6568 results about "Reference image" patented technology

In a measurement processing apparatus for measuring a geometric feature of an object image: a measurement-reference-element setting unit automatically sets at least one first measurement reference element for use in measurement of the geometric feature of the object image, at at least one first position on the object image based on first image data representing the object image and position information indicating at least one second position of at least one second measurement reference element which is set on a measurement reference image corresponding to the object image; and a geometric-feature measurement unit measures the geometric feature of the object image based on the at least one first position of the at least one first measurement reference element.

Disclosed in the present application are a multi-modal data processing method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring a reference image and a reference text; extracting a reference visual feature of the reference image; by means of a multi-modal large language model, determining an embedding of the reference text, an embedding of a start mark of the reference visual feature, an embedding of the reference visual feature, and an embedding of an end mark of the reference visual feature; on the basis of the multi-modal large language model, splicing the embedding of the reference text, the embedding of the start mark, the embedding of the reference visual feature, and the embedding of the end mark into a target embedding sequence, performing attention processing on the basis of the embedding of the start mark, the embedding of the end mark, and an embedding selected by a sliding window in the target embedding sequence, and outputting a predicted sequence; and generating a predicted image and a predicted text on the basis of the predicted sequence.

Provided is a low-illumination image adaptive enhancement method, an apparatus thereof, a device and a storage medium. The method includes: inputting a low-illumination image to be enhanced into a pre-trained low-illumination image adaptive enhancement model to obtain an enhanced image. A method of training the low-illumination image adaptive enhancement model includes: acquiring a training set containing the low-illumination image and the corresponding reference image; constructing the low-illumination image adaptive enhancement model based on a Retinex algorithm. The low-illumination image adaptive enhancement model includes a projection module, an illumination component module, a reflectance component module and an enhancement module. The training set is used to train the low-illumination image adaptive enhancement model, and the trained low-illumination image adaptive enhancement model is obtained. The brightness and the contrast of the image are enhanced, and at the same time, the color and structure information of the image is restored effectively.

Processing circuitry may configure a system to implement a CAR-OnMap (“CAROM”) air framework for vehicle localization and traffic scene reconstruction using the aerial video of the traffic scene. Such a system may obtain aerial video of a traffic scene including vehicles that traverse the traffic scene and a satellite map image of the traffic scene as a distinct reference image. In such an example, processing circuitry may determine aerial image reference points within the aerial image which correspond to reference points in the satellite map image of the traffic scene. Processing circuitry may responsively generate calibrated images of the traffic scene from individual frames of the aerial video and determine unique keypoints on the vehicles in the traffic scene. In such an example, processing circuitry may track the vehicles across the individual frames of the aerial video utilizing the unique keypoints. Processing circuitry may output vehicle metrics for the vehicles.

The invention discloses an AOI optical scheme automatic optimization method based on reinforcement learning, and belongs to the technical field of automatic optical detection. According to the method, a reinforcement learning technology is applied to automatic adjustment of optical schemes in a machine vision system, wafer images under different optical schemes are collected by using an AOI system, and scoring is performed by using a plurality of semantic segmentation models; constructing and training a non-reference image quality evaluation network, evaluating the optical imaging quality in real time, and designing a reward function on the basis; by using the Actor-Critic algorithm, the intelligent agent can autonomously learn an optimal parameter adjustment strategy, quickly adapt to different scenes, realize dynamic optimization of light source parameters and remarkably improve image quality, and an efficient and intelligent solution is provided for efficient application of a machine vision system in a complex industrial environment.

A computing system is described herein that implements a diffusion-based framework for animating reference images. The computing system includes a video diffusion model that is utilized to encode temporal information. The computing system further includes a novel appearance encoder that is utilized to retain the intricate details of the reference image and maintain appearance coherence across frames. The computing system further employs a video fusion technique to smooth transitions between animated segments in long video animation. Potential benefits of the computing system include enhanced temporal consistency, faithful preservation of reference images, and improved animation fidelity in the generated animation sequences.

The invention provides a video generation control method and a computer readable storage medium, and is applied to the technical field of computers. According to the method, the structural defect of a split script is solved through a large split model, the consistency of split image features is ensured through image generation based on reference image feature constraint, audio and picture synchronization is realized in combination with emotional speech synthesis and dynamic mouth shape alignment, the video fidelity is improved through a split video cue word guiding multi-subject generation algorithm, and the user experience is improved. Therefore, the technical problems of missing emotion logic, poor consistency of split image features, difficulty in audio synchronization, insufficient controllability of video generation and the like in film and television creation in the existing AIGC technology are effectively solved, and the efficiency and the quality of video content production are improved.

Embodiments of the disclosure provide a solution for action planning. A method includes: generating a sequence of images for an action execution plan based on description information and a reference image related to an environment with an action executor located, the description information describing the action execution plan to be executed by the action executor; extracting a sequence of visual feature representations from the sequence of images, respectively; and for a respective visual feature representation of the sequence of visual feature representations, determining control information for controlling an action to be executed by the action executor in the environment to complete the action execution plan at least based on the respective visual feature representation, a reference visual feature representation prior to the respective visual feature representation in the sequence and observed information of the action executor in the environment during execution of a reference action prior to the action.

An automated damage severity assessment system and method is disclosed. The present system scans and assesses roof damage, and generates a damage severity report without the oversight of a roof inspector. The system comprises a central database of reference images and data that is constantly reviewed and updated to ensure accuracy of the reports, and aims to reduce the errors and waste inherent in the current methods.

The invention relates to the technical field of computers, in particular to a multi-mode-based three-dimensional model editing method and device, equipment and a medium. The method comprises the following steps: acquiring a multi-modal fusion image personalized generation method and a source three-dimensional model; obtaining a personalized image generation network corresponding to the source three-dimensional model, and training the personalized image generation network; based on the reference image list and the corresponding text description, multi-modal embedded information is obtained after fusion; taking the trained personalized image generation network as a skeleton, and adding a multi-personalized learning module to obtain a multi-personalized image editing network; and editing the source three-dimensional scene image according to the multi-modal embedded information by using a multi-personalized image editing network to obtain a single-view-angle edited image. The multi-mode embedded information generated by the method allows a user to input prompt information of multiple modes such as texts, images, sketches and the like, complementarity of prompt of each mode is mined, so that controllability of editing is improved, and high-quality three-dimensional model editing is realized.

Systems and methods for enhancing a live scene to be captured by a camera of a user device based on one or more best matched reference images that include similar subject matter and attributes of the live scene are disclosed. A preview of a live scene is displayed on the user device. Attributes of the live scene are identified. The live scene is segmented separately into a foreground and a background. Vector representations of the attributes of the segmented live scene are calculated and user device parameters are obtained, and both are used to identify reference images that include the attributes of the live scene. The identified reference images are displayed on the user device and a selection is made. The user device parameters are configured based on the selection made to capture the live scene to obtain similar effects of the selected reference image.

The invention relates to a multi-stage filtering road spilled object detection method based on dynamic difference analysis, which is suitable for automatic identification of unstructured foreign matters in video monitoring. The method comprises the following steps: firstly, extracting a reference image road mask, eliminating vehicle and pedestrian interference by using YOLOv8 detection, and extracting a motion candidate area through a frame difference method and background modeling; and then context expansion and super-resolution reconstruction are carried out on the candidate region, the candidate region is converted into an HSV space, multi-dimensional features such as color similarity, structural similarity and shadow determination are synthesized for screening, false detection is further removed in combination with inter-frame time sequence consistency, and finally a stable detection result is output. The method provided by the invention has the advantages of strong anti-interference capability, high adaptability, high detection precision and the like, and is suitable for the intelligent recognition task of the expressway thrown objects in a complex environment.

The invention discloses a metal structural part surface damage identification method based on machine vision, and belongs to the field of machine vision, and the method comprises the steps: obtaining reference image data with known damage features, carrying out the preprocessing, analyzing the change trend of a system detection state, and judging whether there is a deviation correction demand or not. And if the deviation exists, carrying out geometric correction processing on the lens distortion error to obtain a corrected reference image. Further separating the real change of the damage from the system deviation, and combining low-resolution and high-resolution detection to obtain the distribution data of the suspected damage area and the specific characteristic parameter data of the damage. According to the method, quantitative data of damage levels are obtained through automatic classification, detection differences among multiple devices are calibrated, visual presentation information of damage positions and levels is generated, and finally camera parameters and algorithm thresholds for subsequent detection are optimized and adjusted, so that high-precision damage detection and evaluation are realized.

The embodiment of the invention discloses a method and a device for generating a voice-driven digital human video, which are used for improving the generation quality of the voice-driven digital human video. The method comprises the following steps: acquiring driving voice and a figure reference image; the driving voice and the figure reference image are input into a digital human video generation model, continuous video frames are obtained, and the digital human video generation model is obtained through training of a training sample constructed based on the figure reference image and audio of a figure video and a training label constructed based on the continuous video frames of the figure video; and performing audio and video coding on the driving voice and the continuous video frames to obtain a digital human video.

The invention provides a dance video generation method based on multi-mode music driving and frequency domain-space double-flow decomposition, and the method comprises the steps: extracting multi-granularity music features through a composite encoder (Librosa + Jukebox), employing a beat gating attention mechanism, enabling the key actions such as dancing hand raising, kicking and the like to be strictly aligned with a music re-beat point, and enabling the synchronization error to be reduced to 118 ms through the verification of a test data set; for the problem of visual detail loss, a frequency domain-space double-flow decomposition architecture is provided, a Butterworth filter bank is used to decouple a reference image into a low-frequency energy diagram and a high-frequency residual error, and a double-flow diffusion mechanism is used to optimize a global attitude and local details respectively; a joint confidence prediction module is introduced for the generation stability in a shielding scene, and the motion trail of an abnormal joint point is dynamically corrected through a time domain sliding window weighted fusion strategy, so that a reasonable action conforming to ergonomics can still be generated under a 50% limb shielding rate.

There is provided a system and method of defect examination on a semiconductor specimen. The method comprises obtaining an input image indicative of difference between an inspection image of the specimen and a corresponding reference image; and processing the input image using a trained machine learning (ML) system, to generate an output image representative of a defect map indicative of distribution of defect of interest (DOI) candidates in the input image. The ML system comprises a plurality of ML models operatively connected therebetween and previously trained together to perform defect detection on the input image based on Fourier transform. The output image is usable for further defect examination.

The invention discloses a real-time digital human generation system and method based on low-calculation-amount voice driving, and relates to the technical field of digital human interaction, and the system comprises an audio processing module which is configured to receive voice input in real time and extract an audio feature vector; the driving and rendering module is used for mapping the audio feature vectors into parameters representing mouth movement, generating a dynamic mouth image based on the preprocessed static face reference data and the parameters, and fusing the dynamic mouth image with the reference image; the synchronous control module is used for ensuring the synchronization of the audio features and the rendering video frames according to a timestamp alignment mechanism and a PID feedback control algorithm; and the dynamic scheduling module is used for monitoring a hardware resource load in real time and realizing dynamic allocation of computing resources through multi-thread parallel and task priority adjustment. According to the technical scheme provided by the invention, the breakthrough of low delay, high fidelity and low energy consumption of the digital human can be realized in mobile terminal, embedded and multi-platform scenes.

The invention relates to an image frame insertion method and device in virtual shooting and a storage medium. The method comprises the following steps: acquiring at least two frames of reference images generated by a rendering engine; obtaining motion information and depth information of at least two frames of reference images generated by a rendering engine; and based on the motion information and the depth information of the at least two frames of reference images, determining a pixel value of a frame insertion image between the at least two frames of reference images, the frame insertion image and the at least two frames of reference images being used for sending to a display screen for display, and the display screen serving as a shooting background in virtual shooting. According to the embodiment of the invention, on-screen content can be smoothly played without improving the performance of a rendering end, the picture quality of a picture is remarkably improved, the data output capability of a rendering engine is fully played, redundant calculation is avoided, the computing power is saved, the remarkable frame rate improvement can be realized at relatively low cost, and the user experience is improved. The method meets the requirements of a virtual shooting scene, and has good stability and robustness.

A computer implemented method for defect detection comprises obtaining an imaging dataset of a wafer, and verifying a defect criterion in a subset of the imaging dataset of the wafer. The defect criterion comprises an observation representation of the subset of the imaging dataset with respect to a number of characteristic elements derived from reference images of semiconductor structures. The observation representation and the characteristic elements define a reconstruction of minimal reconstruction error, and a tolerance statistic on defect-free representations of subsets of defect-free observed imaging datasets. Each of the defect-free representations and the characteristic elements define a reconstruction of minimal reconstruction error of a subset of the defect-free imaging datasets. The computer implemented method further comprises generating defect information.

A laser radar-camera online calibration method and system based on a reflectance map includes: data preprocessing, generating data that meets the input of the neural network part; inputting the preprocessed data into the trained neural network; the neural network will output the edge point features of the reference image and the reflectance map, that is, the reference image and the reflectance map descriptor; by comparing the similarity of the reference image and the reflectance map descriptor, find the matching relationship between the reference image feature and the reflectance map feature point; use the mapping relationship between the reflectance map and the 3D point cloud to find the matching relationship between the reference image and the point cloud; use the EPnP algorithm to estimate the transformation matrix from the given 2D-3D point pair to complete the calibration process.

The invention relates to the technical field of image processing, and discloses a packaging material printing quality detection method and system based on machine vision. The method comprises the following steps: acquiring a multispectral image sequence and three-dimensional shape data of a moving packaging and printing material under different illumination, and constructing a dynamic three-dimensional physical attribute field; generating a virtual reference image and a dynamic reference image, constructing a multi-modal reference image, carrying out space-time registration on the multi-modal reference image and the dynamic three-dimensional physical attribute field, and calculating the difference between the multi-modal reference image and the dynamic three-dimensional physical attribute field in different dimensions to generate a multi-dimensional difference quality field; each dimension difference is enhanced through local statistics, and the comprehensive defect confidence coefficient is calculated based on the enhanced dimension difference; and extracting a defect region based on the comprehensive defect confidence, generating a defect evolution sequence and a defect track, analyzing defect track characteristics, constructing a correlation model in combination with process parameter time sequence data of the printing equipment, and positioning a defect reason. According to the invention, high-precision, multi-dimensional and self-adaptive printing defect detection and traceability can be realized.

The present invention belongs to the field of medical image registration, and more specifically, relates to a building method of a multi-modal three-dimensional medical image segmentation and registration model, and an application thereof. The method includes: acquiring medical images of two modalities of each target, and respectively using the images as a reference image and a floating image to acquire a training sample; and using the training sample to simultaneously optimize three network parameters, so as to acquire a segmentation and registration model formed by a reference image segmentation model, a floating image segmentation model, and a registration model. The reference image and floating image segmentation models are respectively used to perform multi-scale segmentation on corresponding images to acquire multi-scale segmentation results having the same maximum scale as the original images. The registration model are used to acquire a multi-scale deformation field on the basis of the reference image, the floating image, a maximum scale reference image segmentation result, and a maximum scale floating image segmentation result. Each of a segmentation loss and a registration loss are the sum of segmentation and registration losses in each scale, and the segmentation loss includes a first-order gradient loss and / or a level set energy function loss. The present invention can improve registration accuracy.

The invention belongs to the technical field of quality detection, and discloses a printing quality online detection method based on artificial intelligence. The method comprises: collecting a printed matter image; preprocessing the printed matter image to obtain a preprocessed image; acquiring a reference image corresponding to the printed matter image; aligning the preprocessed image with the reference image, calculating an overall offset, and judging whether an overall offset instruction is generated or not; if the overall offset instruction is not generated, performing image segmentation on the preprocessed image to obtain a segmented image; key point matching is carried out on the segmented image, local offset is calculated, and whether a local offset instruction is generated or not is judged; pattern deviation in the printing process can be automatically recognized, manual intervention and errors are reduced, the rejection rate is reduced, and therefore the production efficiency and the product quality of printed matter are improved.

The invention discloses an image feature matching optimization method based on intra-class space consistency in the technical field of computer vision and image processing. The method comprises the following steps: feature point extraction and preliminary matching: extracting feature points from a query image and a reference image and performing preliminary matching; initialization and transformation model estimation: initializing a matching point set and a residual error, and calculating an initial transformation model; error calculation and matching point set updating: calculating the error of the matching point pair, and updating the matching point set by adopting a dynamic screening method; performing residual optimization: judging whether the optimal condition is reached or not based on the residual, and deciding whether to continue iteration or not; performing intra-class space consistency clustering and isolated cluster elimination: performing clustering analysis after the optimal residual error is obtained, and eliminating isolated clusters based on an intra-class space consistency separation ion structure; and outputting a result: outputting a matching point set after the isolated clusters are removed. The method solves the problem that a traditional feature matching method is difficult to completely remove mismatching in a complex scene and is sensitive to noise.

The invention relates to the technical field of engineering materials, in particular to an engineering material quality detection method and system based on image recognition, and the method comprises the steps: reference image acquisition, sampling point selection and marking, image acquisition, image comparison and positioning, secondary acquisition and anomaly analysis. Compared with the defects that a detection system in the prior art is rigid in process, poor in adaptability and difficult to cope with a complex and changeable engineering field environment, the scheme constructs a full-process automatic system from intelligent sampling, self-adaptive image acquisition, precise registration and semantic level difference detection to intelligent post-processing and analysis; the method has high intelligence, adaptivity and robustness, and can stably and efficiently complete quality detection tasks in a complex engineering environment.

The invention discloses an image-based three-dimensional reconstruction method and device, a medium and a product, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: obtaining one of m original images as a reference image, and N-1 original images as source images; the reference image and the N-1 source images are sent to a pre-trained segmentation model and a pre-trained feature pyramid network, so that a reference feature images with different scales and a * (N-1) source feature images with different scales are obtained; sending the image to a pre-trained cost body-depth map model so as to enable the pre-trained cost body-depth map model to output an ultimate depth image; taking each image in the m original images as a reference image in turn, and executing the steps to obtain m ultimate depth images; and sending the m ultimate depth images to a point cloud fusion algorithm to output a three-dimensional point cloud model of the target object. According to the invention, the perception of the weak texture region is improved, so that the precision of three-dimensional reconstruction is improved.

An electronic device is disclosed. The electronic device comprises an image acquisition unit configured to acquire medical images captured at a plurality of positions, and a processor configured to generate tree structure data of three-dimensional candidate points corresponding to points along center lines of blood vessel regions of a reference image and a sub-image of the medical images, determine a three-dimensional path based on the tree structure data, determine a three-dimensional diameter for each of the points on the three-dimensional path, and reconstruct the three-dimensional shape of the blood vessels corresponding to the three-dimensional path based on the determined three-dimensional diameter.

The invention belongs to the technical field of artificial intelligence image generation, and particularly relates to an image generation method and system based on style feature injection, and the method comprises the steps: carrying out the multi-dimensional feature analysis of a reference image specified by a user side through a pre-trained multi-level style extraction model, extracting a style feature vector, and carrying out the feature extraction of the style feature vector; the style feature vector comprises a color feature vector, a texture feature vector, a composition feature vector and an illumination feature vector; obtaining a text description input by a user side, and outputting triple information including a scene entity, an entity attribute and a spatial relationship; performing weighted fusion on the triple information and the style feature vector to obtain a style enhanced semantic embedding vector; and inputting the style-enhanced semantic embedding vector into a generative adversarial network for splicing, and outputting an image. The method has the effect of remarkably improving the similarity between the generated image and the reference style.

The invention discloses an intelligent manufacturing system and method based on an industrial robot, and relates to the technical field of industrial robot control, and the method comprises the steps: constructing a workpiece posture-space structure topological graph according to a cross-scale visual recognition result, carrying out the space comparison with an original trajectory planning graph, outputting a space deviation mapping relation, and carrying out the calculation of a spatial deviation mapping relation; performing spatial correlation analysis and path reachability evaluation on the workpiece attitude-spatial structure topological graph by using a graph neural network to obtain an end execution path instruction; driving the industrial robot to perform intelligent manufacturing operation through the tail end execution path instruction, and obtaining an actual operation result image; and performing feature alignment and difference comparison processing on the actual operation result image and the process reference image to obtain an operation deviation feature mapping relation, performing operation quality evaluation, and outputting an operation result visual detection label. According to the method, the adaptability and the manufacturing precision of the operation path of the industrial robot are effectively improved.

A method, apparatus, non-transitory computer readable medium, and system for image generation include obtaining, via a user interface, a reference image and generating, using an image generation model, a synthesized image based on the reference image. A layered image is generated including the synthesized image in a first layer of the layered image and the reference image in a second layer of the layered image. The layered image is then presented for display in the user interface.