24 results about "Sobel edge detection" patented technology

The invention discloses an intelligent identification system for an osteoporosis area, belongs to the field of image processing calculation, and aims to solve the problems of limited technical coverage and lack of a dynamic optimization mechanism. In a feature extraction stage, a system combines traditional image processing and deep learning technologies in parallel, extracts bone trabecula multidirectional texture features by using a Gabor filter and a local binary pattern algorithm, analyzes bone contour curvature by combining Sobel edge detection and morphological operation, constructs geometric morphological parameters, and performs feature extraction on the bone trabecula. The local branch focuses on the porosity and arrangement rule of the bone trabecula by adopting a 3D convolutional network, the global branch is embedded into a compression excitation module based on an improved MobileNetV3 network to strengthen the overall morphological expression of the bone, the response intensity of a local microstructure is enhanced by space attention, the weight of global bone topological characteristics is calibrated by channel attention, and a multi-scale splicing strategy is combined, so that the overall morphological expression of the bone trabecula is optimized. And finally, outputting a fusion feature matrix after noise suppression, and remarkably improving the expression ability of pathological features.

The invention discloses an AI-based child story video generation method and system, and relates to the technical field of artificial intelligence and multimedia crossing, and the method comprises the steps: generating a script from an original text input by a user through constructing an AI model fusing an emotion modeling capability; constructing an image generation combination model, defining a joint loss function, calculating an edge intensity graph of the contour image by using a Sobel edge detection algorithm, calculating an optical flow field of frame change by using a block matching algorithm, and performing color image dynamic frame alignment; a fine tuning WaveNet model is used to generate audio; through constructing an image generation combination model, combining a StyleGAN3-T model and an LDM model, defining a joint loss function, and using a Sobel edge detection algorithm and a block matching algorithm to calculate an edge intensity graph and an optical flow field, dynamic frame alignment of a color image is realized, and inter-frame continuity of a generated video is improved.

The invention discloses a wheat grain protein spatial distribution difference identification method. The method comprises the following steps: S1, constructing a semantic segmentation data set; an improved U-Net network is adopted as a model architecture, and preliminary background removal is completed; s2, binarization processing is carried out, and preliminary segmentation is completed; obtaining an endosperm edge contour by using a Sobel edge detection algorithm; determining the center position of an endosperm area; carrying out layer-by-layer corrosion to obtain a radial layered structure image; s3, acquiring a sampling matrix; a K-means + + algorithm is adopted to initialize the clustering center for iteration; and identifying the protein and calculating the space distribution proportion of the protein. According to the method, semantic segmentation of the U-Net model, dynamic layering of radial corrosion and component clustering of the K-means + + algorithm are improved, full automation from image preprocessing to protein quantitative analysis is achieved, tedious operation of manual description is reduced, image processing is more accurate, layering processing can be conducted on the image, and the protein distribution gradient can be accurately reflected.

The invention discloses a cabin boundary identification method and system based on a point cloud map, and relates to cabin unmanned driving, and the method comprises the steps: collecting point cloud data in a cabin, and carrying out the fusion processing of the point cloud data, and obtaining a fusion point cloud; preprocessing the fused point cloud to obtain a static point cloud; according to the static point cloud, boundary extraction is carried out through an RANSAC algorithm, and a first boundary is obtained; converting the static point cloud into a grid map, and performing boundary extraction on the grid map by adopting a Sobel algorithm to obtain a second boundary; performing cross validation on the first boundary and the second boundary, and judging boundary consistency by calculating the overlapping degree of the two boundaries; and according to a cross verification result, taking the boundary passing the consistency verification as a cabin boundary, and correcting the boundary not passing the consistency verification by adopting a threshold value adjusting method. In view of low boundary extraction precision in a complex cabin environment, boundary extraction and the like are performed in combination with RANSAC plane fitting and Sobel edge detection, so that the cabin boundary extraction precision is improved.

The present application discloses a cabin boundary recognition method and system based on a point cloud map, which relates to unmanned cabin operation, including: collecting point cloud data in the cabin, and fusing the point cloud data to obtain a fused point cloud; pre-processing the fused point cloud to obtain a static point cloud; extracting the boundary using the RANSAC algorithm based on the static point cloud to obtain a first boundary; converting the static point cloud into a raster map, and extracting the boundary of the raster map using the Sobel algorithm to obtain a second boundary; cross-validating the first boundary and the second boundary, and determining the boundary consistency by calculating the overlap between the two; based on the cross-validation results, using the boundary that passes the consistency verification as the cabin boundary, and correcting the boundary that fails the consistency verification by adjusting the threshold method. In view of the low boundary extraction accuracy in complex cabin environments, the present application combines RANSAC plane fitting with Sobel edge detection for boundary extraction, etc., to improve the cabin boundary extraction accuracy.

The invention discloses a laser center line extraction method based on improved non-maximum suppression, and the method comprises the steps: (1) preprocessing a laser image, and carrying out the graying of the image; (2) carrying out noise filtering on the image by using a Gamma algorithm, deleting too dark or excessive noise in the image, and reserving a proper pixel point set; and (3) gradient acquisition by a Sobel operator: determining the position and direction of the edge by calculating the gradient value of each pixel point in the image through a Sobel edge detection algorithm. The gradient size (Gx) and the gradient direction (Gy) in the laser image are calculated by using a Sobel operator. And (4) suppressing the pixel points according to the size and direction of the gradient: finding out a local maximum point on the same gradient by using a non-maximum suppression algorithm, and extracting a center line. And (5) the straight line obtained by the method of interpolation only in the Y direction is smoother and more continuous. According to the method, the problems of overlarge exposure and excessive noise are effectively solved, the center line of the laser image is accurately extracted, and the method is of great significance to extraction of the center line.

The invention relates to an image processing technology, provides an image recognition system for oral mucosa lesions, and aims to improve the precision and efficiency of oral image processing. The system comprises a data acquisition module, a data preprocessing module, an image segmentation module, a lesion area classification module and a report display and visualization module. Through an image pyramid, Sobel edge detection, Otsu threshold segmentation and a local contrast weighted optimization technology, an image segmentation module realizes high-precision lesion region segmentation; the lesion region classification module is combined with technologies such as feature combination, feature interaction, an HEMish activation function and variable expansion causal convolution to accurately classify oral lesion regions; through a multi-level and multi-dimensional technical means, the recognition precision, robustness and adaptability of the lesion area are remarkably improved, the problems of low precision, instable recognition and the like in a traditional system are solved, and the technical progress in the field of oral mucosa lesion image recognition is promoted.

The application discloses an AI-based children's story video generation method and system, relates to the field of artificial intelligence and multimedia cross technology, and comprises the following steps: constructing an AI model integrating emotion modeling capability to generate a script from original text input by a user; constructing an image generation combined model, defining a joint loss function, using a Sobel edge detection algorithm to calculate an edge intensity map of a contour image, using a block matching algorithm to calculate an optical flow field of frame changes, and performing dynamic frame alignment of a color image; using a fine-tuning WaveNet model to generate audio; constructing an image generation combined model, combining a StyleGAN3-T model and an LDM model, defining a joint loss function, using a Sobel edge detection algorithm and a block matching algorithm to calculate an edge intensity map and an optical flow field, realizing dynamic frame alignment of a color image, and improving interframe continuity of a generated video.

The application discloses a binocular stereo matching FPGA accelerator architecture, method and device, belongs to the binocular stereo vision field, and comprises a line buffer module, an initial cost calculation module, a Sobel edge detection module, a cost aggregation module, a right view cost matrix construction module, a uniqueness detection module, a left-right consistency detection module, a hole filling module and a sub-pixel interpolation module. The application solves the problems of low precision, poor efficiency and low resource utilization rate of binocular stereo matching, improves the precision, and guarantees the real-time performance and the efficiency of operation resources.

The invention discloses a skylight mounting plate welding quality detection method based on a convolutional neural network, and the method comprises the following steps: obtaining image data of a detected part through an industrial camera, and inputting the image data into a model based on the convolutional neural network; the convolutional neural network is combined with a Sobe l edge detection operator, local texture and edge features of the welding surface are extracted, and the similarity between the features is calculated based on cosine similarity and used for recognizing fine texture changes of the welding surface; and extracting the shape of the welding spot and the geometric structure characteristics of the rivet nut through a region proposal network for identifying the dimensional deviation. A template matching method based on small sample learning is adopted. The model firstly extracts key features in input data, maps the key features to a unified feature space, and then performs similarity comparison by means of a pre-constructed template library to realize rapid defect type identification.

The invention discloses a lightweight real-time video super-division method and a monitoring system, and relates to the technical field of artificial intelligence image processing and computer vision. According to the invention, a lightweight network only containing a 3 * 3 convolution and a Relu activation function is designed, and the number of channels of all feature maps is reduced as much as possible; secondly, a dense depth separable convolution module is designed, conventional convolution is replaced with depth separable convolution, and good performance is kept while the complexity of the model is reduced through a dense residual connection mode; then, an inter-frame hidden state transmission mode is designed, and the inter-frame information aggregation efficiency is improved while the reasoning speed is increased by updating the cycle states of front and back frames; and finally, designing a texture loss function based on a Sobel edge detection operator, and combining the texture loss function with a Charbonier loss function to guide the network to enhance the attention on the high-frequency texture signal.

This invention discloses a UAV-based shield tunnel inspection system and method, applied in the field of tunnel inspection technology. By manually specifying the three-dimensional coordinate axes of the shield tunnel profile and combining this with UAV automatic navigation and computational vision, the system automates UAV tunnel inspection. The invention utilizes an onboard microcomputer to reproduce the manually set coordinate system. Simultaneously, it uses the tunnel environment and the distance from the tunnel wall to calculate the coordinates of the UAV's position within the tunnel cross-section coordinate system. Furthermore, it uses the Sobel edge detection 3D method to identify the standard lining rings of the shield tunnel, thereby calculating the axial distance, establishing a 3D model of the tunnel, and determining the UAV's travel distance within the tunnel. Finally, it uses Gaussian filtering and computational vision algorithms such as the Sobel edge detection algorithm to analyze and identify tunnel damage in the image information. This invention has advantages such as low cost, high efficiency, and high automation, and can be widely applied to tunnel inspection.

The invention discloses a broken soybean particle instance segmentation method based on YOLOv8, a new Particle Seg-YOLOv8 model is constructed, and the model fuses a DBB module in a detection head so as to enhance effective information interaction between feature channels and accurately extract detail information of broken soybean particles. In an NECK structure, a feature fusion module is improved based on ASF-YOLO, and a Sobel edge detection operator is integrated, so that the sensitivity and the recognition capability of broken edges are enhanced, and the edge features of broken particles are captured more clearly. In addition, the RFA convolutional layer is used for replacing part of traditional convolutional layers, so that the responsiveness of the model to spatial features is enhanced, and the detection capacity of small targets and broken particles is improved. According to the improved model, on the basis of ensuring high calculation efficiency, the segmentation precision and robustness of the broken soybeans are remarkably improved, and a more accurate solution is provided for breakage rate detection and quality control in the agricultural field.

The invention relates to an intelligent order making method and system based on natural language processing, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring a document data set and a document template set, and sequentially performing gray conversion, Sobel edge detection, binarization processing, VGH histogram calculation, non-text region elimination and noise reduction on image data to obtain text region data after noise reduction; performing character correction on the noise reduction data, segmenting characters through a standard character segmentation technology, extracting stroke direction density characteristics after normalization processing, and finally generating a character extraction document through an OCR technology; using a PyMuPDF library to convert the PDF type data into text data, calculating classified data through a text classification model in combination with a text document extracted by OCR, and retrieving a document template set to obtain a filling template; and converting the converted text data and the OCR text extraction document into plain texts, extracting key entities through an entity extraction model, and filling the key entities into a template to generate a form. And automatic filling of the receipts is realized.

The present application relates to the technical field of visual detection, and particularly relates to an online visual detection method and system for quality of iron-on picture finished products based on industrial vision, which comprises the following steps: obtaining an initial image matrix, triggering Gaussian filter denoising through brightness variance analysis to eliminate noise interference caused by light changes; adopting a Sobel edge detection algorithm to extract pattern gradient change characteristics, generating a preliminary pattern mask based on color difference distribution analysis; combining local entropy value analysis to identify edge blur risk areas, expanding the mask boundary to contain the blurred edges, and realizing the optimization of the pattern mask; superimposing the mask to separate the main body area and verify the proportion to obtain the complete pattern area; and adopting a watershed algorithm to refine the boundary details to determine the quality result. Through the double verification mechanism of color difference distribution analysis and local entropy value analysis, the present application can adapt to complex backgrounds such as dark cloth, complex texture and color gradient, effectively improving the accuracy of pattern main body area extraction and the edge recognition precision.

The application discloses a kind of multi-modal pipeline detection data acquisition systems based on FPGA architecture, ultrasonic detection module is used to generate 8 40kHz pulse excitation signal produces ultrasonic wave, and AD9226 analog-digital converter is controlled to collect echo data, and after being internally cached, original sampling data is output through high-speed interface;Image detection module is used to collect the image data output by high-definition image sensor, executes gray scale conversion, filtering, sobel edge detection and morphological operation preprocessing, and outputs the processed image data through fiber interface, and displays on HDMI display after being cached;Two kinds of modal data are accurately time-aligned and uniformly transmitted in the system interior.This application integrates ultrasonic detection and image detection processing, transmission functions in one system, realizes the synchronous acquisition and processing of multi-dimensional information of pipeline inner wall, significantly improves the integration and data consistency of detection system, and is suitable for high-precision, low-delay acquisition and transmission of multi-modal sensing data in industrial pipeline detection equipment.

The invention discloses an Ethernet transmission method based on FPGA (Field Programmable Gate Array) real-time video image measurement splicing, and relates to a video processing and network security technical method. The method comprises the steps of real-time video image transmission through an Ethernet UDP protocol, real-time video encryption and decryption through a Lorenz chaos algorithm, Sobel edge detection combined with dynamic threshold optimization verification and real-time display, read-write interaction with a DDR3 memory through an AXI4 protocol, and real-time video encryption and decryption through a Lorenz chaos algorithm. The video image after encryption, decryption and edge detection verification is converted into a row signal, a field signal and image data of the video image through the FPGA, the row signal, the field signal and the image data are written into the DDR3, finally, the VGA equipment scans the row signal and the field signal of the video image, and the display of the encrypted, decrypted and verified image is completed through the HDMI. According to the invention, high-security and time-delay-free video image processing and transmission are realized, and the method is suitable for the fields of high-definition video monitoring and the like with strict security transmission requirements.

The invention belongs to the technical field of industrial automation, and discloses an industrial camera production line data analysis control method and system, and the method comprises the steps: obtaining industrial camera production line data which comprise industrial camera production parameter data, production equipment operation data and industrial camera production image data; identifying and removing abnormal values in the industrial camera production parameter data, the production equipment operation data and the industrial camera production image data; a wavelet transform denoising technology and a multi-dimensional feature extraction technology are fused, an image denoising method based on two-dimensional convolution and weight dynamic adjustment, Sobe l edge detection, gray level co-occurrence matrix texture analysis and color histogram extraction are combined, and a production parameter feature data set, an equipment operation feature data set and a production image feature data set are obtained; and the production efficiency and the productivity of the industrial camera are improved.

The present invention discloses a method for identifying the spatial distribution difference of wheat grain proteins, comprising the steps of: S1, constructing a semantic segmentation data set; using an improved U-Net network as the model architecture to complete preliminary background removal; S2, performing binarization processing to complete preliminary segmentation; using the Sobel edge detection algorithm to obtain the endosperm edge contour; determining the central position of the endosperm region; performing layer-by-layer erosion to obtain a radially stratified structure image; S3, obtaining a sampling matrix; using the K-means++ algorithm to initialize the clustering center for iteration; identifying proteins and calculating the proportion of protein spatial distribution. The present invention realizes full automation from image preprocessing to protein quantitative analysis through semantic segmentation of the improved U-Net model, dynamic stratification of radial erosion, and component clustering of the K-means++ algorithm, reduces the cumbersome operations of manual drawing, is more accurate in image processing, can also perform hierarchical processing on images, and accurately reflects the protein distribution gradient.

The present invention discloses a target collaborative perception method and system based on intelligent fleet navigation. Target spatial information is acquired through AIS and laser radar fusion positioning, and multi-perspective video streams are collected in real time based on multiple intelligent ships. After the system extracts the video key frames, it uses Sobel edge detection and PCA analysis technology to construct an object feature set, and combines the network status dynamic evaluation to determine the data transmission volume. The feature set is analyzed for correlation using a pre-trained self-attention model to screen out features with high attention scores, thereby achieving efficient target recognition and shared perception. The present invention can significantly improve target recognition accuracy and system response speed, rationally utilize multi-intelligent ship data for target collaborative perception, and effectively improve the comprehensive environmental perception capabilities of multiple ships.

The invention discloses a point cloud image identification-based ship berthing identification anti-collision early warning monitoring method and system. The method comprises the following steps of: carrying out initial segmentation on a preprocessed image by adopting a Sobel edge detection operator; edge pixel point extraction operation is carried out on the first fine segmentation processing image, and third edge pixel points are obtained by screening pixel points which do not accord with target object identification detection conditions; based on the third edge pixel points and the second edge pixel points, ship feature angular points and a ship frame are formed through intersection point fusion, and the ship feature angular points and the ship frame are connected to obtain a third ship image; obtaining a laser point cloud image of the current ship, and secondarily verifying whether the current target object is of the same ship type or not; determining the minimum safety early warning distance of the current target object according to the current ship type; according to the method, two detection means are complementary and parallel through a visual identification processing method and three-dimensional point cloud data identification, and the accuracy of ship identification is remarkably improved.

The present invention discloses an intelligent identification method for underwater dredging pipelines based on multi-temporal remote sensing images, including: obtaining multi-temporal remote sensing images of the same study area; extracting the blue band, green band and near-infrared band, or the blue band, green band and short-wave infrared band; calculating the optical remote sensing water body index and displaying it using a rainbow color map; determining the optimal threshold for water body segmentation of the optical remote sensing water body index; clarifying the water body and non-water body areas; using a Lee filter for image enhancement; using a custom convolution kernel for directional filtering enhancement; using a double-threshold sobel edge detection method to obtain an edge detection result map; superimposing the edge detection result map on the rainbow color map, patching the discontinuous parts at the middle position of the continuous line of the edge detection result map, and removing the debris non-segment edges to form a complete display map of the distribution of dredging pipelines; it can achieve efficient, large-scale scanning and precise positioning of the pipelines without disturbing the normal operation of the reservoir.

The present invention relates to image processing technology and provides an image recognition system for oral mucosal lesions, aiming to improve the accuracy and efficiency of oral image processing. The system includes a data acquisition module, a data preprocessing module, an image segmentation module, a lesion area classification module, and a report display and visualization module. The image segmentation module achieves high-precision lesion area segmentation through image pyramid, Sobel edge detection, Otsu threshold segmentation, and local contrast weighted optimization technology. The lesion area classification module combines feature combination, feature interaction, HEMish activation function, and variable dilated causal convolution to accurately classify oral lesion areas. The present invention significantly improves the recognition accuracy, robustness, and adaptability of lesion areas through multi-level and multi-dimensional technical means, solves the problems of low accuracy and unstable recognition in traditional systems, and promotes technological progress in the field of oral mucosal lesion image recognition.

This invention relates to the field of target detection and localization, and particularly to an image enhancement-based target detection and localization method for mobile phone assembly scenarios. This method enhances the original image acquired by a visual sensor, specifically enhancing both the input image for target detection and the input image for target edge calculation. For target detection, overall brightness enhancement, histogram equalization, and Laplace sharpening are applied to enhance brightness and contrast. The enhanced image is then input into a deep learning network for target detection. For edge detection, the input RGB image is linearly enhanced using a single R-channel, the grayscale image is enhanced using the Sigmoid function, and finally, Sobel edge detection is performed, thereby achieving the target localization process.