1077 results about "Image map" patented technology

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.

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.

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

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 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 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 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.

An image fusion device and an image fusion parallel optimization method are provided. After a plurality of images are aligned to each other to form a superimposed image, the image fusion device filters the superimposed image to obtain a feature image. The image fusion device compares color depths of pixel points in each of a plurality of pixel regions of the feature image with each other to determine a maximum color depth of each of the plurality of pixel regions. When a relationship between the color depth of any one of the pixel points and the maximum color depth in each of the plurality of pixel regions does not meet a preset relationship, the image fusion device adjusts the color depth of the one of the pixel points according to the maximum color depth. The image fusion device generates a fused image according to the adjusted feature image.

The invention is suitable for the field of intelligent jet cleaning, and provides a dirt recognition cleaning method, device and equipment, and the dirt recognition cleaning method comprises the steps: obtaining an original image corresponding to target dirt and a sound reflection signal corresponding to the target dirt; converting the sound reflection signal corresponding to the target dirt into an image representation; obtaining a fused image corresponding to the target dirt according to the original image corresponding to the target dirt, the image representation and a preset multi-channel image fusion algorithm; inputting a fusion image corresponding to the target dirt into the improved deep neural network to obtain segmentation mask data and size data of the target dirt; adjusting the pose of the dirt cleaning equipment according to the segmentation mask data and a preset coordinate conversion algorithm; according to the size data of the target dirt, dirt cleaning parameters are adjusted, and dirt cleaning equipment is controlled to execute cleaning operation. According to the method, the dirt recognition and cleaning precision and efficiency are remarkably improved, and various cleaning requirements and operation environments can be better met.

The invention discloses a local-global information aggregation remote sensing image change detection method based on Version Mama, and belongs to the technical field of remote sensing image change detection. The method adopts a Dual-LGNet network model for prediction, and comprises the following steps: inputting a dual-time-phase remote sensing image map before and after change into a twin encoder to obtain a feature map aggregating global features and local features; inputting the feature map aggregating the global features and the local features into a feature fusion module to obtain a fused difference feature map; and inputting the difference feature map into a progressive decoder to obtain a final prediction result. Wherein the LG-SS2D is introduced into the encoder to carry out feature extraction, and an LG-SS2D block comprises a parallel convolution branch and a Mama-based global branch. Through combination of local-global information aggregation and boundary enhancement, the recognition and detection precision of a remote sensing change area can be improved on the premise of keeping linear complexity, and especially the detection precision of a large building and a small target change area can be improved.

The invention provides a disease and pest epidemic prevention system based on image recognition, and the system comprises a data collection and preprocessing module which is used for collecting optical images, sound wave images and environment information of crops, and carrying out the preprocessing and feature extraction; the image enhancement module is used for processing the sound wave features, enhancing the optical features through a sound wave weight map obtained through processing, and marking the pest and disease region through a neural network to obtain an optical enhancement image; the image segmentation module is used for carrying out region division on the optical enhancement image and then carrying out pixel-level segmentation to obtain a to-be-identified region map; and the disease and pest recognition module is used for outputting a disease and pest recognition result graph through a disease and pest recognition model after learning a double mapping relation between the to-be-recognized area graph and the environment weight vector through a neural network. According to the invention, through comprehensive application of multi-modal information fusion and a deep learning technology, the efficiency and precision of crop disease and insect pest detection are significantly improved.

The invention discloses a building effect picture generation method based on multi-condition weighted fusion and block control. The building effect picture generation method comprises the following steps: 1, training a customized model; 2, preliminarily generating a rough effect picture A; 3, extracting and calibrating a global cue word to obtain a global cue word P1; 4, style condition modeling: obtaining a style condition vector C3 by using a Redux model of Flux; 5, amplifying the image to obtain a high-resolution image B; 6, image partitioning and partitioning cue word obtaining are carried out, and a partitioning cue word set P2 is obtained; 7, condition fusion; 8, geometric control preprocessing, which is used for constraining a geometric structure in a block generation process; 9, block refinement generation is carried out, and a high-resolution block graph with rich details and accurate geometry is realized; and 10, performing block merging and consistency repairing to obtain a final building effect picture. According to the method, the problems that the existing generation resolution is limited, the geometric structure of the building is distorted, global and local prompts are inconsistent, the style drifts, the block splicing trace is obvious, engineering parameter intervals are lacked and the like are solved.

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

The invention provides a cold region greenhouse multispectral visual positioning picking method and system. The method comprises the following steps: synchronously acquiring an RGB image, an NIR image, greenhouse environment temperature and humidity data and depth information of a target; based on the RGB image, the NIR image and the environment data, image correction is carried out through an image correction network, and a sharpened RGB image is output; inputting the corrected RGB image, NIR image and depth image into a maturity identification model for processing, and outputting the maturity grade and three-dimensional space coordinates of the fruit; based on the three-dimensional coordinates and maturity information of the fruits and a pre-constructed environment three-dimensional point cloud map, a collision-free optimal picking path is planned for a mechanical arm; the mechanical arm is controlled to execute the picking action, the success rate of the picking result and the recognition confidence coefficient serve as feedback signals, and online fine adjustment is conducted on parameters of the image correction network and the maturity recognition model; the problems of low picking efficiency, low precision and high damage rate of the greenhouse in the cold region are solved.

The invention relates to the technical field of mask heat dissipation control, and discloses a heat dissipation control method and system for a protective mask, and the system comprises an image collection module which carries out the splicing processing of a plurality of visual images, determines a spliced visual image of a heat dissipation fan, an image analysis module which generates a visual recognition mark based on visual pixel points, and carries out the recognition of the visual recognition mark. The visual identification module is used for obtaining visual identification marks, judging whether dust parameters are obtained or not according to the visual identification marks, determining the dust parameters based on a visual identification model and a spliced visual image and determining the reasonability of a heat dissipation environment according to the dust parameters, and the image processing module is used for comparing the dust parameters in a historical heat dissipation database to determine an approximate result. Whether data of a historical heat dissipation database is called or not is judged based on the approximation result, heat dissipation rotating speed data is determined based on the dust parameters and a dust correlation model, and the heat dissipation control module conducts heat dissipation based on the heat dissipation rotating speed data. The heat dissipation reliability of the protective mask is ensured.

The invention relates to the technical field of computer vision, in particular to a sea surface image semantic segmentation method based on boundary feature constraint and electronic equipment, and the method comprises the following steps: S1, obtaining an image; s2, image preprocessing; s3, making a data set; s4, preprocessing the data set; s5, constructing a model; s6, training the model; and S7, outputting a result. According to the double-branch model based on the semantic branches and the boundary branches, expansion of the semantic information in the up-sampling process is restrained by extracting the boundary information and mutual query and constraint of the semantic information, the overall sensing value for a target is constructed in an auxiliary mode, and the problem of dynamic interference is effectively solved; by designing an attention multi-scale sensing module and a bidirectional attention fusion module, the segmentation precision of a water surface small target can be improved, and detail information can be supplemented; by providing the boundary query module and the boundary constraint module, a more accurate target boundary can be obtained on the premise of not introducing excessive parameters.

The invention discloses a wafer surface macroscopic defect detection method and system based on a CIS camera, and the method comprises the steps: collecting an image, carrying out the preprocessing of the image, eliminating the inherent brightness nonuniformity of a system, and obtaining a corrected image; performing contrast enhancement processing on the preprocessed image to highlight a defect area and suppress noise, and obtaining an enhanced image; defect area positioning is carried out on the enhanced image, and a candidate defect area set is obtained in combination with an image analysis method in the positioning process; multi-dimensional features are extracted from the candidate defect area, the features comprise the form, texture and intensity information of defects, and feature expression under multiple light sources is fused; inputting the extracted features into a classifier, and automatically judging the type of the defect; and integrating information such as classification, position and size of the defect, generating a detection report and visualizing the position of the defect. According to the invention, high-speed, high-precision, high-uniformity and distortion-free detection of wafer macroscopic defects is realized.

Aspects of the method, apparatus, non-transitory computer readable medium, and system include estimating extrinsic camera parameters of a plurality of cameras based on detection of corners in a camera calibration arrangement by projecting an image of the detected corners onto an image plane of at least one of the plurality of cameras. The aspects further include incrementally tuning a location estimate for the detected corners based on pixels of the projected image, and adjusting the extrinsic camera parameters of the at least one of the plurality of cameras based on the tuned location estimate. The aspects further include generating a virtual image based on the pixels of the projected image mapped to a virtual image plane.

Embodiments of the present disclosure provide an image processing method, an apparatus, a device, a computer-readable storage medium, and a product. The method includes: obtaining an image to be processed and an image expansion text; performing a padding operation on the image to be processed based on a preset background to obtain a padded image; inputting the padded image and the image expansion text to a preset target model, the target model being obtained after a preset model to be trained is iteratively trained based on a preset training dataset, a training data pair including an original image, an image expansion description text, a random mask, a masked image, and a cropped image obtained through cropping based on the original image and the random mask; and performing an image expansion operation on the image to be processed based on a predicted noise that is output by the target model.

A method of a user device to track objects in an extended reality (XR) environment includes capturing an image of a physical environment associated with the XR environment, sending the image, a first timestamp for the image, and object information for at least one object in the image to a tracking service, receiving an object identifier, object tracking information, and a second timestamp for the at least one object from the tracking service, performing time and motion compensation on the object tracking information to correlate the object tracking information and object identifier with the at least one object in the image of the physical environment, and generating or updating an XR overlay for the at least one object using time and motion compensated object tracking information.

The invention discloses a radar anti-interference method based on frequency domain modeling and image collaboration, and the method comprises the steps: S10, carrying out the preprocessing of an original echo, and finally packaging a preprocessing result into a three-order tensor form; s20, performing FNO interference feature extraction and identification, and outputting interference type probability distribution and an interference-to-signal ratio estimation value through a de-wharf; s30, selectively processing the image, and reconstructing the histogram into an SAR image in an equalization manner; s40, evaluating the credibility of the image, and outputting an imaging credibility score between 0 and 1 through a regression head; s50, generating a credibility-interference combined criterion for judging the reliability of the radar guidance state; s60, a loss function is designed, back propagation is carried out through a total loss function, the model gradient is updated, and model parameters are optimized; and S70, dynamic anti-interference strategy matching: retrieving candidate anti-interference actions from a preset strategy library based on the interference types, performing grading and sorting according to complexity, and when the joint risk score is lower than a set threshold value, activating the optimal high-complexity anti-interference process of the corresponding interference type.

Systems and methods for detecting artificial intelligence generated images are provided. The system accepts an input image (e.g., a digital still image, or a frame from a digital video file or image stream) and subdivides the input image into a set of patches using a patch partitioning algorithm. The system then processes each patch and produces a feature embedding for each patch within a high dimension space. The system then utilizes these patches with further processing as input to machine learning models, which allows the system to achieve image, patch-level, and video-frame generated image classification and localization alongside identification of the generative model used to synthesize the image.

Embodiments include systems and methods for generative image filling based on text and a reference image. In one aspect, the system obtains an input image, a reference image, and a text prompt. Then, the system encodes the reference image to obtain an image embedding and encodes the text prompt to obtain a text embedding. Subsequently, a composite image is generated based on the input image, the image embedding, and the text embedding.

An information processing device includes an instruction information acquisition unit configured to acquire first print instruction information including designation of a drawing target, an image request unit configured to, when an image corresponding to the acquired first print instruction information is not stored in a storage, request, using the first print instruction information, another server to transmit an image in which the drawing target is drawn, an image acquisition unit configured to acquire the image from the other server, and a print control unit configured to cause an image forming device to print the acquired image.