43 results about "Local binary patterns" patented technology

The invention discloses an ultra-high-definition video stream adaptive coding method based on deep learning visual saliency, and the method comprises the steps: carrying out the five-scale Gaussian filtering processing and image pyramid construction of a video frame, and combining Sobel gradient, Laplacian edge and local binary pattern feature extraction to generate a multi-scale feature map; a pre-training saliency detection network is adopted, and a smooth saliency thermodynamic diagram is generated through processing of a feature adaptation layer, a residual encoder, a self-attention mechanism and a transposed convolution decoder; dividing the video frame into a high region, a middle region and a low region according to the saliency thermodynamic diagram, and establishing a regionalization coding parameter table; performing differentiated prediction modes, motion estimation and quantization strategies on different salient regions; and organizing coded data according to an H.265 / HEVC standard, and embedding the saliency thermodynamic diagram into supplementary enhancement information for transmission. According to the method, the important region concerned by the user can be intelligently identified, a differentiated coding strategy based on content semantics is realized, and the coding efficiency is remarkably improved.

The invention relates to the technical field of machine vision detection, in particular to an automobile part surface defect online detection method and system. The method comprises the following steps: acquiring a grayscale image, calculating the size of a structural element for each pixel based on a local Gaussian Laplacian operator response variance, filtering to obtain a substrate image according to the size of the structural element, and differentiating to obtain a texture image. Determining a Gabor scale and a gray-level co-occurrence matrix statistical direction by using the size, and extracting a cooperative direction gray-level co-occurrence matrix feature; and a weight is set based on the size and is subjected to weighted fusion with a multi-scale rotation invariant local binary pattern feature to generate a texture saliency map, and texture defects are judged. On the substrate image, taking the gray value as the height, and determining a neighborhood calculation curvature feature based on the size to detect the substrate defect. According to the scheme, the image scale can be adaptively analyzed, the background texture is effectively inhibited, and therefore different types of tiny defects such as scratches and pits can be reliably detected.

The invention discloses an AI linkage data system based on Internet of Things multi-source data fusion, and relates to the technical field of image recognition, and the system comprises a collection module which is used for collecting a real-time image of a fire, and selecting a suspected region with the brightness higher than a brightness threshold value in the real-time image; the preprocessing module is used for performing Gaussian filtering preprocessing on the suspected area; the method comprises the following steps: denoising a suspected region by dynamically adjusting Gaussian filtering parameters, calculating a brightness change rate of pixels on the inner and outer sides of an edge to obtain a gradient band width, dividing suspected region sub-blocks, extracting sub-block texture feature vectors by adopting a local binary pattern algorithm, and calculating a region overall texture entropy value by combining a region texture similarity matrix to obtain a region texture similarity matrix; the difference between the strong reflection light spots and real flames can be accurately recognized, the AI chip is prevented from misjudging the strong reflection light spots as newly added fire behaviors, then the linkage control module is prevented from mistakenly starting a spraying system in a non-fire behavior area, and damage of a large number of materials due to water stains caused by mistaken spraying is reduced.

The invention discloses a bridge concrete apparent damage identification method and system based on texture analysis, and belongs to the crossing field of bridge engineering detection and computer vision technology, and the method comprises the steps: employing an unmanned plane to collect a bridge member surface image, and generating an orthoimage; extracting contrast, correlation and entropy features based on the gray-level co-occurrence matrix; extracting histogram features based on a local binary pattern; detecting a corrosion dialysis area based on an HSV color space; fusing the multi-dimensional features, inputting the fused multi-dimensional features into a multi-label classifier, and outputting pixel-level segmentation masks of various damages such as cracks, spalling, exposed reinforcement corrosion and dialysis at the same time; according to the method, the complementary texture features are fused to realize simultaneous identification of multiple types of damages, a pixel-level segmentation result is output, and an evaluation report conforming to engineering specifications is automatically generated.

The invention provides a video coding method, which comprises the following steps of: inputting a coding tree unit to be coded, and extracting texture features of a coding block, the texture features comprising local binary pattern similarity of all pixel points in the coding block and direction dispersion of each pixel point; inputting the texture features into the trained multi-type division prediction architecture, and sequentially judging whether to execute quadtree division, horizontal or vertical division, horizontal binary tree or ternary tree division and vertical binary tree or ternary tree division or not through a binary classification problem method; and calculating the rate distortion cost under each division mode to obtain an optimal division mode and a suboptimal division mode of the coding block, and outputting an optimal mode or a combination of the optimal mode and the suboptimal mode. According to the invention, the accuracy of the prediction mode in coding is improved.

The present application provides an image processing method, device and equipment, which can be applied to the technical field of image processing. The image processing method comprises: pre-processing an input image to obtain input features; inputting the input features into a visual encoder and a multi-layer perception machine in a visual center decoupler respectively to obtain enhanced special features and salient object detection special features; the visual encoder aggregates local region features based on the input features to obtain the enhanced special features, and the multi-layer perception machine captures edge information based on the input features to obtain the salient object detection special features; inputting the enhanced special features into an enhancement network to obtain enhanced output features; the enhancement network takes illumination weights of different color channels and local binary pattern features of the input image as illumination constraints, and enhances the enhanced special features to obtain the enhanced output features; and inputting the salient object detection special features and the enhanced output features into a salient object detection network to detect a salient object.

The invention relates to a material processing method and system based on sensitive content detection and a medium, and belongs to the technical field of multimedia content security. The material processing method comprises the following steps: receiving an input original material, and analyzing the input original material into an image stream, a video stream and a text stream; executing HSV color space conversion, and extracting texture features; performing semantic analysis on the text stream to generate a word segmentation sequence and a named entity recognition result; random frequency noise is injected based on the saturation channel, and local binary pattern features are extracted from the saturation channel after noise injection; generating a semantic vector according to the word segmentation sequence and a corresponding named entity recognition result, and calculating a risk entropy value; and establishing a space-time mapping relationship between the image stream / video stream and the text stream, constructing a cross-modal incidence matrix, executing a grading decision according to an output result, executing a material processing operation, and feeding back a processing result to the cross-modal incidence matrix for weight updating. The sensitive content identification accuracy and timeliness can be improved.

This invention discloses a method and system for identifying apparent damage to bridge concrete based on texture analysis, belonging to the interdisciplinary field of bridge engineering inspection and computer vision technology. The method includes: acquiring surface images of bridge components using a drone and generating orthophotos; extracting contrast, correlation, and entropy features based on the gray-level co-occurrence matrix; extracting histogram features based on local binary patterns; detecting corrosion and seepage areas based on the HSV color space; fusing multi-dimensional features and inputting them into a multi-label classifier, simultaneously outputting pixel-level segmentation masks for multiple types of damage such as cracks, spalling, exposed rebar corrosion, and seepage; generating a damage level score and maintenance recommendation report according to bridge technical condition assessment standards. This invention integrates complementary texture features to achieve simultaneous identification of multiple types of damage, outputs pixel-level segmentation results, and automatically generates an assessment report that conforms to engineering specifications.

This invention belongs to the field of surface defect detection technology for spring products, and relates to a machine vision-based method for detecting surface defects in springs. The method includes the following steps: acquiring a sequence of surface images of the spring under multiple preset rotation angles; for the images to be registered in the image sequence, extracting the spiral contour of the spring through edge detection and curve fitting, calculating the curvature along the spiral contour, and identifying local extreme points where the absolute value of the curvature exceeds a preset curvature threshold as key points; for each key point, calculating local binary pattern features and gradient direction histogram features in the neighborhood of the key point, and weighting and fusing the two features according to the local curvature value at the key point to generate a hybrid feature representation, which can improve the reliability of spring surface defect detection and reduce the false negative and false positive rates.

The invention provides a tooth image caries recognition method based on frequency domain enhancement, and relates to the technical field of image processing, the method adopts a dynamically adjusted annular band-pass filter to adaptively enhance an intermediate frequency component of a tooth image caries region in a frequency domain, so that the feature discrimination can be effectively improved, and the recognition accuracy is improved. Based on improved phase consistency edge optimization and gamma correction, the quality of a tooth image can be remarkably improved, multi-modal features are extracted through an improved local binary pattern and a gray-level co-occurrence matrix, and a dual-channel deep convolutional network is constructed to realize fusion of global and local features, so that the image quality is improved. The caries probability heat map of the tooth image can be accurately generated, and accurate positioning of the caries area can be realized by adopting adaptive threshold segmentation and morphological processing.

The application provides a mountainous area night animal tracking method and system based on multispectral and thermal imaging data fusion, and relates to the technical field of night animal tracking, and comprises the following steps: synchronously acquiring a multispectral image sequence and a thermal imaging image sequence in a mountainous area at night; performing multiscale Retinex enhancement processing on a registered near-infrared image and a thermal image respectively to obtain a strengthened candidate target image; extracting a multi-source texture signature of a fusion local binary pattern feature vector and a thermal gradient direction histogram for each candidate target, and combining geometric features obtained through morphological skeletonization to construct a multi-dimensional feature set; realizing target correlation between adjacent frames by using a nearest neighbor greedy matching algorithm to form a preliminary trajectory segment; and finally eliminating abnormal trajectories, solving trajectory intersection and occlusion conflicts through a sparse dynamic time warping algorithm, and generating an accurate and complete animal moving trajectory. The application can solve the conflicts caused by trajectory intersection and occlusion, and output continuous and accurate single animal moving trajectories.

This invention discloses a coal and rock image recognition and classification method based on the fusion of improved CLBP and receptive field theory, belonging to the field of image processing technology. First, the coal and rock image is converted to grayscale, and the pixel values ​​of each point in the grayscale image are extracted to form a pixel value matrix. Then, median processing is used to denoise the pixel matrix to enhance the algorithm's noise resistance. Based on the new pixel matrix obtained after median processing, an improved Complete Local Binary Pattern (CLBP) image feature descriptor is introduced for coal and rock feature extraction. Finally, a network model is used for recognition and classification. Experimental results show that the recognition accuracy of coal and rock in the coal and rock database reaches 94%, and the recognition speed is also better than the original algorithm. This invention can effectively solve the problem of low recognition efficiency of the original algorithm and can efficiently solve practical problems in coal and rock recognition methods, possessing certain practical value.

This invention discloses an intelligent fire detection method based on artificial intelligence video analysis, belonging to the field of artificial intelligence video analysis technology. This method targets multiple video streams deployed on an edge computing gateway. It utilizes an asynchronous decoupled buffer queue to bind video frames and timestamps into frame units to be processed. An incremental sequence identifier is assigned to each frame unit to be processed via a hardware resource scheduler, and the frame units are then distributed to a first processing pipeline and a second processing pipeline based on the sequence identifier. In the first processing pipeline, background removal and dynamic texture extraction are performed to generate a thermal activation map. In the second processing pipeline, frequency domain transformation and multi-scale local binary mode decomposition are performed to generate a flame frequency domain texture feature map. The thermal activation map and the flame frequency domain texture feature map are then pixel-level weighted fusion at the same time reference to generate a fusion confidence tensor. Finally, non-maximum suppression and spatial connectivity analysis are performed to output the bounding box of the flame target and the initial fire intensity level.

The application discloses a kind of offshore area high-resolution three-dimensional reconstruction method and system, including using perspective size invariant feature to match the multi-view angle image of offshore area, according to the feature similarity guide sampling, obtain sparse feature point cloud;Based on structured local binary pattern, the depth of each pixel is obtained using graph cut method, and the sparse feature point cloud is encrypted to generate dense point cloud;Global error optimization is carried out to the point cloud of all view angles, and according to the depth information of the matching feature points, the three-dimensional coordinates of the feature points in space are generated;Strong and weak connection relationship between plane primitives is constructed, and the surface model is obtained by graph structure and energy function.The application can obtain wide area large range panoramic high-resolution information of sea area, construct three-dimensional model, and can intuitively display the space-time big data of offshore area.

The invention relates to the technical field of image analysis, and discloses a PCB (Printed Circuit Board) production defect detection method based on image analysis, which comprises the following steps: acquiring a surface image of a PCB, and carrying out image preprocessing on the surface image, including graying, noise filtering and image enhancement; multi-scale feature extraction is carried out on the preprocessed image to obtain feature maps under multiple scales, and the multi-scale feature extraction is based on Gaussian pyramid decomposition and local binary pattern texture feature fusion; and based on the feature map, performing registration comparison on the collected feature map and a feature map of a standard template. According to the method, through fusion of multi-scale image feature extraction and defect quantitative analysis, the recognition precision of PCB tiny defects and dense line anomalies is remarkably improved, the limitation that a traditional detection means is insufficient in subtle feature perception capacity is effectively overcome, and accurate mapping from the image level to defect features is achieved.

The application discloses a sofa posture adaptive adjustment system based on multi-modal sensor fusion and belongs to the technical field of smart home. The system acquires the pressure space-time sequence of a distributed pressure sensor array and the temperature space-time sequence of an infrared temperature sensor array on a sofa cushion through a data acquisition module, a feature extraction module performs local binary pattern transformation on the two sequences respectively to obtain a pressure texture feature map and a temperature texture feature map, a posture recognition module inputs the two types of texture feature maps into a three-dimensional convolutional neural network after stacking along a time axis, outputs a three-dimensional posture heat map of a user's body, a template matching module performs dynamic time warping matching on the current three-dimensional posture heat map and a plurality of preset comfortable posture templates, determines a target posture template with the minimum matching deviation, and drives a regulation module to drive the sofa backrest motor and the cushion air bag to be linked and regulated based on the actuator control amount corresponding to the template.

The invention relates to the technical field of image analysis, and discloses a bone and meat distribution visual identification method for beef segmentation, and the method comprises the steps: carrying out the spectral image data capture of a to-be-segmented beef part, and obtaining source image data; performing local binary pattern coding on the source image data to obtain a surface texture feature response map; performing multi-scale wavelet decomposition on the source image data, and performing gradient magnitude analysis on the image to obtain a gradient energy tensor; performing adaptive threshold segmentation on the meat feature response region and the skeleton feature response region to obtain an initial distribution marking graph; carrying out perspective projection transformation on the collection view angle to obtain a skeleton space occupation prior mask; performing spatial position consistency verification on the skeleton feature response region to obtain a spatial distribution feature map; performing feature vector cascade splicing on the surface texture feature response map and the spatial distribution feature map to obtain a part distribution marking map; according to the invention, the efficiency of beef bone segmentation and meat distribution visual identification can be improved.

The application discloses a kind of needle textile surface defect visual detection methods, it is related to computer vision technical field, the method includes: real-time acquisition surface image and utilize adaptive band-stop filter to shield periodic texture to obtain residual image;Adopt multi-scale gabor wavelet to enhance tiny defect, construct multidimensional feature vector by fusing local binary pattern, grey level co-occurrence matrix and information entropy feature;Vector is input deep residual convolutional neural network to realize the classification identification and positioning of defect.The application significantly improves the detection sensitivity and identification accuracy of microscopic defects, has extremely strong complex environment adaptability and real-time processing capability, can match the demand of high-speed production line, realizes intelligent quality monitoring and closed-loop management in the production process of needle textile.

The present application relates to the technical field of machine vision, in particular to a multi-angle license plate image automatic recognition processing system and method, electronic equipment and storage medium, the method comprising: acquiring multi-angle license plate images, using an adaptive threshold segmentation algorithm to perform binary processing on the images and extract license plate contours; using perspective transformation to correct license plate contour images at different angles; extracting local binary pattern features and HSV color histogram features of the corrected license plate contour images, using a support vector machine (SVM) classifier to cut the license plate character regions, correcting the cutting results according to the license plate character arrangement rules to obtain accurate character cutting images, building a multi-layer convolutional neural network, inputting the cut character images into the network for recognition and classification, and mapping and outputting license plate number information. The present application comprehensively utilizes image processing, machine learning and deep learning technologies, and comprehensively improves the accuracy and robustness of license plate recognition, and has a broad application prospect.

The invention relates to the technical field of crossing of computer vision and image processors, and discloses a CZ66 tobacco leaf curing degree detection method based on image analysis. The method comprises the steps of obtaining a tobacco visible light image and extracting a main body area; converting to a CIEL * a * b * chromaticity space, and calculating an L * mean value, a * standard deviation and b * skewness; quantizing a texture energy attenuation index through a local binary pattern; calculating a theoretical moisture removal rate by combining temperature and humidity data; the features are input into a double-branch stage discriminator, a first branch is a neural network, a second branch is embedded into a rule reasoning module based on drying dynamics, the first branch and the second branch cooperatively output a baking stage and verify consistency, and an abnormity rechecking mechanism is triggered when the two branches are not consistent. According to the method, the detection robustness, the interpretability and the engineering reliability are remarkably improved by fusing a physical mechanism and data driving.

The invention provides a digital ray image splicing and identification method and system based on high contrast characteristics. The method comprises the following steps: carrying out Gaussian filtering denoising and adaptive histogram equalization preprocessing on an aerospace digital ray image; extracting multi-scale high-contrast features by using a local binary pattern algorithm and carrying out series fusion; matching feature points through a scale invariant feature transformation algorithm, and estimating an affine transformation model in combination with a random sampling consistency algorithm to realize panoramic stitching; and constructing a convolutional neural network, and training the model by using a cross entropy loss function to complete panoramic image intelligent identification. The system correspondingly comprises a preprocessing module, a feature extraction module, a panoramic stitching module, an intelligent identification module and a data storage module. According to the method, the problems of low splicing precision and low recognition accuracy when the spaceflight digital ray image is processed by a traditional method are effectively solved, the image recognizability, the splicing precision and the target recognition accuracy are improved, and the requirements of detection of the internal quality of spaceflight parts and defect recognition are met.

The invention provides a lung image recognition method and system for clinical diagnosis of the respiratory medicine department. The method comprises the steps that a binary mask containing complete lung parenchyma is obtained from a lung image; segmenting a target lung field image from the lung image based on the binary mask, and extracting a texture saliency map from the target lung field image; performing pixel-level fusion on the texture saliency map and the target lung field image to obtain a lung feature image, and performing multi-resolution pyramid decomposition on the lung feature image to obtain a Gaussian pyramid layer and a Laplacian pyramid layer; direction gradient histogram features and local binary pattern features are extracted from the Gaussian pyramid layer and the Laplacian pyramid layer respectively, and then a multi-resolution joint feature vector is constructed; and inputting the multi-resolution joint feature vector into a pre-trained image analysis network, and positioning a focus area in the lung image. According to the technical scheme provided by the invention, the lesion area in the lung image can be identified under the coupling interference of the anatomical structure and the pathological features.

The application discloses a multi-channel synchronous sampling cable insulation fault diagnosis method and system, and relates to the technical field of insulation diagnosis.The application synchronously collects electric field intensity, temperature data and panoramic images along the whole length of the cable by unifying the sampling frequency, constructs an electric field intensity-temperature correlation graph and a full-section panoramic graph, screens electric field and temperature abnormal areas based on double-threshold analysis, determines the abnormal position section by interval merging and generates a secondary inspection list, adopts a pixel row division strategy to expand the three-dimensional panoramic graph into a two-dimensional image for each abnormal section, extracts contrast and entropy values by using a gray level co-occurrence matrix, analyzes texture uniformity by combining local binary pattern, constructs a multi-feature fusion degradation index calculation model, and finally realizes insulation state grading diagnosis through overall degradation degree evaluation, thereby realizing accurate positioning and quantitative evaluation of cable insulation defects and effectively improving the accuracy and reliability of fault diagnosis.

The invention relates to the technical field of mechanical part defect detection, in particular to a mechanical part defect detection method and system based on machine vision, and the system comprises an image acquisition module, a texture feature extraction module, an internal structure analysis module and a defect classification module. A part surface image is obtained through a high-resolution industrial camera, textural features are extracted by using an improved local binary pattern algorithm, a comprehensive feature model is constructed in combination with data of an ultrasonic flaw detector, and defect classification is realized by using a deep learning algorithm. The method can improve the recognition precision of micro cracks or recesses, shortens the detection period, enhances the classification accuracy of complex defect types, reduces the rework risk and resource waste, and expands the application range and reliability of the detection method.

The invention discloses a multi-source heterogeneous point cloud registration method based on image key points and a related device, and relates to the technical field of three-dimensional data processing, and the method comprises the steps: extracting a ground point cloud from target and source point clouds, determining a unit normal vector and a height mean value, correcting the source point clouds through a rotation matrix and a vertical translation vector, and carrying out the registration of the target and source point clouds. The method comprises the following steps of: projecting a target image and a source image, extracting feature key points, screening through descriptor similarity and main direction consistency to obtain initial matching point pairs, calculating a pixel motion vector field through dense optical flow improved by a local binary pattern feature map, selecting same-name matching point pairs according to a displacement threshold value, and combining and de-weighting to obtain a potential image matching point set. According to the method, geometric constraints are established, optimal affine transformation parameters are obtained, point cloud corresponding coordinates are obtained through back projection, horizontal two-dimensional rigid transformation is solved, a coarse registration optimal spatial transformation matrix is calculated in combination with a rotation matrix and a vertical translation vector, and finally multi-source heterogeneous point cloud fine registration is completed through an iterative nearest point algorithm.

This invention belongs to the field of fishing net repair robot technology, and provides a method and system for detecting damaged areas in fishing net repair robot operations. By judging whether the fishing net is shaking, and adjusting the angle between adjacent robot bodies when shaking occurs, the problem of robot instability caused by shaking flexible fishing nets is solved. At the same time, the net is stereo matched based on binocular views; by integrating visual saliency and local binary pattern texture difference analysis, suspected areas with abnormal features are initially identified; the damaged area is determined based on the suspected area and laser signal; by first identifying the suspected area and then accurately determining the damaged area, the problem of insufficient damage positioning accuracy caused by the large detection blind zone of monocular vision detection is solved. Furthermore, the damage type and aging degree are determined by analyzing the force of scanning, puncturing and hooking of the damaged area by preset probes and in-situ component analysis.

The invention discloses a machine vision-based lining paper pattern integrity detection method, which comprises the following steps of: performing high-angle annular bright field illumination and low-angle dark field illumination on lining paper to be detected, and respectively acquiring lining paper images in different illumination modes; respectively carrying out denoising and enhancement processing on each acquired lining paper image, and extracting a texture primitive of a local binary pattern to obtain comprehensive local texture features of the lining paper to be detected; inputting the defect regions into a lining paper defect detection model, and outputting a pixel-level positioning result of each defect region and a corresponding defect type; and based on a pixel-level positioning result and a defect type, comprehensively analyzing a defect position and a defect distribution characteristic, and generating an integrity detection report. According to the method, the precision, robustness and intelligent level of pattern integrity detection of the lining paper are improved, and fine defects, such as incomplete gold stamping transfer, deficiency of yin and yang lines and pinholes of a metal coating, which are difficult to detect by a traditional method can be effectively identified.

The invention discloses a carburized gear internal oxidation rating method and system based on deep learning, and belongs to the technical field of carburized gear quality detection.The carburized gear internal oxidation rating method comprises the steps that a scanning electron microscope image of a carburized gear is obtained and preprocessed, and after noise is reduced through anisotropic diffusion filtering, an internal oxidation structure area is segmented through morphological operation; textural features are extracted based on gray-level co-occurrence matrix and local binary pattern fusion, and feature vectors are screened and optimized through Pearson's correlation coefficients; inputting the feature vectors into a support vector machine classification model subjected to particle swarm optimization to obtain an internal oxidation degree preliminary classification result; and in combination with an internal oxidation comprehensive evaluation system, a final quantitative rating result is output through probability threshold judgment, deviation matrix linear analysis correction and service condition and material characteristic matching. According to the carburizing gear internal oxidation grading method, automation, standardization and precision of carburizing gear internal oxidation grading are achieved, and scientific support is provided for gear quality detection and service safety guarantee.

This invention relates to the field of image processing technology and discloses a rapid detection method for surface defects in fire-resistant recycled graphite composite homogeneous plates. The method first acquires an image of the plate surface and performs illumination equalization processing to obtain a preprocessed image. Then, it uses a local binary pattern and histogram of oriented gradients (HOR) fusion algorithm to extract surface texture and structural features, generating a fused feature map. This fused feature map is then input into a trained lightweight convolutional neural network, which employs depthwise separable convolution and channel attention mechanisms to output a binary mask containing suspected defect regions. Finally, morphological closing operations are used to connect adjacent defect regions, and the defects are classified and located based on morphological features. This invention effectively improves detection efficiency and accuracy, meeting the needs of industrial production.