156 results about "Color texture" patented technology

The invention relates to the technical field of defect detection, and particularly discloses a railway fastener defect detection method and system, and the method comprises the steps: controlling a high-speed industrial camera to scan a track region so as to collect a track image, carrying out the image preprocessing of the track image, extracting an ROI image block containing a potential railway fastener based on template matching, and then carrying out the detection of the potential railway fastener. The method further introduces a target detection algorithm to perform fastener accurate positioning and segmentation on the ROI image blocks to obtain finer fastener ROI image blocks, further extracts visual features of the fastener ROI image blocks, and performs feature space distribution saliency enhancement on the visual features to enhance expression effects of the visual features such as surface color, texture and shape of the fastener, so as to improve the image quality of the fastener. Therefore, intelligent identification of railway fastener defects is realized on the basis. According to the method, the limitation of a traditional detection method can be effectively overcome, the reliability of railway fastener defect detection in a complex environment is improved, and the method has relatively high universality and adaptability.

The invention provides a real-time monitoring method and system for milk powder stirring processing, and the method comprises the steps: collecting a current frame image of a milk powder material in a stirring container, and obtaining a reference frame image of the current frame image before a preset time interval; calculating a space texture feature set, a time sequence color texture feature set and a dynamic flow field feature set of the current frame image; cascading the space texture feature set, the time sequence color texture feature set and the dynamic flow field feature set to obtain a high-dimensional state vector; calculating a mahalanobis distance between the high-dimensional state vector and a target uniform state cluster core in a pre-constructed state space; when the mahalanobis distance is smaller than a first threshold value, it is judged that the current stirring state is uniform mixing; when the mahalanobis distance is greater than a second threshold value, determining an abnormal state; when the Mahalanobis distance is between the first threshold and the second threshold, it is determined that mixing is being performed.

The invention relates to the technical field of fruit and peach screening, in particular to a fruit and peach multi-stage screening and quality detection method which comprises the following steps: S1, fruit and peach variety judgment: collecting multi-angle images, extracting color, texture and contour features, and outputting variety labels and parameter sets in a classified manner; s2, growth feature mapping: extracting structure key points, matching the structure key points with a variety template, and generating a growth deviation vector; s3, area perception analysis: dividing fruit surface areas based on growth deviation, and independently identifying defects and maturity; s4, grade discrimination: fusing identification results, combining varieties and deviations, and dynamically evaluating a comprehensive quality label; and S5, screening instruction generation: converting the quality label into a control instruction, and driving the sorting device to complete output. According to the invention, integrated automatic processing of intelligent identification of fruit and peach varieties, accurate detection of regional defects and grade screening control is realized, and the accuracy and flexibility of fruit and peach screening are significantly improved.

The invention relates to the technical field of computer vision, and discloses a jade defect intelligent detection method and system based on machine vision and deep learning, and the method comprises the following steps: S1, based on a high-resolution industrial camera and a laser three-dimensional scanner, adopting a multi-mode synchronous collection strategy, and rotating a jade sample through a precise motion control system, a jade surface high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are respectively obtained, and a jade multi-mode original data set is generated. A high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are integrated through a multi-modal synchronous acquisition strategy, and multi-dimensional feature expression under unified coordinates is constructed, so that the limitation of a single data source is effectively overcome; an image registration algorithm and a feature pyramid network are combined with a point cloud network to perform multi-modal feature fusion, complementarity of color texture and geometric morphology information is enhanced, and image quality is optimized based on adaptive histogram equalization and non-local mean filtering.

The invention relates to the technical field of computer graphics and deep learning, in particular to a simulation bait automatic coloring method and system based on a 3D model. The method comprises the following steps: acquiring an uncolored 3D model and a reference image, and generating standard data through analysis verification, curvature grid division and image compliance detection; performing color conversion, texture enhancement and multi-scale downsampling on the compliant image to construct an image pyramid; performing multi-level feature extraction and adversarial training optimization based on a pre-trained convolutional neural network and a generative adversarial network, and generating an enhanced color texture map; performing UV expansion, color mapping and normal mapping fusion in combination with the model topology, and constructing an intermediate model with physical rendering attributes; and batch color consistency verification is realized through color histogram comparison, adaptive threshold segmentation and iteration parameter adjustment, and a standard model group is generated. According to the invention, efficient and highly realistic automatic coloring of the simulated bait is realized, and color consistency and rendering quality in batch production are guaranteed.

The invention provides a 3D target detection tracking method based on visual image and radar tensor sparse proposal fusion, which comprises the following steps: firstly, carrying out generalization extraction on color texture information of a visual image, and establishing multi-scale semantic high-dimensional features; secondly, extracting multi-scale space high-dimensional features of a radar tensor by using SCAN, respectively mapping a learnable sensing probe to radar and visual high-dimensional feature spaces, and performing generalization sparseness on different modal features by means of multi-head deformable attention to form radar and visual proposal features; sparse proposal fusion of radar and visual proposal features is carried out, and 3D target detection is completed; and finally, carrying out mixed multi-feature cascade matching and batch track management on a detection result, and feeding back generated track time sequence information to a front-end learnable sensing probe to realize an active target detection and tracking integrated circulating progressive network based on time sequence information guidance. According to the scheme of the invention, an integrated active sensing framework of single-frame target detection and time sequence target tracking is established, and the reliability of environment target sensing by a multi-source sensor in automatic driving is enhanced.

The invention provides a historic building heritage colored drawing layer AR visual analysis method and system. The method comprises the steps that correlation analysis is conducted through a multi-dimensional mapping method according to feature vectors of colored drawing colors, texture details and historical information obtained through separation, and a fusion relation matrix between attributes is obtained; according to the fusion relation matrix, a virtual reconstruction algorithm is utilized to carry out spatial recombination on colored drawing colors, texture details and historical information, and a three-dimensional virtual colored drawing model is generated; for the generated three-dimensional virtual colored drawing model, performing change simulation on color and texture details in a time dimension through a dynamic rendering technology to obtain a dynamic presentation effect; according to the dynamic presentation effect, a user control interface is embedded in the interaction environment, a switching function is set for different historical information nodes, and an interactive virtual display framework is determined; according to the corrected virtual model, dynamic rendering and interaction environment configuration are executed again, and a final visual visualization result is obtained.

The invention discloses a fruit and vegetable maturity inspection and classification method based on fruit and vegetable identification, and the method comprises the steps: collecting a fruit and vegetable RGB image and a near-infrared image through a high-definition camera and a near-infrared sensor, carrying out the preprocessing of denoising, enhancement, segmentation and the like, and extracting a fruit and vegetable region; extracting features such as colors, textures and shapes by using a pre-trained convolutional neural network, inputting the features into a support vector machine classification model, judging the maturity of the fruits and vegetables, and classifying the fruits and vegetables into immature fruits, mature fruits and over-mature fruits and vegetables. According to the invention, the method has a self-learning capability, can optimize the classification model through quality inspection feedback, improves the classification accuracy, and provides effective technical support for the quality control and management of fruits and vegetables.

Image rendering techniques for use with virtual worlds and interactive media are described herein. Techniques may include: acquiring a first scene depth texture map and a first scene color texture map of a first image frame; acquiring, based on the first scene depth texture map, a first spatial position of a vertex of a target triangle face in a first clipping space; mapping, based on a first camera parameter and a second camera parameter, the first spatial position to a second spatial position in a second clipping space; generating a second scene depth texture map and a second scene color texture map based on the second spatial position and the first scene color texture map; and displaying a second image frame based on the second scene depth texture map and the second scene color texture map. Image display frame rates for a virtual scene may be improved, thereby improving visual effects.

The invention discloses a multi-modal three-dimensional point cloud semantic segmentation method based on adaptive noise filtering, and belongs to the technical field of automatic driving environment perception. The method comprises the following steps: firstly, acquiring data by using a laser radar and a monocular camera, and constructing a multi-modal panoramic feature tensor containing a geometric structure and color textures through projection and mapping; extracting shallow geometric distribution features through a residual context module, and extracting multi-scale environment features through an expanded residual encoder; performing global context aggregation by using a self-attention mechanism of a Transform architecture, and establishing a full-image pixel dependency relationship to make up for a convolution locality defect; in the decoding stage, a channel cross fusion attention module (CCA) is adopted to process deep semantic features and shallow jump connection features, and a channel weight mask is dynamically generated to adaptively screen effective features and suppress high-frequency noise; and finally, outputting a two-dimensional semantic segmentation result and back-projecting the result to a three-dimensional space to obtain a semantic point cloud. According to the method, through global semantic integration and local detail screening, the problems of terrain misjudgment and noise interference of severe weather (such as rain, snow and dust) in a cross-country scene are effectively solved, and the robustness and precision of automatic driving perception are remarkably improved.

The invention provides a dynamic feature contribution degree-based color textile fabric image retrieval method. Innovation and improvement are carried out aiming at single feature limitation and fixed weight fusion defects existing in a traditional retrieval technology. Color, texture and shape features are separated through a multi-modal feature decoupling technology, and coupling interference between the features is eliminated; a dynamic contribution degree quantification mechanism is innovatively constructed, gradient back propagation is used for calculating the contribution proportion of features to retrieval results in real time, and weight self-adaptive adjustment is achieved. According to the method, the problem of retrieval deviation caused by complex characteristics such as color gradient and blending structure difference of the colored spun yarn fabric is solved in a breakthrough mode, weight distribution can be intelligently matched according to inquired image characteristics (for example, the color gradient fabric focuses on color characteristics, and the jacquard fabric focuses on shape edges), and the retrieval result better fits human visual perception. According to the technical scheme, the image retrieval precision of complex fabrics in the textile industry is effectively improved, and an intelligent solution is provided for production process data association.

A simulation scene image generation method, an electronic device and a storage medium are provided. The method includes: acquiring semantic segmentation information and instance segmentation information of a white blank 3D environment model; receiving instance text information of the white blank 3D environment model, the instance text information being editable information and used for describing an instance attribute; and generating a simulation scene image based on the semantic segmentation information, the instance segmentation information, the instance text information and a pre-trained generative adversarial network. In the method, only the establishment of the white blank 3D environment model is required, so that the simulation scene image can be generated based on the semantic segmentation information and the instance segmentation information of the white blank 3D environment model, and attributes such as color, texture and illumination do not need to be refined during establishment of the scene, thereby improving a generation efficiency.

The invention discloses a dual dynamic three-dimensional code based on random color textures and a generation method, a coding method, a recognition method, a decoding method and a generation device thereof, a unique three-dimensional code is generated by adopting random combination and arrangement of specified color textures on the basis of a two-dimensional code, the three-dimensional code has two-dimensional and three-dimensional layers, and the two-dimensional and three-dimensional functions are integrated. Meanwhile, part of information in the encoding process is recorded and encrypted and converted into a decoding core for decoding, the dynamic decoding core provides dynamic three-dimensional hierarchical information under the condition that the corresponding three-dimensional code is not changed, and the content of the dynamic decoding core can point to a specific crowd and is dynamically updated. A two-dimensional level is generated by adopting a standard specification, so that a three-dimensional code is downwards compatible, and the public can directly obtain open information through mobile equipment. By designing a three-dimensional code template, using a multiple three-dimensional code information error correction technology in the generation and decoding process and using a double-flow image processing technology in the image recognition process, the self-adaptive repair capability, the recognition accuracy and the recognition speed of the three-dimensional code are improved, and the equipment requirement is reduced.

The invention belongs to the technical field of material identification management, and particularly relates to a molybdenum concentrate dryness evaluation method and system based on a material image, and the method comprises the steps: extracting depth features capable of representing colors, textures and luster from a material image sequence through a convolutional neural network; constructing a multi-scale feature pyramid, and performing motion alignment and differential operation in combination with an optical flow method to obtain a feature change rate field sequence representing instantaneous change of the material; generating a smooth rate diagram through scale fusion and time window filtering, and calculating a statistical moment of the smooth rate diagram to obtain a dynamic feature vector representing a drying macroscopic trend and spatial uniformity; and combining the dynamic characteristics with the instantaneous static evaluation value, and constructing a normalized comprehensive decision cost function to obtain a final comprehensive evaluation result. According to the invention, dynamic, comprehensive and accurate evaluation of the drying process can be realized.

The present invention discloses a distributed intelligent recognition and processing system for collaborative aerial images of a cluster of unmanned aerial vehicles (UAVs), which belongs to the technical field of UAV image processing. The system specifically comprises: UAVs collect aerial images according to a geographical division scheme and store them in a local edge cache, and then split the aerial image data into two-layer tasks according to color and texture features and upload them to a regional cache; each node obtains data from a three-level cache system, completes target recognition using an intelligent collaborative recognition algorithm after preliminary processing, and stores the results in a global cache; the central node monitors the load weight of each node in real time, and allocates new tasks using a weighted minimum connection number algorithm; the central node aggregates the recognition results in the global cache according to the grid ID, eliminates splicing dislocations through a consistent hashing algorithm, and generates an aerial image recognition report output; the present invention effectively solves the problems of low processing efficiency and large collaborative errors in a centralized architecture, and improves the efficiency and accuracy of aerial image processing of UAV clusters.

The invention relates to the technical field of film and television infringement evaluation, in particular to a film and television infringement evaluation method and system based on an image matching technology. The method comprises the following steps: collecting to-be-evaluated film and television image data; through constructing an adaptive multi-scale convolution denoising network and an image enhancement algorithm, film and television image data are subjected to denoising, enhancement and cutting processing so that image quality is improved and sizes are unified. Extracting image color, texture and shape features, constructing a multi-modal convolution feature extraction network, and improving the matching accuracy by using an adaptive weighted image matching algorithm; after image matching, similarity is calculated, and infringement probability evaluation is carried out in combination with legal standards; an evaluation result is displayed through a report, and copyright party processing suggestions are provided; in addition, a template image matching method is introduced, and first-order and second-order matching codes are used for carrying out rapid preliminary matching on the images, so that the high efficiency and accuracy of evaluation are ensured. The method comprehensively considers image quality, feature matching and legal standards, and has high practicability and flexibility.

The invention discloses an unmanned ship navigation scene complexity integrated classification evaluation method and system based on color texture visual field joint features. The method comprises the following steps: dividing a water area data set into a training set and a test set according to a preset proportion; classifying and marking the water area data set according to scene complexity; performing color, texture and view feature extraction on the data set, and fusing feature extraction results to obtain a feature matrix; training a navigation scene complexity classification model based on a feature matrix corresponding to the training set; and performing model performance evaluation on the trained navigation scene complexity classification model based on the feature matrixes corresponding to the training set and the test set. Based on the data processing flow, the limitation of a single type of features can be overcome by fusing color, texture and visual field features, meanwhile, a multi-classification model integrating multiple machine learning algorithms is constructed, and quantitative decision is made for visual complexity perception of a navigation scene.

The invention relates to the technical field of sewage treatment, and discloses a sewage biochemical treatment process abnormity intelligent early warning method and system based on image recognition, and the method comprises the steps: periodically collecting a sludge image and water quality sensing data; extracting floc form, bubble distribution and color texture features of the image, and performing space-time alignment fusion with the sensing data to construct a multi-source fusion feature matrix; inputting the matrix into a pre-trained digital twin evaluation model, calculating a dynamic deviation degree between a current state and a normal working condition, and identifying an abnormal mode; and generating a graded early warning signal according to the deviation degree and the abnormal mode and pushing the graded early warning signal. The system comprises an online image acquisition unit and a water quality sensor array which are used for data acquisition, and a feature processing module used for feature processing and fusion. According to the scheme, early-stage, accurate and intelligent early warning of abnormality in the sewage biochemical treatment process is realized, the false report and missing report rate is reduced, and the accuracy and the reliability of the system are improved. And the operation stability and the regulation and control efficiency of the system are improved.

The invention discloses a traditional Chinese medicine face image color quantitative characterization method fusing color space and texture features, and relates to the technical field of image processing and medical image analysis. The method comprises the following steps: performing illumination normalization and noise suppression processing on a facial image of a patient to obtain an enhanced and denoised image, and constructing a structure response graph and a color fusion feature tensor; introducing a structure-guided color enhancement mechanism, and obtaining a structure-enhanced multi-channel color tensor based on the structure response diagram and the color fusion feature tensor; constructing a yellow saliency guide map and a multi-scale texture response tensor, and generating a joint feature map through a color-texture joint decoupling mechanism under saliency weighting; normalizing the combined feature map, and extracting texture features and colors of the normalized combined feature map; and combining the color and texture features into a joint quantitative feature vector, and quantitatively representing the color of the traditional Chinese medicine facial image. The problems that color distinguishing is unstable and color and texture decoupling is difficult under complex illumination are solved.

The application discloses an infrared image registration method and device, a medium and a parallel double-light handheld infrared equipment, and relates to the field of infrared temperature monitoring. The first infrared image and the first visible light image are registered according to imaging rules, then the second visible light image and the first visible light image are registered according to marks on a target object, and finally the second visible light image and the second infrared image are registered again according to imaging rules, so that the registration of the infrared image is realized. In the method, key points, feature vectors of the key points and the like do not need to be extracted from the infrared image, and the method is not affected by the resolution of the infrared image, so that the stability and accuracy are higher, and the success rate of infrared image matching is improved. Furthermore, the method is not limited by the color texture of the pseudo-color image, so that the registration effect is better. In addition, key point feature extraction and key point feature matching are omitted, so that the calculation amount is greatly reduced, and the registration efficiency is improved.

A simulation scene image generation method, an electronic device and a storage medium are provided. The method includes: acquiring semantic segmentation information and instance segmentation information of a white blank 3D environment model; receiving instance text information of the white blank 3D environment model, the instance text information being editable information and used for describing an instance attribute; and generating a simulation scene image based on the semantic segmentation information, the instance segmentation information, the instance text information and a pre-trained generative adversarial network. In the method, only the establishment of the white blank 3D environment model is required, so that the simulation scene image can be generated based on the semantic segmentation information and the instance segmentation information of the white blank 3D environment model, and attributes such as color, texture and illumination do not need to be refined during establishment of the scene, thereby improving a generation efficiency.

The invention relates to the technical field of image processing, in particular to a plant raw material grading method and system based on image analysis. The method comprises the steps of collecting an RGB image of a to-be-detected frozen plant raw material, converting the RGB image into a CIELAB color space to obtain a corresponding color texture image, and performing super-pixel segmentation on the color texture image to obtain a plurality of super-pixel blocks; based on the brightness feature, the gradient feature and the saturation feature of each super-pixel block, obtaining a frost shielding index of each super-pixel block, and obtaining a final quality score of each super-pixel block according to the saturation feature of each super-pixel block and the difference between the gradient feature and the frost shielding index; taking the superpixel blocks belonging to the same berry as each superpixel block set; the areas of the super-pixel blocks serve as weights, the weighted quality score of each super-pixel block set is obtained, grading processing is conducted on each fruit based on the weighted quality scores, and the grading accuracy of frozen plant raw materials is remarkably improved.

This disclosure provides a method, apparatus, computer device, and storage medium for generating textures. The method includes: acquiring a three-dimensional model of a target object, an original shadow texture corresponding to the three-dimensional model, and a shadow tendency texture; performing a correction process on the original shadow texture based on the lighting direction corresponding to the three-dimensional model to obtain a target shadow texture corresponding to the three-dimensional model; and performing a light and shadow mapping process on the target shadow texture using the shadow tendency texture to obtain a shadow color texture corresponding to the three-dimensional model.

A method and a system for performing image editing based on an attribute-specific text prompt includes acquiring a noise code (z), a textual instruction (Ai) specifying a target facial attribute to be edited, and a target camera pose (pt). Upon acquiring, mapping the noise code (z) to a latent code (w), via a mapping network. Once the mapping is done, editing the latent code (w) based on the textual instruction (Ai) to generate an edited latent code (ŵ), via a text-driven Latent Attribute Editor (LAE). Further, based on the edited latent code (ŵ), generating a color texture image and a set of alpha maps via a three-dimensional Generative Adversarial Network (3D GAN). Furthermore, based on the color texture image and the set of alpha maps, generating a 3D-aware and view-consistent image at the target camera pose (pt) via a differentiable renderer.

The application provides a food freshness evaluation method and system based on image processing. Wherein, multi-view images of food are acquired, the images are transmitted to a central processing center, personalized image acquisition parameters are set, and a food image database is formed; based on the food image database, high-precision dynamic monitoring is performed on the color and texture changes of food in different time periods to generate a preliminary evaluation result; according to the preliminary evaluation result, the freshness of food is predicted and modeled in combination with the type, storage condition and historical sales data of food, and a final freshness evaluation report is obtained; the inventory strategy is adjusted in real time, the food distribution plan is optimized, and an optimized management process is generated by using the freshness evaluation report. The technical scheme provided by the application improves the accuracy of food freshness evaluation.

The invention provides a breast ultrasound image segmentation method based on deep learning, which comprises the following steps of: performing data enhancement on an image in a data set by using a breast cancer ultrasound data set which is widely used in previous research through methods of coordinate transformation, gray mapping and the like so as to extract features such as color, texture, granularity and the like; a medical expert carries out manual annotation on a tumor part in a breast ultrasound image data set, and due to the fact that breast image annotation needs to consume a large amount of labor cost and time cost, in order to more effectively utilize existing data, a training data set is enhanced; according to the breast ultrasound image segmentation method based on deep learning, in an actual diagnosis site, more lesion areas can be quickly and accurately identified, and meanwhile, the phenomena of false detection and missing detection are fewer; the training data is greatly increased after data enhancement, the semantic information of the image is richer, the number of effective features learned by the model during training is increased, and meanwhile, the oscillation condition generated in network training is reduced.

The application provides a color spun fabric image retrieval method based on dynamic feature contribution degree, and innovatively improves the single feature limitation and fixed weight fusion defects existing in traditional retrieval technology. Color, texture and shape features are separated through a multi-modal feature decoupling technology to eliminate the coupling interference between features; a dynamic contribution quantification mechanism is innovatively constructed, the contribution proportion of features to retrieval results is calculated in real time by using gradient back propagation, and adaptive adjustment of the weight is realized. The method breaks through the retrieval deviation problem of color spun yarn fabrics caused by complex characteristics such as color gradient and difference in blended structure, can intelligently match the weight distribution according to the query image features (such as color gradient fabric focusing on color features and jacquard fabric focusing on shape edges), and makes the retrieval results more consistent with human visual perception. The technical scheme effectively improves the image retrieval accuracy of complex fabrics in the textile industry and provides an intelligent solution for production process data association.

Method for examining a multicolor print (101) of a printed color texture on a surface (103) of a substrate, in an ongoing printing process of a printing system, comprising the following steps: - Capturing the first produced multicolor print (101) of the printed color texture on the surface (103) of the substrate in the ongoing printing process of the printing system with an image sensor (109) as a reference of the multicolor print to be printed, - Providing the reference for the multicolor print to be printed (101), - Illuminating the printed surface (103) of the substrate with visible light of a first light color and with visible light of a further light color, wherein the visible light of the first light color and of the further light color corresponds to a defined wavelength or a defined wavelength range; - Recording an image of the surface (103) of the substrate when illuminated with the visible light of the first light color and recording another image of the surface (103) of the substrate with visible light of the other light color of an area of ​​the multicolor print (101) using an image sensor (109) with at least one image sensor row (109a), - Evaluation of light reflection in the image recording of the first light color as well as the other light color and detection of an anomaly in the printed color texture, in particular a faulty or missing color application, based on the respective light reflection of the first light color as well as the other light color in a comparison to the reference of the multicolor print to be printed (101) by means of a processor (111); - Detection of a defective nozzle of the printing system by detecting the anomaly in the printed color texture, whereby the reference of the multicolor print to be printed (101) of the color texture to be printed is only captured once on the basis of the first printed multicolor print (101).

The present invention relates to the field of remote control technology, and in particular to a scraper machine image recognition method based on image processing; the method is implemented by a data acquisition module, a camera module, a color feature analysis module, a texture feature analysis module, a contour analysis module, an image recognition output module and an intelligent management module; the color, texture and contour feature analysis of the scraper machine image are respectively performed by the color feature analysis module, the texture feature analysis module and the contour analysis module to output the scraper machine image recognition result, which can more comprehensively analyze the scraper machine image and improve the accuracy of the scraper machine image recognition result; the scraper machine is stopped by controlling the distance between the responsible employee and the scraper, which can avoid the responsible employee stopping the scraper machine in advance before reaching the position of the abnormal warning scraper, resulting in unreasonable downtime and further waste of production resources of the coal preparation plant; and the abnormal scraper machine can be repaired while maximally ensuring the working efficiency of the coal preparation plant.