pixel and one or more additional labeled pixels as part of the first boundary line of the driving lane, a first line drawing of the first boundary line of the driving lane is constructed. A lane segment based on a combination of the first line drawing of the first boundary line of the driving lane and a second line drawing of a second boundary line of the driving lane can be created.

The application discloses a multi-modal image data automatic recognition system and method based on machine learning, relates to the technical field of image data processing, and comprises the following steps: image data is called, pixel point feature information is extracted, and a pixel point feature sequence is constructed; feature matching degree analysis is performed on the pixel points, and hierarchical division is performed; spatial feature joint index analysis is performed on the pixel points in the same layer, and hierarchical pixel correlation index is obtained by integrating the analysis data; interlayer pixel classification analysis is performed on the pixel points between adjacent layers, pixel classification correlation analysis is performed on the adjacent layers, and pixel classification correlation index of the adjacent layers is determined; correlation evaluation of pixel layer correlation logic is performed on non-repeated image data, and the best image matching combination is output; the application realizes accurate recognition of multi-modal image data, reduces the misrecognition rate and interference rate caused by coarse recognition and coarse matching, and improves the accuracy of image recognition.

This disclosure provides methods, devices, and systems for controlling robotically assisted medical systems. The present implementations more specifically relate to techniques for vision-based detection of an access sheath. In some aspects, a controller for a medical system obtains a series of images captured by a camera disposed on a distal tip of a medical instrument that is at least partially inserted through an access sheath. The controller infers a respective segmentation mask from each image in the series based on a machine learning model trained to classify each pixel of the image as depicting the access sheath or not depicting the access sheath, where the segmentation mask indicates how many pixels of the image are classified as depicting the access sheath. The controller further determines a position of the distal tip of the instrument relative to the access sheath based on the segmentation masks for the series of images.