63 results about "Texture extraction" patented technology

The invention relates to the technical field of image processing, and provides an enteromorpha remote sensing image detection method and system based on deep learning, and the method comprises the following steps: carrying out the edge gradient extraction and small target enhancement processing of an obtained to-be-detected remote sensing image, and obtaining a first feature map after the edge enhancement and small target feature enhancement; transmitting the first feature map to a U-shaped backbone network formed by cascading multiple stages of Ep-VSS block modules, performing multi-stage feature extraction, and obtaining a detection result of the enteromorpha remote sensing image based on the feature map output by the last stage of Ep-VSS block module; according to the method, through edge enhancement, small target sensitive detection, multi-scale texture extraction and spatial context modeling, precise boundary segmentation and long-range dependence modeling are realized, and the accuracy, real-time performance and reliability of enteromorpha remote sensing monitoring are improved.

The invention relates to a cutting path generation system based on visual positioning, cutting equipment and a cutting method. The system comprises a scanning camera control module, an image acquisition and recognition module, an image processing module, a cutting path planning module and a cutting control module. According to the system, by combining a Laplace algorithm and an LBP and GLCM texture extraction technology, the boundary recognition problem under the condition that a pattern and a background are in the same color or low contrast is solved; the path smoothness and the cutting quality are improved by adopting contour optimization and curvature compensation algorithms, the cutting sequence is optimized by combining weighting functions of the area, complexity and overlapping degree, and material deformation is reduced. And motor driving is dynamically adjusted through PID control, and stable control over the speed of the tool bit is achieved. The method has the advantages of being high in recognition precision, high in adaptability, intelligent in path planning and stable in cutting process, the automatic cutting efficiency and the finished product quality of complex patterns or same-color materials are remarkably improved, and the overall cutting precision fluctuation is controlled within + / -2%.

The invention discloses an infrared image super-resolution reconstruction method based on visible light correlation feature fusion, and belongs to the technical field of image processing. The method comprises the following steps: inputting an up-sampling low-resolution infrared image into a learnable texture extraction module, and extracting query features from the learnable texture extraction module; the visible light image and the high-resolution visible light image which are subjected to down-sampling and up-sampling processing are input into a texture feature coding module, and key features and value features are extracted from the visible light image and the high-resolution visible light image; generating a correlation guide graph and a corresponding feature weighted graph according to the query features and the key features; obtaining a migration feature graph according to the corresponding feature weighted graph and the value feature; and inputting the shallow layer features, the correlation guide map and the migration feature map into a cross-modal feature fusion module to obtain fusion features, and inputting the fusion features into a Transform image reconstruction module to generate a super-resolution infrared image. According to the method, the guiding effect of visible light on infrared detail reconstruction is remarkably improved, so that the reconstructed image is clearer and sharper in texture details.

The invention discloses an infrared small target detection method based on comparative learning and frequency gradient feature fusion, and aims to improve the detection precision. A current method faces three challenges that the contrast of an infrared image is low, space information is limited and is interfered by clutters, so that global context is missing, and robustness is insufficient; target structure features are weak and are similar to background gray, and texture extraction is difficult; an image background is complex, a target is tiny, missing detection and false alarm are caused frequently, and the distinguishing capacity of a foreground and the background is affected. For the first problem, a frequency attention perception fusion module is designed to improve semantic consistency and positioning precision. In order to solve the second problem, a textural feature enhancement module is designed to enhance textural feature representation. In order to solve the third problem, a temperature sensing comparison learning module is designed to improve the foreground and background distinction degree. In combination with the three designs, the model designed by the invention can accurately detect the small target in the infrared image, and is worthy of vigorous popularization.

The invention discloses a nighttime light remote sensing image super-division method and system using multi-source remote sensing data, and the method comprises the steps: collecting an NPP-VIIRS low-resolution nighttime light image, a Landsat-8 image and a Luoma No.1 high-resolution nighttime light image, carrying out the preprocessing, feature texture extraction, resampling, and data set construction. Constructing a U-Net neural network model for increasing a channel attention mechanism to carry out super-resolution processing from a low-resolution image to a high-resolution image; according to the method, detail information in the low-resolution image is effectively supplemented, the image super-resolution precision and the image integrity are improved, compared with the prior art, the high-quality and richer night light super-resolution image is provided, and the method has the advantages of being advanced in algorithm, high in precision and high in intelligent degree and is suitable for popularization and application. And a high-quality data basis is provided for subsequent remote sensing image analysis and data extraction.

According to the high-speed fuzzy license plate character recognition method based on deep learning, a deep convolutional generative adversarial network BLPDCGAN generates a high-quality fuzzy image similar to a real fuzzy license plate, a data set is expanded, and the model generalization ability is improved. The generation module introduces a noise image to enhance the blurring effect, and optimizes the quality of the generated blurred image through MSE loss and adversarial loss. The blurred license plate character recognition module uses a Unet architecture for reference, integrates a CBAM attention mechanism and multi-scale feature fusion, enables the model to pay more attention to character features rather than noise, and improves the feature extraction capability of blurred images. The feature enhancement module further enhances the structure and texture extraction of the fuzzy characters, and increases the robustness and recognition precision of the model to the characters in the fuzzy scene. According to the multi-scale feature fusion strategy, features with different resolutions are aggregated, so that the model can capture key feature details of a fuzzy region and optimize the overall recognition effect.

The application discloses a multi-focus microscopic image fusion method, system and program based on depth map correction. The method comprises the following steps in sequence: image sequence acquisition, texture extraction, definition evaluation, depth map generation, confidence map generation, depth correction and color image reconstruction. According to the definition curve, the depth map and the confidence map are determined, the unstable points are determined by using the confidence map, the depth data of the non-texture and weak-texture areas are corrected by HSL color space guided filtering, and thus the phenomenon that the depth map is wrong in focus plane judgment due to the non-texture and weak-texture areas of the image source is avoided.

The invention relates to an infrared-visible light dual-mode layered registration method for pavement defect detection. The method comprises the following steps: acquiring infrared and visible light images to be registered, and respectively inputting the infrared and visible light images into upper and lower flow channels of a physical attribute decoupler; the upper stream carries out thermal radiation intensity filtering on the infrared image to obtain a light intensity characteristic pattern, the lower stream carries out gradient texture extraction on the visible light image to obtain a texture characteristic pattern, and the two patterns output pure characteristics through a cross-modal suppression gate. Inputting the pure light intensity and the texture features into an infrared encoder and a visible light encoder respectively to obtain features of each layer; according to a self-adaptive layered weighting mechanism, performing weighted fusion on the features of each layer of the bimodal to obtain weighted features; performing projection, similarity calculation and region division on the weighted features by using a dynamic region clustering engine, and determining key registration anchor points and de-noising feature regions; and finally, adjusting a fusion weight by means of an environment self-adaptive compensation mechanism, and outputting a final fusion feature map in combination with the target optimization function constraint precision. By adopting the method, cross-modal high-precision alignment of microscopic details and macrostructures can be achieved.

The application provides a three-dimensional reconstruction method and device based on biomimetic stereovision and a storage medium, the method comprising: acquiring left and right images captured by a double-camera device when focusing on an interest target; performing texture extraction on the left and right images respectively to obtain a total texture map of the left image and a total texture map of the right image; performing feature matching on the total texture map of the left image and the total texture map of the right image to generate a dense disparity map; calculating three-dimensional coordinates of common feature points matched in the dense disparity map, and estimating three-dimensional coordinates of non-common feature points according to the three-dimensional coordinates of the common feature points to obtain a three-dimensional coordinate dataset; and performing three-dimensional reconstruction according to the three-dimensional coordinate dataset to generate a stereoscopic view of the interest target; the application can effectively improve the matching rate of feature points and improve the stereoscopic effect.

The invention relates to a small target detection method and system based on adaptive texture perception and frequency domain-cross-layer collaborative optimization, and the method comprises the steps: providing a cross-scale texture guide module, independently capturing the spatial distribution characteristics of heterogeneous features through a multi-scale texture extraction unit, and constructing scene-level texture prior through an additive fusion mechanism; a frequency domain detail enhancement module is provided, features are mapped to a complex frequency spectrum space through real number fast Fourier transform, dynamic reweighting is carried out on medium-high frequency components by using an adaptive weight vector, and target contour information is explicitly enhanced in combination with inverse transform and a residual connection mechanism; a cross-layer feature stabilization module is designed, context clues of different granularities are captured through lightweight multi-scale receptive field extension, and a dynamic channel gating modulation mechanism is introduced to perform active intervention on a feature evolution path. Compared with the prior art, the method has the advantages of characteristic enhancement, stable positioning, balanced detection precision and operation efficiency and the like.

The application provides a polarization and visible light image fusion texture extraction method and device. A visible light reflection component is first extracted to screen a reliable linear polarization degree image texture and generate a preliminary fusion texture image. Then, a deep learning network model is trained by taking the preliminary fusion texture as a supervised true value, combining a reasonable data enhancement method and a scientific training strategy, so as to eliminate residual block effects and noises in artificial textures. Finally, a fusion texture with high information quantity, high precision, low noise and low block effect is output. In this way, the complementary characteristics of the polarization and visible light images are fully utilized, the texture extraction result is clear and free of artifacts, and the method is suitable for image fusion, edge detection and other real complex scene image detail enhancement and quality optimization.

The invention discloses a pest prevention method, system and device based on computer vision, and the method comprises the steps: obtaining a plant X-ray image, carrying out the texture extraction, obtaining a plant texture image, and obtaining a pest mask image through gradient analysis; identifying the specific position information of the pests of the specific type, and obtaining a conversion mask image of the pests of the specific type through distance conversion based on the specific position information and the pest mask image; obtaining an intersection point of the image boundary of the transformation mask image of the specific type of pests and the pest area, taking the intersection point as a current guide point, obtaining a next guide point through a neighborhood pixel point set of the current guide point, and carrying out iterative analysis to obtain a next guide point set; calculating the distance between all the guide points, obtaining the length of the pest body, analyzing the age of the pest, determining the age of the pest, and preventing the pest according to the age of the pest. By analyzing the X-ray image of the plant, the length of the pest body is obtained, and pest prevention is achieved through pest age analysis.

The present invention discloses an infrared image super-resolution reconstruction method based on visible light correlation feature fusion, which belongs to the field of image processing technology. The present invention inputs an upsampled low-resolution infrared image into a learnable texture extraction module to extract query features; inputs a visible light image and a high-resolution visible light image that have been processed by downsampling and upsampling into a texture feature encoding module to extract key features and value features; generates a correlation guidance map and a corresponding feature weighted map based on the query features and the key features; obtains a migration feature map based on the corresponding feature weighted map and the value features; inputs shallow features, the correlation guidance map and the migration feature map into a cross-modal feature fusion module to obtain fused features and inputs them into a Transformer image reconstruction module to generate a super-resolution infrared image. The present invention significantly improves the guiding effect of visible light on the reconstruction of infrared details, making the reconstructed image clearer and sharper in texture details.

The invention relates to the technical field of image recognition, in particular to a catheter positioning method and system based on image recognition, and the method comprises the steps: extracting multi-direction gray changes from a catheter image, generating main direction angle mapping, comparing adjacent pixel directions to form an edge continuous set, recognizing the trend, matching direction textures, and extracting an extension path. And constructing a connected node chain by screening directions and gray features, and connecting node coordinates to generate a catheter positioning path set. According to the method, a main direction angle distribution diagram is constructed through multi-direction gray scale changes, the edge trend with extension features is extracted through direction continuity and angle stability, linear track classification is completed through direction texture comparison, a coherent node link is obtained through synchronous constraint of direction and gray scale trend, and the linear track classification is completed. A conduit track set with direction correlation and node connection is formed through spatial serialization, path derivation continuity and spatial structure integrity are kept in an area with complex texture or frequent structure change, and the phenomena of trend drift and path jump are reduced.

This invention discloses a crop disease diagnosis method that integrates spatial multi-scale perception with environmental decoupling, belonging to the field of agricultural remote sensing image processing technology. It utilizes large-kernel deep convolution to perform wide-area spatial perception on remote sensing images, generating a spatial weight map encoding global environmental background information. Subsequently, the spatial weight map is reshaped into a dynamic convolution kernel, driving small-kernel group convolution to perform context-adaptive fine-grained disease texture extraction within local neighborhoods, achieving cross-scale feature collaborative modeling. Deep features are decomposed into disease features and environmental features. Adversarial training using gradient inversion layers forces disease features to be free from environmental interference, and mutual information minimization and counterfactual reinforcement learning eliminate environmental spurious correlations, ultimately obtaining robust disease diagnosis results to environmental changes. This invention effectively solves the problem of insufficient generalization performance of existing technologies under varying environmental conditions and has significant application value in multi-temporal and multi-plot agricultural remote sensing monitoring scenarios.

The present application belongs to the technical field of computer vision and intelligent agriculture, and particularly relates to a remote sensing image cultivated land fine segmentation method and system fusing global-local feature perception. The method comprises: acquiring cultivated land remote sensing image data and performing preprocessing; constructing a feature encoder based on a ResNet architecture to perform feature extraction on the preprocessed cultivated land remote sensing image data to obtain shallow features and deep features; constructing a GLC-Mamba bottleneck module to perform local texture extraction and global long-distance dependence modeling on the deep features, and obtaining fused features through a gated adaptive fusion mechanism; and constructing a U-Net decoder to up-sample the fused features and fuse the shallow features through a skip connection to obtain a pixel-level cultivated land segmentation result map. The present application solves the problems of strong spatio-temporal heterogeneity and easy-to-fuzz boundary in cultivated land segmentation, maintains a low computing cost while ensuring high precision, and is suitable for large-scale agricultural condition monitoring and land resource management.

This invention discloses an intelligent fire detection method based on artificial intelligence video analysis, belonging to the field of artificial intelligence video analysis technology. This method targets multiple video streams deployed on an edge computing gateway. It utilizes an asynchronous decoupled buffer queue to bind video frames and timestamps into frame units to be processed. An incremental sequence identifier is assigned to each frame unit to be processed via a hardware resource scheduler, and the frame units are then distributed to a first processing pipeline and a second processing pipeline based on the sequence identifier. In the first processing pipeline, background removal and dynamic texture extraction are performed to generate a thermal activation map. In the second processing pipeline, frequency domain transformation and multi-scale local binary mode decomposition are performed to generate a flame frequency domain texture feature map. The thermal activation map and the flame frequency domain texture feature map are then pixel-level weighted fusion at the same time reference to generate a fusion confidence tensor. Finally, non-maximum suppression and spatial connectivity analysis are performed to output the bounding box of the flame target and the initial fire intensity level.

The application provides a polarization and visible light image fusion texture extraction method and device. A visible light reflection component is first extracted to screen a reliable linear polarization degree image texture and generate a preliminary fusion texture image. Then, a deep learning network model is trained by taking the preliminary fusion texture as a supervised true value, combining a reasonable data enhancement method and a scientific training strategy, so as to eliminate residual block effects and noises in artificial textures. Finally, a fusion texture with high information quantity, high precision, low noise and low block effect is output. In this way, the complementary characteristics of the polarization and visible light images are fully utilized, the texture extraction result is clear and free of artifacts, and the method is suitable for image fusion, edge detection and other real complex scene image detail enhancement and quality optimization.

The invention relates to the field of cargo identification, and discloses a pallet cargo identification method and device and a storage medium. The method comprises the following steps: collecting a depth point cloud and a color image; carrying out cargo extraction processing on the depth point cloud to obtain a cargo point cloud cluster; performing projection cutting processing on the color image to obtain a cargo image block, and cutting a point cloud cluster; performing geometric feature extraction on the cutting point cloud cluster to generate query features; performing texture extraction on the cargo image blocks to generate texture features; performing dot product normalization processing on the query features and the texture features to generate similarity features; performing feature fusion processing on the similarity features and the cargo image blocks to generate cargo fusion features; and based on a preset classifier and a preset regression device, carrying out classification identification processing on the cargo fusion features, and generating cargo type and pose data. In the embodiment of the invention, the cargo identification precision is improved, the influence of illumination on the identification result is overcome, and the data cost and deployment time of new SKU online are reduced.

The application discloses an image texture extraction method, device and computer readable storage medium, the method comprising: converting the color value of each pixel of a target image into a different base to obtain a digital array of each pixel; wherein the base of the color value of the converted pixel is lower than the base of the color value of the pixel before conversion; according to the number of bits of the digital array, the target image is decomposed into several layers; for each layer, a window mask is used to extract texture information to obtain a sub-texture map of each layer; and all the sub-texture maps are superimposed with weights to synthesize a total texture map of the target image. Compared with traditional edge detection technology, the application can greatly improve the texture information extraction rate and texture information extraction accuracy of the target image, and provide more complete texture and edge information for downstream image information processing procedures.

The present application belongs to the technical field of image processing, and particularly relates to a quantum heuristic progressive focusing plant cell microtubule image segmentation method and system. The method of the present application takes quantum heuristic progressive focusing Transformer as the backbone network, takes the feature texture extraction module as the bypass network, and loads the Hamilton growth layer at the output end of the segmentation head to form a plant cell microtubule segmentation network; each Transformer layer of the backbone network comprises, in sequence, a progressive attention layer, a full connection layer, a normalization layer, a global state cognition module, a Bell non-local module and a quantum interference gate. The multi-quantum feature coupling mechanism composed of the global state cognition module, the Bell non-local module and the quantum interference gate effectively solves the segmentation fracture and blur problem of the plant cell microtubule image under the condition of few samples due to low signal-to-noise ratio and complex structure, and improves the accuracy, topological integrity and robustness of segmentation.

This invention relates to the field of image processing technology and proposes a method and system for detecting Ulva prolifera in remote sensing images based on deep learning. The method includes the following steps: for the acquired remote sensing image to be detected, edge gradient extraction and small target enhancement processing are performed to obtain a first feature map after edge enhancement and small target feature enhancement; the first feature map is transmitted to a U-shaped backbone network composed of cascaded multi-level Ep-VSS block modules for multi-level feature extraction; and the detection result of the Ulva prolifera remote sensing image is obtained based on the feature map output by the last-level Ep-VSS block module. This invention achieves accurate boundary segmentation and long-range dependency modeling through edge enhancement, sensitive small target detection, multi-scale texture extraction, and spatial context modeling, thereby improving the accuracy, real-time performance, and reliability of Ulva prolifera remote sensing monitoring.

The invention is applicable to the technical field of color sorters, and provides a color sorter impurity distinguishing method based on texture feature extraction, which comprises the following steps: S1, image acquisition: acquiring an original image of a to-be-detected material through an industrial camera of a color sorter; according to the color sorter impurity distinguishing method based on texture feature extraction, two texture extraction algorithms of gray level co-occurrence matrix and Gabor wavelet transform are fused, texture features are extracted from two dimensions of spatial distribution and direction scale, and compared with a single texture extraction method, the texture difference between materials and impurities can be reflected more comprehensively, so that the color sorter impurity distinguishing method based on texture feature extraction is more accurate. The method has higher distinguishing precision especially for impurities with similar colors and different textures, dimension reduction processing is carried out on initial texture features through a principal component analysis algorithm, redundant features are effectively eliminated, the calculation amount of a classification model is reduced, the sorting efficiency of a color sorter is improved, and the requirement for high-speed material sorting is met. And meanwhile, a support vector machine classification model is adopted, so that the model generalization ability and the classification stability are improved.

The application discloses a pest prevention method, system and device based on computer vision, and the method comprises the following steps: acquiring a plant X-ray image, performing texture extraction to obtain a plant texture image, and obtaining a pest mask image through gradient analysis; identifying specific position information of a specific type of pest, obtaining a specific type of pest conversion mask image based on the specific position information and the pest mask image, and performing distance conversion; acquiring the intersection of the image boundary of the specific type of pest conversion mask image and the pest area, taking the intersection as a current guide point, obtaining a next guide point through a neighborhood pixel point set of the current guide point, performing iterative analysis to obtain a next guide point set; calculating the distance between all guide points, obtaining the length of a pest body, analyzing the instar, determining the instar of the pest, and preventing the pest through the instar of the pest. The application can analyze the plant X-ray image, obtain the length of the pest body, and realize pest prevention through instar analysis.

The ancient painting color virtual restoration system based on artificial intelligence provided by the application has the advantages that the system takes the ancient painting to be repaired as the target, combines visual analysis operation to segment the ancient painting to be repaired, extracts the ancient painting texture as the clue to distinguish different images, tracks the texture extraction result and transmits it to other reference images, helps the ancient painting restorer to quickly locate the reference image, acquires the color of the reference image, models the mixing process of pigments, constructs a color reproduction recommendation algorithm based on human color mixing behavior, helps the ancient painting restorer to understand the pigment mixing process, improves the accuracy and authenticity of the ancient painting color restoration, and promotes the restoration work of faded ancient paintings.

The present invention discloses a method and system for processing turbidity disease images of underwater bridge structures, and relates to the technical field of underwater bridge detection. The present invention comprises: acquiring underwater defect images of bridge piers, and numbering each acquired image to form a turbidity image dataset of underwater bridge structures; further dividing the turbidity image dataset into a training set and a test set, wherein the training set includes TrainA and TrainB, and the test set includes TestA and TestB; constructing a CycleGAN-deturbidity model for adaptive deturbidity removal of underwater bridge structure images; inputting the training set into the deturbidity removal model CycleGAN-deturbidity for training to obtain the model weights. The present invention enhances the texture extraction capability of underwater images by introducing TEM and PTFEM into the original CycleGAN model, and further enhances the model's perception capability of images by adding perceptual loss to the loss function. This method can achieve effective deturbidity removal of underwater bridge structure images under different turbidities and effective restoration of pier images.

The invention discloses a small target detection system based on an instance relation enhanced attention mechanism, which comprises a detail texture extraction module, a context relation extraction module and a high-level semantic extraction module which are sequentially connected with each layer of a YOLOv5 network neck, the section texture extraction module, the context relation extraction module and the advanced semantic extraction module are all connected with the cross-layer feature fusion module, and the cross-layer feature fusion module is connected with the prediction head; the advanced semantic extraction module is used for further emphasizing important targets and generating enhanced semantic features; context features generated by the context relation extraction module comprise feature relations between large targets and small targets and are used for promoting fusion of features of different levels; the detail texture extraction module is used for extracting more textures and detail information to enrich small target features; the cross-layer feature fusion module is used for performing cross-layer feature fusion based on high-level feature guidance of potential space and feature relationship between the large target and the small target; and the detection performance of the network on the small target in a real industrial scene is effectively improved.

The application discloses a kind of defect detection method and system based on two-stage generative adversarial network.There are following steps: obtaining defect sample and good sample dataset, marking defect sample to make it become defect mask sample;Defect mask sample is used to train BayesGAN defect mask generation network;Non-defect texture extraction network is used to obtain the feature map of good sample and calculate its style conditional gram matrix;The style conditional gram matrix of good sample, defect sample and defect mask sample are used to train mask-driven defect generation network;The output of the generator of the two trained generation networks is used to train the defect detection network based on segmentation decision;The sample to be detected is input into the defect detection network, and the defect detection result is output.The application proposes two-stage generative adversarial network to generate defect sample, provides a large amount of training data containing annotation for defect detection model, without manual post-labeling.

1. Name of the product of this design: A graphical user interface derived from a model of an electronic device. 2. Purpose of the product of this design: an electronic device. 3. The key design features of this design product are the part of the graphical user interface in the electronic device for which protection is sought. 4. The picture or photo that best illustrates the key points of the design: main view. 5. Purpose of the graphical user interface: The product interface is an interactive interface for model-derived secondary creation; the 3D model of the cultural relic is scanned in all directions to extract the texture of the cultural relic, and the user rotates the 3D model to select the texture of a specific part for AI secondary creation; the main view interface is a 3D model of the cultural relic scanned in all directions, and the texture is displayed while it is being extracted; in the main view interface, after the texture extraction is completed, the interface change state diagram 1 is displayed; in the interface change state diagram 1, after dragging and rotating the left model area, the interface change state diagram 2 is displayed; in the interface change state diagram 2, after clicking the lower right corner of the left model area, the interface change state diagram 3 is displayed, and you can switch to view the main body of the cultural relic; the gray area of ​​the interface is the content screen.

The invention relates to the technical field of image processing, in particular to a color rice grain color genetic locus rapid positioning and gene detection method. The method comprises the following steps: step 1, placing a colored rice sample to be detected in a constant-temperature and constant-humidity environment, and performing brightness normalization and size unification on the obtained image to obtain a colored rice standardized image set; 2, inputting a color rice standardized image set, obtaining rice grain surface texture features through a single texture extraction channel, and generating a fusion matrix; and 3, calling an asynchronous hash mapping controller by using the fusion matrix, and finally generating a color rice grain color genetic locus positioning result. And 4, sampling a rice surface layer region corresponding to the positioning result of the color rice grain color genetic locus to generate a color rice grain color gene detection result. The invention provides a detection scheme which is high in throughput, low in cost and easy to popularize for colored rice breeding.