363results about How to "Clear edges" patented technology

The invention discloses a method for classifying polarimetric SAR (synthetic aperture radar) images on the basis of Cloude decomposition and K-wishart distribution and mainly solves the problem that the prior art is poor in classification effect and high in computation complexity. The method includes the steps of firstly, reading a polarimetric SAR image to be classified, subjecting each pixel of the image to Cloude decomposition to obtain an entropy H and a scattering angle alpha; secondly, initially partitioning the polarimetric SAR image according to the values of the entropy H and the scattering angle into eight classes; thirdly, subjecting results of eight classes of the whole polarimetric SAR image to iteration to obtain more accurate classification results. Compared with classic classification methods, the method is stricter in partitioning the polarimetric SAR image, better in classification effect, lower in computation complexity and applicable to terrain classification and target recognition of the polarimetric SAR images.

The invention discloses a polarization synthetic aperture radar (SAR) image classification method based on spectral clustering. The polarization SAR image classification method mainly solves the problem that an existing non-supervision polarization SAR classification method is low in accuracy. The polarization SAR image classification method comprises the steps of extracting scattering entropy H of representation polarization SAR target characteristics to serve as an input characteristic space of a Mean Shift algorithm combining with space coordination information; diving in the characteristic space with the Mean Shift algorithm to obtain M areas; choosing representation points of all areas on the M areas to serve as spectral clustering input to spectrally divide all areas, and further finishing spectral clustering on all pixel points to obtain pre-classification results; and finally classifying the whole image obtained from the pre classification with a Wishart classifier capable of reflecting polarization SAR distribution characteristics in an iteration mode to obtain classification results. Tests show that the polarization SAR image classification method is good in image classification effect and can be applied to non-supervision classification on various polarization SAR images.

The invention relates to a Bacillus subtilis strain YB-05, and a preparation method of an inoculant of the Bacillus subtilis strain YB-05. The Bacillus subtilis strain YB-05 is preserved at China General Microbiological Culture Collection Center, and has a preservation number of CGMCCNO.6498. The microbial inoculant is a pulvis prepared through the spray dry preparation of a fermentation solution of the Bacillus subtilis strain YB-05. The YB-05 strain and its inoculant have the advantages of wide antibacterial spectrum, good antimicrobial effect, and strong inhibition effects on the wheat take-all disease, tomato Botrytis cinerea, wheat sheath blight fungi, Phytophthora melonis and the like; and the inoculant has the advantages of high efficiency, non-toxicity, simple use, and no environmental pollution.

The invention discloses a transfer print type decorative film suitable for a cold wave technology. The transfer print type decorative film comprises a base material, a release layer, a metal layer and a bonding layer, wherein the release layer comprises the following components in part by weight: 600 to 2,000 parts of acrylic resin, 100 to 400 parts of rosin resin, 30 to 120 parts of amino resin, 3 to 50 parts of aid and 2,000 to 3,000 parts of solvent. According to the decorative film, stripping force between the release layer and the base material can reach more than 100 grams, powder cannot fall in the cold wave process, and graphics and words are high in definition; and the decorative film can be widely applied to decorative processing of surfaces of plastic films, plastic packaging products and the like in a package printing industry.

The invention provides a real-time three-dimensional face modeling method based on depth information; the method does not adopt any previous standard face models but realizes the three-dimensional real-time modeling through registration and integration of a plurality of face depth images. The method comprises the following steps: systematically building depth information acquired from kinect; separating facial images from the depth images; back projecting, so as to form three-dimensional point cloud; smoothing, registering, integrating, drawing and displaying. With the adoption of the real-time three-dimensional face modeling method used for the computer graph and image processing and based on the depth information, a three-dimensional real-time face model required by a user can be rapidly generated only through that the user sits in the front of kinect, so that the cost and time spent on three-dimensional face modeling through laser scanning in the past are greatly reduced, and errors caused by using two-dimensional images for three-dimensional face modeling are avoided, so that three-dimensional real-time face modeling is realized at a lower cost.

An image segmentation method based on a Gaussian smoothing filter is provided. The method comprises the following steps: carrying out a weighted average operation on three components (RGB) by means of different weights according to the importance of the three components and other indexes; adopting histogram equalization to correct a histogram at first, correcting the histogram of an original image and enabling the histogram to be evenly distributed through a gray scale transformation function, and then conducting histogram equalization; conducting Gaussian smoothing filtering on the image, adopting a method utilizing only one global threshold T during a binary process, comparing the gray value of each pixel of the image with the T, and taking a foreground color if the gray value is greater than the T, otherwise, taking a background color; taking a central point P corresponding to the maximum number of pixels within L gray scale ranges as an initial mean value; checking each pixel during the i iteration, and calculating the distance between each pixel and the mean value of each gray scale, and giving each pixel the mean value closest to the class of the pixel; for j=1,2,...1, calculating a new cluster center, and updating a class mean value; and checking all the pixels one by one.

The invention discloses a SAR image denoising method based on a contour wave domain block hidden Markov model, belonging to the field of image processing, mainly solving the problem of space adaptability shortage, serious detail information loss and fuzzy edge of current method, and comprising the following steps: (1) carrying out logarithmic transformation and Contourlet decomposition; (2) carrying out Block HMM modeling and training on a Contourlet coefficient; (3) correcting the Contourlet coefficient by estimation parameters; (4) carrying out Contourlet inverse transformation and antilogarithm transformation to the corrected Contourlet coefficient in order to obtain a one-time denoising image; (5) denoising a difference value image to obtain a second-time denoising image; and (6) fusing the one-time denoising image and the second-time denoising image and rotating and translating the fused image to obtain and output a final denoising image. The SAR image denoising method improves the space adaptability, reduces the calculation complexity and increases the edge resolution of the denoised image.

The invention discloses a natural image denoising method based on regionalism and dictionary learning. The natural image denoising method based on the regionalism and the dictionary learning mainly solves the problems that in an image denoising method based on kernel singular value decomposition (KSVD), blurring occurs in a weak texture region and fake texture occurs in a smooth region. The realization scheme includes that: removing high-frequency information of a noise-contained image through alternation of a stationary wavelet, and extracting structural information through a primal sketch algorithm, dividing the noise-contained image into three regions including a structural region, a texture region and a smooth region; obtaining a dictionary of the structural region and the texture region through a KSVD method; denoising the three regions respectively, merging denoising results, and obtaining a denoising image. An idea of combination of the regionalism and the dictionary learning is utilized, a dictionary which is obtained by the dictionary learning is enabled to conduct sparse presentation on corresponding signal composition of the image , information of edges and texture of the image is kept effectively, a denoising effect is improved, and the natural image denoising method can be used for obtaining high-quality images from noise-contained low-quality images.

The invention discloses a polarized synthetic aperture radar (SAR) image classification method based on Freeman decomposition and data distribution characteristics and mainly solves the problems of high computation complexity and poor classification effect in the prior art. The polarized SAR image classification method includes step: (1) performing Freeman decomposition for polarized SAR images to be classified to obtain plane scattering power, dihedral angle scattering power and volume scattering power; (2) initially dividing the polarized SAR images into three classes according to the three scattering powers; (3) calculating the distribution characteristic parameter xL of each pixel point in each class; (4) subdividing each of the three initially divided classes into three classes according to the distribution characteristic parameters xL to divide the whole polarized SAR images into nine classes; and (5) performing complex Wishart iteration for the obtained nine-class dividing results to obtain the final classification result. Compared with the typical classification method, the polarized SAR image classification method is rigorous in polarized SAR image dividing, good in classification effect and small in computation complexity and can be applied to terrain classification and object identification of the polarized SAR images.

The invention discloses a LED tunnel light. It includes shell and lighting module. The former includes lamp body fixed structure. The latter includes lens shade formed by lens array which formed by astigmatism convex and condense edge. The astigmatism includes light incidence and emergent planes. The lamp body fixed structure includes fixed frame and fixed plated surface. The invention can control the light distribution to make faculae form rectangle, have even illumination in effective area, little or no parasitic light out the area.

The invention relates to the field of video processing and discloses a de-interlacing method and a de-interlacing device for interpolating pixel points. The method and the device perform edge detection, select points for participating in median filtering in the direction of a detected edge and make the margin of an inclined edge more clear on the premise of ensuring high capability of saw-teeth elimination and have the capability of inhibiting noises and high stability. Through motion detection, a static point to be inserted is directly, accurately and simply predicated according to a pixel value of a contiguous field at the position of the point to be inserted.

The invention discloses a salient target detection method based on a cascade convolutional network and adversarial learning. The method comprises the following steps: 1, designing a global saliency estimator E; 2, designing a local saliency refiner R; 3, combining the global saliency estimator E and the local saliency refiner R into a generator G based on a cascade convolutional neural network forgenerating a saliency map; 4, optimizing the generator G; 5, designing an adversarial learning discriminator D to distinguish a real saliency map from a predicted saliency map generated by a generator G; and 6, the generator G and the adversarial learning discriminator D follow the CGAN strategy and are trained in a complete end-to-end manner, so that the generator G can better understand the structure information of the salient object, and a good saliency detection result is obtained. According to the method, the structural information is learned implicitly through confrontation learning, sothat significance target detection can be well carried out, and a best result is obtained on a plurality of databases.

The present invention relates to a preparation method for a laser-markable halogen-free fire retardation polyester having a variable ground color, and belongs to the technical field of polymer processing. The halogen-free fire retardation polyester comprises, by weight, 40-87% of polybutylene terephthalate, 0.1-2% of a coloring agent, 0.2-1% of a metal oxide, 0-2% of a silicate mineral, 10-30% of a halogen-free fire retardation agent, 2-10% of a toughening agent, 0.2-1% of an antioxidant, 0.5-1.5% of a processing auxiliary agent, and 0-40% of continuous glass fibers. The preparation method comprises: drying various components, uniformly mixing according to a certain ratio, conveying into a twin-screw extruder, and carrying out melting, mixing, extruding, strip drawing, cooling, drying and palletizing through the extruder to obtain particles. The laser-markable halogen-free fire retardation polyester has characteristics of excellent fire retardation, excellent mechanical property and multiple colors, and presents the black marker with the clear edge and high contrast degree in the laser beam irradiation. The preparation method is simple and is suitable for continuous mass production.

The invention relates to a super-resolution processing method of a face image integrating a global feature with local information, in particular to a method for processing a low-resolution face image by an image pyramid to obtain a high-resolution face image. The method comprises the following steps: (1) establishing a controllable pyramid database of high-resolution face images; (2) solving a global high-resolution image of a low-resolution face image; (3) establishing a high-resolution residual face image database; (4) calculating and inputting global high-resolution images of the low-resolution face image; (5) calculating and inputting high-resolution residual face images of the low-resolution face image; and (6) calculating and inputting the final high-resolution image of the low-resolution face image. The method has the advantages of easy realization, strong robustness, high precision, etc., and can provide real-time reliable feature information for face identification.

The invention relates to a video saliency target detection method based on a cascade convolutional network and an optical flow, and the method comprises the steps: carrying out the pixel-level saliency prediction of an image of a current frame in the high scale, the middle scale and the low scale through employing a cascade network structure. A cascade network structure is trained by using an MSAR10K image data set, a saliency annotation graph is used as supervision information of training, and a loss function is a cross entropy loss function. after the training is ended, static saliency prediction is carried out on each frame of image in the video by using the trained cascade network. A classic Locus- Kanada algorithm is used to carry out optical flow field extraction. a three-layer convolutional network structure is used to construct a dynamic optimization network structure. the static detection result and the optical flow field detection result of each frame of image are spliced toobtain input data of the optimized network. And a Davis video data set is used to optimize the network, and pixel-level significance classification is carried out on the video frame by using a staticdetection result and optical flow information.

The invention relates to a printing processing method. The printing processing method comprises the following steps that ink is arranged on a light-transmitting base material in a full plate printing manner to form an ink layer; a printing layer is formed through silk printing, and the ink layer is located between the light-transmitting base material and the printing layer; an infrared picosecond laser device is utilized for rough scanning to remove ink in a preset area; and the infrared picosecond laser device is utilized for fine scanning to remove ink left in the preset area. The ink is removed through lasers in the preset area so as to be used for backlight transmission, the infrared picosecond laser device weak in energy is utilized for processing, and the light-transmitting base material is prevented from being destroyed by the too strong lasers. The ink removing step comprises rough scanning and fine scanning, most ink of the preset area can be fast removed through rough scanning, and the processing efficiency can be guaranteed easily. After rough scanning, further laser processing is quite close to the light-transmitting base material, and fine scanning is adopted to be beneficial to protecting the light-transmitting base material; and the ink is removed thoroughly, and the light-transmitting effect is good.

The invention discloses an improved SMOTE re-sampling method for unbalanced data classification. The method comprises clustering a minority class of samples in a sample set by using a K-Means method, deleting the noise sample class with a centroid closest to a majority of samples in each class cluster, classifying each class cluster into three classes by using a KNN method and removing the noise sample class, finally inputting a random number in each class cluster and selecting a certain sample set according to the proportion relation of the random number to the sample set type in the class cluster to carry out the SMOTE method oversampling. Compared with a traditional SMOTE method, the improved K-Means-SMOTE method is significantly improved in effect in a model of predicting the complaint of network television set-top box users.

The invention discloses an automatic online mark spraying machine for steel plates. The automatic online mark spraying machine for steel plates comprises an equipment foundation rack, a three-dimensional coordinate positioning mechanism is installed on the equipment foundation rack and provided with an alternating current servo device connected with a computer, the computer controls the alternating current servo device to achieve three-dimensional movement positioning of the three-dimensional coordinate positioning mechanism, and the three-dimensional coordinate positioning mechanism is provided with a flow restraining device. Compared with the prior art, the automatic online mark spraying machine for steel plates can solve the problems that existing mark spraying equipment is huge and large in occupied space, specific temperature requirements are provided when the surfaces of steel plates are marked, the spray printing quality is poor, the existing mark spraying equipment is incompatible with existing enterprise equipment, the cost is high as the whole set of equipment needs to be imported, a spraying mechanism is not provided with a flow restraining device, and the environment of working sites is poor.

The invention relates to a novel noise image fusion method based on a cycle spinning (CS) technology and Contourlet domain context hidden Markov model (CS-CT-CHMM). First, a source image containing a certain level of Gauss white noise is de-noised by use of the CS-CT-CHMM, wherein the structure of the context is calculated based on neighborhood entropies of a father node, two nearest cousin nodes and a current node of a Contourlet decomposition coefficient; then, Shearlet transform is performed on the de-noised image, and a fusion rule is designed by adopting a weighted average fusion strategy for low-frequency sub-band coefficients and adopting an improved pulse-coupled neural network (IPCNN) model for high-frequency sub-band coefficients; and finally, reverse Shearlet transform is performed to obtain a fused image. The CS-CT-CHMM de-noising method can protect detailed information of the image and suppress the pseudo Gibbs effect while effectively removing noise, while the fusion method combining Shearlet transform and IPCNN is advantageous in terms of image contrast enhancement and the amount of information. Compared with the traditional fusion method, the quality of the fused image is greatly improved.

The invention discloses an object random walk-based visual saliency detection method and an object random walk-based visual saliency detection system for a remote sensing image. The method comprises the following steps: performing multi-scale segmentation, and combining adjacent regions with similar color characteristics under each scale respectively; for a division result under each scale, extracting visual characteristics of each divided region to construct of an object set under the current scale respectively; for the object set under each scale, calculating a corresponding edge weight by virtue of inter-object characteristic differences, and calculating the transfer probability of a focus of attraction between objects to obtain a transfer probability matrix of the focus of attraction, calculating the stable distribution of the focus of attraction among all the objects according to the transfer probability matrix of the focus of attraction respectively, further calculating visual saliency by virtue of the probability of each object in the stable distribution, and performing normalization to obtain a normalized visual saliency map under the current scale; fusing the visual saliency maps under each scale to obtain a final visual saliency map of the remote sensing image.