43 results about "Image transformation" patented technology

83 20 25 28 74 03 30 D ec 2 02 5 A B S T R A C T 2 0 2 5 2 8 7 4 0 3 3 0 D e c 2 0 2 5 8 3 IL LU M IN AT IO N PA TT ER N (S ) 11 0 A SE CO ND T YP E OF IM AG ES (E .G ., FL UO RE SC EN CE IM AG ES ) 12 4 A FI RS T TY PE O F IM AG ES (E .G ., BR IG HT -F IE LD IM AG ES 12 2 DI SC RI MI NA TO R 10 4 GE NE RA TO R 10 2 Tr ain in g Da ta 12 0 GA N Mo de l 1 00 FI G. 1 1 / 26 Training Data 120 GAN Model 100 ILLUMINATION A FIRST TYPE OF IMAGES (E.G., PATTERN(S) BRIGHT-FIELD IMAGES 110 122 GENERATOR 102 A SECOND TYPE OF IMAGES (E.G., DISCRIMINATOR FLUORESCENCE IMAGES) 104 124 FIG. 1 20 25 28 74 03 30 D ec 2 02 5 2 0 2 5 2 8 7 4 0 3 3 0 D e c 2 0 2 5 T r a i n i n g D a t a 1 2 0 ILLUMINATION A FIRST TYPE OF IMAGES (E.G., P A T T E R N ( S ) BRIGHT-FIELD IMAGES A SECOND TYPE OF IMAGES (E.G., FLUORESCENCE IMAGES)

A system and method for detecting and diagnosing faults in a building automation system (100). Time-series data (202) are received from the building automation system (100). Label rationality (214, 216, 218, 220, 222) is determined for each set of time-series data and the corresponding label associated with that set, based on a tree-based classifier (224) and an image transformation classifier (226). The tree-based classifier (224) and the image transformation classifier (226) receive the same data input and operate independently of each other.

This invention discloses a data generation method for correcting rotational nonuniformity of endoscopic probes, relating to the field of medical image processing technology. The method includes: selecting an acquired image composed of multiple scan line data; performing a random image transformation on the acquired image to obtain a reference image; simulating diverse nonuniform rotational distortion patterns using a mathematical model to randomly synthesize an offset vector corresponding to the number of line data in the reference image; applying the offset vector to the reference image, obtaining corresponding line data based on the offset value at each position, and arranging the newly arranged line data to form a distorted image; and generating a distorted-reference image data pair with known labels from the reference image and the distorted image. This invention can meet the quantitative and diverse requirements for training data in nonuniform rotational distortion correction at extremely low cost, making the training set more complete and exhibiting good applicability and generalization for correcting endoscopic probe imaging of different modalities and systems.

The application provides a large-scale satellite image automatic registration and enhancement method based on adaptive multi-source fusion, comprising: performing orthorectification and multi-resolution unified projection on a multi-source original data set obtained to obtain a coarse registration image set; calculating a local tensor structure of the coarse registration image set, performing repeated texture recognition, generating a final structure representation and a structure reliability; performing regional registration to obtain a full-image dense deformation field, then performing image transformation to be aligned and registration uncertainty estimation to obtain a high-precision registration image and structure registration uncertainty estimation; combining a pseudo-change probability of the high-precision registration image and the structure registration uncertainty estimation to obtain a change reliability weight; under the constraint of the change reliability weight, performing consistency enhancement on the high-precision registration image to output a quantitative and reliable enhanced image. The application realizes high-precision, low-false alarm and quantifiable and reliable automatic registration and enhancement of multi-source large-scale satellite images.

The application discloses a temperature measurement error attribution analysis method and device for an infrared thermal imager. The method comprises the following steps: based on a preset temperature value set of a blackbody radiation source, temperature measurement of the blackbody radiation source is performed by using the infrared thermal imager to obtain a measurement temperature set; image transformation processing is performed on the measurement temperature set to obtain a measurement temperature image set; temperature measurement error attribution analysis processing is performed on the measurement temperature image set to obtain temperature measurement error region information of the infrared thermal imager.

Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.

This invention relates to the field of image recognition, specifically to an automated tunnel construction monitoring system based on image data. The system includes an edge computing node that acquires the original tunnel image and constructs an atmospheric scattering physical model using measured data from an optical dust concentration sensor. The edge computing node transforms the original image to the frequency domain, extracts low-frequency brightness features and high-frequency texture features using discrete cosine transform, and adaptively compensates the high-frequency texture features with weights based on the transmittance matrix calculated by the physical model to suppress diffuse light interference. The compensated features are then input into a lightweight convolutional neural network via inverse transform. This scheme introduces dust physical parameters as prior constraints into the frequency domain feature weight calculation, reducing the probability of image edge artifacts and color distortion under conditions of localized strong light and high dust, ensuring the physical authenticity of target edge features in the reconstructed image, and improving the accuracy of feature extraction under harsh conditions.

This application discloses a visual tracking method, apparatus, device, and medium for industrial robots, relating to the field of industrial robot technology. The method includes: acquiring a continuous image stream and global motion data of the industrial robot's end effector camera; performing optical flow tracing on the current frame and the previous frame to obtain feature point displacement vectors, and calculating mechanical vibration energy indices based on these vectors; adaptively adjusting a smoothing factor based on the vibration indices, filtering the global motion data to obtain a stable image transformation matrix, and performing a geometric transformation on the current frame image to generate a stable image; inputting the stable image into a Transformer architecture detection model, and obtaining the detection result through cross-attention calculation; extracting the target feature vector of the current frame based on the target bounding box, performing dual checks of occlusion gating and consistency gating, executing a corresponding memory update strategy, and outputting the target tracking result. This method can achieve accurate tracking.

This invention discloses an event camera calibration method based on wavelet transform, which can quickly extract feature points and improve calibration accuracy and speed. The target, displayed as a striped pattern on an LCD screen, serves as the two-dimensional calibration target, showing both horizontal and vertical stripe images. The screen flashes at a certain frequency to trigger events. During calibration, the event camera acquires and captures horizontal and vertical stripe event stream data. An appropriate time window is selected, and the event stream is accumulated into two-dimensional event frames, which are then converted into image information. Wavelet transform is then used to calculate the wrap-around phase map in both directions. The feature points are located at the 2π phase value between the horizontal and vertical wrap-around phases, thus obtaining the pixel coordinates of the feature points. This invention uses image transformation technology to extract the phase map, eliminating reliance on image intensity for feature point detection and achieving accurate feature point extraction.

The present application belongs to the technical field of image processing, and particularly relates to an airborne imaging non-uniform low-light scene image enhancement method. The method comprises the following steps: S1, processing a non-uniform low-light scene image to obtain H, S and V channel images; S2, performing dark area correction calculation on the V channel image to obtain a first image; S3, performing bright area suppression calculation on the V channel image to obtain a second image; S4, fusing the first and second images to obtain a fused image; S5, inputting a pixel distribution optimization image into a nonlinear image transformation tone adjustment model for processing to obtain a mid-term enhancement image; and S6, performing pixel value range stretching processing on the mid-term enhancement image, and combining the H and S channel images to obtain an optimized enhancement image. The present application can effectively improve the visibility of the dark area of the image and retain the detail feature information of the bright area while retaining the color richness and level of the image through the regional differentiation calculation and adaptive fusion mechanism.

This invention provides a method and system for automatic vertex picking of DAS-based microseismic events, relating to the field of microseismic location technology. The method includes acquiring multi-channel data corresponding to microseismic events and performing demodulation analysis to form multi-channel waveform data; performing correlation analysis on the waveform data from different channels to establish an automatic picking correlation model; performing image transformation based on the automatic picking correlation model to form corresponding grayscale images; and performing location discrimination analysis based on the grayscale images to determine the event vertex channel. This method improves the interpretation accuracy and reliability of geological analysis of seismic images by removing noise while preserving geological structural features such as faults and strata.

The application relates to a motor bearing fault identification method, device, equipment, medium and product, wherein the method comprises the following steps: converting a current signal of a motor bearing to be identified into a two-dimensional Markov image by using a Markov transform field; processing the two-dimensional Markov image by using a student model to obtain an identification result of the motor bearing; the identification result is used to represent the probability that the motor bearing belongs to a preset state category; and the identification result is obtained by converting the two-dimensional Markov image into an initial capsule vector by the student model, and performing dynamic routing inference based on a preset sparse ratio and the activation intensity of the initial capsule vector. By using the above method, the accuracy, interpretability and robustness of the identification result of the running state of the motor bearing can be improved.

This invention discloses a plug-and-play model inversion attack method based on a generative model in a collaborative reasoning environment. This method uses a pre-trained StyleGAN2 as the target-independent image prior, leveraging its mapping and synthesis networks in the generator structure to map latent vectors into intermediate representations to generate images. During the attack optimization process, to address the gradient vanishing problem easily caused by traditional cross-entropy loss, a Poincaré loss function is introduced, utilizing the gradient preservation properties of non-Euclidean space to ensure the stability of the optimization process. Simultaneously, through standard image transformation and random image transformation techniques, the difference between the target data distribution and the image prior is effectively reduced, enhancing the robustness of the generated features. This method effectively overcomes limitations such as high computational resource consumption, insufficient flexibility, and sensitivity to changes in dataset distribution. In high-resolution scenarios such as face recognition, it demonstrates outstanding attack efficiency and generated image quality.

The application discloses a football training posture analysis method and system based on image enhancement, and belongs to the field of image processing. The method comprises image acquisition, low-light training image processing, motion blur detail reconstruction, posture image transformation, training posture joint node estimation model design, and football training posture analysis. The scheme ensures the visibility of the player's jersey number and facial features in the shadow by local gain improvement and histogram equalization enhancement. Through motion blur detail reconstruction, the blurred texture and sharp outline are restored. The design of the fidelity loss includes a modulus deviation term and a structure correlation error term, which makes the predicted joint heat map more accurate in the direction of the toe and the degree of finger opening. Through phased timing fine adjustment, the details of early frames are avoided from being smoothed, and the details of the football training posture are protected. The analysis effect of the football training posture is improved.

The application provides a colorectal cancer staging algorithm and system based on a self-supervised learning mode, which can judge the severity of colorectal cancer without using labeled samples. First, image transformation and low-level processing are performed on the data set to generate different groups of new data sets. The different groups of data sets are input into different encoder networks to obtain feature outputs, and the image information is saved through the conversion of the conditional attention mechanism and the cross-model hybrid maximum. Finally, the model is trained and optimized by merging and calculating the contrast loss and the reconstruction loss. The application can effectively classify the severity of colorectal cancer without a labeled training set by learning the similarity and difference of medical images to construct features, and has a wide application prospect.

This invention discloses a spatial target semantic segmentation method and apparatus based on ISAR echoes, belonging to the field of radar target recognition technology. The method first uses a trainable domain alignment module (DAM) to directly perform a learnable orthogonal transformation on the original ISAR echo without imaging transformation. While maintaining the same amount of information, the echo domain of the ISAR echo is mapped to a feature domain compatible with the mask domain of the semantic segmentation generation mask, obtaining a first feature. This first feature is then fed into a complex domain encoder for scattering feature extraction. The scattering feature is further processed by a complex domain decoder to achieve the recognition of the semantic segmentation mask. This invention differs from the traditional "image first, segment later" process, avoiding potential information loss and time consumption during the imaging stage, and achieving a direct mapping from the original ISAR echo to pixel-level segmentation.

The disclosure provides an image classification method and device based on endogenous feature denoising and a storage medium, including: obtaining a to-be-processed image, performing specification unification and numerical normalization operations on the to-be-processed image through a preset image transformation process to obtain standardized image data; inputting the standardized image data into a pre-trained anti-disturbance residual network model to output a classification result of the to-be-processed image; wherein the anti-disturbance residual network model is constructed based on a residual network model, and an adaptive feature denoising model is embedded in a feature transmission path of the residual network model; the adaptive feature denoising model is used for purifying high-dimensional features output by the residual network. The disclosure uses machine learning, such as artificial intelligence, neural networks and training models, takes neural networks as a core technology carrier, supports the construction and training of the residual network model and the adaptive feature denoising model, and solves the problem of deviation in image data classification caused by anti-disturbance.

The application belongs to the technical field of face recognition, and discloses a large-angle side face recognition method and system based on face recognition, wherein the method comprises the following steps: acquiring a face image, constructing a training data set, then representing pixel modules of the face image as a plurality of feature points, then analyzing coordinates of the feature points to determine the orientation and side angle of the face image, selecting corresponding face images from the training data set to perform perspective transformation, then performing data augmentation on the face images after the perspective transformation, and comparing the face images after the data augmentation with face images of the training data set. Through face image acquisition and feature point analysis, when the angle of the face image does not meet the recognition requirement, the large-angle face can be reconstructed, the face image can be converted into a recognizable type, and thus the face recognition precision is improved.

This invention discloses a method, system, and related equipment for trapezoidal correction of projected images. The trapezoidal correction method is applied to a projection device equipped with a distance sensor and includes the following steps: acquiring the distances from the projection device to at least three different distance points on the projection surface; calculating, based on the distances from the projection device to the at least three distance points, the rotation angle of the projected image relative to a plane perpendicular to the projection surface using a preset calculation method; and performing image transformation processing on the projected image based on the rotation angle to achieve trapezoidal correction. The trapezoidal correction method for projected images in this invention can significantly reduce the cost of projection devices and expand their applicability, making them suitable for a wider range of development platforms.

This application relates to interpreting machine learning models using image transformation. A system and method for identifying visual features that influence predictive models are provided. This technique employs an image transformation function to introduce visual features into an image to create a modified image that can be fed into a predictive model. When the predictive model generates a prediction for a given image that differs from the prediction generated for a modified version of the image, a further modified version exaggerating the introduced visual features can then be created using the image transformation function. Therefore, this technique helps identify visual features that influence the predictive model, enabling the understanding of the model's conclusions and allowing for further study and testing of these visual features.

The present application relates to the technical field of computer graphics and computer vision, and discloses a homography matrix estimation method and system, the method comprising the following steps: S1, collecting images with different overlap rates as a training data set; S2, constructing a neural network model, including an overlap monitoring network and a homography matrix estimation network; S3, using a loss function to guide and optimize the neural network model, calculating the loss value between the result of image transformation by the network output homography matrix and the target image; S4, training the neural network model to generate a trained neural network model; S5, using the overlap detection network to crop out the common area of the two estimated images; S6, using the deep homography matrix estimation network to achieve coarse-to-fine homography matrix estimation. The system comprises an acquisition unit, a model construction unit, a model training module and a homography matrix estimation module. The present application also discloses an electronic device and a computer readable storage medium.

The application provides a visual enhancement method and system based on AR glasses, the method comprising: respectively pre-processing a target visible light image and a target infrared light image; respectively performing pyramid image registration on the processed visible light image and the processed infrared light image; respectively performing image transformation decomposition on the visible light registration image and the infrared light registration image; fusing visible light low-frequency coefficients and infrared light low-frequency coefficients, and fusing visible light high-frequency coefficients and infrared light high-frequency coefficients; and performing inverse image transformation decomposition on the fused low-frequency coefficients and the fused high-frequency coefficients to obtain an enhanced visual image. The application combines heat source information of an infrared image and detail texture information of a faint light image, and significantly improves the visual effect and the definition of the image.

This request will provide methods and sets of image processing equipment, computer equipment, storage media, and Products and software related to fields of arts and sciences such as artificial intelligence and learning. Intelligent machinery and transportation are being used to respond to requests for changes to the featured section. The unique characteristics of the source image and its prominent features indicate initial features with at least one scale. For example, the target image will be fed into the function change model where the change request is made. The page will be used to request that the target page in the target image be changed to the source page in the image. Origin: And the fusion of distinctive elements will be performed in a repetitive manner with the defining characteristics. And the highlighted part describes the initial characteristics that have at least one scale in order to obtain the subsequent highlighted part. The fusion process uses a page transformation model; the target page transformation image will be... The creation of the design is based on post-merger prominence using page and image transformation models. Changes to the target page will export the page in the image; changes to the target page will be merged. Combined with key features that define the identity of the source page and key features that describe the target characteristics. Target page;

The application discloses a kind of identification method of rephotographing image, the training method and device of rephotographing identification model.The identification method of rephotographing image includes: obtaining target image block from the target object area of the image to be identified, and the first transformed image of image feature enhancement is obtained by image transformation processing to the image to be identified;The image to be identified, target image block and first transformed image are input into target rephotographing identification model to determine whether the image to be identified is the rephotographing image of target object;Target rephotographing identification model includes feature extraction module, fusion module and classification module;Feature extraction module is used to extract features to the image to be identified, target image block and first transformed image respectively, and obtain multiple image features;Fusion module is used to fuse multiple image features to obtain fused image features;Classification module is used to rephotographing identification based on fused image features to the image to be identified, to determine whether the image to be identified is rephotographing image.

An information processing device includes: a capture unit configured to capture a graph structure input by a user as instruction data; a generation unit configured to generate an image based on the instruction data; a conversion unit configured to convert the generated image into a graph structure; a computation unit configured to calculate the difference between the graph structure of the instruction data and the graph structure converted from the generated image; a regeneration command unit configured to instruct the generation unit to regenerate the image such that the difference is reduced if the difference is greater than or equal to a predetermined value; and an output unit configured to output the generated image to the user if the difference is less than the predetermined value.

The application discloses a template image coordinate transformation circuit and a target matching device, and belongs to the technical field of image matching and recognition. The circuit comprises an interface module, a reference coordinate system transformation module and an intermediate cache memory. The reference coordinate system transformation module comprises a matrix operation submodule and a linear accumulation submodule. The interface module is used for receiving an original template image. The matrix operation submodule is used for performing a rotating coordinate transformation on the original template image according to a rotation angle of a to-be-matched image, so as to obtain pixel coordinates of each pixel point. The linear accumulation submodule is used for performing a weighted accumulation on the gray values of the pixel coordinates adjacent to the reconstructed pixel coordinates, so as to obtain a reconstructed gray value of the reconstructed pixel coordinates and store the reconstructed gray value into the intermediate cache memory, and thus a rotated template image is obtained. The template image coordinate transformation circuit takes into account the speed and accuracy of template image transformation. The template image coordinate transformation circuit is used to generate a template image, which is beneficial to the deployment on an embedded system and the fast and accurate realization of target matching and recognition.

The application discloses a vehicle double-lamp projection correction method and device, a vehicle and a storage medium, and relates to the technical field of vehicles. The method comprises the following steps: acquiring a distortion mapping relationship when the double-lamp projection correction trigger condition of the vehicle is met; the distortion mapping relationship is determined based on an image transformation matrix, and the image transformation matrix is used to represent the mapping relationship between a left projection image and a right projection image obtained based on double-lamp projection calibration information; pre-distortion correction is performed on the to-be-projected content corresponding to the double lamps based on the distortion mapping relationship, so that corrected to-be-projected content is obtained; and the corrected to-be-projected content is synchronously projected by the double lamps. In this way, the accuracy of the double-lamp collaborative projection of the vehicle can be improved.

A multi-dimensional magnetocardiogram (MCC) feature extraction system based on dynamic masks with multiple regions of interest (ROIs) and multi-scale image transformation can automatically extract multi-dimensional MCC features. It boasts strong feature representation capabilities and high robustness. The system is characterized by: a module for drawing two-dimensional temporal isomagraphs (TDAs), used to construct the cardiac magnetic field distribution in preprocessed multi-channel MCC signals using spatial interpolation algorithms, drawing a complete two-dimensional isomagraph of a single heartbeat cycle, and arranging them chronologically to form a complete two-dimensional isomagraph sequence; an automated dynamic mask generation module, used to divide the MCC cycle into multiple key bands based on the two-dimensional isomagraph sequence and MCC physiological characteristics, automatically generating dynamic masks with multiple regions of interest for each band; a multi-scale image transformation module, used to perform multi-scale image transformations on the two-dimensional isomagraph sequence to obtain various transformed images of the two-dimensional isomagraph sequence; and a multi-dimensional MCC feature extraction module, used to extract a multi-dimensional MCC feature set.

The invention, which relates to the technical field of image detection, discloses a high-generalization-oriented counterfeited region self-guiding collaborative learning double-flow detection system comprising a data processing module, an attribute tag generation module, a region guiding feature extraction module, a collaborative feature discrimination module and a training test optimization module. According to the method, random image transformation is performed on a standard sample to generate a twin image pair and an initial region mask, an input generator automatically outputs three types of attribute tags including a counterfeit type, a counterfeit region mask and a tampering proportion value, and a dynamic weighting mixing strategy is adopted to synthesize an enhanced training sample according to the counterfeit type. According to the design, diversified training materials and stable supervision signals can be obtained without manual annotation, the problems that manual annotation is high in cost and limited in coverage range are solved, the adaptability to different counterfeit modes can be improved through rich training samples, and the robustness of the system is improved. And a solid foundation is laid for feature extraction and discrimination tasks.

The application discloses a strategy learning method based on an optimizable image conversion for mechanical arm grabbing, comprising the following steps: designing a task environment, setting parameters of a mechanical arm and a target object, and setting hyperparameters of a reinforcement learning algorithm; building a virtual environment consistent with the task environment; manipulating the mechanical arm to interact in the virtual environment, collecting training data, and calculating a task reward function according to the distance between a grabbed object and a target position, and storing the training data and the task reward function in an experience replay pool; determining a calculation mode of an optimal invariant metric under feature learning by using the optimizable image conversion; collecting a batch of data from the experience replay pool, and training an optimal strategy of the mechanical arm for moving a grabbed object to a target position in a dynamic environment by using a reinforcement learning algorithm combined with the optimizable image conversion. The application can improve the training sample efficiency of an intelligent agent based on visual perception under the condition of ensuring the migration of the strategy, thereby improving the learning efficiency and the convergence rate of the visual perception reinforcement learning algorithm.