92results about How to "Good details" patented technology

A method for segmentation of paint cracks on an ICGA image based on a conditional generative adversarial network includes: (1), collecting an original ICGA image, extracting a complete fundus oculography image, labeling it with gold standard, normalizing fundus oculography image and gold standard, splicing it into a group of images as sample data, distributing the sample into a training set and atest set according to proportion; (2) based on the principle of conditional generative countermeasure network, constructing the network of generators and discriminators; (3) inputting that data of thetraining set into the network for adversarial train, defining a loss function, and generating a paint crack image correspond to the original picture by the training generator; (4) in the testing phase, inputting the data of the test set, and getting the corresponding paint crack segmentation result diagram through the trained generator G. The segmentation method provided by the invention can be used for solving the problems that the sample size of the ICGA image is small and the acquisition of the contrast image is difficult, and has the characteristics of high accuracy of the segmentation result.

The invention discloses a quick magnetic resonance imaging method based on a recursive residual U-type network. The method comprises the three steps of data processing, model training and image reconstruction. By adopting the method, a recursive residual module is added to the U-type network, the problems of gradient blast and gradient vanishing caused by back propagation of the network are solved, new network parameters are not added while the layer number of the network is increased, the quality of a reconstructed image is obviously improved, and more image details can be restored.

The invention relates to tooling fabric with a cooling function and an uvio-resistant function. A method for manufacturing the tooling fabric with the cooling function and the uvio-resistant function comprises a first step of using cooling master batch as raw materials and manufacturing the cooling master batch to slicing sheets, a second step of drying the slice sheets, a third step of carrying out spinning on the slicing sheets through a spinning box to manufacture nylon base cooling fibers, a fourth step of adopting a siro spinning technology to spin cotton fibers and the nylon base cooling fibers into blended yarn, and a fifth step of adopting technological measures of 'early opening, later weft insertion and on-machine tension slightly larger' to carry out weaving. A formula of the cooling master batch comprises PA6, bowlder powder, zirconium dioxide powder, silicon dioxide powder, stearic acid dispersants, polystyrene dispersants, low molecular wax dispersants and titanate coupling agents. The tooling fabric has functions of fast heat absorption and fast heat dissipation, and an everlasting cooling feature is guaranteed. Meanwhile, the tooling fabric further has a notable uvio-resistant feature.

The embodiments of the invention disclose an image shooting method and device, an electronic device and a storage medium and relate to the technical field of electronic devices. The image shooting method is applied to the electronic device. The electronic device is provided with a rotatable camera. The method comprises the following steps: when a shooting request is received, obtaining a first image shot by the camera; determining a plurality of shooting main bodies in the first image based on the first image; controlling the camera to rotate and obtain a plurality of second images, wherein the preset position of each second image in a plurality of the second images focuses one shooting main body in a plurality of the shooting main bodies respectively; the shooting main bodies focused by each second image are different; and a plurality of the second images are synthesized to obtain a target image. According to the image shooting method and device, the electronic device and the storagemedium provided by the embodiments of the invention, a plurality of images, which respectively focus different shooting main bodies, are shot in different angles and synthesized through the camera arranged on the electronic device, so as to obtain an image with finer details.

A computer-implemented method for processing data includes receiving a collection of traces corresponding to signals received over time at multiple locations due to reflection of seismic waves from subsurface structures. A measure of correlation among the traces as is computed a function of a set of wavefront parameters, which determine respective moveout corrections to be applied in aligning the traces. A matrix having at least three dimensions is generated, wherein the elements of the matrix include the computed measure of the correlation. Using the matrix, values of the wavefront parameters are identified automatically or interactively along the time axis or along selected horizons to maximize the measure of the correlation, and a seismic image of the subsurface structures is generated by aligning and integrating the traces using the moveout corrections that are determined by the identified values of the wavefront parameters.

The invention provides a dark channel prior and edge information-based image defogging method, and belongs to the technical field of image defogging methods. The method mainly comprises the following steps of: (1) estimating a value of atmospheric light by adoption of a block-based technology; (2) carrying out edge detection and expansive operation on a fog-degraded image, and extracting edge information of an original foggy image; (3) self-adaptively selecting a window size according to an obtained edge information graph so as to obtain a rough transmissivity graph; (4) refining the transmissivity graph by adoption of a rapid gradient domain steerable filter method, and importing a tolerance coefficient and a correction factor to correct the refined transmissivity graph; and (5) substituting the value of the atmospheric light and the corrected transmissivity graph by utilizing a fog-degraded model, so as to obtain a fog-free image. According to the method provided by the invention, when the transmissivity graph is solved, the edge information of the image is combined to self-adaptively select the window size, so that the halo phenomenon at the edge area can be effectively solved.

The invention discloses a single-frame image super-resolution reconstruction method based on cascade regression base learning. The method comprises the following steps: taking a super-resolution reconstruction technology of a single-frame low-resolution image as a research object, learning a multi-layer over-complete sub-dictionary for representing an image structure, constructing a mapping relation between a low-resolution image and a high-resolution image, and learning an optimized regression base and a corresponding coding coefficient; and then, complete super-resolution reconstruction is realized for the low-resolution image set, and the reconstructed image is used as a low-resolution image of the next layer for feature extraction. The invention discloses a single-frame image super-resolution reconstruction method. learning by utilizing a meta-dictionary learning method to obtain a low-resolution dictionary; a weighted linear regression method is used for carrying out multilayer regression base learning on a reconstructed high-resolution training set image and an original high-resolution image in a cascading mode so as to approach a complex nonlinear mapping relation between alow-resolution image and a high-resolution image, and instance regression super-resolution reconstruction with high processing speed, small memory occupation and high reconstruction quality is achieved.

The invention relates to a pedestrian detection data expansion method based on a generative adversarial network, and the method comprises the steps: S1, building a three-layer cascaded generative adversarial neural network model, and setting a target function of model training, wherein each layer of generative adversarial neural network adopts a Bicycle GAN structure, the generator adopts a residual Unet structure, and the input of the next layer of network is the pedestrian instance mask picture and the output of the previous layer of network; s2, preprocessing the training data; s3, traininga three-layer cascade generative adversarial neural network model by adopting the preprocessed data; and S4, completing expansion of pedestrian detection data through the three-layer cascade generative adversarial neural network model. Pedestrians generated by adopting the scheme of the invention are fused with the background more naturally, and details of the generated pedestrians are finer by improving the Unet structure of the generator; the multi-scale pedestrian picture is generated based on the cascade structure, so that the quality of the large-size and high-resolution pedestrian picture is improved; diversified pedestrians can be generated, and the data expansion efficiency is improved.

The invention discloses a nighttime traffic monitoring enhancement method based on gradient domain fusion, and relates to a digital image processing method. Because there are vehicle lamps, street lamps, building lamps and other active light sources at night, a large amount of halo will be generated till the objects nearby the lamps cannot be seen. In particular, the strong halo generated by the lamps of vehicles on a highway severely affects the visual effect of traffic sign boards, and causes the severe degeneration of the quality of the traffic sign boards. The method enables image fusion and image halo removal to be combined, achieves the processing of the image in the gradient domain, and enables the traffic signs in a final result image to be easy to recognize through the interframe complementation information of a video.

The invention discloses an RGB-D saliency object detection method based on a cascade improved network, and belongs to the technical field of image processing. Most existing RGB-D models directly aggregate the features from the CNN networks of different levels, and the noise and interference information contained in low-level features are easily introduced. The invention creatively provides a cascade improved structure, a saliency map generated by the features of a high-level part is used as a mask to improve the features of a low-level part, and then a final saliency map is generated by aggregating the improved low-level features. In addition, in order to eliminate the interference information of the depth map, the invention provides a depth enhancement module for preprocessing before thedepth features and the RGB features are mixed. According to the method, four evaluation indexes are used for carrying out experiments on seven data sets, and the results show that the method surpassesall current most advanced RGB-D saliency object detection methods.

The invention relates to the field of computer vision, and aims to remove moire and output a corresponding clean picture. The invention discloses a screen shot image moire removing method based on a convolutional neural network AMNet, and the method comprises the following steps: analyzing moire features, and building a moire model in a screen shot image; building a data set; designing a network framework; designing a network structure; setting the learning rate of the network and the weight of the loss function of each part, training the convolutional neural network by using a deep learning framework Tensorflow until the loss converges, and generating a training model; and inputting the test picture with the moire pattern into a network to obtain a corresponding clean picture without themoire pattern. The method is mainly applied to occasions for removing the screen moire patterns.

The invention belongs to the technical field of intelligent cooking, and discloses an intelligent cooking control system and method with a thermal image. The intelligent cooking control system with the thermal image comprises a thermal image collection module, a temperature detection module, a cooking parameter setting module, a main control module, an image processing module, a timing module, a heating module, a stirring module and a display module. Through the cooking parameter setting module, the problem of cumbersome operation caused by the fact that a user manually conducts adjusting stepby step through a button to obtain cooking parameters can be avoided, and the setting efficiency and intelligence of the cooking parameters are improved; and meanwhile, the threshold of a food boundary is calculated through the image processing module, outline extraction of food is achieved through threshold segmentation and a segmentation threshold window extraction method for color components,green components and blue components, and the precise outline of the food is obtained.

The invention discloses an image generation method and device. The method comprises the steps of acquiring a first image of a shooting target and a first ambient light angle used for indicating the relative position relation between an irradiation light source and the shooting target when the first image is shot; obtaining a first 3D model generated by fusing the depth information of the shootingtarget, the plurality of second images and the first lighting information; wherein the plurality of second images are a plurality of two-dimensional images shot from a plurality of angles of the shooting target; wherein the first lighting information comprises a first illumination angle which is the same as the first ambient light angle and first light intensity of which the corresponding brightness is greater than or equal to a preset brightness threshold; and according to the first image and the first 3D model, performing fusion to generate a third image. By adopting the method, when a userperforms selfie in a dark environment, the 3D model containing the first lighting information can be fused with the actually shot three-dimensional image to obtain the selfie image which does not generate stereoscopic impression and skin detail loss, so that the user experience is better.

A material dispensing unit (1; 10) for a three dimensional printing apparatus (100; 200) has a nozzle (3) for depositing particulate material (5; 220a, 220b; 224; 226) on a build surface (7), where the nozzle defines a through passage (9) for the material. The through passage has an inlet end (11) for receiving the material and an outlet end (15) for dispensing the material. A valve (21) is provided at least one of at, within or in fluid communication with the through passage for controlling flow of the material via the through passage, the valve being operable between open and closed positions. Flow of said material into the through passage is blocked when in the closed position and, when in the open position, flow of the material into the through passage is allowed. A method of forming a three dimensional object (31) is also disclosed, as is a three dimensional printing apparatus (200) comprising one or more dispensing units (la; b) for dispensing particulate material (220a, 220b; 224; 226), an enclosure (137) for containing the material dispensed by the one or more dispensing units and one or more heating elements (210, 212) for heating the material contained in the enclosure to a first predetermined temperature.

The invention provides a grid generation method and device and a storage medium. The method comprises the following steps: firstly, acquiring a point cloud and a corresponding relation between the point cloud and an original image; then, based on the point cloud, constructing a tetrahedron corresponding to the point cloud; taking the tetrahedron as a vertex and the overlapped surfaces between the adjacent tetrahedrons as directed edges to construct a directed graph; according to the corresponding relationship between the point cloud and the original image, determining the weight of a directed edge contained in the directed graph; and according to the weight of the directed edge, generating a target grid corresponding to the point cloud through a graph cut algorithm. In the application, the weighted value of the directed edge is determined according to the corresponding relationship between the point cloud and the original image, and compared with a weight set as a constant, the target grid corresponding to the point cloud generated according to the weight is more accurate, so that when the target grid obtained through the method is used for reconstructing a large-scale scene, the higher the precision of a reconstructed three-dimensional model is, the better the detail effect is.

The invention provides a pollen image detection method based on feature fusion, and the method comprises the steps: inputting a preprocessed pollen image into a convolutional neural network, and obtaining shallow features; generating a spatial attention weighted feature map and a spatial attention weight matrix through a spatial attention module based on the shallow features; generating a deep feature map from the spatial attention weighted feature map through convolution and down-sampling, and generating a channel attention weighted feature map from the deep feature map through a channel attention module; inputting the space attention weight matrix and the channel attention weighted feature map into a cross-connection attention mechanism to obtain a feature map after feature fusion; and inputting the feature image after feature fusion into a prediction module to obtain a detection result of pollen information in the pollen image. The pollen detail information in the superficial layer features is weighted and fused to the deep layer features through the cross-connection attention mechanism, feature fusion is performed after the deep layer features are optimized, and more pollen details of the pollen image can be recovered.

The invention discloses an image restoration method based on gradient sparsity and non-local similarity information, comprising a first stage of image non-blind restoration based on gradient sparsityand a second stage of image quality improvement based on non-local autoregressive model. The non-blind image restoration based on gradient sparsity in the first stage includes the following core steps: current gradient image auxiliary variable calculation; Current restored image update; Lagrange multiplier calculations. The invention is based on the sparsity of the image transformation domain, sothat the edge structure in the image can be recovered, and on this basis, the quality of the image restoration is further improved by utilizing the non-local similarity information in the image.