1508 results about "Image map" patented technology

The invention discloses a packaging box printed matter printing quality detection method, which comprises the following steps of: acquiring a high-resolution printing image through an image acquisition module, preprocessing and segmenting the printing image by an image processing module, optimizing image quality, classifying the preprocessed image by an image classification module according to characters, patterns and color blocks, and detecting the printing quality of a packaging box printed matter. The defect detection module performs multi-dimensional detection on the classified preprocessed images based on image parameter data, the quality analysis module quantitatively evaluates quality scores of characters, patterns and color blocks based on detection results, and the computer terminal generates a comprehensive score through weighting calculation and automatically generates a detection report; according to the packaging box printed matter printing quality detection method, complex defects such as font errors, stroke breakage, pattern deviation, color deviation and stains are effectively recognized through multi-dimensional defects, the manual reinspection cost is reduced through full-process automatic processing, and the quality of printed matter is visually reflected through a quantitative evaluation system.

The invention discloses an oral cavity panoramic image processing system, and the system comprises an image collection module which is used for collecting an initial oral cavity panoramic image containing a plurality of oral cavity diseases; the image processing module is connected with the image acquisition module and is used for performing target detection and instance segmentation labeling on the initial oral panoramic image and expanding a training data set by adopting a data enhancement technology to obtain a target oral panoramic image; a model construction module connected with the image processing module and used for training an MMC-YOLO network model through the target oral panoramic image to obtain a target MMC-YOLO network model; and the analysis and detection module is connected with the model construction module, and is used for performing synchronous detection of tooth concurrent oral diseases, comprehensive analysis of multi-dimensional pathological information and accurate recognition of multi-scale pathological changes through the target MMC-YOLO network model to obtain an analysis result. The system provided by the invention realizes synchronous detection and accurate grading of multiple types of focuses in the oral panoramic image.

Systems and methods for multi-modal image editing are provided. In one aspect, a system and method for multi-modal image editing includes identifying an image, a prompt identifying an element to be added to the image, and a mask indicating a first region of the image for depicting the element. The system then generates a partially noisy image map that includes noise in the first region and image features from the image in a second region outside the first region. A diffusion model generates a composite image map based on the partially noisy image map and the prompt. In some cases, the composite image map includes the target element in the first region that corresponds to the mask.

The invention relates to an unmanned aerial vehicle lens stain identification and cleaning method and system based on deep learning. The method comprises the following steps: generating an image acquisition instruction according to a set sampling period to obtain a to-be-detected image; the image acquisition instruction is used for instructing an unmanned aerial vehicle camera to perform image acquisition operation and feeding back in the form of a to-be-detected image; preprocessing the to-be-detected image to obtain a to-be-detected standard image; inputting a to-be-detected standard image into the stain detection model to obtain a stain identification result; the stain identification result comprises a central coordinate of the polluted area, bounding box data, a confidence coefficient score and a stain type label; generating a cleaning parameter and a cleaning instruction according to the stain identification result; the cleaning instruction is used for instructing the cleaning equipment to clean the unmanned aerial vehicle lens according to the cleaning parameters. By adopting the method, the problems of difficult stain identification and extensive cleaning decision in an existing unmanned aerial vehicle visual system can be solved, and the accuracy of stain detection and the intelligence of cleaning control are realized.

The invention discloses a concave-convex point detection and defect evaluation system based on multistage detection and feature change, and the system comprises an image collection unit which is used for capturing a product image in real time; the image preprocessing unit is used for carrying out noise reduction processing on the image and improving the brightness and the contrast ratio of the image; the primary product defect positioning module is used for selecting Gaussian difference extraction, dynamic threshold extraction, fixed threshold extraction, template matching and a deep learning algorithm module to extract and position suspicious defects according to the size of the product and the characteristic form of the defects; the secondary defect classification and judgment module is used for carrying out preliminary classification and type judgment on the defects in the region; the third-stage defect contour extraction unit is used for performing fine contour extraction on each detected defect area after the defect classification is clear; and the four-stage defect feature analysis and evaluation unit is used for deeply extracting key features of each defect and carrying out scientific and quantitative evaluation on the key features.

Methods and apparatus for relighting images. According to an example embodiment, inverse rendering is applied to an input image to extract a plurality of channels including an original lighting channel. A first neural network is used to determine a first latent feature corresponding to the input image based on a first set of channels including a shading channel generated using a replacement lighting channel. A second neural network is used to determine a second latent feature corresponding to the input image based on a different second set of channels including the replacement lighting channel. A relighted image is generated by propagating samples of a latent image map corresponding to the input image through a conditional diffusion model to which the first and second latent features are applied as first and second conditions.

The invention relates to an end-to-end automatic driving method based on Transform multi-modal feature fusion, and the method comprises the steps: collecting an RGB image and an original depth image, and converting the original depth image into an HHA image; inputting the RGB image and the HHA image into a sensing module for fusion to obtain a fusion feature map, and performing global average pooling and flattening operation to obtain environment features; acquiring the real-time speed of a vehicle, an advanced navigation command and a target position, connecting in series to form measurement input, and processing based on an MLP measurement encoder to obtain measurement characteristics; adding the environment features and the measurement features element by element to obtain combined features, performing downsampling step by step, and connecting a ReLU activation function behind each layer to obtain track features; and a loss estimator is constructed, training loss is dynamically predicted in real time based on the track features, and the weight of each feature parameter is dynamically adjusted according to the loss, so that the vehicle is dynamically controlled and adjusted in real time. According to the method, the multi-modal features can be well fused, so that a more accurate end-to-end automatic driving method is realized.

The invention discloses a rail transit fastener model identification system and method, and relates to the technical field of fastener overhaul, an initial image of a rail is acquired through an image acquisition module, and an image preprocessing module is used for preprocessing to obtain a reference image; the image analysis module inputs the reference image into a preset model for processing, completes segmentation of the fastener assembly and the steel rail assembly, and obtains segmented images; the positioning module is used for screening the images of part of fastener assemblies and the images of the rail bottom of the steel rail and restoring the images to the reference images; the feature acquisition module is used for acquiring a reference adjustment line based on the segmented image of the rail bottom, acquiring feature parameters of the partial components based on the segmented images of the partial components, and correcting by using the reference adjustment line to obtain reference feature parameters; and the model identification module is used for matching the reference characteristic parameters of the screening component with the data in the constructed database and outputting a corresponding fastener model. According to the invention, efficient identification of the models of the fasteners can be realized, and the identification accuracy is also guaranteed.

The invention provides a method and a system for generating deviation guide information in an approaching landing section of an aircraft and a readable storage medium, and the method comprises the steps: S1, real image collection and processing: capturing an outboard environment image through an airborne visual sensor, and extracting key feature points of a target landing airport runway; s2, virtual image generation: based on the airport three-dimensional high-precision map, generating an approach glide image under a visual angle of a virtual camera; s3, image feature registration: carrying out spatial alignment on the key feature points of the runway in the real image and the key feature points of the runway in the virtual image; the pixel deviation of the runway center point in the real image and the virtual image is calculated, glide guidance is provided for a pilot or is directly input into an automatic flight system.The method can be used for the aircraft in the approaching landing section, the enhanced outboard visual display is provided for the pilot in the cockpit based on the equivalent ILS deviation guidance method, and the visual guidance effect is improved. And providing approaching glide deviation guide information.

The invention discloses an underwater image enhancement method based on cross-domain feature representation and semantic segmentation guidance, and belongs to the field of underwater image processing, and the method comprises the steps: constructing an unpaired sample data set, and dividing the unpaired sample data set into positive and negative samples; designing and initializing an image enhancement network; respectively and randomly selecting an image IH and an image IL from the positive sample and the negative sample, inputting the image IL into an image enhancement network to obtain an enhanced image IE, inputting {IL, IH, IE} into an image feature extraction model, and calculating and comparing learning loss; pre-training the semantic segmentation model on a semantic segmentation data set, then performing fine tuning on the semantic segmentation data set of the underwater image, inputting an image IH and an image IE, generating a corresponding semantic prediction map, and calculating semantic segmentation loss; training an image enhancement network by combining and comparing learning loss and semantic segmentation loss, updating parameters of the image enhancement network through end-to-end training, and generating a high-quality enhanced underwater image; the method has the effects of image precision enhancement, detail recovery, strong generalization ability and low data acquisition cost.

The invention discloses an environmental health risk assessment system, electronic equipment and a nonvolatile storage medium. The system comprises an environment image acquisition module which is used for acquiring an environment image of a target area; the image feature extraction module is used for performing feature extraction on the environment image to obtain a first feature corresponding to the streetscape image and a second feature corresponding to the remote sensing image; the environment variable prediction module is used for fusing the first feature and the second feature, and determining a parameter value of an environment variable corresponding to the target area according to a fused feature obtained after fusion; and the comprehensive index determination module is used for determining a comprehensive environment index corresponding to the target crowd object in the environment of the target area according to the parameter value of the environment variable. According to the method and the device, the technical problem that the environmental health risk cannot be effectively assessed due to the fact that a method for measuring and analyzing the environmental variables mainly depends on field monitoring, the efficiency is low and the data coverage is narrow in the prior art is solved.

The invention discloses a wide-angle video monitoring system, method and equipment based on multiple cameras and a medium, and relates to the technical field of video monitoring, the wide-angle video monitoring system comprises a multi-camera acquisition module, a variable-focus high-definition camera multi-angle acquisition module, a high-quality image obtained through distortion correction and an image splicing processing module, an improved algorithm is used for accurately matching feature points and fusing the images, and the high-quality image is obtained. The intelligent analysis module detects and tracks a target and analyzes behaviors in real time by means of an improved model, the storage and transmission module performs cluster storage, advanced coding cost reduction and high-speed link transmission, and the control module performs intelligent regulation and control according to multiple information and can also perform remote management. The system has the advantages of multi-angle acquisition without dead angles, good image splicing, accurate intelligent analysis, efficient storage and transmission, adaptability to severe environments, flexible power supply, guarantee of data safety, improvement of monitoring quality and efficiency, meeting of complex scene requirements, and enhancement of practicability, reliability and safety.

A computer system and a computer-implement method include obtaining a source image and a modification input that indicates a target edit to the source image and generating a modification encoding representing the target edit. An image generation model generates an output image that depicts the source image with the target edit based on the source image and the modification encoding. The image generation model is trained to perform a pose modification task and a part replacement task.

The invention discloses an Alzheimer disease risk prediction method based on feature fusion, a medium and equipment. The method comprises the following steps: firstly, acquiring an sMRI image, an FDG-PET image, a biomarker and intelligence score data of a target subject; then, sMRI and FDG-PET image data are respectively input into a three-dimensional convolutional neural network module, and a corresponding first image feature map and a corresponding second image feature map are extracted; secondly, inputting the two feature maps into a cross attention fusion module to obtain image fusion features; and inputting a U-shaped up-sampling network to segment a hippocampus region to obtain hippocampus features. And then, merging the image fusion features, the hippocampus features, the biomarker data and the intelligence score data, and inputting the merged data into an extreme learning machine for calculation, thereby obtaining an onset risk prediction result of the Alzheimer's disease. According to the method, by means of cross-modal data fusion, multi-modal data complementary information is extracted, and the accuracy of onset risk prediction can be effectively improved.

Disclosed herein are system, apparatus, device, method and / or computer program product embodiments for determining, in a remote deposit system, whether a deposit attempt is illegitimate (e.g. fraudulent). Whether the deposit attempt is illegitimate may be assessed based on one or more of the following processes: comparing location data to a location parameter determined from past deposits, comparing an image capture location with a deposit location, and analyzing image-of-image characteristics obtained through image processing to identify whether an image associated with the deposit attempt is an image of an image. In some embodiments, a remote deposit status related to acceptance of the deposit attempt may be provided in real-time

A warehouse goods intelligent identification and positioning management system based on an image segmentation algorithm comprises an image acquisition and preprocessing module, an image segmentation algorithm module, a goods positioning and feedback module, an inventory management module, a data communication and integration module and a user interaction and management module. The image segmentation algorithm module is used for segmenting and extracting image information. The cargo identification and positioning module is used for identifying and positioning cargoes. The position feedback and alarm module is used for feeding back cargo positions and giving an alarm. The inventory management module is used for managing and maintaining cargo information. The data communication and integration module is used for transmitting information in real time. According to the warehouse goods intelligent identification and positioning management system based on the image segmentation algorithm, the warehouse image segmentation algorithm based on deep learning is put forward to classify goods, and the goods identification and positioning algorithm based on improved label template matching is put forward to intelligently position the goods.

The invention discloses a method for guiding accurate maintenance, operation and maintenance of an urban pipe network by using an SWMM model. The method comprises the following steps: carding and generating a pipe network image map, confirming key nodes, confirming boundary adjustment, building a pipe network model, operating the SWMM model, checking sedimentation risk prediction and evaluation of a pipe section in a simulation area in an SWMM output report, and giving a sedimentation risk map. On the basis of the SWMM model, in combination with the pipe network topological relation, the actual sewage flow direction, the sewage discharge amount and the sewage discharge process, an intra-region drainage pipe network model is built, full-process and full-region simulation of a drainage system is achieved, the process of pipe networks of all key nodes and key regions can be quantified, the trend can be displayed, and the state can be evaluated; the defect that branch pipe sections and nodes in the middle cannot be detected in the traditional operation and maintenance process of a pipe network is overcome.

This disclosure describes a framework for determining the category-based image relevance of digital images associated with entities or topics. Specifically, this disclosure describes an image relevance system that determines outlier images within a set of images associated with an entity or topic by correlating semantic content with visual content. For example, the image relevance system ensures that only images relevant to the entity or topic are provided in response to a user query about the entity or topic. The image relevance system can also filter out images from an image set that do not correspond to user input in a search query before providing the image set. Furthermore, the image relevance system can prevent irrelevant images from being added to an image set associated with an entity or topic.

The invention relates to the technical field of artificial intelligence technology and oil and gas engineering information processing, and discloses an intelligent questioning and answering method and system for offshore drilling, logging and completion based on large language model fine tuning and multi-modal retrieval enhanced generation (RAG) technology. The method comprises the steps that multi-source data such as texts, images, charts and tables related to well drilling, well logging, well logging and well completion are collected, format normalization, semantic segmentation and vectorization processing are carried out, and a multi-mode knowledge base with unified coding is constructed; wherein the image and the complex table are not directly subjected to vector modeling, but are associated with descriptive text paragraphs in a link mode, and the text paragraphs participate in unified retrieval after semantic coding. According to the method, multi-round interaction is supported, related images, charts or structured table resources are embedded according to the answer types, and multi-modal mixed output is achieved. The system collects user feedback information and constructs a knowledge credibility label for driving knowledge base updating and model continuous optimization.

The invention relates to the technical field of part surface defects, in particular to an industrial part surface defect recognition system and method, and the system comprises a high-resolution industrial camera which is used for collecting an industrial part surface image and is provided with an automatic focusing and light source compensation module; therefore, clear images can be obtained in different light environments; the image preprocessing unit has the functions of denoising, contrast enhancement and edge detection and ensures the saliency of defect features in the original image; a defect detection model based on deep learning, wherein the model is designed through a convolutional neural network structure and can automatically identify and classify various surface defects including but not limited to scratches, pits, cracks, air holes and the like; the defect positioning module is used for accurately marking the specific position of the defect on the surface of the part in combination with an image processing algorithm and calibration information; the system further comprises a data storage and management unit, a system control unit and an abnormity alarm module.

The invention discloses an intelligent material recommendation method and system based on image communication, and belongs to the field of image communication and intelligent recommendation, and the system comprises a demand analysis and image collection module which is used for analyzing a user demand and collecting and transmitting a reference image; the image feature deep analysis module is used for extracting multi-level features of the reference image; the material library feature index module is used for material characterization and index establishment; the intelligent material generation and collaborative screening module is used for calling a generation model to generate candidate materials and screening an optimal generation material through a multi-dimensional scoring system; the intelligent matching recommendation module is used for performing multi-dimensional matching on the materials in the material library and the optimal generated materials to generate a recommendation list; according to the method, candidate materials can be dynamically generated based on reference image features and demand keywords through fusion of generative AI, and multi-scene personalized demands such as personal life records, enterprise commercial propaganda and mobile terminal short videos are covered.

The invention belongs to the technical field of image processing, and particularly relates to a mixed U-Net brain tumor segmentation method and system based on quantum attention, and the method comprises a data preprocessing module which is used for obtaining and preprocessing a medical image; the image segmentation module is used for segmenting an image through a trained QHAU-Net segmentation model, and the QHAU-Net segmentation model comprises an encoder used for extracting multi-level features of the input image; the bottleneck layer is used for receiving the semantic features of the highest dimension output by the encoder and generating enhanced features modulated by quantum through a quantum multi-head attention module; the decoder is used for recovering the spatial resolution of the feature map and fusing the features through jump connection; the quantum boundary enhancer is used for performing quantum enhancement optimization on the output features of the decoder and outputting the output features; and the result output module is used for outputting and visualizing the segmentation result graph. The problem of low boundary medical image segmentation precision in medical image processing is solved.

The invention discloses a corn detection method based on visual identification, and relates to the technical field of corn ear detection, and the method comprises the steps: collecting corn images of a corn, including an RGB image, an NIR image and a Depth image; registering the corn image based on the transformation matrix to respectively obtain a registered RGB image and a registered NIR image; creating a blank image to obtain a binary mask image; based on the binary mask image, segmenting the Depth image, registering the RGB image and registering the NIR image to respectively obtain a Depth cluster area, an RGB cluster area and an NIR cluster area; constructing an image plane corresponding to a camera visual angle under a standard attitude, and obtaining an aligned RGB image and an aligned NIR image based on the image plane, the Depth cluster area, the RGB cluster area and the NIR cluster area; and inputting the aligned RGB image and the aligned NIR image into a double-branch deep learning fusion network to obtain a detection result. The problem that the existing corn ear detection method is high in subjectivity, low in efficiency and easy to be interfered by the environment and omission and misjudgment are caused by ear attitude diversity is solved.

The invention provides an ore particle granularity detection device and method based on a flexible vibration disk, and the device comprises the flexible vibration disk which is used for vibrating an ore material at a certain frequency and amplitude, so that the ore material is uniformly distributed and is in a proper posture; the image acquisition device is used for continuously shooting the ore materials on the flexible vibration disc to obtain ore images; the image processing device is used for carrying out preprocessing, segmentation, feature extraction and particle size analysis on the ore image and outputting the ore particle size distribution condition and an accumulative distribution curve; and the result output device is used for displaying the ore image and the granularity analysis result. According to the invention, while material accumulation is prevented, real-time, accurate and efficient detection of attitude distribution of ore materials is realized, and the method has the advantages of low cost of detection equipment, easy maintenance, strong interference resistance, high accuracy and stability of detection results, and the like.

The invention relates to the technical field of massage instruments, and discloses a fascia gun and a working method thereof.The fascia gun comprises an image acquisition module used for acquiring a real-time image of a human body part; the image processing and analyzing module is used for processing and analyzing the real-time image and identifying a corresponding human body part; the control module is used for selecting a specified striking mode based on the recognition result of the human body part and sending out a corresponding striking mode instruction; and the execution mechanism module is used for adjusting and outputting a corresponding striking mode based on the striking mode instruction. The fascia gun provided by the invention can automatically identify human body parts and automatically select a proper massage mode according to the human body parts, so that the injury to the human body caused by improper use of the fascia gun is avoided, and meanwhile, the effect of recovering fatigue of a user is ensured.

The invention belongs to the technical field of mold machining, and discloses a mold sensor monitoring and data analysis system and method based on the Internet of Things, and the method comprises the steps that a data collection module collects mold data and injection molding part data; the data acquisition module acquires temperature, pressure and time data of the mold; the image acquisition module acquires plastic part and mold images; the image preprocessing module preprocesses the image; the feature extraction module performs feature extraction on the image; the image analysis module identifies abnormal conditions of the mold and the plastic part; the injection molding simulation module simulates an injection molding process; and the data fusion analysis module comprehensively judges the states of the mold and the plastic part. Mold cracks and plastic part deformation abnormity can be recognized, and batch waste products are avoided; the injection molding simulation module predicts a melt flow path and optimizes the sprue design and the cooling system layout; the influence of thermal expansion on size precision is simulated, and mold testing times are reduced; and the data fusion analysis module combines the image and the sensor data to improve the state judgment reliability.

The invention relates to the technical field of image processing, in particular to a concrete member surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a surface image of a concrete member, and dividing the surface image into a plurality of image blocks; and performing frequency domain transformation on any image block to obtain a power spectrum. According to the method, the feature space period of each image block is analyzed and calculated through frequency domain transformation, and adaptive weighted fusion is carried out on texture features at different distances by using Gaussian weight on the basis of the feature space period. According to the method, the feature extraction process can dynamically adapt to the physical scale of image local textures, namely, small distance analysis is automatically emphasized on fine textures and large distance analysis is automatically emphasized on rough defects, so that scale-perceived composite texture features are constructed; and the accuracy of identifying the concrete surface defects under the complex texture background is obviously improved.

The invention discloses a sole wear degree detection system based on image recognition, and relates to the technical field of image recognition, the system comprises an image acquisition module, an image processing module, a wear degree analysis module, a database module and a decision output module; the image acquisition module is used for acquiring a multi-angle high-definition image of the shoe sole; the image processing module is used for carrying out impurity removal processing on the multi-angle high-definition image; the database module is used for storing data of different shoe types; the wear degree analysis module is used for distinguishing normal wear from abnormal wear according to the database module and the three-dimensional distribution data; and the decision output module is used for outputting a quality guarantee judgment conclusion and a maintenance scheme. According to the shoe sole wear degree detection system based on image recognition, natural wear and abnormal wear are accurately distinguished through three-dimensional shape reconstruction and a deep learning model.

The invention belongs to the technical field of medical image processing, and particularly relates to a glioma segmentation method based on a multimodal fusion network guided by anatomical symmetry, which comprises the following steps of: jointly inputting an FLAIR image, a T2 image, a T1 image and a T1c image of the same glioma into a trained image segmentation model, and outputting a predicted segmentation image by the trained image segmentation model, the prediction segmentation image is a glioma MRI image with three segmentation areas obtained through prediction, and the three segmentation areas are an edema area, an enhanced tumor area and a necrosis area respectively; the image segmentation model comprises an encoder, a jump connection part and a decoder; the encoder comprises an ASG module, the jump connection part comprises an IMP module, and the decoder comprises a CMF module. Through a three-module cooperation mechanism, the performance of tumor localization, cross-modal fusion, subregion segmentation and the like is improved, and a reliable image basis is provided for glioma operation plan formulation, prognosis evaluation and personalized treatment decision.

The invention discloses an unmanned aerial vehicle bridge detection method based on machine vision. According to the method, an unmanned aerial vehicle is used as an autonomous mobile platform, and full-coverage and high-resolution image data acquisition of structural surfaces such as a bridge deck, a beam body and a bridge pier is realized through systematic task planning. The collected data is processed by a motion recovery structure and a multi-view three-dimensional algorithm to generate a high-precision three-dimensional live-action model and a digital orthophoto map. On the basis, a deep learning target detection algorithm is adopted to perform intelligent analysis on the image, apparent defects such as cracks, spalling and corrosion are automatically identified, positioned and classified, and accurate quantification of parameters such as crack width and spalling area is realized. According to the method, the problems of high risk, low efficiency, high subjectivity, existence of detection blind areas and the like in traditional manual detection are effectively solved, and key technical support is provided for digital and intelligent transformation of bridge operation and maintenance management.