184 results about "Quadtree" patented technology

The invention belongs to the technical field of image processing, and particularly relates to a monitoring image defogging enhancement method in a high-dust environment of a mineral separation site, which comprises the following steps of: performing smooth denoising on an original image by using weighted guided filtering, and obtaining global atmospheric light through a quadtree subdivision method; constructing a same-color heterogeneous discrimination index combining a spectrum similarity factor and a texture confidence factor for distinguishing dust and ore with similar colors; calculating a pixel-level dynamic defogging coefficient based on the discriminant index, and obtaining an adaptive transmissivity in combination with dark channel prior; and finally, restoring the image by using an atmospheric scattering model and carrying out contrast-limited adaptive histogram equalization processing. According to the method, the problem of misjudgment caused by the fact that the colors of the ore and the dust on the ore dressing site are similar is effectively solved, powerful defogging of the dust area and detail reservation of the ore area are achieved, and the definition and the contrast ratio of the monitoring image are improved.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention belongs to the field of unmanned aerial vehicle (UAS) traffic management (UTM) and computer science, and particularly relates to a method and system for airspace modeling and path planning of a logistics unmanned aerial vehicle, and the method comprises the steps: S1, receiving multi-source data, and constructing a hierarchical mixed data model; s2, coding the control constraint data and the dynamic environment data into semantic attributes of nodes in a two-dimensional quadtree layer and / or voxels in a three-dimensional octree layer so as to generate a semantic airspace model containing computable rules; s3, based on the semantic airspace model, executing a hierarchical path planning algorithm to determine a flight path from the starting point to the ending point; and S4, combining the refined paths, and outputting a four-dimensional flight path containing a timestamp. The airspace rule is internalized into the model attribute, the real-time performance, safety and compliance of path planning are remarkably improved, the airspace resource utilization efficiency is improved, and key technical support is provided for large-scale urban unmanned aerial vehicle logistics operation.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention discloses a seepage field adaptive analysis method and device based on a flexible polygon unit, and the method comprises the steps: dividing the polygon unit into a plurality of sub-regions, and constructing an internal water head field of each sub-region; recognizing a free water surface according to water head features extracted from the internal water head field of each sub-region; if the free water surface is recognized for the first time at present, or the error of the positions of the free water surfaces recognized by two adjacent rounds of iteration does not meet a preset condition, quadtree decomposition is carried out on the free water surface to obtain new sub-regions; returning to the step of extracting the water head features according to the internal water head field of each sub-region until the error of the positions of the free water surfaces identified by two adjacent rounds of iteration meets a preset condition, and obtaining a grid division result of the polygon unit; and performing seepage field analysis on the polygon units based on a grid division result to obtain a seepage field analysis result. According to the method, the complex seepage field is accurately simulated, and the optimal balance between high-precision discretization near the free water surface and the overall calculation efficiency is realized.

The invention provides a dynamic grid updating and scheduling method and system for watershed scale ecological restoration. The system comprises a data fusion access module, a dynamic grid generation module and a central cooperative controller. The data fusion access module is used for receiving multi-source heterogeneous data, the dynamic grid generation module is used for constructing an initial watershed grid by adopting a quadtree space division algorithm based on the multi-source heterogeneous data, and the central cooperative controller is used for periodically scanning a full-watershed grid set. Comprehensively judging information value density and updating urgency of each grid according to a multi-dimensional index; by adopting the above technical scheme, the autonomous evolution and precise adaptation of the watershed spatial discretization unit can be realized, the global monitoring breadth and the local description precision are dynamically balanced, and the response sensitivity to an emergent ecological event is improved.

The invention relates to the technical field of mobile robot autonomous navigation path planning, and discloses a complex orchard scene-oriented hybrid sampling global path planning method, which comprises the steps of adopting a Gaussian hybrid sampling strategy, switching between global uniform sampling and Gaussian offset sampling through a probability threshold, and considering global exploration and key area guidance; a dynamic node expansion strategy is adopted, the expansion step length is adaptively adjusted according to the distance between the expansion step length and a target point, and the expansion direction is optimized by fusing vectors facing a sampling point and the target point and introducing an obstacle influence factor; a layered collision detection strategy is adopted, and step-by-step filtering is carried out through rough detection based on a quadtree, secondary screening based on an axis alignment bounding box and accurate geometric judgment in sequence so as to improve the efficiency; and a dynamic iteration termination mechanism is adopted, and the minimum number of iterations and the path quality convergence state are combined as composite termination conditions. The method can quickly generate a smooth and safe path, and is especially suitable for structured scenes such as orchards.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention discloses a Web-based visual editing method, belongs to the technical field of industrial configuration, and aims to solve the problems of complex deployment of traditional configuration software and insufficient Web scheme performance. The method comprises the following steps: constructing a front-end architecture (Vue. Js + Konva.js) containing a graphic element module, a canvas management module and the like; the primitive adopts JavaScript prototype chain inheritance + SVG / Canvas double-layer rendering, and a canvas is divided into four layers and is provided with a quadtree index; an editor and an expression engine are generated by using a factory pattern for attribute configuration; page management supports state capture recovery; real-time communication is based on WebSocket + PubSub, and data aggregation and incremental updating optimization are combined. Cross-platform zero deployment and Web environment near-desktop performance are achieved, editing efficiency is improved, industrial protocols such as OPC UA are compatible, and the method is suitable for the field of energy and intelligent manufacturing.

The invention relates to the technical field of geological disaster monitoring, and discloses an unmanned aerial vehicle aerial survey intelligent identification and grading method and system for geological disaster hidden danger points, and the method comprises the steps: converting a pixel-level identification result into hierarchical data which retains a spatial topological structure through constructing a confidence quad-tree; establishing a weighted decision grid based on aerial survey parameters to realize coupling modeling of observation conditions and confidence uncertainty, calculating topology difference to generate a spatial difference field to quantify the deviation degree of a confidence field and a standard grading decision surface, and finally executing self-adaptive grading judgment to output a risk grade result with a confidence label; according to the method, the problem of confidence field and decision boundary space topology mismatch caused by truncation of confidence continuous measurement information in a grading stage in a traditional serial architecture is solved, systematic underestimation of misjudgment risks in observation condition limited areas such as alpine and gorge areas is avoided, and consistency of actual credibility of a risk quantification result and a nominal grade is realized.

The invention relates to the technical field of industrial data processing, in particular to a PDF drawing vector review method and system based on the creative technology, and the method comprises the steps: receiving a PDF drawing data stream through a creative security communication channel, and converting the PDF drawing data stream into a standardized graphic primitive containing a two-dimensional coordinate and engineering metadata through a sandbox security parser; and on the basis, a spatial semantic data structure integrating quadtree spatial indexes and semantic grouping information is constructed. According to the method, a dynamic performance benchmark test data model is also constructed; and during rendering, predicting and selecting an optimal rendering scheme capable of meeting preset target performance based on the model, efficiently generating an interactive graphic review interface by utilizing the data structure, and finally receiving and storing a user review instruction associated with the graphic object. By constructing the adaptive rendering model and fusing a data structure of space and semantic information, high-performance and safe vectorization interactive review of the PDF drawing in a credential environment is realized.

The invention provides a water body pollution spatial-temporal distribution dynamic monitoring system based on multi-source remote sensing data fusion and deep learning, and relates to the technical field of data processing, and the system comprises the steps: obtaining a satellite hyperspectral image, unmanned aerial vehicle multispectral data, and water quality parameter data collected in real time through ground Internet of Things equipment; the inversion module is used for performing pollutant concentration inversion on the satellite hyperspectral image to obtain pollutant concentration data; performing local pollution source positioning on the unmanned aerial vehicle multispectral data to obtain pollution source position data; acquiring real-time water quality parameter data from ground Internet of Things equipment; the spatial discretization module is used for carrying out spatial discretization processing on pollutant concentration data, pollution source position data and real-time water quality parameter data, establishing a quadtree index and carrying out recursion division on the quadtree index into sub-regions; and generating a dynamic space correction parameter according to the statistical characteristics of the data points in each sub-region. The method can effectively improve the water body pollution time-space distribution monitoring precision and the pollution early warning reliability.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention provides a geographic element spatio-temporal topology establishment method for a large ocean forecast disaster reduction model, and belongs to the technical field of ocean observation forecast. Multi-source heterogeneous ocean data is fused and cleaned in real time, sparse data complementation of physical constraints is realized by using a temperature-salt flow collaborative interpolation model and a graph neural network, and the real-time fusion and cleaning of the multi-source heterogeneous ocean data are realized. A spatial index is established based on quadtree Z curve coding, a main filter and an auxiliary filter are executed in parallel through a GPU double-layer sub-thread grid to achieve millisecond-level query response, and local increment updating is conducted on a topological relation through spatial partitioning and an event-driven mechanism. Bayesian Kriging interpolation and Monte Carlo simulation are introduced to add confidence labels for topological nodes to quantify uncertainty, and finally standardized spatio-temporal topological relation data are output, so that the technical problem that a large ocean forecast disaster reduction model is difficult to understand and a geographic element spatio-temporal topological relation under the condition of observation forecast data is efficiently constructed is solved.

A geometry cleanup method for a 2.5D model for characterizing a layered circuit board structure, comprising: merging adjacent points on the basis of an adaptive quadtree; performing an edge intersection operation; removing a sharp angle; aligning points to edges; simplifying a polygon; and performing local update.

The invention provides an alignment method for territorial space planning multi-modal data, and belongs to the technical field of big data processing.The alignment method comprises the steps that a quadtree structure is adopted to segment a remote sensing image, a three-dimensional index is established, a graph segmentation algorithm is utilized to divide vector data space subgraphs, a space-time weight matrix fusing space weight, time weight and credibility weight is constructed, and the three-dimensional index is established; inputting the preprocessed multi-modal data into a multi-modal semantic fusion model comprising a residual neural network, a bidirectional encoder and a graph convolutional network to extract a unified semantic representation vector, identifying conflict data by calculating semantic similarity, constructing a rule priority directed graph, and calculating a rule importance score by adopting a random walk algorithm; and executing the conflict resolution rule according to the priority and outputting the aligned data set. The technical problem that efficient alignment of the multi-source heterogeneous territorial space planning data on the semantic level is difficult to realize is solved.

The invention discloses an unmanned aerial vehicle patrol and security monitoring linkage management method. According to the system, urban geographic information data and key monitoring area coordinates are acquired, hierarchical grid division is realized by using a quadtree spatial index algorithm, and a patrol task distribution basic data structure is established; solving an approximate optimal solution of the patrol path of the unmanned aerial vehicle by adopting an improved greedy strategy, and performing optimization adjustment according to technical parameters and weather conditions of the unmanned aerial vehicle; the unmanned aerial vehicle collects video data in real time through high-definition camera equipment in the patrol process, target detection is conducted through a deep learning model, and potential safety hazards such as personnel gathering and vehicle abnormity are recognized; detecting violent conflicts and illegal intrusion behaviors in combination with a time sequence analysis algorithm, and generating an abnormal event report; cooperative work of the unmanned aerial vehicle, the ground robot and the command center is realized; and a patrol scheme is dynamically adjusted according to meteorological conditions, and robot scheduling is optimized through energy consumption management.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The present application relates to the technical field of video coding, and especially relates to a coding unit division fast decision method and system based on multi-functional video coding, comprising constructing a quadtree division prediction network, using the network to judge whether a coding tree unit needs to perform quadtree division, and obtaining a coding unit after performing the quadtree division; constructing a mixed tree division prediction network, using the network to judge whether the coding unit needs to be divided, stopping division if the coding unit does not need to be divided, otherwise judging whether the coding unit adopts binary tree division or ternary tree division, and taking a smaller coding unit division prediction value in parallel branches as a final prediction result. The present application improves coding efficiency, and can balance the relationship between visual quality and coding efficiency by selecting different threshold values according to specific scene needs.

This disclosure provides a video processing method and apparatus. An example method includes: determining whether a coded block includes samples outside an image boundary; and, in response to the coded block being determined to include samples outside an image boundary, performing quadtree segmentation of the coded block regardless of the value of a first parameter, wherein the first parameter indicates whether the quadtree is permitted for segmenting the coded block.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention provides a video coding method, which comprises the following steps of: inputting a coding tree unit to be coded, and extracting texture features of a coding block, the texture features comprising local binary pattern similarity of all pixel points in the coding block and direction dispersion of each pixel point; inputting the texture features into the trained multi-type division prediction architecture, and sequentially judging whether to execute quadtree division, horizontal or vertical division, horizontal binary tree or ternary tree division and vertical binary tree or ternary tree division or not through a binary classification problem method; and calculating the rate distortion cost under each division mode to obtain an optimal division mode and a suboptimal division mode of the coding block, and outputting an optimal mode or a combination of the optimal mode and the suboptimal mode. According to the invention, the accuracy of the prediction mode in coding is improved.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

A neural network device, a computing system, and a method of processing a feature map are provided. The neural network device includes at least one processor configured to implement: an arithmetic circuit configured to generate third data including a plurality of pixels based on a neural network performing an arithmetic operation on first data and second data; and a compressor configured to generate compressed data by compressing the third data, wherein the compressor is further configured to generate bitmap data as the compressed data based on a quadtree structure, the bitmap data including position information of non-zero pixels having non-zero data values among the plurality of pixels.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The application discloses a dynamic charging coordination method, system, equipment and medium under open sea grid monitoring, relates to the technical field of open sea monitoring and energy management, and comprises grid dynamic division and equipment priority allocation; the light receiving efficiency is optimized by adjusting the inclination angle of the photovoltaic film, a task adjustment and energy management balance mechanism is constructed, and abnormal detection and cooperative response are carried out based on a multi-sensor fusion technology. The method disclosed by the application carries out grid dynamic division and equipment priority allocation, optimizes the grid boundary by using a hierarchical quadtree algorithm and real-time data, improves the utilization rate and monitoring accuracy of monitoring resources, optimizes the inclination angle of the photovoltaic film and the task energy management balance mechanism, improves the task completion efficiency while ensuring long-time endurance of the equipment, and accurately identifies complex abnormal events and quickly allocates response tasks based on the abnormal detection and cooperative response of the multi-modal sensor fusion technology, thereby improving the efficiency and intelligent level of the monitoring system as a whole.

This invention discloses an underwater image enhancement method based on a diffusion model with red-dark channel prior constraints. The method includes inputting a single-frame underwater raw image into the model and passing it through a joint estimation module of red-dark channel prior and quadtree, outputting transmittance and background light. These are then encoded by a physical condition encoder to generate a physical condition vector containing spatial attenuation priors and global background light constraints. This vector is used by the diffusion network for encoding and utilizes transmittance distribution and global background light information. In the reverse denoising process, a physical consistency layer and the Jaffe-McGlamery equation are combined to construct a physical consistency residual. The residual gradient calculated based on the physical model is injected as a guiding term into the noise prediction process. The enhanced image is iteratively generated through a denoising diffusion implicit model sampling process. The enhanced image is then forward-degraded and re-rendered through a cyclic consistency reconstruction branch. The cyclic consistency loss is calculated and used for reverse optimization of network parameters. This invention can reduce the color shift problems caused by background light misjudgment and transmittance overestimation.

The invention belongs to the technical field of three-dimensional modeling, and particularly relates to a novel multi-detail-level terrain rendering method, which comprises the following steps: S1, inputting terrain data; s2, calculating a vertex, a normal and a texture coordinate array, and transmitting into a rendering pipeline; s3, calculating an LOD maximum value, a resolution ratio scale factor and a sight distance switching factor; s4, setting basic attributes of root nodes of the quadtree, and completing construction of terrain geometry; s5, rendering terrain geometry in real time; the method has the advantages that by setting basic attributes of root nodes of the quadtree, a topographic data structure with the quadtree as an organization form is constructed; a topographic organization mode of'single-source geometric data + multi-level index rules' is established, and a mode of'repeatedly storing complete geometric data in each level of LOD 'in a traditional tile pyramid is converted into a mode of only storing a vertex, a normal and a texture coordinate array; the memory pressure in a high-precision terrain scene can be reduced, and the terrain real-time rendering efficiency can be improved.

The application discloses a point of interest recommendation method based on regional perception dynamic hypergraph and double-layer modeling, generates a check-in record representation containing a region code through a quadtree region code and coarse-grained geographic information; constructs a dynamic hypergraph; captures the dependency relationship between the check-in records in a single user target track through a self-attention mechanism to obtain check-in record representation; aggregates the check-in record representation through hypergraph convolution to generate an initial feature representation of each track; performs convolution operation on a hyperedge to fuse high-order correlation information of the tracks associated with the target track in a track-track correlation matrix between the tracks to obtain a final target track feature representation; and maps the final target track feature representation to an ID space of a point of interest (POI) to recommend a next point of interest for a user. The application effectively alleviates the data sparsity problem, enhances the capture ability of the dynamic moving mode of the user, and improves the accuracy and reliability of the recommendation through regional perception and double-layer modeling.