721 results about "Imaging condition" patented technology

The invention provides a wound surface grading model construction method and a wound surface self-analysis system, and the method comprises the steps: processing an obtained wound surface image, clinical text description data and structured background information data through a specific feature extraction strategy, and obtaining an image feature vector; clinical text description data and structured background information data are subjected to cleaning, word segmentation, entity labeling and other operations, a wound semantic evolution graph is constructed, related vectors are fused to obtain text embedding, wound background context vectors are generated according to the structured background information data, dynamic weighting, splicing and fusion are performed on image and text feature vectors, and the image and the text feature vectors are subjected to image fusion. According to the method, the fusion feature vector is obtained, the corresponding grading model is constructed according to the fusion feature vector, more accurate, comprehensive and explainable grading evaluation can be performed on different types of wounds, and a self-analysis system designed for the constructed model can quickly and accurately judge the wound image condition shot by a user.

The invention provides a mobile phone glass quality detection method and system based on image detection, and relates to the technical field of image defect detection, and the method comprises the steps: configuring an imaging condition for collecting a mobile phone glass image, and collecting an original image of mobile phone glass according to the configured imaging condition; constructing a transparency enhancement algorithm according to a transparent background existing in the collected original image, and processing the original image according to the transparency enhancement algorithm to obtain an enhanced image; a crack boundary self-enhancement step is introduced, micro cracks and fuzzy scratches existing in an enhanced image are positioned, the enhanced image is marked according to the positioning result of the micro cracks and the fuzzy scratches, and a marked image is obtained. The phenomenon of false detection or missing detection easily occurring in mobile phone glass detection under a transparent background in related technologies is solved through a transparency enhancement algorithm; and the problem that boundary fuzzy defects such as microscopic cracks and fuzzy scratches with extremely low contrast are difficult to identify is solved through the crack boundary self-enhancement step.

The invention discloses a highway vehicle track monitoring method and system, and the method comprises the steps: carrying out the preprocessing of a pavement vibration signal of a certain road segment through employing Hilbert transform, and obtaining an original vehicle track image with noise, and taking the image as training data in a data set; performing enhancement processing on the original vehicle track image by using a Gaussian kernel function, and then marking a label of the enhanced vehicle track image as label data in the data set; taking vehicle track images with different characteristics of label data in a similar data set as track image conditions, and generating a synthetic noisy image with real track characteristics under the control of the track image conditions; taking the synthesized noisy image and the track image condition as training data and label data in a data set to obtain an expanded data set; and training a de-noising generative adversarial model based on the expanded data set to obtain a de-noising model for de-noising the road vibration signal of any road section to obtain vehicle trajectory information.

The invention belongs to the technical field of image processing, relates to a multi-scale road crack detection method and device in extreme weather and a medium, and relates to the technical field of image processing, and the method comprises the steps: constructing an extreme weather data set; constructing a target detection network; training a target detection network by adopting the data set to obtain a target recognition model; according to the method, the lightweight backbone network CAPNet is adopted to replace a backbone network of the RT-DETR, and the feature extraction efficiency is improved; a cascade multi-branch feature fusion module is used for replacing a CCFM module in a traditional neck network, dynamic integration of multi-scale features is achieved, a multi-scale edge enhancement module is additionally arranged in a network structure, the perception ability of the model to crack edges of different scales is enhanced, the feature expression problem under the low contrast and blurred image conditions is effectively solved, and the dynamic integration of the multi-scale features is achieved. And the balance between the detection precision and the efficiency is realized.

The invention provides a calcium imaging neural signal extraction method and product based on self-supervised pre-training, and relates to the technical field of artificial intelligence. Based on a pre-training data set containing a large amount of unlabeled two-photon calcium fluorescence imaging data, a pre-training decoding network and a corresponding pre-training task are self-supervised, and a pre-trained space-time coding network is trained by taking minimization of a pre-training loss function as a target; the method comprises the following steps of: firstly, establishing an encoder and decoder network, establishing fine-tuning data with accurate space-time marked neuron calcium signal positions through simulation, simultaneously performing fine-tuning on the encoder and decoder network by taking minimization of a fine-tuning loss function as a target, and checking the algorithm extraction precision based on various actual imaging data to obtain a calcium imaging signal extraction algorithm network with strong generalization performance. Therefore, a calcium signal extraction model which can be generalized in various imaging conditions and model biological brain regions can be obtained through training without manual labeling, a high-accuracy neural signal extraction result is obtained, the application difficulty that a deep learning model lacks data generalization ability is overcome, and the accuracy of a neural signal extraction result is improved. Calcium imaging neural activity analysis based on self-supervised pre-training and wide in application is achieved.

The invention relates to a high-resolution remote sensing image change detection method and system based on decoupling representation learning, and solves the defects of false change and non-ideal detection precision caused by inconsistent imaging conditions of different time phases compared with the prior art. The method comprises the following steps: obtaining a training sample; constructing content branches; constructing style branches; training a multi-branch parallel framework; obtaining a high-resolution change result; and separating image styles and obtaining a recovery result. According to the method, a reliable high-resolution remote sensing image change detection result can be obtained under the condition of multi-temporal complex imaging condition interference.

The invention discloses a mold defect visual inspection method, particularly relates to the technical field of industrial machine visual inspection, and is used for solving the technical problem of image spatial variation blurring caused by mechanical vibration under a mobile shooting condition. The method comprises the following steps: acquiring a to-be-detected image on the surface of a mold, analyzing the gradient magnitude of each region, determining the fuzzy characteristics of different regions in the to-be-detected image according to the difference of the gradient magnitudes, evaluating the expected confidence of each region for executing the deblurring operation based on the fuzzy characteristics, and executing the deblurring operation on the regions to obtain a preliminary restored image; an artifact index is calculated in the uniform background area of the preliminary restored image, the distribution concentration degree of image components in each local feature area in the frequency domain is analyzed in the preliminary restored image, and the distribution concentration degree is compared with a preset defect judgment threshold value adjusted according to the artifact index; judging whether the corresponding local feature region is a defect region or not; accurate recognition of mold surface defects under complex imaging conditions is realized.

The invention provides a rapid Marchenko imaging method for undulating surface conditions, and the method comprises the following steps: S1, inputting an earthquake shot record of a fixed surface and a depth domain undulating migration surface, and correcting the earthquake shot record to the migration surface through a static correction method; s2, inputting a depth domain seismic velocity model, and constructing an initial downlink focusing function from an underground target area imaging point to an undulating surface migration surface through a ray tracing algorithm; and S3, taking the shot record on the fluctuating offset surface and the initial downlink focusing function as input, and calculating uplink and downlink Green functions from the underground imaging point to the ground through an iterative method. And S4, applying an energy normalization cross-correlation imaging condition to the reconstructed uplink and downlink Green functions to obtain a Marchenko imaging result. According to the Marchenko imaging method, the problems that conventional Marchenko imaging is low in calculation efficiency and undulating surface conditions cannot be processed are solved, and the Marchenko imaging quality under the undulating surface conditions is ensured while the calculation efficiency is improved.

The invention discloses a road broken line automatic repair method based on unmanned aerial vehicle image segmentation and fitting reconstruction, and relates to a remote sensing image semantic processing and road connectivity recovery method. The method comprises the steps of firstly extracting a road category region in an image; skeletonizing the extracted road area to form a road trunk structure; introducing a multi-scale density clustering algorithm to perform multi-level spatial clustering on the breakpoints, and superposing clustering results to enhance breakpoint coverage; screening out breakpoint pairs conforming to broken line characteristics; for each pair of matched breakpoints, backtracking along the road direction to obtain a certain number of skeleton points, and reconstructing a road connection path; and restoring the repaired road in combination with the width information of the original road to form a complete connected road structure. The method can effectively improve the automatic detection and continuity recovery capability of the road broken line area in the remote sensing image, and is suitable for a road structure intelligent restoration task under a low-quality image condition and a complex road network scene.

The invention discloses an X-ray thermoacoustic imaging method based on a diffusion model and sound velocity compensation, and relates to the technical field of X-ray thermoacoustic imaging and deep learning imaging reconstruction. According to the method, the influence of sound velocity non-uniformity on a thermal sound wave propagation path and time delay is fully considered, and a new method combining sound velocity compensation and diffusion model reconstruction is provided; on the premise that the hardware complexity of an imaging system is not increased, accurate correction of a wave propagation path is achieved through sound velocity modeling, and imaging artifacts and space positioning errors are effectively avoided; meanwhile, the image inversion framework based on the conditional diffusion model has excellent noise suppression and high-frequency detail recovery capabilities, and can generate a high-quality thermoacoustic image with a clear structure and remarkable contrast under the imaging conditions of low signal-to-noise ratio, low energy dose and the like.

Provided by the present disclosure is an ultrasonic phased array-based in-situ imaging method for melt flow in injection molding. The ultrasonic phased array is used for the detection of an injection molding process for the first time, and an effective dynamic monitoring imaging method for a melt front position is developed. A melt flow process in a mold cavity is dynamically monitored by collecting an FMC (Full matrix capture) dataset online. A mapping relationship between an incident angle and a target pixel point is established to rapidly determine time delay of each point in a measurement target region, and a melt bottom image is acquired using TFM (Total focusing method) imaging conditions, from which the melt front can be localized. The provided method is high in measurement accuracy, short in imaging time, and capable of effectively improving imaging efficiency of online measurement.

The invention discloses a process casting image surface defect automatic identification method based on machine vision, which comprises the following steps: acquiring an original image of a process casting, recording an imaging parameter set, and preprocessing to obtain a standardized image; performing homomorphic filtering to obtain an enhanced image and an illumination component, and inputting the enhanced image into an improved OfficientAD algorithm to generate an abnormal score map and a feature residual map; updating homomorphic filtering parameters according to the abnormal score map, the feature residual map and the illumination component, and performing homomorphic filtering for three times to obtain an enhanced image; inputting the enhanced image into an improved OfficientAD algorithm to obtain an abnormal score graph; and performing threshold segmentation and connected domain extraction on the abnormal score graph to obtain a defect candidate region, extracting texture statistics and residual statistics, and matching a defect type feature library to output an identification result. According to the method, stable and consistent automatic identification is realized under complex texture and imaging conditions, and the manual visual inspection cost is reduced.

A computer-implemented method includes converting by a pose tokenizer, based on a learned codebook, pose parameters of a body into a sequence of discrete pose tokens; randomly masking a portion of the sequence of discrete pose tokens; predicting the randomly masked sequence of discrete pose tokens based on multi-scale features extracted from a monocular image by an image conditioned masked transformer; optimizing the sequence of discrete pose tokens by aligning a re-projected three-dimensional (3D) pose with an estimated two-dimensional (2D) pose; directly regressing, from the multi-scale features, a shape parameter of the body and a weak perspective camera parameter; and generating a 3D mesh reconstruction of the body based on the shape parameter and the weak perspective camera parameter.

The invention discloses a method and device for detecting color reducibility of a mobile phone camera and a medium, and relates to the technical field of image quality detection.The method comprises the steps that after geometric alignment is conducted on a color card image set, color block areas are divided, and a cross-light-source color block observation matrix is obtained through color block feature extraction; converting the cross-light-source color block observation matrix into a target color space coordinate, and performing illumination correction to obtain a cross-light-source color coordinate matrix; inputting the cross-light-source color coordinate matrix into a deep learning model, outputting a color block level high-dimensional color embedding matrix, and obtaining a high-dimensional color embedding vector set corresponding to the tested mobile phone camera in combination with an attention weight; calculating a color reducibility error index based on the high-dimensional color embedding vector set in combination with the standard reference library; and performing qualification judgment according to the color reducibility error index to generate a qualification detection conclusion. According to the invention, a stable and reliable technical basis is provided for camera quality detection under a multi-light-source imaging condition.

The invention discloses a hyperspectral fast imaging method and system based on spectral difference depth matrix decomposition, and aims at low signal-to-noise ratio data under a limit imaging condition to realize high-quality image recovery through structural modeling of an average spectrum chart and a spectral difference chart. The method comprises the following steps: firstly, quickly collecting and splicing hyperspectral data to form a three-dimensional tensor, and constructing a forward observation model under a weak light condition; decomposing into an average spectrum chart, a spectrum difference chart and a noise item; rearranging the spectral difference graph into a two-dimensional low-rank matrix, and obtaining a space and spectral principal component matrix by adopting PCA decomposition; an unsupervised dual-path optimization network is innovatively constructed; a 2D jump network is used to optimize a spatial principal component so as to model structural information, and a 1D jump network is used to optimize a spectral principal component so as to fit detail changes; and finally, outputting an optimized spectrum difference graph by the network after joint training, and fusing the optimized spectrum difference graph with the average spectrum graph to complete reconstruction. High-fidelity image reconstruction is realized under single-frame and unsupervised conditions, and the reconstruction quality and robustness are significantly improved.

Means for rigorous quantitative assessment of sperm motility are presented, based on a number of movement and morphology parameters measured using various image processing methods. The quantitative assessment thereby achieved is objective and multidimensional, allowing for detailed and repeatable assessment of samples. Deep learning methods are also disclosed for detection and classification of sperm cells (or other types of cells or particles) using neural networks, adapted to work both in good and in poor imaging condition, such as low magnification and resolution. Specific methods for both supervised and unsupervised deep learning approaches are delineated. In a non-limiting disclosure, the methods are particularly adapted to deal with cases of azoospermia, where there is a very low number of sperm cells (most of which do not swim) and a lot of debris in the imaged field of view.

The invention discloses a radar point cloud-based train body modeling method and device and a related medium, and the method comprises the steps: obtaining an original point cloud frame sequence, and carrying out the parameter synchronous mapping of the original point cloud frame sequence, and obtaining a standardized point cloud sequence; performing data preprocessing on the standardized point cloud sequence to obtain a preprocessed point cloud sequence; performing time sequence registration and continuous modeling by using the preprocessed point cloud sequence to generate continuous vehicle body characterization; performing semantic segmentation and region extraction on the continuous vehicle body representation to obtain a door region set; extracting morphological characteristics of the vehicle body based on the door area set to obtain a structured parameter set with a door area state label; and performing formatting packaging on the structured parameter set, and outputting vehicle body modeling parameters. According to the method, three-dimensional modeling is carried out on the train, and the feature state is extracted, so that train state identification can be stably completed under different illumination and shielding conditions without depending on external identification and imaging conditions, and the positioning accuracy is improved.

A method for training a generative image model includes receiving training data having training images and image captions and annotating the training data resulting in annotated training data having the training images, the image captions, and moderation labels, such that each training image is associated in the annotated training data with a corresponding image caption and at least one corresponding moderation label. The method further includes performing a training process to train a generative image model using the training images, the image captions, and the moderation labels, resulting in a trained model that generates images conditioned to both the image captions and the moderation labels.

The invention discloses a depth physical parameter integrated network and method for lensless microscopic imaging, and the method comprises the steps: firstly building a physical forward model based on fractional Fourier transform, and quantizing a propagation distance into an adjustable fractional order parameter; secondly, constructing a multi-stage progressive network framework, wherein each stage comprises a physical model fidelity module and an image regularization updating module; a self-adaptive near-end network and a self-adaptive compression-excitation network are innovatively embedded in a regularization module, and real-time self-adaption of network parameters to imaging conditions is achieved; finally, end-to-end single-frame reconstruction is realized, complex amplitude information is directly recovered from a single intensity observation image, and retraining for different imaging parameters is not needed. According to the method, the reconstruction physical reliability, the noise robustness and the cross-scene generalization ability are remarkably improved, meanwhile, the lightweight real-time deployment requirement is met, and the method is suitable for lens-free microscopic imaging scenes such as biological living body observation and portable pathological diagnosis.

An information processing apparatus includes a processor configured to acquire a plurality of pieces of image data obtained by imaging an object by changing at least one of a relative positional relationship between the object and an imaging unit and an imaging condition of the imaging unit for setting brightness of an image to be acquired, acquire a region where a point group can be generated from each of the plurality of pieces of image data, and set the imaging condition in a case where the object is imaged by the imaging unit on a basis of the regions acquired.

A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing includes acquiring a first face image, specifying a first state of elements of an imaging condition from the first face image, generating a second state of the elements of the imaging condition changed such that the first state is improved, inputting the second state to a machine learning model for each of action units (AUs) that represent movements of facial expression muscles, with states of the elements of the imaging condition as features and errors in estimated values with respect to ground truth values of intensities of the AUs as ground truth data, to estimate prediction errors for each of the Aus, determining whether or not predetermined criteria are satisfied by all of the prediction errors, and specifying the elements of the imaging condition suitable to be improved on the first face image.

The invention discloses a welding seam defect X-ray image intelligent diagnosis and credible traceability method based on multi-modal heterogeneous information collaboration, which is characterized in that a defect analysis network fusing multi-domain feature modeling and graph structure expression is constructed on the basis of bimodal data formed by a welding seam X-ray image and an industry detection standard text. In the image mode, dividing the weld seam image into a plurality of local area units through superpixel segmentation, taking the areas as image nodes, respectively extracting spatial domain, frequency domain, wavelet domain and edge domain features, and constructing a weighted graph structure by combining the spatial adjacency relation and the feature similarity relation between the areas; realizing overall modeling and correlation analysis of weld defect structure information by using a graph convolutional network; in a text mode, feature coding is carried out on an industry detection standard text, and the feature coding is used as an important prior constraint for defect judgment. Collaborative modeling of an image detection result and standard semantic information is achieved through a gating fusion mechanism, a mapping relation between a detection conclusion and a standard term is established, and interpretable expression and result credible traceability of the weld defect diagnosis process are achieved. And a welding seam X-ray film automatic digital acquisition and observation device is adopted in a matched manner, so that stable transmission, positioning observation and high-resolution digital imaging of the industrial ray film are realized, and reliable and consistent image data input is provided for the intelligent diagnosis method. The method is suitable for intelligent defect detection under complex welding seam structures and multi-working-condition imaging conditions, and has high engineering application value and popularization prospect.

The invention discloses an ISAR image ship coarseness identification method and system based on scattering feature estimation, and relates to the technical field of target identification, and the method comprises the steps: constructing and training a target identification model; the target recognition model comprises a small grazing angle image feature enhancement module based on scattering characteristic estimation, a feature extraction fusion module based on motion attitude decoupling and a full connection module; the small grazing angle image feature enhancement module comprises a scattering center parameter estimation module, a complex waveform feature extraction module and a physical data feature fusion module; the feature extraction fusion module comprises a spatial frequency domain attitude mode representation module, an attitude adaptive deformation convolution module and a frequency domain space double-attention fusion module; obtaining an ISAR image of a to-be-identified marine ship; and inputting a to-be-identified marine ship ISAR image into the trained target identification model for ship type identification. According to the invention, the identification precision of the ship type under the conditions of small grazing angle and complex motion imaging is improved.

The invention discloses an EMSDH-YOLO network-based detectable target detection method for a low-quality image, and relates to the technical field of target detection in industrial automation. According to the method, partial convolution in a fourth C3k2 module of a backbone network of a YOLO11 model and a C3k2 module at the tail end of a Neck network is replaced by efficient multi-scale convolution, and an EMS-ConvBlock module is constructed; adding large-kernel separable convolution into a spatial pyramid structure of a backbone network of the YOLO11 model, fusing a two-dimensional convolution solution idea, and constructing an LSK-SPPF module; the method comprises the following steps: adding the idea of Dynamic Tanh into a C2PSA module of a backbone network of a YOLO11 model, realizing adaptive calibration of attention weight by using hyperbolic tangent gating, and constructing a DyT-C2PSA module; the method comprises the following steps: adding an idea of channel shuffling and space rolling mixing into an up-sampling architecture in a YOLO11 model Neck network, and constructing an EU-SCMixer module; and a loss function of the YOLO11 model is optimized by adopting a Wise-IoU v3 method. According to the method, the detection precision of the mechanical arm on various grabbable objects can be improved when the mechanical arm carries out grabbing operation under the low-quality imaging condition.

A method for image generation includes receiving a training data that includes training images, image captions each corresponding to one of the training images, and contextual features each associated with one or more of the training images. The method further includes performing a training process to condition a generative image model using the training images, the image captions, and the contextual features, resulting in a conditioned model that generates images conditioned to both the image captions and the contextual features.

The invention provides a scene recognition method based on rendering data and electronic equipment, and the method comprises the steps: obtaining real-time rendering data of a first image frame before the rendering operation is executed on the first image frame; based on the real-time rendering data, identifying at least one of a picture zooming type corresponding to the first image frame, a carrier type used by a game role, a game scene and scene light complexity; determining an optimization strategy for the first image frame based on at least one of a picture zooming type corresponding to the first image frame, a carrier type used by a game role, a game scene and scene light complexity; and performing a rendering operation on the first image frame based on the optimization strategy. In this way, before the image is rendered, the picture scene which can influence the optimization strategy can be recognized in advance. The subsequent optimization algorithm can adopt the most appropriate optimization strategy to perform rendering operation on the first image frame based on the image condition recognized in advance, so that accurate rendering resource supply is realized, and the user experience is improved.

The invention discloses a steel industry material steel seal character recognition method and device and a storage medium, and belongs to the field of motor control. According to the method, an original steel seal character image is subjected to low-light image enhancement by adopting a low-light enhancement model, so that the brightness of a steel seal character area in a picture is improved, meanwhile, the picture is prevented from overexposure, and after a low-light enhanced image is obtained, character positioning and tilt correction are performed, and finally, each row of steel seal characters are identified. The problem that the material character recognition accuracy is low due to the fact that the low-illumination image is collected under the condition that the field environment and the imaging condition are poor is solved, and the steel seal character recognition efficiency is effectively improved.

The invention relates to the technical field of material microstructure analysis, in particular to a method for manufacturing a transmission electron microscope sample based on image processing, which comprises the following steps: acquiring an initial optical image of an original sample to determine a target area and a corresponding positioning coordinate; preliminarily cutting according to the positioning coordinates to obtain a pre-processed sample containing a target area; performing optical imaging on the pre-processed sample based on a preset imaging condition to obtain a plurality of groups of gray level images, and determining the thickness distribution condition of the pre-processed sample according to the surface roughness of the pre-processed sample, the preset imaging condition and the plurality of groups of gray level images; and performing thinning treatment on the pre-processed sample to obtain a transmission electron microscope sample, generating a thinning path according to the thickness distribution condition to perform thinning treatment, determining the thickness change trend of the sample according to the real-time thickness of the sample in the thinning process, adjusting a preset thinning parameter according to the thickness change trend of the sample, and determining the thinning termination opportunity. The thickness of the transmission electron microscope sample is uniform, and the observation requirement is met.

An electronic device according to an embodiment may include a camera, memory, a driving device configured to move a location of the electronic device, a projector configured to output a beam, an adjustment device configured to adjust at least one of an angle or a height of the beam output from the projector, and a processor. The processor according to an embodiment may be configured to identify, when the electronic device is located in a predetermined space, first values for locations at which a beam corresponding to a content is to be output through the projector in the predetermined space. The processor according to an embodiment may be configured to determine a first location at which a beam corresponding to a content is to be output through the projector, based on the first values, and control the driving device so that the electronic device is moved to the first location. The processor according to an embodiment may be configured to output, when the electronic device is moved to the first location, a first beam for testing through the projector on a first surface corresponding to the first location. The processor according to an embodiment may be configured to identify whether the first image satisfies a designated image condition by inputting, to a first artificial intelligence (AI) model stored in the memory, a first image which is captured through the camera, corresponding to the first beam which is displayed on the first surface. The processor according to an embodiment may be configured to control, based on identifying that the first image does not satisfy the designated image condition, at least one of the adjustment device or the driving device so that the first image satisfies the designated image condition. The processor according to an embodiment may be configured to, based on identifying that the first image satisfies the designated image condition, output the first beam corresponding to the content through the projector on the first surface while controlling, at least one of the adjustment device or the driving device to a state corresponding to the designated image condition.

The invention provides a medical magnetic resonance image reconstruction method and system, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining sampling coding and under-sampling k space data; processing the under-sampled k space data through inverse Fourier transform, and converting the k space data into an image domain to obtain under-sampled image data with blurring or artifacts; carrying out preliminary restoration on the undersampled image data based on sampling coding, further applying data consistency operation on the image to obtain a rough image, and taking the rough image as an image condition vector after underspace coding; acquiring a text serving as a cue word, inputting the text into a text encoder, and encoding the text into a high-dimensional semantic embedding vector by the text encoder; and inputting the high-dimensional semantic embedding vector and the image condition vector into a reconstruction model based on the correction flow to predict a full-sampling MRI image, and generating a final reconstructed MRI image.