63679 results about "Pattern recognition" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

Since defect areas in collected hydro turbine defect images account for a small proportion and there is a significant color difference between surrounding background and defects, which creates too much redundant information and greatly affects detection speed, a method for detecting defects in a hydro turbine top cover based on an improved YOLOv8 model involves first cropping collected defect images to obtain cropped images, then expanding the cropped images by image enhancement techniques such as image flipping and image blurring to obtain a training dataset. A defect detection network based on YOLOv8-CBAM is constructed and trained to generate a defect detection model for detecting the defects in the hydro turbine top cover. The method achieves high precision in defect detection for the hydro turbine top cover.

A multi-modal information tagging method, apparatus and device, and a storage medium and a product. An information tagging effect is effectively improved by means of determining visual feature information of visual information by means of a multi-modal tagging system, performing multi-modal fusion processing on the basis of the visual feature information, the visual information, recognized text information and descriptive text information to obtain image-text fusion features, determining image-text correlation information between the visual information and both the recognized text information and the descriptive text information, on the basis of the image-text fusion features and the image-text correlation information, determining a tagging result for information to be tagged, and performing multidimensional information tagging on the basis of multi-modal information fusion and cross-modal correlations.

An exemplary image segmentation method and system that employs, in a deep neural network, (i) an attention gate that fuses features with skip connections and (ii) a graph or multi-scale convolutional attention module that enhances the long-range and local context. The segmented region or image data derived in part from the segmented region can be subsequently employed for diagnosis, controls, planning, assessment, or analysis of various diseases. The image data can be medical images (e.g., from medical instruments), sensor images or videos (e.g., from smart phones, cameras, satellites, etc.), as well as 3D objects or volume.

The invention discloses a stainless steel surface defect detection method based on machine vision, and relates to the field of material defect detection.The stainless steel surface defect detection method comprises the steps that stainless steel surface images are collected, the collected multispectral surface images are subjected to image fusion, and a multispectral composite image is generated; performing detail enhancement on the low-resolution image through a super-resolution reconstruction algorithm to obtain a high-resolution image set; positioning a defect area position from the high-resolution image set through a defect detection algorithm, and obtaining a defect position data set; extracting a historical image defect feature set and a real-time image defect feature set from the defect position data set, and constructing a defect classification model according to the historical image defect feature set; inputting the real-time image defect feature set into a defect classification model, and performing defect classification and evaluation to obtain a defect classification data set; and generating and distributing a defect detection report. By combining the multispectral imaging algorithm, the super-resolution reconstruction algorithm and the defect classification model, the automation, precision and efficiency of stainless steel surface defect detection are improved.

A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

The present application discloses a face image restoration method based on a state space model, which includes inputting the to-be-restored face image to a restoration model to obtain a restored face image; the restoration model includes an encoder and a decoder; the encoder sequentially includes a first image fusion module, a first multi-scale state space module, a second image fusion module, a second multi-scale state space module, a third image fusion module, and a third multi-scale state space module; the decoder sequentially includes a fourth multi-scale state space module, a first multi-scale attention fusion module, a fifth multi-scale state space module, a second multi-scale attention fusion module, a sixth multi-scale state space module, and a third multi-scale attention fusion module. This method ensures the consistency of face semantic information while restoring the detailed texture, especially low quality face images in real degraded scenes can achieve better restoration results.

Locator systems are disclosed for locating buried utilities such as conduits, cables, pipes. or wires. The locator system may include image-capturing and position and orientation measuring devices such as magnetic compass, accelerometers, gyros, GPS, and DGPS. The system may associate images captured during the locate process with buried utility position data and the other sensor data. These data and images may be further associated with terrain images from satellites or aerial photography to provide a highly precision map and database of buried utility locations and visual images of the burial site. Information so obtained may be transferred to a hand-held personal communication device, such as a smart phone to show the location of buried utilities in combination with photo-images and / or terrain maps.

The invention discloses a large-scene three-dimensional reconstruction method based on three-dimensional Gaussian sputtering, and relates to computer graphics. The method comprises the following steps: collecting a multi-view image set of a large scene; obtaining a scene sparse point cloud according to the multi-view image set; performing monocular depth estimation on the multi-view image by using a pre-trained depth prediction network to obtain monocular depth estimation priori; the method comprises the following steps of: performing global training on a scene by utilizing scene sparse point cloud and monocular depth estimation prior to obtain an initial three-dimensional Gaussian model, and performing space grid division on the initial three-dimensional Gaussian model to obtain a plurality of scene blocks with axis alignment bounding boxes; setting image view angle data of each scene block; performing deep supervised training on the Gaussian ellipsoids in the plurality of scene blocks by using a parallel GPU (Graphics Processing Unit); combining the trained scene blocks to obtain a final three-dimensional Gaussian model; in view of low geometric structure reconstruction precision caused by only depending on color information of a multi-view image in large-scene three-dimensional rendering, the method improves the reconstruction precision of large-scene rendering.

A method is provided for encrypted network traffic analysis. The method includes capturing network traffic data; calculating entropy of said data to classify traffic as encrypted or non-encrypted; applying statistical and sequential feature hybridization on encrypted traffic to extract comprehensive features; analyzing the features using a neural network model to identify encrypted traffic types and detect anomalies; and refining the analysis based on entropy and neural network insights through a feedback loop.

The invention discloses a video content intelligent management method and system based on multi-modal analysis, and belongs to the field of video content management.The method comprises the steps that multi-modal features of video content are extracted; performing fine structure segmentation on the video content based on the multi-modal features, and segmenting the video into fine segments related to semantics; the fragments obtained after fine structure segmentation are subjected to tagging, and metadata is generated for each fragment; constructing a multi-modal semantic index of the video content based on the generated metadata; according to the method, a semantic retrieval function is provided by utilizing multi-modal semantic indexing, fine fragments required by a user are accurately recalled in combination with text label matching, measurement numerical value scoring, cross-modal vector retrieval and filtering based on a shot language and a shooting technology label, and the user queries and positions a specific time point or the fine fragments in a video through a natural language. According to the invention, the accuracy of video content analysis is improved, the user experience is optimized, and the operation complexity is reduced.

The invention discloses a historical block scene three-dimensional reconstruction method and system based on Gaussian sputtering, and the method comprises the steps: collecting a historical block video sequence through a lightweight panorama camera, extracting a multi-frame panorama, and generating an image data set through a projection converter; monocular depth and normal estimation is carried out through a pre-training visual model, and a prior depth and normal graph data set of a historical block scene is constructed; sparse reconstruction is carried out on the multi-view image data set based on the SfM technology, initial point cloud and camera pose information are acquired, and a Gaussian ellipsoid is optimized in combination with prior depth and normal information; dynamically calculating the geometric width estimation value of the street, and guiding and adjusting the adaptive density of the Gaussian ellipsoids of the vertical surfaces on the two sides; rendering the optimized Gaussian ellipsoid through an improved rasterization renderer; and designing a multi-modal loss function and a regularization mechanism to optimize reconstruction and rendering results. According to the method, high-fidelity three-dimensional reconstruction of the historical block scene is realized, and the adaptability of the Gaussian sputtering method to the complex block scene is enhanced.

Disclosed in the embodiments of the present application are a defect detection method and apparatus, an electronic device, a storage medium and a program product. The method comprises: acquiring an image under test of a target workpiece and a template image of the target workpiece, and comparing the image under test with the template image so as to obtain a differential image; inputting the image under test and the differential image into a defect detection model, and executing the following processing: on the basis of the image under test and the differential image, extracting a target image feature and, on the basis of the target image feature, performing defect detection on the image under test to obtain defect detection box information of the image under test; positioning a defect area in the differential image and, according to the defect area, determining a first detection result of the target workpiece; and, according to the defect detection box information and the first detection result, determining a second detection result of the target workpiece.

The invention discloses an intelligent multi-modal virtual digital human interaction system based on an AI language large model. The system comprises a high-authenticity face generation module; the high-authenticity face generation module uses an AdaAN network, based on adaptive feature fusion and voice driving and time sequence modeling of voice features, feature information related to voice is extracted, the extracted voice features are processed through a deep neural network, it is ensured that the voice and facial expressions are highly aligned in time and space, and the face recognition accuracy is improved. Collecting a bio-electricity signal, mapping the signal to facial muscle movement, generating a final facial expression, and interacting with a user; the system further comprises an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scene adaptation module and a feedback optimization module. The invention further discloses a multi-mode digital human interaction method which has wide application value.

Aspects of the subject technology provide for human subject Gaussian splatting using machine learning. A method includes receiving a video input having a scene and a subject. The method also includes obtaining a three-dimensional (3D) reconstruction of the subject and the scene from the video input. The method includes generating a 3D Gaussian representation of each of the scene and the subject. The method also includes generating a deformed 3D Gaussian representation of the subject by adapting the 3D Gaussian representation of the subject to the 3D reconstruction of the subject. The method includes rendering a visual output comprising an animatable avatar of the subject and the scene using differentiable Gaussian rasterization based at least in part on the deformed 3D Gaussian representation of the subject and the 3D Gaussian representation of the scene.

A small-size vehicle detection deep learning model based on feature fusion of multi-scale modules is provided, which solves the problem of small-size vehicle image detection. The model includes a Backbone network, a Neck layer and a Head network, wherein a C2f_DCNv3 module based on the combination of deformable convolution v3 (DCNv3) and a cross stage feature fusion (C2f) module and an SPPF_LSKA module based on the combination of a spatial pyramid pooling fast (SPPF) layer and a large separable kernel attention (LSKA) module are introduced into the Backbone network; a C2f_SCConv module based on the combination of spatial and channel reconstruction convolution (SCConv) and a C2f module is introduced into the Neck layer; and a multi-scale kernel detection (MSK_Detect) module is introduced into the Head network.

Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate a three-dimensional voxel representation of a scene based, at least in part, upon a plurality of two-dimensional images of the scene.

The invention relates to the technical field of intelligent conference processing, and relates to a conference summary processing method and system using AI, and the method comprises the steps: carrying out the real-time noise suppression of a collected conference audio stream and associated text data through a noise suppression algorithm, and carrying out the cross-modal alignment of the denoised data through a cross-modal alignment algorithm; a domain-specific attention head is inserted into an attention layer of the pre-trained Transform model, a domain-enhanced speech recognition model is constructed, and audio is converted into a text sequence with a speaker tag; adopting a heterogeneous graph neural network to construct a structured topic evolution graph; key decision nodes in the structured topic evolution graph are extracted based on a reinforcement learning strategy, and a final conference summary document is generated. In the decoding stage, the fusion proportion of the acoustic model and the language model is dynamically adjusted based on the real-time acoustic confidence coefficient, the recognition rate of the vocabularies in the professional field is increased, and the problems of frequent term transcription errors and poor semantic coherence in the professional conference are effectively solved.

A ground visual question-answering method based on dynamic dual-level visual information fusion includes using a dual-level multiscale network, which is divided into language-guided pixel-level features and region-level features. These two scale branches are combined to predict the final textual answer and ground answer. Furthermore, a question-guided dynamic region-level feature localization network is proposed to locate visual information guided by the question and adaptively assign masks of different sizes to ground answers, thereby enhancing the accuracy of locating and segmenting small targets. Additionally, a cross-modal aggregation module is designed to fuse features from both levels, enhancing the fusion of pixel-level and region-level features to improve the segmentation effect of ground answer masks' edges. The ground visual question-answering system built by the language-guided adaptive dual-level feature fusion network in this invention can effectively improve the accuracy of the entire model while answering questions and generating answer ground masks simultaneously.

The invention discloses a hybrid enhanced index and system based on vector retrieval and a BM2 algorithm, and the method comprises the steps: uploading a document by a user, analyzing the document by the system to obtain text content, segmenting the text content, converting the segmented text content into dense vectors, storing the dense vectors in a vector library, extracting keywords of segmented texts by using a large model, processing the keywords, and inserting the keywords into a word list. Word segmentation is carried out on the text based on the constructed word list and a word segmentation device; in response to the received user query, vectorizing the user query, and calculating the similarity with each vector in the vector library to obtain a preliminary query result; performing word segmentation on user query, performing keyword retrieval by using a BM25 algorithm according to a word list and a word segmentation device to obtain query results, and filtering the similarity of the two query results by the reordering model according to a set threshold value; selecting a result with the highest similarity according to large model parameter limitation and an upper limit set by business requirements; and splicing the document content corresponding to the result and the user query into a cue word, inputting the cue word into the large model, and analyzing a semantic relationship and a logic structure in the cue word to generate an answer.

A computer-implemented method of training a neural network system comprising a visual encoder neural network and a text encoder neural network is provided The method comprises obtaining a plurality of training data items (each training data item comprising an image and associated text defining a sequence of text tokens) and at each of a plurality of training steps processing at least one of the training data items by: processing pixels of the image in the training data item using the visual encoder neural network to generate a set of patch embeddings for the image; processing the sequence of text tokens using the text encoder neural network to generate a sequence of token embeddings, processing the set of patch embeddings and the sequence of token embeddings to generate a set of language-aware patch embeddings (based on similarities between patch embeddings and token embeddings), and training at least the visual encoder neural network by backpropagating gradients of a contrastive objective function evaluated over the language-aware patch embeddings and the sequence of token embeddings.

The invention relates to the technical field of defect detection, in particular to a special equipment defect automatic identification method and system, and the method comprises the following steps: marking a continuous path based on an image gray scale gradient and adjacent differences, identifying the pixel connection intensity to obtain a contour image layer, extracting continuous pixels, analyzing gray scale and gradient features, and expanding a texture direction to generate a defect image block. And marking and connecting the small regions to obtain a closed layer, analyzing texture and edge differences, matching classification tags, and clustering and coding to generate a defect identification tag set. According to the method, boundary structure recognition is enhanced through gray gradient and direction continuity analysis, a texture aggregation area is expanded in combination with gray consistency and edge direction stability, brightness abrupt change points are removed, contour extraction precision is improved, micro crack continuity is recovered, and pseudo defects are eliminated; and multi-dimensional image attributes are fused to identify key region feature differences, so that the classification precision and the spatial mapping consistency are improved, and efficient and accurate defect identification and stable classification are realized.

The invention discloses a large language model knowledge base question answering system based on a multi-path fusion recall retrieval algorithm, and relates to the technical field of artificial intelligence application, and the system comprises the steps of S1, data collection, S2, data preprocessing, S3, knowledge base construction, S4, analysis and clustering, S5, knowledge recall, S6, weight adjustment, and S7, weight fusion. According to the method, a dynamic weight distribution module is arranged, so that various paths of recall weights such as keyword matching, semantic similarity and a knowledge graph can be flexibly adjusted according to different semantic scenes such as a technical scene and a product query scene, a recall result can be effectively optimized, knowledge related to problems can be accurately screened, and the method is high in practicability and high in practicability. And irrelevant information interference is reduced, the answer quality and the system efficiency are improved, and the user satisfaction is improved.

The invention provides an intelligent sound box voice processing method and system based on artificial intelligence, and the method comprises the steps: obtaining audio data and mouth shape video data, carrying out the processing of the audio data and the mouth shape video data, and carrying out the multi-modal feature fusion, and obtaining a fusion feature; performing bimodal voice activity detection on the fusion features to obtain effective voice data; performing context sensing recognition of audio and video fusion on the effective voice data to obtain a first text; constructing a user feature model, and performing semantic understanding on the text based on the model to obtain an understanding result; performing intention recognition and slot filling based on the understanding result to obtain user intention and key information; generating a response strategy in combination with the user intention, the key information and the environment perception data; generating response voice according to the response strategy; and monitoring feedback information of the user to the response voice in real time, and updating the user feature model and the response strategy evaluation model based on feedback. According to the scheme, the voice can be recognized more accurately, and the safety and robustness of the system are enhanced.

A system and method for generating multimedia artifacts with managed scene continuity in visual and multimedia using an AI-based and scene continuity aware media generation platform. The system receives a user or AI agent specification or simulation result(s), selects or trains generative models based on the specification, preprocesses relevant data, and generates scene narrative or frame-specific, sequence specific or broader continuity aware content using the selected or trained model(s). The generated content may be further enhanced using frame interpolation and view synthesis techniques to create smooth transitions or novel viewpoints or to aid in more efficient transmission or viewing or persistence of resultant content. The system enables efficient and customizable generation of high-quality scene continuity aware content for various applications in visual and multimedia production using neuro-symbolic and simulation enhanced compression, representation and generation processes.

The invention provides a semiconductor defect detection and process optimization method based on deep learning. The method comprises the following steps: acquiring semiconductor wafer surface multispectral image data, wafer reflectivity distribution data and thickness distribution data through multispectral imaging equipment; performing multi-modal fusion preprocessing to generate a defect enhanced image; extracting micro-nano scale topological features of the surface of the wafer through a lattice structure guided deep learning network; performing frequency domain-space domain joint modeling on the topological features to generate a defect feature energy distribution diagram; fusing real-time process parameter data to dynamically classify defect features and output defect types and confidence coefficients; generating a process equipment control instruction according to the defect classification result; a defect cause knowledge graph is constructed through a causal inference engine, and a process optimization scheme is output; and feeding back the optimized process parameters to the semiconductor manufacturing equipment to form closed-loop control. The semiconductor manufacturing efficiency and the product quality can be improved, and the production cost is reduced.

The invention relates to an optimization method and device for sparse view angle three-dimensional Gaussian splash, and belongs to the technical field of three-dimensional reconstruction in computer vision, and the method comprises the steps: collecting a sparse view angle image; a multi-view stereoscopic vision model based on deep learning generates a geometrically consistent depth map for the sparse view image, converts the depth map into point clouds and fuses the point clouds to obtain dense point clouds; sampling dense point clouds by adopting voxel-guided farthest point sampling to obtain initialized point clouds, and constructing a three-dimensional Gaussian field; rendering the three-dimensional Gaussian field through an enhanced geometric renderer to obtain a rendering depth and a rendering normal; constructing a multi-level geometric regularization loss function, and optimizing the three-dimensional Gaussian field; and performing optimization adjustment on the three-dimensional Gaussian field based on a shape-scale constraint criterion and a two-stage adaptive opacity constraint strategy to obtain an optimized three-dimensional Gaussian field. According to the method, the problems of initialization failure, insufficient geometric supervision and element out-of-control of 3D Gaussian splashing under the sparse view angle are solved.

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention provides a water level distributed monitoring method and system based on a visual sensing network, and relates to the technical field of water level monitoring, and the method comprises the steps: constructing a water level propagation prediction model through multi-modal comparative learning and a graph neural network, processing a real-time water level image through the combination of adaptive wavelet transform, decoupling representation and a dense connection network, and obtaining a real-time water level prediction model; the water level measurement value and the reliability score are obtained, anomaly analysis and emergency monitoring are carried out based on state estimation, causal discovery and hierarchical reinforcement learning, high-precision and high-reliability distributed water level monitoring can be achieved, abnormal water level changes can be effectively recognized, and timely emergency monitoring parameters are provided.

The invention relates to the technical field of image processing, can be applied to business scenes of medical health, financial science and technology and the like, and discloses an image sensitive character desensitization method which comprises the following steps: extracting a plurality of feature maps from a to-be-processed image, and inputting a feature pyramid network to generate pyramid features, fusing a spatial attention mechanism to enhance the response of a sensitive region in each level of feature map, generating candidate region coordinates through a region candidate network, mapping the candidate region coordinates to a to-be-processed image to obtain a local image, extracting text semantic information in the local image by using a codec based on a self-attention mechanism, and obtaining a local image; and performing sensitive word detection in combination with the semantic classification model, and performing local texture repair operation on the sensitive area according to a detection result to generate a desensitized image. According to the method, a space attention mechanism and a semantic understanding module are introduced on the basis of multi-scale feature fusion, so that the capability of detecting small-size sensitive characters in an image is improved.