74 results about "Color texture" patented technology

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 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.

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 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 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.

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.

A packaging system for a buffing pad is provided where a single package can be used with different types of pads. In one embodiment, a rear perspective view of a pad is provided on the face of the package, where common portions are printed. A transparent portion is further provided whose size and shape resembles uncommon portions of the pad, such as color and texture. The buffing pad is then inserted into the package so that the front surface of the pad is visible through the transparent portion. This will provide the user with a depiction of a buffing pad that is include therein, including its color and texture and its backside, including how the pad can be affixed to a power tool. This together with a legend will allow the user to identify the specific type of buffing pad that is included therein.

The application discloses a geometric self-attention semantic segmentation method and device and a storage medium, and comprises the following steps: acquiring a color texture image and a depth topography image in a multi-modal image; obtaining a color texture feature map and a depth topography feature map according to the color texture image and the depth topography image; guiding the depth topography feature map to perform geometric correction through the color texture feature map to obtain a corrected geometric feature; performing depth uncertainty evaluation according to the depth topography image to obtain an uncertainty mask; dynamically fusing the color texture feature map and the corrected geometric feature according to the uncertainty mask to obtain a fusion feature map of n scales; and inputting the fusion feature map into a classification module to obtain a semantic segmentation result. The geometric correction of the depth topography feature map is used to eliminate the cross-modal boundary misplacement; the reliability evaluation of the depth data is used to dynamically adjust the weight of the geometric information in the attention calculation, so that the dynamic fusion between different modalities is realized, and the segmentation accuracy of the semantic segmentation result is ensured.

The invention relates to the related technical field of image recognition analysis, in particular to a sesame quality detection method and system based on image recognition, and the method comprises the steps: obtaining sesame epidermis and endosperm spectral data and microscopic imaging features, setting a texture matrix, extracting morphological features, finely tuning a pre-training model for multi-dimensional fusion, and dynamically calibrating exposure time and light source intensity. And performing exposure compensation control when the environment changes. The technical problems of unstable image acquisition quality, difficulty in accurately distinguishing tiny defects and limited sesame quality detection precision caused by ambient light intensity fluctuation and sesame stacking density change are solved, and the purpose of accurately detecting the sesame quality by synchronously acquiring reflection spectrum data of an epidermal wax coat and internal endosperm and dynamically calibrating exposure time and light source intensity in combination with a microscopic texture feature matrix is achieved. Interference of ambient light intensity fluctuation and sesame stacking density change is effectively resisted, weight distribution is performed on form, color and texture features in combination with an attention mechanism, classification boundaries are optimized, and the technical effects of sesame quality detection precision are guaranteed.

The invention discloses a dual-coding adversarial learning and multi-scale expansion fusion attention image cartoonalization method, and relates to the technical field of image processing, and the method comprises the steps: constructing a generator comprising a dual-encoder structure, and enabling a content feature encoder to extract the structural semantic features of an input real image, a style feature encoder extracts texture style features of the target cartoon image; the style features are injected into the content features through adaptive instance normalization; processing fusion features by adopting a multi-scale expansion fusion attention mechanism, and adaptively enhancing a key channel and a space region; the decoder reconstructs and generates a cartoon image; meanwhile, a double-discriminator framework composed of a global discriminator and a local discriminator is adopted, the overall style consistency and the local detail authenticity of the image are evaluated respectively, and the generation process is optimized through adversarial learning. According to the method, the high-quality cartoon graph can be generated, the color texture is natural, the edge is clear, and details are rich.

This invention discloses a method and system for intelligent recognition and analysis of images from shield tunnel faces in complex geological formations, relating to the field of intelligent monitoring technology for shield tunnel construction. The method includes: a multi-source spoil image data acquisition step, acquiring endoscope images from the spoil chamber, video images from the spoil conveyor belt, and spoil sampling photos, and performing preprocessing; a spoil image multi-feature extraction and fusion step, extracting color, texture, and morphological features and fusing them through an attention mechanism; an intelligent formation type recognition step, outputting formation type, water content, and particle size distribution; an intelligent recommendation step for tunneling parameters, generating recommended values ​​for propulsion speed, cutterhead rotation speed, and spoil chamber pressure; and a recognition model feedback optimization step, updating model parameters based on actual tunneling results.

The present application relates to the technical field of image reconstruction, in particular to a kind of imaging method of 3D space position information and 2D color texture information fusion, comprising the following steps: according to 2D color texture information, extract 2D color texture information internal gray level co-occurrence matrix pixel parameter, generate two-dimensional color mutation boundary, obtain 3D space position information accompanying space isomorphism determination item.In the present application, three-dimensional feature distribution skeleton coordinate set is used to participate in the solution of curved surface control point, so that the spatial curved surface reconstruction has continuity and geometric consistency;Finally, by converting two-dimensional color mutation boundary coordinates into Lagrange multiplier constraint variable and embedding into the solving process, the boundary constraint directly participates in the three-dimensional curved surface generation process, and then the texture pixel value is mapped to the external connection surface of three-dimensional curved surface grid node set, to realize the synchronous expression of space structure and texture information.

The application discloses a non-transparent point cloud and grid rendering method and device based on pixel sampling, and relates to the technical field of rendering. The method comprises the following steps: obtaining non-transparent point cloud data or grid data to be rendered; converting vertex coordinates corresponding to the point cloud data or the grid data into screen coordinates, and discretizing the screen coordinates into a plurality of pixel grids with an actual screen size, so that the vertex falls into the corresponding pixel grid; wherein one pixel grid corresponds to one pixel point of the screen; determining the color of each pixel grid according to the relationship between the vertex corresponding to the point cloud data or the grid data and each pixel grid and the distance relationship between the point cloud or the grid and a viewpoint, and then determining the color of the corresponding pixel point, so as to finally obtain a corresponding color texture map; and transmitting the obtained color texture map to a rendering device to complete the display of the texture by the rendering device. The application utilizes the pixel grid and depth comparison to eliminate data that is not finally displayed, reduces the rendering amount, and improves the rendering efficiency.

The present disclosure relates to a three-dimensional virtual object generation method and apparatus, and a device, a storage medium and a program product. The method includes: receiving a three-dimensional grid model; constructing a height map on the basis of the three-dimensional grid model, wherein the height map refers to a grayscale image that includes height data of a three-dimensional virtual object; processing the height map to obtain a transparency map, a basic color texture image and a roughness map; and fusing the transparency map, the basic color texture image and the roughness map, so as to obtain the three-dimensional virtual object.

The application provides a foreign matter detection method, system and device in a railway scene and a storage medium, and belongs to the technical field of computer vision and railway safety monitoring. The method comprises the following steps: synchronously collecting visible light, low-light and infrared images in front of a train, determining corresponding track regions of interest on various images through geometric projection; extracting color texture, edge contour and temperature distribution features in respective regions, performing brightness fitness evaluation on the visible light image based on a double Sigmoid function; performing signal-to-noise ratio evaluation on the low-light image based on gradient information entropy and noise intensity; performing thermal contrast evaluation on the infrared image based on temperature distribution standard deviation; dynamically calculating fusion weights according to the confidence of the three, adaptively weighting and fusing multi-modal features to generate fusion features. The features are input into a pre-trained classifier to complete accurate detection and classification of track foreign matters. The system accuracy and environmental adaptability are significantly improved, and real all-weather safety monitoring is realized.

The invention relates to the technical field of image data processing, in particular to a fatty liver lesion image analysis method based on color texture features, which comprises the following steps: extracting a rat liver region through image segmentation, and converting the rat liver region into a CIELAB color space to separate brightness, red-green degree and yellow-blue degree components; calculating a local texture difference feature based on a pixel space neighborhood, and coupling a brightness value to construct a pixel-level validity weight to suppress specular reflection; and weighting and updating a K-Means clustering center by using a pixel-level validity weight, distinguishing a fatty degeneration region from a non-lesion region, and counting the number of pixel points to realize analysis of the fatty liver lesion image. The optical noise interference is accurately inhibited, the clustering accuracy is improved, the fatty liver lesion analysis accuracy is improved, and reliable technical support is provided for drug curative effect evaluation and pathological mechanism research.

The application provides a model rendering method and system based on mixed reality interactive creation, and relates to augmented reality technology, comprising: calculating an optimal rendering view angle based on the geometric structure of a three-dimensional model; obtaining a preset optimal viewing angle, and calculating a final rendering view angle by weighting the optimal viewing angle and the optimal rendering view angle; projecting the three-dimensional model based on the final rendering view angle to generate a baseline sketch map; obtaining an interactive coloring image with the baseline sketch map as a bottom map; identifying the interactive coloring image, extracting a regional image matched with the baseline sketch map, and performing geometric correction; extracting color features from the regional image, coloring the baseline sketch map based on the color features, and generating a color texture map; and rendering the three-dimensional model based on the color texture map. The application enables users to input creativity in a hand-drawing manner, automatically generates a high-quality map conforming to a target three-dimensional object, and finally renders and displays in real time in an augmented reality environment.

The invention provides an amphibious three-dimensional model seam color transition method based on surface primitive Poisson fusion, and relates to the field of data processing. According to the method, after three-dimensional models of a water area and a land area are geometrically fused, water area pseudo-color textures are constructed based on water depth information, land area real image textures are obtained, a water and land boundary line is extracted through unified topology analysis, and a plane primitive buffer area is constructed around the boundary line; local color change constraints of a water area and a land area are introduced into a buffer area, a guiding gradient field is constructed, then a Poisson fusion model is established based on a surface primitive topological relation, surface primitive colors are solved in a unified mode under boundary color constraints, and continuous transition and visual consistent fusion of texture colors of a water and land joint area is achieved. By implementing the technical scheme provided by the invention, the color transition quality of a large-range land and water joint area can be effectively improved.

The invention provides a cultivated land boundary identification method and system, and the method comprises the steps: collecting a remote sensing image of a target cultivated land, carrying out the cultivated land semantic segmentation of the remote sensing image, obtaining an initial cultivated land probability graph, and determining a color-texture gradient magnitude graph of the remote sensing image; the category probability gradient of each pixel in multiple directions in the cultivated land probability graph and the color-texture gradient magnitude graph of the remote sensing image are initialized, and a boundary significance graph is constructed; according to the semantic probability gradient magnitude and the color-texture gradient magnitude of each pixel point in the boundary saliency map, determining the semantic-geometric feature mismatch degree of each pixel point, and performing adaptive filtering on the boundary saliency map through the semantic-geometric feature mismatch degrees of all the pixel points; and generating a cultivated land boundary vector of the target cultivated land based on the filtered boundary saliency map. By adopting the scheme of the invention, the dislocation of the semantic boundary and the visual edge in the cultivated land boundary image recognition process can be quantified.

A face image rotation method, device and storage medium, the method comprises the steps of: acquiring three-dimensional face data; constructing a three-dimensional face model database according to the three-dimensional face data; training a deep convolutional neural network using the three-dimensional face data; generating a rotated two-dimensional face grayscale image according to the deep convolutional neural network; and generating a rotated two-dimensional face color texture image using style transfer rendering according to the two-dimensional face grayscale image. The face image rotation method, device and storage medium provided by the application rely on a neural network model focusing on three-dimensional face model shape parameter regression, can improve the accuracy of rotating face shape structure, and retain face deep features; the style transfer adversarial neural network is used to extract face decoupling features, which can well render the high-frequency details of the rotated face; while ensuring the accuracy of the rotated face contour positioning feature, the face texture color and high-frequency details can be accurately rendered, and the face deep features are retained.

The invention discloses an AI-based three-dimensional model simplification method and system, a medium and equipment, and belongs to the technical field of lightweight processing of three-dimensional graphic models.The method comprises the steps that an original three-dimensional model is obtained, and texture information (including texture numbers and images) and primitive data (including primitive numbers, texture numbers and vertex data) of the original three-dimensional model are extracted; the vertex data comprises a position, a UV coordinate and a normal; inputting the texture images into a recognition model, screening pure color textures and outputting representative color values; and deleting UV coordinates and normal lines of the primitives associated with the pure-color textures, adding vertex color attributes and assigning values as representative colors to obtain first primitive data, carrying out surface reduction based on an edge collapse geometric surface reduction algorithm, and carrying out rendering in combination with texture information: if the vertexes contain the color attributes, directly rendering, otherwise, using texture images, and finally outputting a simplified model. By implementing the method and the device, the technical problem of low efficiency in the surface reduction process of the existing three-dimensional model in the prior art can be solved.