4 results about "Colored graph" patented technology

The present disclosure describes systems and methods for assigning nodes in a circuit design to layers. The method includes identifying a plurality of structures in a circuit design. Each of the plurality of structures includes four nodes of the circuit design and each of the four nodes are connected to every other node in the respective structure. The method also includes removing, from the plurality of structures, an even number of pairs of connected nodes of the circuit design to form a reduced node structure and assigning circuit layers for the nodes in the reduced node structure. The method further includes assigning, by a processing device, circuit layers for the pairs of connected nodes removed from the plurality of structures based on the assigned circuit layers for the nodes in the reduced node structure.

The invention provides a color filling game image click rate prediction model training method, prediction method and equipment, and the method comprises the steps: obtaining each training sample, each training sample comprises color filling game image data of which the total historical click number is higher than a preset threshold value, and multi-modal information, the multi-modal information comprises a line draft graph, a color graph, a text label and a color block graph; training a preset deep learning model to enable the deep learning model to respectively extract feature vectors of the training samples in each iterative training process, splicing the feature vectors to obtain a fused feature vector, and enabling the multi-layer perceptron to output click rate level prediction result data according to the fused feature vector; and performing click rate prediction by taking the deep learning model as a color filling game image click rate prediction model. According to the method, the game experience of the user on the color filling game can be met, the image data of the color filling game can be effectively integrated, and the accuracy of click rate prediction is improved.

The invention discloses a novel texture character creating method and device, equipment and a storage medium. The method comprises the steps that character file data and texture picture data are acquired; forming structural data by the character file data and the texture picture data, and transmitting the structural data into a character rendering engine; in a character rendering engine, mask data are obtained from the character file data through a text shaping engine and a font rendering engine, and a character drawing area is determined; the mask data and the character drawing area are transmitted into a typesetting engine for paragraph layout, and a mask character pattern set is obtained; generating a color mask glyph set by copy construction based on the mask glyph set; setting the texture picture data into texture filling effect attributes of the paintbrush structural body, and configuring a rendering mode of the paintbrush structural body; generating a color image set based on the color mask font set and the configured paintbrush structural body; and rendering and outputting the color image set to obtain texture characters. According to the method, the reusability of textures and characters is enhanced.

The application provides a depth map super-resolution method and system based on entropy map guidance, and belongs to the technical field of computer vision and deep learning; comprising: generating a global entropy guide map according to a low-resolution depth map and a high-resolution color map through an entropy map guidance module; extracting depth features and color features respectively; in a hierarchical direction perception module, decomposing the features into low-frequency and high-frequency components by wavelet decomposition; according to the global entropy guide map, adaptively fusing the components to generate fused depth features; and generating a high-resolution depth map based on the fused depth features. A composite loss function containing a spatial domain loss and an entropy domain loss is used during training; the application adaptively guides by introducing an entropy map, and cooperatively processes high-frequency and low-frequency information by using wavelet decomposition, effectively solving the texture migration error and structure inconsistency problem, and comprehensively improving the quality and realism of the super-resolution depth map.