418 results about "Single image" patented technology

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

The invention relates to the technical field of intelligent inspection of power equipment, and particularly discloses a power transmission line inspection system based on an autonomous correction long-endurance unmanned airship. The system aims to solve the problems of short endurance, small operation radius, single image angle, intermittent operation, leak detection, low precision, poor environmental adaptability and the like caused by dependence on manpower or GPS navigation in the conventional unmanned aerial vehicle inspection. The system comprises an energy supply module, a data acquisition module, an intelligent control module and a communication and data return module. Wherein the energy supply module is integrated with a solar energy collection, storage and intelligent scheduling unit to realize day-and-night continuous energy supply; the intelligent control module adopts an RT-DETR visual algorithm to identify a tower in real time, performs automatic deviation correction by fusing pose information, and accurately tracks a preset track; the data acquisition module supports multi-angle high-definition imaging, and the communication module guarantees real-time data return. And high-precision unmanned inspection with ultra-long endurance, full-automatic and wide-area continuous coverage is integrally realized.

The invention relates to the technical field of image processing, and discloses a fluorescent lifetime microscopic image large-view-field splicing method and system and electronic equipment, and the method comprises the steps: constructing a tissue region mask for a to-be-spliced image, and constructing a vignetting model in a tissue region to complete vignetting correction; counting brightness indexes in an organization area to determine a global brightness reference, and realizing image group brightness unification through global zooming and single image brightness adaptive correction; further realizing alignment of adjacent images through feature point matching, and finishing image block splicing in combination with a minimum color difference suture fusion method to obtain an image band; an overlapping area is extracted from an image belt to generate an effective content mask, relative displacement is estimated by using a phase correlation method after low-frequency suppression, image belt splicing is completed by multiplexing a minimum color difference suture fusion method, a large-view-field spliced image is output, the automation degree and robustness of splicing are improved, the spliced image is geometrically consistent and visually seamless, and the splicing efficiency is improved. And the requirements of medical research on high-resolution and large-field-of-view fluorescence lifetime microscopic images are met.

The invention discloses a target detection method and system based on dynamic memory enhancement, relates to the technical field of computer vision and target detection, and aims to solve the problems that a traditional method depends on local features of a single image, is difficult to detect a target, is low in precision and is weak in generalization ability. The method comprises the steps that after a to-be-detected image is preprocessed, multi-stage multi-scale features are extracted through a backbone network; an encoder outputs single image global features for feature interaction fusion, and a global distribution extraction module constructs and updates a data set level global feature distribution library; the decoder adopts a hierarchical structure, and optimizes the target query token layer by layer in combination with self-attention, cross attention and global distribution fusion sub-modules; and finally outputting a target category and bounding box coordinates. Through global context injection, small target and shielding target detection precision is improved, model training stability and generalization ability are enhanced, and the method is suitable for diversified detection scenes.

The invention discloses a multi-modal data feature representation optimization method based on comparative learning and two-stage masks. The method comprises the following steps of: firstly, aiming at an input single image-text pair, generating two groups of heterogeneous image-text data with mode missing as two inputs of a model by applying two random mask strategies with different mask areas and proportions; wherein the first group of data applies a high-proportion mask to the image and applies a low-proportion mask to the text; the second group applies a low-scale mask to the image and a high-scale mask to the text. Then, the two groups of data respectively pass through an image encoder and a text encoder which share parameters, and two different multi-modal fusion feature vectors are generated through a cross-modal fusion encoder; according to the method, masked modal information is recovered through a decoder, and the reconstruction loss of the difference between a recovery result and original data is calculated. Meanwhile, two multi-modal fusion feature vectors generated twice are subjected to comparative learning, and the comparative loss of feature representation distances under different enhanced views for approaching the same image-text pair is calculated. And finally, performing weighted summation on the reconstruction loss and the comparison loss to form an overall loss function, and optimizing model parameters through back propagation. When the trained encoder is used for a downstream multi-modal classification task, the classification precision and generalization ability of the model can be effectively improved.

The utility model discloses a peripheral out-of-focus lens assembly and glasses, and the assembly comprises a basic lens which generates perspective focal power for perspective light; the single image source is arranged at the edge position of the near-eye side of the basic lens and is far away from the basic lens; the plurality of light splitting surfaces are embedded into the basic lens and are distributed in the basic lens at different inclination angles, and the central normal lines of the plurality of light splitting surfaces are inclined to the side where the image source is located relative to the central normal line of the lens; the light irradiation range of a single image source covers all the light splitting surfaces; each light splitting surface and the shared image source form an out-of-focus unit, each light splitting surface is used for reflecting light rays emitted by the image source to form convergent light rays, the convergent light rays are emitted to an exit pupil position, and an out-of-focus image is formed after the convergent light rays enter human eyes; a plurality of out-of-focus images formed by reflection of the plurality of light splitting surfaces are respectively distributed in a peripheral view field. The peripheral out-of-focus lens assembly and the glasses are used for realizing out-of-focus stimulation, inhibiting eye axis growth and achieving the purpose of preventing and controlling myopia.

The invention discloses a single image defogging method based on block-by-block nonlinear brightness prior, which belongs to the technical field of image processing, and comprises the following steps of: dividing a fog-containing image into local blocks, calculating the average brightness of the blocks, and constructing prior block-by-block monotone increasing nonlinear mapping to represent the corresponding relationship between the brightness of fog-containing blocks and the brightness of clear blocks; an atmospheric scattering model is combined, an atmospheric light vector is modeled into a vector, the vector and a PPWF form a parameterized recovery model, three scalar parameters are taken as a core, an optimal parameter is obtained through multi-target joint optimization, alternate optimization and golden section search, and finally a defogged image is generated. The method has the advantages of few parameters, low complexity and no need of training data, the definition and global contrast of far and near scenery can be remarkably improved while the image structure is maintained, and halo, excessive enhancement and color cast are effectively inhibited; the method has good robustness for different fog densities and illumination conditions, and is suitable for real-time and embedded defogging application of single-channel or multi-channel images.

The invention discloses a text-to-three-dimensional object (3D) generation method based on dual color consistency matching, and aims to solve the problem of 3D asset quality reduction caused by multi-view color inconsistency in the prior art. According to the method, in each training iteration, a group of multi-view images are firstly rendered from a 3D model, and a high-fidelity forward view is specified as a color reference; subsequently, optimization is carried out through a double matching mechanism: 1, matching among images: aligning colors of other views to the reference view by using depth semantic features, thereby breaking circulation of error accumulation; 2, image internal matching: in a single image, color and geometric smoothness in the same semantic region are standardized by improving total variation loss; through the dual mechanism, accumulation and propagation of unnatural color distortion are effectively prevented, color inconsistency is reduced, and the visual fidelity and color consistency of the generated 3D model are remarkably improved.

The invention relates to the technical field of multi-modal medical image fusion, provides a medical image fusion method based on a visual state space and a gated attention mechanism, and effectively captures cross-modal local details and a global dependency relationship through collaborative combination of a convolutional neural network and a state space model. Specifically, a multi-scale feature extraction module is introduced to extract hierarchical features of different scales of each mode, and the module further introduces an optimized channel-space attention module to enhance cross-scale feature expression and inter-channel interaction. In order to model long-distance spatial dependence and enhance cross-modal feature interaction, a multi-scale visual state space module is developed based on SSM, and the module can realize global context aggregation while keeping fine-grained structure details. And finally, integrating the information of the two modes together through a specific fusion strategy, and generating a single image with rich details.

The invention discloses a visual precise positioning method based on pixel fusion alignment, and relates to the technical field of visual positioning, and the method comprises the steps: obtaining a target image shot by a to-be-positioned terminal and rough pose data, screening a candidate grid set in a pixel fusion grid template dictionary based on the rough pose, and obtaining a candidate grid set; performing multi-channel pixel fusion alignment on the target image in the candidate grid set, performing step-by-step detailed search from top to bottom according to grid levels, and calculating a target three-dimensional coordinate in a minimum grid according to a pixel fusion alignment result; multi-source three-dimensional scene modeling, multi-layer grid organization, multi-channel pixel-level alignment and an online self-learning updating mechanism are comprehensively utilized, and the problem that in the prior art, in the indoor and outdoor integrated complex environment where satellite signals are weak and illumination and scenes change frequently, the multi-source three-dimensional scene modeling, multi-layer grid organization, multi-channel pixel-level alignment and online self-learning updating are difficult to achieve is solved. The method solves the problem that high-precision and high-robustness three-dimensional visual positioning is realized only by means of a single image of the terminal, and is particularly suitable for navigation guidance and intelligent equipment positioning application of large comprehensive transportation hubs, airports and urban complexes.

Systems and methods for generating a 3D model from a single input image are described. Embodiments are configured to obtain an input image and camera view information corresponding to the input image; encode the input image to obtain 2D features comprising a plurality of 2D tokens corresponding to patches of the input image; decode the 2D features based on the camera view information to obtain 3D features comprising a plurality of 3D tokens corresponding to regions of a 3D representation; and generate a 3D model of the input image based on the 3D features.

The invention discloses a single-image three-dimensional generation method and system, and relates to the technical field of three-dimensional object generation. According to the method, a three-dimensional field is initialized from a single input image through micro rendering, three orthogonal view angle images are synthesized by using a multi-view angle diffusion model, and a supervised depth map is obtained by combining monocular depth estimation; obtaining an initial three-dimensional object with a basic geometric structure through scale-invariant depth consistency loss and geometric optimization loss; and rendering the initial three-dimensional object from multiple perspectives, generating a prior image through a pre-training diffusion model under the condition of the input image, and performing texture refinement by adopting a frequency spectrum-space distillation strategy to generate a high-quality three-dimensional object. The method can improve texture details and structure completeness of a three-dimensional object generated by a single input image.

The invention discloses a steel wire rope defect detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a to-be-detected picture and a prompt text; inputting the to-be-detected picture and the prompt text into a multi-modal large model for image feature coding, text feature coding and modal fusion processing, and positioning and classifying different types of defects to obtain a detection result; and outputting the detection result. According to the method, the defects that in the prior art, dependence of a single image or manual marking is serious, multi-modal information fusion is insufficient, the detection precision of internal and tiny defects is low, and the online monitoring automation degree is poor can be overcome, and efficient, accurate and robust diagnosis of the health state of the steel wire rope is achieved.

Provided are systems and methods to perform novel view synthesis of a three-dimensional (3D) scene with a machine-learned diffusion model. Example implementations of the proposed models may be referred to as “3D Diffusion Models” or 3DiM. The models described herein can be or include an image-to-image diffusion model that takes one or more (e.g., a single) reference views and one or more (e.g., a single) relative poses as input and generates the target view. Thus, the machine-learned diffusion models described herein can perform novel view synthesis from as few as a single image.

Fluorescence imaging systems and methods of are described herein that enable imaging of sequencing samples with a light source of a single color and a single image sensor. The optical systems herein provides higher signal efficiency, simpler optical filter design, and lower costs for DNA sequencing analysis and other imaging applications.

The invention discloses a medical image recognition method and system based on artificial intelligence, and the method comprises the steps: obtaining CT image data, pathological section data and PET metabolic activity data, introducing a GNN graph neural network to model the semantic association between multi-modal features, and outputting a fusion feature vector; inputting the fusion feature vector into a DSRN double-flow space-time recursive network, extracting 3D lesion morphological features of a single image by using a spatial flow, analyzing lesion growth kinetic parameters of a historical image sequence through a time flow, and generating a lesion malignancy probability index based on a gating fusion unit; based on the focus malignancy probability index, Monte Carlo Dropout sampling is utilized to generate a confidence interval, a clinically interpretable credibility score is output, and when the confidence is smaller than a threshold value, a low-confidence area is displayed for a doctor to check. The deep fusion of multi-source information is realized, the one-sidedness of a single mode is avoided, and the recognition accuracy and efficiency are improved.

The invention relates to the technical field of intravenous infusion monitoring, and discloses a real-time monitoring method and system for a static therapy infusion process based on image recognition, and the method comprises the steps: obtaining visible light image data, non-contact distance data and structured light image data, and adaptively selecting a liquid level height determination mode according to the transparency of liquid in an infusion bag, thereby achieving the real-time monitoring of the static therapy infusion process. When the liquid is transparent, visible light and structured light image data are utilized, and when the liquid is turbid or non-transparent, non-contact distance data are utilized, so that the problem that liquid level identification is inaccurate when the traditional single image identification method is used for infusion liquid with different transparency is effectively solved.

The invention discloses a multi-source heterogeneous data intelligent analysis method based on a large model, relates to the technical field of data intelligent analysis, and solves the technical problems that complex characteristics of multi-source heterogeneous data are difficult to adapt, and deep fusion and accurate analysis of the data cannot be realized. According to the method, a simple similarity judgment mode of a single image and a text is abandoned, image ambiguity calculation is introduced, effective images are screened, blurred images are removed, then the similarity mean value of the screened images and the complaint text is calculated, double preset similarity threshold values are combined, a closed-loop mechanism of primary judgment-review correction is constructed, and the complaint text is obtained. According to the method, the identification error of primary judgment is effectively corrected, the misjudgment rate of image-text semantic inconsistency is reduced, the accuracy and reliability of cross-modal data consistency judgment are improved, meanwhile, the limitation of single auxiliary verification dimension in the prior art is broken through, a multi-dimensional auxiliary verification system of commodity category suitability inspection and user historical behavior risk scoring is constructed, and the accuracy and the reliability of cross-modal data consistency judgment are improved. And accurate decision-making requirements of different scenes are met.

The invention relates to an image spatial feature fusion method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the spatial feature quantification processing and spatial compression processing of a medical image in a time domain, and obtaining a spatial time domain feature and a spatial compression feature; the dimensions of the spatial time domain feature and the spatial compression feature are both smaller than the dimension of the medical image; performing frequency domain feature analysis on the space time domain feature to obtain a frequency domain feature; and obtaining fusion features based on the space time domain features, the frequency domain features and the space compression features, wherein the fusion features are used for executing classification and / or detection tasks. According to the invention, more effective deep and multi-dimensional feature extraction and fusion can be carried out on single image information.

This invention belongs to the field of image processing and computer vision technology, and discloses a lightweight single-image super-resolution reconstruction method based on a directional stripe hybrid network. It captures directional structural information of the image from both the spatial and frequency domains through horizontal stripes, vertical stripes, local window attention of the directional stripe self-attention module, and direction-specific convolution of the directional high-frequency enhancement module. The directional stripe self-attention module effectively captures long-distance dependencies extending along specific directions; the directional high-frequency enhancement module extracts high-frequency components through frequency domain decomposition, combines them with direction-specific convolution, and introduces a dual-guided adaptive enhancement mechanism. It identifies texture-rich regions through local variance maps and structural boundaries through edge maps, generating adaptive weights to guide feature enhancement, accurately enhancing high-frequency edges and textures with different orientations while effectively suppressing noise. The two work together to achieve spatial-frequency domain dual-level directional modeling, significantly improving the reconstruction quality of images rich in directional textures, such as architectural scenes and cartoon images, while maintaining a lightweight design.

The invention relates to the technical field of image recognition, and discloses a binary logic image recognition sample set method based on single image construction, and the method comprises the following steps: S1, image acquisition: obtaining a forward image of a specific field; s2, image preprocessing: aiming at the obtained image, carrying out one or more random superposition adjustment on the saturation, image quality, gray level, rotation and contrast parameters of the image by utilizing an existing image processing method, integrally generating N forward image samples as required, marking as S1-SN, and taking N as 400; and S3, marking, and obtaining a sample set irrelevant to the specific field according to the specific field. According to the method, sample expansion is performed based on a related image processing algorithm by using a single forward image in a specific field, and a basic binary logic sample set for training forward image recognition in the specific field can be quickly formed in combination with reverse sample sets in other irrelevant fields.

The invention discloses a three-dimensional target reconstruction and instance association method based on a multi-view image. The method comprises the following steps: firstly, acquiring initial three-dimensional representation and camera parameters of a scene through a three-dimensional reconstruction model; secondly, carrying out two-dimensional target detection on the plurality of input images; the method comprises the core steps that cross-view instance association is carried out on a detected target, a six-dimensional feature vector containing a three-dimensional position and a camera observation direction is constructed, clustering is carried out by adopting a greedy matching algorithm with constraints, and the algorithm ensures that one instance only corresponds to one detection frame in a single image; finally, the model can be selectively optimized through instance segmentation and secondary reconstruction, and a three-dimensional bounding box is generated for each instance. According to the method, the problem that instances are wrongly merged in a traditional clustering method is solved, the accuracy and robustness of multi-view target association are remarkably improved, and a high-quality independent three-dimensional target model can be generated.

The embodiment of the invention relates to the technical field of digital image processing and computer vision, and provides a single image defogging method based on iterative transmissivity correction and a related device, and the method comprises the steps: obtaining a to-be-processed foggy image; performing atmospheric light value estimation processing according to the to-be-processed foggy image to obtain a global atmospheric light value; on the basis of dark channel prior, according to the to-be-processed foggy image, transmissivity graph calculation processing is carried out, and an initial transmissivity graph is obtained; according to the to-be-processed foggy image and the global atmospheric light value, performing optimization iteration processing on the initial transmissivity graph to obtain a target transmissivity graph; based on the atmospheric scattering model, image defogging processing is carried out according to the to-be-processed foggy image, the global atmospheric light value and the target transmittance graph, so that a more accurate target restored image can be obtained, and the defogging processing accuracy of a single image can be effectively improved.

A method and system for generating a depth map corresponding to a frame of a sequence of frames in a video clip is disclosed. This can involve generating a single image depth map for each of a plurality of frames, scaling the single image depth maps, and processing a time sequence of scaled single image depth maps to generate said depth map corresponding to the frame of the sequence of frames in the video clip.

The application discloses a 3D multi-target detection method based on semantic driving single image for ATS, which processes input RGB images, extracts 3D bounding boxes of each object in the images, generates all potential 2D projections of 3D objects in a scene, extracts keywords, phrases and semantic information in the description to form feature information P representing the language description t ; fuses 2D image information and 3D geometric information of the object to obtain complete object representation a ; associates language features extracted from the language description with the detected 3D object, captures the semantic correspondence between the text and the visual modal; filters the target according to the generated matching score to obtain all targets meeting the natural language description. The application improves the accuracy and efficiency of retrieval and recognition, realizes higher recognition accuracy, significantly reduces the computational complexity, can improve the accuracy and speed of cross-modal retrieval, and greatly improves the accurate recognition and positioning ability of traffic events.

The invention discloses a vehicle-mounted projection system and an automobile product, which can decompose the same to-be-projected image into two parts of 2D to-be-projected pixel information or 3D to-be-projected pixel information so as to control a DLP projection module to simultaneously project and form a visual effect of a 2D plane image corresponding to the 2D to-be-projected pixel information. And the visual effect of the 3D stereoscopic image corresponding to the 3D pixel information to be projected is realized, so that the vehicle-mounted projection technology with both the 2D projection effect and the 3D projection effect is realized. According to the technical scheme of the invention, the personnel on the vehicle can simultaneously watch the 2D projection effect and the 3D projection effect, so that different attention can be easily generated for different parts of the projection effect of the same to-be-projected image according to the difference between the 2D projection effect and the 3D projection effect, and the user experience can be improved in the aspects of improving the information transmission efficiency of the to-be-projected image and the like. The invention is widely applied to the technical field of automobiles.

The application discloses an elevator left-over detection method and system, and the method comprises the following steps: collecting front and rear images; extracting a front feature map from the front image and a rear feature map from the rear image; calculating the cosine similarity of the front feature map and the rear feature map; generating a binary image based on the cosine similarity; and judging whether there is a left-over based on the binary image. Thus, the images before and after taking the elevator are collected to extract features, and whether there is a left-over is judged by the similarity between the two images, instead of only identifying a single image, so that the influence of environmental factors is effectively excluded, and the identification accuracy is significantly improved.

This invention provides a face recognition method, device, system, and storage medium based on feature detection. The method includes: acquiring image data; inputting the image data into a face recognition model to identify several facial feature information; the face recognition model includes an encoding / decoding deep learning neural network and a predictive feature neural network; filtering identical facial feature information through an IOU module; classifying target features through a candidate box classification module, aggregating each facial feature information element and its related target elements into a feature set and assigning a unique code to each feature set; filtering each feature set through the IOU module; inputting each feature set into a logic judgment module for occlusion judgment, obtaining a mapping between the code and the occlusion judgment result; and outputting the face recognition result. This invention supports simultaneous recognition of multiple faces occluded in a single image, meets the real-time requirements of face recognition, and offers high accuracy and stronger robustness.

An image presentation system for an amusement park includes a first display with an opening extending therethrough, wherein at least a portion of the first display is viewable from a viewing area, a movable arm, wherein the movable arm is positioned within the opening, a second display supported by the movable arm and offset from the first display, wherein the second display conceals the opening from the viewing area, and a controller configured to provide a first image via the first display and a second image via the second display such that the second image comprises an image effect that accounts for the offset and coordinates with the first image to present as though the first image and the second image are a single image displayed by the first display.

The invention relates to the technical field of medicine, in particular to an ophthalmology clinic system and method simulating a three-level diagnosis and treatment mode and medical device.The system comprises a triage module provided with a first diagnosis model and capable of achieving eye disease screening and early triage based on single-mode image data and clinical data of eyes of a user; the diagnosis module is provided with a data fusion model and a second diagnosis module and can realize accurate recognition and disease progress prediction of complex eye diseases, the decision module is provided with a third diagnosis model and can provide personalized eye disease treatment scheme suggestions for patients, and the patients are propagandized and educated in combination with a large language model, so that the diagnosis efficiency is improved. And the clinical decision-making efficiency and the self-management ability of the patient are improved. Therefore, the problems of limited screening capability, low accuracy, incapability of effectively predicting the development trend of diseases and the like due to the fact that ophthalmology diagnosis mostly depends on single image analysis in related technologies are solved.