12 results about "Image pattern recognition" patented technology

The present application relates to the field of urinary surgery stone image pattern recognition, and provides a urinary surgery stone image pattern recognition method and system. By acquiring the urinary surgery stone image, the stone image is recognized to determine the suspected stone area and its boundary area; for each pixel point in the boundary area, based on its gray scale feature and neighborhood feature, the probability of the pixel point belonging to the stone is calculated to generate a boundary probability map; based on the boundary probability map, the size, volume and Hounsfield unit value of the stone are determined through a preset multi-scale feature fusion manner. Thus, the problem of missed diagnosis, misdiagnosis and Hounsfield unit value deviation of small stones under low-dose CT images in the prior art can be effectively solved, and the accuracy and robustness of urinary surgery stone image pattern recognition are significantly improved.

The invention relates to the technical field of image pattern recognition, in particular to a diopter parameter analysis model optimization method and system, and the method comprises the following steps: collecting image sequence recognition region features, constructing edge segments, judging the structural continuity, executing concurrent recognition and trajectory analysis, and outputting a diopter parameter analysis optimization graph. According to the method, through constructing curvature section and gray vector distribution in an image sequence, extracting continuous features of a boundary structure, screening stable graph frames in combination with inter-frame direction consistency, recording corresponding boundaries by using a dual-channel recognition rhythm alignment mode, establishing a structure difference value and generating an error trajectory graph; the stable nodes are identified based on the offset trend, and the key graph segment is backtracked, so that the restoration coherence and response consistency of the image structure are enhanced, and the expression ability of the graph result to the change characteristics of the crystalline lens and the accuracy of parameter extraction are improved.

The invention discloses a weed efficacy automatic evaluation method and system based on image pattern recognition, and relates to the technical field of agricultural remote sensing, and the method comprises the steps: obtaining unmanned aerial vehicle multispectral images before and after pesticide application, and screening candidate samples according to NDVI attenuation after preprocessing; extracting vein structure enhancement features of a multi-scale Hessian matrix and physiological dispersion field features based on local Weber contrast from the sample; and generating a necrosis mask by utilizing probability model fusion features, and calculating a comprehensive pharmacodynamic index in combination with a necrosis area proportion and damage severity so as to judge the grade. According to the method, the internal structure and tissue degradation of the vegetation are represented in a combined manner, so that the recognition capability of fine-grained phytotoxicity forms such as partial necrosis and residual veins is remarkably enhanced, and the suitability and the stability of pesticide effect judgment are improved.

The specification discloses a method and device for image recognition, a storage medium and an electronic device. First, a sequence of images to be recognized is received, and a human body part recognition model is used to determine the part labels of each slice image in the sequence of images to be recognized, and a first image is determined according to the part labels. Then, the recognition result of each first image is determined. Finally, the final result of recognizing the images to be recognized is determined according to the recognition result of each first image. By determining the part labels of each slice image and selecting part of the slice images as the first image, the final recognition result of the images to be recognized is determined according to the recognition result of the recognized first image, so that when the images are recognized, part of the slice images are recognized using the pre-trained image pattern recognition model, the accuracy of image recognition is improved, and the efficiency of image recognition is improved.

The application discloses a kind of offline handwritten Chinese character scoring method, device and storage medium, wherein method includes: obtaining handwritten Chinese character image to be scored;Handwritten Chinese character image is input into trained Chinese character scoring model, the characteristic structure of handwritten Chinese character is extracted, the corresponding Chinese character in standard library is compared and analyzed with the characteristic structure extracted, and the comprehensive score of handwritten Chinese character is obtained;And / or, based on the distribution law of Chinese character pixel points, the two-dimensional distribution of Chinese character pixel points in handwritten Chinese character image is analyzed, a plurality of two-dimensional features are obtained, and the corresponding Chinese character in standard library is compared and analyzed according to the plurality of two-dimensional features obtained, and the score on different dimensions of handwritten Chinese character is obtained.The application processes handwritten character image by model, or processes handwritten character image according to Chinese character pixel points, can score handwritten Chinese character as a whole, and provides improvement reference for writer.The application can be widely applied in image pattern recognition technical field.

The invention relates to the technical field of computer external equipment camera module image mode recognition, in particular to a computer external equipment camera module image mode recognition method and system, and the method comprises the following steps: obtaining image data captured by a multi-camera module, the time information corresponds to the image data and reflects the actual exposure time; according to the spatial position information of the target object in the image data, combining with the time information to deduce the motion state of the target object; and based on the motion state of the target object, performing time correction on the image data from different camera modules, and fusing the image data to construct three-dimensional information of the target object so as to identify the defect of the target object. According to the invention, accurate alignment and fusion of the multi-view images are realized, so that the three-dimensional information of the target object can be accurately constructed and defects can be identified, and the accuracy of industrial quality inspection and the efficiency of security monitoring are improved.

The invention relates to the technical field of image mode recognition, in particular to a crankshaft fatigue life prediction method and system based on image analysis, and the method comprises the following steps: obtaining a crankshaft fatigue image sequence and sorting a frame sequence, recognizing a gray abrupt change boundary, extracting a crack front end track, extracting a boundary gray value and calculating a change rate. According to the method, time arrangement and gray boundary extraction are carried out on a crankshaft fatigue monitoring image sequence, crack boundary mapping is constructed in combination with frame numbers and space coordinates, the track expression precision is improved, crack propagation is tracked through path coordinate changes, and the fatigue residual life interval is deduced. The method comprises the following steps: extracting a gray level change rate, combining with time information, enhancing coupling expression of a crack state and image data, comparing a path speed with a gray level rate, identifying an extension synchronism difference, combining with a fracture threshold and rate distribution, deducing a residual life interval, and realizing a high-precision fatigue evaluation closed loop.

The present application relates to the technical field of image pattern recognition and deep learning, and particularly relates to a kind of suction powder image quality evaluation method and system based on deep learning, the method comprises: obtaining suction powder image, for each pixel point, extract multi-scale relative darkness and neighborhood gradient direction consistency to determine structure characteristic value;Extract orthogonal spectral residual and inverse weight to determine spectral heterogeneity, fuse the above features to generate physical saliency matrix;Construct double-flow convolutional neural network, map physical saliency matrix to spatial attention weight, use affine transformation to force modulation intermediate layer feature, generate physically enhanced high-dimensional semantic feature data, globally pool high-dimensional semantic feature data and classify, output quality evaluation result.The method avoids the defect feature dilution effect caused by global average pooling through the feature modulation architecture guided by physical prior, improves the accuracy of suction powder quality evaluation result.

The invention relates to the technical field of image pattern recognition and deep learning, in particular to an induced draft powder image quality evaluation method and system based on deep learning, and the method comprises the steps: obtaining an induced draft powder image, and extracting multi-scale relative darkness and neighborhood gradient direction consistency for each pixel point to determine a structure feature value; extracting an orthogonal spectrum residual error and an inverse proportion weight to determine spectrum heterogeneity, and fusing the characteristics to generate a physical significance matrix; constructing a double-flow convolutional neural network, mapping the physical saliency matrix into a spatial attention weight, forcibly modulating interlayer features by using affine transformation, generating physically enhanced high-dimensional semantic feature data, performing global pooling and classification on the high-dimensional semantic feature data, and outputting a quality evaluation result. According to the method, the defect feature dilution effect caused by global average pooling is avoided through a feature modulation framework guided by physical prior, and the accuracy of an air suction powder quality evaluation result is improved.

The application relates to the technical field of image pattern recognition, and provides a drill pipe counting method and device applied to low-illumination underground coal mines. Based on preset space factors and gray scale factors, original images obtained by a camera device are subjected to projection processing to remove noise to obtain first images; the first images are processed through a network structure of channel splicing, the mutual relationship between first components and second components in the first images is extracted on different levels, and the first components corresponding to the reflection side and the second components corresponding to the illumination side are extracted from the first images; the first components and the second components are fused to generate an enhanced image corresponding to the original image; features of the enhanced image are extracted, and the number of drill pipes in the original image is determined according to the extracted features. Through multi-scale feature extraction and detection, real-time counting of drill pipes with high precision and low error under low illumination is realized, and a complete closed loop from environment adaptation to target detection is formed.

This invention belongs to the field of underwater sea state data monitoring technology, and discloses an underwater online intelligent monitoring and analysis system and method. The method utilizes an Acoustic Doppler Current Profiler (ADCP) mounted on a seabed observation system to measure seabed currents, obtaining the velocity and direction of the water flow, and uses a binocular camera mounted on the same system to determine the size of underwater organisms. It employs an image recognition algorithm within the underwater observation system to perform image pattern recognition and classification of underwater organisms, identifying their species and quantity. Data is transmitted via an armored cable to the communication module of a virtual moored unmanned platform, and error control is implemented on the data transmitted via the armored cable to detect and correct errors that occur during transmission. The success of the returned information is determined through an information transmission control method within the underwater observation system. This invention enables image recognition of amphioxus in complex sea conditions, improving operational efficiency and safety.

This invention discloses an image target recognition and region discrimination method and apparatus, relating to the field of image pattern recognition and classification technology. The invention obtains a pre-set recognition and discrimination model including feature generation and dual classifier output modules. It preprocesses the source domain image (carrying pixel-level ground truth annotations of the target category) and the target domain image, and inputs them into the feature generation module to obtain spliced ​​predicted features and cross-domain alignment constraint loss. The spliced ​​predicted features are then input into the dual classifier output module to obtain cross-domain classification prediction results. Based on the ground truth annotations, alignment constraint loss, and prediction results, the pre-set model is trained in multiple stages to obtain a target region recognition and region discrimination model with cross-scene generalization capabilities. Using the image to be recognized as input, the invention outputs a target region recognition label map. This invention effectively compensates for insufficient fine-grained feature alignment, optimizes the output spatial difference measurement logic, and improves training stability and cross-domain adaptability.