17 results about "Fourier descriptor" patented technology

The invention discloses an image edge matching degree calculation and inspection point position deviation correction control method and system, and innovatively adopts a semantic segmentation and multi-level feature fusion mechanism to solve the problems of low precision and poor robustness of traditional IMU coarse adjustment, single template matching and SLAM algorithms. The method comprises the following steps of: acquiring an image through a pan-tilt camera, performing de-noising processing, and calculating an initial translation offset by using an RANSAC algorithm; generating a mask matrix in combination with the pre-training model, and extracting the edge gradient direction and strength; region pairing is established based on semantic consistency, and the matching degree is calculated through Fourier descriptor conversion; the control quantity is reversely deduced through the pixel-holder coordinate mapping relation, image acquisition is executed recursively until the matching degree reaches a threshold value, and semantic-level high-precision automatic correction and intelligent control of the target area are achieved.

This invention relates to the field of image processing technology and discloses a posture deviation recognition method based on image recognition. The method involves acquiring a posture image at the moment a specified rehabilitation movement is completed, preprocessing and segmenting it to obtain a binary contour of the target posture, representing the contour points as a complex sequence, and performing closure processing and arc length resampling to standardize the number of contour points. Based on this, a DC-free and normalized Fourier descriptor is constructed to obtain the frequency domain features of the posture and the standard posture. The global shape deviation intensity and adaptive judgment threshold are calculated through frequency domain comparison, and the residual signal is reconstructed at the resampling point location to obtain the local deviation distribution. Finally, the posture deviation level and structured results are output according to a grading rule, thereby reducing the dependence on sensors and manual thresholding, realizing the quantitative assessment and presentation of posture deviation, and providing a basis for clinical assessment and training adjustment.

A computer-implemented method for classifying road intersections by means of GPS data, in particular from a plurality of vehicles in a fleet. The method includes: using existing GPS data points of vehicle movements, which were captured using GPS sensors of the vehicles at at least one road intersection; converting the captured GPS data points into a 2D contour by determining a center point of the road intersection and creating a polygon that represents a 2D contour of the road intersection; encoding the 2D contour of the road intersection using elliptic Fourier descriptors using a Fourier transform method and classifying the encoded data into different intersection types.

The invention belongs to the technical field of image processing, and relates to a protection method and system for the design copyright of an automobile cushion. The method comprises the following steps of: extracting a digital image contour of an automobile cushion design drawing, calculating a normalized Fourier descriptor of the contour as a shape feature, dividing a drawing area into grid units according to radial sectors and concentric rings, converting a digital image into a CIELAB color space in each grid unit, and for each grid unit, carrying out CIELAB color space conversion on the CIELAB color space; extracting a gradient direction histogram of a brightness L * value taking gradient intensity as a weight and a quantization histogram of statistical a * b * two-dimensional color distribution of pixels in the grid unit, combining the two histograms into a local feature descriptor of the grid unit, serializing and splicing the local feature descriptor from inside to outside, and combining shape features with a local feature sequence to form copyright feature data, and performing digital signature by using an asymmetric encryption private key, and storing the signature and a timestamp in a distributed account book together to realize copyright protection.

This invention discloses a two-dimensional contour morphology representation method based on current-round Fourier transform, belonging to the field of geometric morphometrics. It solves the technical problems of contour rotation and misalignment caused by elliptical Fourier descriptors. The key point of its technical solution is to complete the reconstruction and normalization of contour shape through current-round Fourier descriptors, achieving contour shape description effect comparable to elliptical Fourier analysis and contour normalization (alignment) effect comparable to the Pu-style method. It can also avoid the graphic pseudo-rotation problem caused by standardization during the extraction of elliptical Fourier descriptors, without the need for prior standardization of contours using other methods, and quantitatively describes contour features based on current-round Fourier descriptors.

The application provides a lung nodule CT image processing method and system, computer equipment and a storage medium, and belongs to the field of image processing. The method comprises the following steps: collecting a lung nodule CT image; segmenting the lung nodule CT image to obtain a nodule segmentation mask; extracting nodule structure semantic features from the nodule segmentation mask; extracting lung nodule texture features from the nodule segmentation mask based on a gray level co-occurrence matrix; extracting lung nodule shape features from the nodule segmentation mask by a Fourier descriptor; splicing the lung nodule texture features and the lung nodule shape features to obtain hand-crafted features; fusing the nodule structure semantic features and the hand-crafted features based on kernel canonical analysis to obtain fused features; and determining the nodule structure complexity corresponding to the lung nodule CT image according to the fused features. The method retains the powerful pattern recognition capability of the nodule structure semantic features, and also integrates the stable discrimination information of the hand-crafted features in a small sample scene, thereby improving the accuracy and stability of classification.

The invention relates to a computer-implemented method for encoding a road intersection contour using Elliptic Fourier Descriptors (EFDs), comprising the steps: encoding by calculating Fourier coefficients of a polygonal representation of the road intersection contour using a forward transformation method (20), wherein the number of Fourier coefficients is determined by a complexity of the contour, and thereby a resulting data set is determined.

This invention discloses a method and apparatus for traffic light shape detection, relating to the fields of computer vision and intelligent transportation technology. The method includes: acquiring an image to be detected; determining the preliminary boundary region of the traffic light based on color and position information; determining whether the size of the preliminary boundary region meets preset size conditions; if not, performing adaptive size enlargement processing on the preliminary boundary region to obtain an enlarged target region; extracting the outline of the traffic light from the target region and calculating multiple coefficients using Fourier descriptors; determining the preliminary shape type of the traffic light based on the Fourier descriptor coefficients; and performing secondary confirmation of the preliminary shape type based on the geometric features of the target region to obtain the final detection result. This invention effectively compensates for the feature loss of small-sized or deformed targets through adaptive size processing, and significantly improves the accuracy and robustness of traffic light shape recognition by utilizing the invariance characteristics of Fourier descriptors and a multi-stage judgment strategy.

This invention relates to the field of computer vision and image recognition technology, and discloses a method and system for visually identifying the authenticity of cards based on floral anti-counterfeiting features. The method includes: macro imaging and adaptive preprocessing of the floral area, extraction of the floral line skeleton based on curvature flow analysis, hierarchical encoding of floral features and construction of multidimensional fingerprints, depth comparison based on Fourier descriptors and KL divergence, and counterfeit detection and comprehensive judgment based on generative adversarial networks. The accuracy rate of floral anti-counterfeiting feature recognition reaches 99.2%, the detection rate of high-quality counterfeit products reaches 98.5%, and the identification time per card is less than 3 seconds.

The application discloses an image edge matching degree calculation and patrol point rectification control method and system, aiming at the low precision and poor robustness problems existing in traditional IMU coarse adjustment, single template matching and SLAM algorithm, and innovatively adopting a semantic segmentation and multi-level feature fusion mechanism.After the image is collected by a holder camera, denoising processing is carried out, and the RANSAC algorithm is used to calculate the initial translation offset; a mask matrix is generated in combination with a pre-trained model, and the edge gradient direction and intensity are extracted; region pairing is established based on semantic consistency, and the matching degree is calculated through Fourier descriptor conversion.The control quantity is inversely deduced through the pixel-hold coordinate mapping relationship, the image collection is recursively executed until the matching degree reaches the threshold, and the high-precision automatic rectification and intelligent control of the target region at the semantic level are realized.

This invention belongs to the field of forensic image processing and pattern recognition technology, and discloses a method and system for image comparison and analysis of morphological features of forensic blunt force trauma wounds. The method includes: acquiring wound images using standardized macro photography with an orthophoto perspective and performing geometric correction; parameterizing the wound contour using an elliptical Fourier descriptor and extracting local geometric parameters; constructing a tissue retraction deformation compensation model based on elasticity mechanics for inverse correction; and matching the wounding tool cross-section with a cosine similarity database and outputting a recommended ranking of the wounding tools. This invention realizes the transformation of wound morphology from qualitative description to quantitative analysis.

The invention provides a pulmonary nodule CT image processing method and system, computer equipment and a storage medium, and belongs to the field of image processing, and the method comprises the steps: collecting a pulmonary nodule CT image; segmenting the pulmonary nodule CT image to obtain a nodule segmentation mask; extracting nodule structure semantic features from the nodule segmentation mask; extracting pulmonary nodule texture features from the nodule segmentation mask based on a gray level co-occurrence matrix, and extracting pulmonary nodule shape features from the nodule segmentation mask through a Fourier descriptor; splicing the pulmonary nodule texture features and the pulmonary nodule shape features to obtain manual features; and fusing the nodule structure semantic features and the manual features based on kernel canonical analysis to obtain fusion features, and determining the nodule structure complexity corresponding to the pulmonary nodule CT image according to the fusion features. According to the method, the powerful mode recognition capability of nodule structure semantic features is reserved, stable discrimination information of manual features in a small sample scene is fused, and the classification accuracy and stability are improved.

The application provides a landslide deformation identification and early warning method based on video monitoring, which comprises the following steps: S1: data acquisition, S2: centroid extraction, S3: deformation judgment, a sliding window range envelope is constructed based on a feature point centroid coordinate sequence, and the coordinate extreme value and standard deviation in the window are counted to expand the confidence interval boundary; when the centroid coordinates of continuous multiple frames exceed the boundary, a deformation early warning signal is triggered, and multi-modal feature verification is carried out in combination with the change rate of the length-width ratio of the circumscribed rectangle and the difference of the Fourier descriptor of the contour. The application realizes high-precision real-time monitoring and reliability verification of the slope deformation by combining an automatic image processing algorithm with a high-fidelity indoor scale physical model test. The scheme provides a complete software and hardware cooperative solution scheme for the problems of high cost, poor real-time performance and weak anti-interference ability of the traditional monitoring technology, and significantly improves the accuracy and adaptability of the landslide early warning.

The invention relates to a method and a device for classifying road junctions. The invention relates to a computer-implemented method for classifying road junctions by means of GPS data, in particular by means of GPS data of a plurality of vehicles of a vehicle fleet, the method comprising the following steps: using existing GPS data points (10, 11) of a vehicle movement, which have been detected in particular at at least one road junction by means of a GPS sensor of the vehicle; converting the detected GPS data points into a 2D contour by determining a center point (13) of the road junction and creating a polygon (12) representing the 2D contour of the road junction; encoding the 2D contour of the road junction by means of a Fourier transform method in the case of using Elliptic Fourier Descriptors (EFDs) and classifying the encoded data into different junction types.

The invention relates to a computer-implemented method for classifying road intersections using GPS data, in particular from several vehicles of a vehicle fleet, comprising the following steps: using existing GPS data points (10, 11) of vehicle movements, which were recorded in particular by means of GPS sensors of the vehicles at at least one road intersection, Converting the recorded GPS data points into a 2D contour by determining a center point (13) of the road intersection and creating a polygon (12) which represents a 2D contour of the road intersection, Encoding the 2D contour of the road intersection using Elliptic Fourier Descriptors (EFDs) via a Fourier transformation procedure and classifying the encoded data into different intersection types.

The present application relates to a kind of multi-view three-dimensional model classification methods of fusing ResNet and CoTNet.The present application is first projected to three-dimensional model, obtains two-dimensional view set.CBAM attention module and SENet attention module are added in 2DResNet model to enhance the learning ability of network.2DResNet-CoTNet network is constructed based on 2DResNet model, and 2DResNet is combined with 2DCoTNet in parallel, view feature is extracted from two-dimensional view.Shape descriptor D1, D2, D3, Zernike moment and Fourier descriptor are used to describe two-dimensional view, and 1DResNet is used to extract shape feature.View feature and shape feature are fused, and finally Softmax function is used to determine the category of three-dimensional model.The present application has good three-dimensional model classification effect, can more accurately solve three-dimensional model classification problem.

The invention provides a reverse aerodynamic performance evaluation method for a fan blade. The method comprises the following steps: S1, establishing blade appearance point cloud data; s2, extracting a cross section airfoil profile and geometric parameters: intercepting a cross section to obtain a cross section airfoil profile under the cross section; s3, section feature recognition and normalization: calculating the chord length and torsion of the cross section; the section airfoil is normalized under the unit chord length and the horizontal chord line reference, and a point set is obtained; s4, Fourier descriptor calculation: obtaining Fourier descriptors F corresponding to each section airfoil; S5, airfoil matching and pneumatic epipolar reconstruction; S51, performing similarity calculation on the descriptors corresponding to each section airfoil and descriptors in an airfoil database, obtaining a minimum distance airfoil, judging whether the minimum distance airfoil meets a set matching condition or not, if the minimum distance airfoil meets the set matching condition, judging whether the minimum distance airfoil meets the set matching condition, and if the minimum distance airfoil meets the set matching condition, performing airfoil matching and pneumatic epipolar reconstruction; and if matching succeeds, directly calling an airfoil profile pole table in an airfoil profile database, and if matching fails, solving the use value of the airfoil profile of the section, and calculating the pneumatic polar line of the airfoil profile.