26 results about "Image pattern recognition" patented technology

The invention belongs to the technical field of underwater sea condition data monitoring, and discloses an underwater online intelligent monitoring analysis system and method. According to the method, an acoustic Doppler current profiler (ADCP) carried by a seabed observation system is used for carrying out seabed flow measurement, the speed and direction of water flow are obtained, and a binocular camera carried by the seabed observation system is used for determining the size of underwater organisms; performing image mode recognition and classification on the underwater creatures by using an image recognition algorithm in the underwater observation system, and recognizing the types and the number of the underwater creatures; transmitting data to a communication module of the virtual anchor system unmanned platform by using the armored cable, performing error control on the data transmitted by the armored cable, and detecting and correcting error information of the data in the transmission process; and judging whether the returned information is successful or not through an information transmission control method in the underwater observation system. According to the invention, the method can achieve the image recognition of Branchiostoma under a complex sea condition, and improves the operation efficiency and safety.

The invention discloses a wound healing state evaluation system based on multi-modal image mode recognition, relates to the technical field of mode recognition, and aims to construct a multi-modal and three-dimensional wound monitoring network structure and improve the accuracy and coverage dimension of wound tissue distribution monitoring, so that efficient and accurate healing state evaluation decision is realized. The healing process and patient individual differences are considered, the monitoring precision and diagnosis response speed of wound healing state evaluation are improved, the healing efficiency and rehabilitation effect in the wound treatment process are remarkably improved, the skin gray threshold value and the deformation index threshold value can be dynamically adjusted according to the individual characteristics of different patients, and the healing effect is improved. The continuous influence of tissue state misjudgment on healing evaluation is effectively prevented, evaluation deviation and treatment delay caused by subjective judgment are avoided, differential evaluation can be performed according to different skin types and healing characteristics, and the intelligent level of wound healing state evaluation and the precision of clinical application are effectively improved.

The invention relates to the technical field of image pattern recognition, in particular to a plate defect detection system based on edge calculation, which comprises a convolution kernel directivity screening module, a convolution kernel separable reconstruction module, a model structured constraint fine tuning module and an edge end parallel reasoning module. According to the method, calculation cores sensitive to specific directional defects are automatically screened, the accuracy of recognizing key features such as scratches is enhanced, complex detection operation is decomposed into two simplified vector convolution and one residual error correction calculation, the design greatly compresses the operation complexity and parameter quantity, and the detection accuracy is improved. By means of a parallel computing architecture of an edge end, separated computing tasks are synchronously processed, results are combined, efficient reasoning is achieved on equipment with limited computing resources, real-time on-site detection of plate defects is achieved, delay caused by a data remote transmission center server is effectively avoided, and the detection accuracy is improved. And the response speed and deployment flexibility of the whole detection process are improved.

The present invention provides a graphic recognition method and obstacle avoidance system for intelligent agricultural robots, which relates to the technical field of image pattern recognition. By introducing polarization imaging, the present invention effectively suppresses water droplet highlights, avoids misjudging optical artifacts as diseases, and significantly improves recognition accuracy. For low-confidence targets, the original micro-area thermal excitation physical review mechanism can confirm their true physical properties online, solving the problem of recognition uncertainty; in addition, the dynamic risk volume constructed based on multi-dimensional information, combined with the adaptive speed control of path risk foresight, realizes differentiated intelligent obstacle avoidance for rigid and flexible obstacles. While ensuring safety, this method greatly improves the robot's travel efficiency and smoothness in complex environments.

A plate strip casting blank rolling blank defect online detection method comprises the steps that a rolling blank surface image detection device is installed in an outlet area of a reversible rolling mill or a roughing mill, rolling blank surface images of different thicknesses are collected online, and the positions and sizes of defects generated on the rolling blank surface of each pass are recognized and analyzed through an image mode; and judging whether the area where each defect source is generated is in the rolling area or the smelting area, and gradually restoring the distribution and size of the defects in the original strip casting blank according to the images of the connected rolling passes in sequence for the defects in the smelting area.

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 application belongs to the field of medical image pattern recognition, and provides a knee osteoarthritis injury prediction method based on CT images, which comprises the following steps: step one, acquiring a group of healthy controls and a group of CT images of subjects with different degrees of knee joint damage; step two, pre-processing the X-ray images, cropping out the knee joint region, and converting the gray-scale images to L, a and b channels by using the LAB color model; step three, performing image enhancement on the images in the L channel by using adaptive histogram equalization; step four, integrating the three channels of L, a and b after enhancement; step five, performing denoising on the enhanced images; step six, marking the images of the healthy controls as 0, and marking the images of the patients with knee osteoarthritis as 1, 2, 3 and 4 according to the severity; and step seven, performing feature extraction by using a discriminative autoencoder; the method can reduce artificial errors, is an effective supplement for clinicians to judge knee osteoarthritis, and is expected to realize accurate prediction of knee osteoarthritis in areas where professional medical personnel are lacking.

The invention provides an intelligent agricultural robot pattern recognition method and an obstacle avoidance system, relates to the technical field of image pattern recognition, and aims at effectively inhibiting water drop highlight by introducing polarization imaging, preventing optical artifacts from being misjudged as diseases and remarkably improving the recognition accuracy. For a low-confidence-coefficient target, an original micro-area thermal excitation physical review mechanism can confirm the real physical attribute of the low-confidence-coefficient target on line, and the problem of recognition uncertainty is solved; besides, based on a dynamic risk volume constructed based on multi-dimensional information, and in combination with adaptive speed control of path risk look-ahead, differential intelligent obstacle avoidance of rigid and flexible obstacles is realized. According to the method, safety is ensured, and meanwhile, the passing efficiency and smoothness of the robot in a complex environment are greatly 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 present disclosure provides a target detection method and device, which relates to the field of computer technology, in particular to the field of computer vision and image pattern recognition technology. The specific implementation scheme is: obtaining a target image and a type identification of the camera device through a camera device; in response to the type identification of the camera device being a ball camera device, inputting the target image into a first feature extraction network, so as to use the first feature extraction network to perform feature extraction and downsampling on the target image, and obtain a first feature map; in response to the type identification of the camera device being a gun camera device, inputting the target image into a second feature extraction network, so as to use the second feature extraction network to perform feature extraction and upsampling on the target image, and obtain a second feature map; performing target detection on the target image according to the first feature map or the second feature map, and outputting the detection result. For images obtained by different camera devices, they are input into different feature extraction networks for targeted feature extraction, which effectively improves the accuracy of target detection and recognition, improves the effect of target detection, and realizes the precise detection of target objects.

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 invention relates to the technical field of image mode recognition, and provides a drill rod counting method and device applied to underground coal mine low illumination. The method comprises the following steps: performing projection processing on an original image acquired by a camera device based on a preset space factor and a gray factor, and removing noise to obtain a first image; processing the first image through a channel spliced network structure, extracting a mutual relation between a first component and a second component in the first image on different levels, and extracting the first component corresponding to the reflection side and the second component corresponding to the illumination side from the first image; fusing the first component and the second component to generate an enhanced image corresponding to the original image; and performing feature extraction on the enhanced image, and determining the number of drill rods in the original image according to the extracted features. Through multi-scale feature extraction and detection, high-precision and low-error real-time counting of the drill rod under low illumination is realized, and a complete closed loop from environment adaptation to target detection is formed.

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 present invention discloses a refrigerator food management method, device and refrigerator, the method comprising: obtaining an action process image of food being stored or taken out of a refrigerator compartment through a main camera arranged on the refrigerator; dividing the action process image into a first effective area, a second effective area and an invalid area, and performing image pattern recognition and sequence comparison on each area to determine the food information and food movement trajectory of the action process image; and determining the operation type of the action process image according to the food movement trajectory. Compared with the prior art, the refrigerator food management method of the present invention only needs to perform image pattern recognition and sequence comparison on the food, and can dynamically identify the storage and access of food in the refrigerator compartment, omitting the step of performing image pattern recognition on the hands, reducing the complexity of the recognition model, alleviating the recognition workload, and improving the recognition efficiency.

The present invention discloses a laser therapy device, comprising: a controller, a laser module, an optical fiber, a spectrometer, and a sensor module; the laser module and the sensor module are both connected to the controller; the optical fiber is used to transmit the laser light emitted by the laser module to the treatment tip at the end; the spectrometer is arranged on the optical fiber, and the output end of the spectrometer is connected to the treatment tip and the sensor module respectively; the controller can adjust the operating parameters of the laser module according to the feedback signals from the laser module and the sensor module. This solution can accurately control the laser power and wavelength; split the light through the spectrometer and connect to the sensor module; automatically identify the tissue in the treatment area, and perform image pattern recognition and visualization; the laser is a multi-wavelength laser, achieving better laser treatment effects and different treatment purposes; the laser module can be mixed with other light sources to achieve better treatment effects and different treatment purposes.

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 invention relates to the technical field of image pattern recognition, in particular to a commodity identification system based on unbalanced data learning, which comprises an image feature extraction module, an unbalanced sample learning module, a fine-grained classification module, an intelligent matching and recommendation module and a model dynamic optimization module. According to the method, the detail degree of feature extraction is enhanced and a high-dimensional feature expression form is constructed by analyzing the mark edge distribution, the contour pixel value, the texture direction and the gray level distribution and the color proportion and the gray level value of the lettering area. Weight adjustment is adopted to optimize low-proportion category sample performance, data distribution differences are balanced, and the accuracy of the classification model is improved. And through refined texture distribution and gray characteristic comparison and in combination with key characteristic comprehensive analysis, the accuracy of category division is remarkably improved. The sample feature weight and category mapping relation is dynamically adjusted, the model performance is continuously optimized, and the adaptive capacity and the real-time learning capacity to complex samples are ensured.

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 present invention discloses a method for estimating the attitude of a space non-cooperative target, comprising the following steps: S1: acquiring an image of a space non-cooperative target; S2: identifying target angular information from the target image based on error ellipse and Hough transform; S3: using Hu's invariant moment to describe the target attitude image, and then using a pattern classifier to identify the pitch angle; S4: based on the target angular information and target machine axis information obtained in step S2 and step S3, using a projective set method to reconstruct the three-dimensional attitude of the target. Aiming at the characteristics of rich close-range imaging information in the terminal guidance stage of infrared seekers, the present invention conducts research on guidance information mining technology, and uses image pattern recognition and computer vision methods to identify the new guidance information of aircraft target attitude, which is of great significance to improving the intelligent level and combat effectiveness of my country's infrared guidance technology and achieving leapfrog development.

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.

The present invention provides a spectrum sensing apparatus including a receiver which receives an analog signal in a frequency band of interest; an ADC(Analog Digital Caonverter) which samples the analog signal to output a digital signal; a correlator which performs the auto-correlation of the digital signal to output a correlation function; and a controller which generates an image pattern corresponding to the correlation function and identifies whether to occupy the frequency band of interest according to the image pattern.

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.