82 results about "Convex hull algorithms" patented technology

The invention discloses a building seismic damage analysis method, and relates to the field of seismic damage assessment. The method comprises the following steps of: setting a contour sampling interval, extracting a point cloud set of different height of a building, then, obtaining a contour section profile line through a convex hull algorithm, and converting three-dimensional point cloud of the building into a two-dimensional contour polygon sequence; extracting shape discrete parameters of the building; and calculating standard deviation of building shape parameters, and through a Fisher discriminant analysis, performing statistics on the standard deviation obtained in a step S22 or a step S23, thereby obtaining a discrimination function corresponding to three types of degree of damage of the building. According to the invention, edge extraction, shape clustering and discriminant analysis are fused, and problems in building contour polygon sequence extraction, shape discrete parameter calculation, irregular building block segmentation and damage analysis are solved effectively.

The invention relates to a single-camera-based gesture convex hull detection and palm positioning method. The method comprises the steps that a single camera acquires a gesture image; denoising preprocessing is carried out on the acquired image; gesture motion, color detection and segmentation are carried out; gesture contour tracking detection is carried out; gesture contour convex hull detection is carried out; and gesture convex hull convex defect detection and gesture palm positioning are carried out. The method uses a convex hull algorithm and a smallest enclosing circle algorithm to realize gesture convex hull detection and palm positioned. The problems of gesture variability and diversity and light factor interference vulnerability are solved. The accuracy, the stability and the practicality of gesture palm positioning are improved. The method has high research significance and broad application prospects in a gesture identification technology based on vision.

The invention provides a grain transportation vehicle grain loading state detection method and a device based on a visual image. The method comprises steps: image acquisition is carried out on the grain transportation vehicle through a color digital camera, and a planar color image for a grain box port and the grain is acquired; smoothing, edge, line detection and other filters are used for processing the planar color image, and a complete edge contour of the grain box port is acquired; an image processing method such as color space conversion and threshold segmentation is used for acquiring the area of the grain in the grain box in the image; a two-dimensional convex hull algorithm is used for acquiring convex points representing edges of the grain area, and the linear distances between the convex points and the four edges of the grain box port are calculated respectively; according to the distances measured in real time, the loading state of the grain at each distribution position of the grain box port is judged through a preset threshold, and detection on the grain loading state when a reaper unloads the grain to the grain transportation vehicle can be realized; and finally, the grain state and the corresponding operation instruction are displayed on a detection terminal in real time. The method and the device have the advantages of convenient mounting and maintenance, easy implementation, quick detection speed, high stability and high precision.

The invention discloses a sea surface small target detection method based on multi-domain multi-dimensional feature fusion. The method comprises the following steps: carrying out the combined processing of echo data under four polarizations: HH, HV, VH and VV, and extracting the multi-dimensional feature information of a time domain, a frequency domain, a time-frequency domain and a polarization domain; through linear fusion of a polarization domain and a feature domain, multi-dimensional features are compressed into a 3D feature space, and the calculation cost is reduced while the high-dimensional information amount is ensured; and in the 3D feature space, a quick convex hull algorithm is adopted to obtain a false alarm controllable judgment area, and target detection is realized. The detection method provided by the invention has steady performance in different marine environments, improves the detection performance of the radar on the sea surface small target, and provides a new thought for the detection of the sea surface small target.

A novel dynamic spatial clustering construction and visualization system is configured to intelligently and autonomously determine optimal office space divisions for improved space efficiency and/or productivity of an organization in a particular office floorplan. Instead of requiring a human operator intervention to input step-by-step instructions or floor plan map manipulations into the system, the dynamic spatial clustering construction and visualization system can autonomously and intelligently determine optimal spatial clustering from a given set of space segments, grouping properties, and geographic features of a computerized office floor plan, by machine-determined isolation and sub-grouping of space marker icons based on known common traits per icon, by determining separate clusters within each subgroup using a density-based clustering algorithm, by creating space marker boundary indicators to approximate required spaces without spatial waste, and by executing a convex hull algorithm on each defined cluster to define the new boundary per cluster without spatial waste.

A multicolor printing system splitting modeling method for maximizing a color gamut comprises steps of manufacturing and printing single color step-wedges of all primary colors, determining the ultralimit threshold value of each single color ink quantity, measuring the spectrum value of each single-color step-wedge subjected to equal ratio compression and establishing a corresponding relation; splitting an original multicolor printing system into printing system submodels; performing ink color space overall sampling to obtain the sampling samples under the sprinting system submodels, and obtaining the prediction spectrum reflectivity information values by simulation; targeting at all submodels, using a convex hull algorithm to calculate to obtain the ink color space overall sampling color gamut volumes; combining K submodels and using the convex hull algorithm to obtain the color gamut volume of each combination, and locking the combination corresponding to the maximum color gamut coverage rate when the maximum color gamut coverage rate is larger than the preset threshold value, thus realizing the splitting modeling of the original printing system. The method has the technical advantages of obviously reducing the splitting modeling complexity of the system, avoiding the ink quantity ultralimit problem, improving the stability of output colors and the like.

The invention provides a system and a method for wireless network signal scope sensing and displaying. A vehicle-mounted terminal device periodically obtains present wireless network signals and a latitude and longitude coordinate, and periodically sends the wireless network signals and the latitude and longitude coordinate back to a rear-end cloud server. The cloud server can draw a transmission scope of each region according to the wireless network signals and the latitude and longitude coordinate set back by the vehicle-mounted device and by means of a convex hull algorithm. Thus signal conditions in road segments which no vehicle passes can be compensated. Conditions that a same area is covered by a plurality of regions are analyzed according to the transmission scope. Which area is not covered by any region or is only covered by one region and located at the edge of the region is determined according to the region covering conditions, thus determination of poor mobile communication receiving or communication corner areas is realized, and reference is provided for operating supporting.

The invention provides a multi-color halftone equipment split modeling method and system oriented to a copy object. The method includes the steps that multi-color halftone equipment is split into multiple four-color models containing black ink in an enumeration mode; a Nigboer primary color step wedge prepared in the ink quantity limited modeling process is used for acquiring color gamut of the four-color models through a convex hull algorithm; a color gamut judgment method is used for acquiring color gamut coverage rates of different four-color model combinations on a to-be-copied image, based on the principle of maximization of the color gamut coverage rates, the four-color model combinations are determined; aiming at the probable condition that multiple sets of combinations meet condition requirements at the same time, a center aberration minimization method is used for determining a final optimal four-color model combination, and equipment split modeling is completed. Aberration samples are prepared in the ink quantity limitation process, under the condition without an additional sample preparation step, the overall modeling process of the multi-color halftone equipment is simplified, and implementation is convenient.

The invention relates to a method for accurately dividing a blade cross section character point cloud. According to the method, firstly, initial dividing is achieved by extracting a midpoint of every two adjacent points in the point cloud; secondly, the parameters of a front edge and the parameters of a back edge are accurately extracted; thirdly, the intersections with the front edge and the intersections with the back edge are deleted from a blade pot point set and a blade back point set respectively so that the character point cloud can be accurately divided, wherein the blade pot point set and the blade point set are formed through initial dividing. The method overcomes the defects of a convex hull algorithm and an algorithm improved on the basis of the convex hull algorithm without loss of generality, and is suitable for dividing cross section character point clouds of blades within a wider range, and accuracy of extracting of the subsequent blade molded face feature parameters is guaranteed.

The invention discloses a human body posture visual identification method of a transfer carrying nursing robot. According to the method, a two-stage network (the first-stage neural network is PAF andthe second-stage neural network is improved ResNet) based on RGBD (color image RGB + depth image Depth) is utilized to realize close-range higher-precision identification; a ResNet network commonly used for classification is improved, and input and output structures of the ResNet network are modified, so that the ResNet network can solve the problem of human joint recognition, serves as a second-level neural network, and obtains higher precision compared with the existing method. According to the nearest neighbor insect following method, under the condition that only two joint coordinates aredepended on, the armpit contour of the human body is automatically tracked, the armpit contour is further repaired through the convex hull algorithm, the armpit center, namely the armpit point, is finally obtained, and by means of the armpit point recognition method, the dependence of the armpit point on the joint position is reduced, and meanwhile accuracy is improved.

The embodiment of the invention provides an outer contour point labeling method and device, electronic equipment and a readable storage medium, and relates to the technical field of image processing. A to-be-processed image is obtained, the to-be-processed image comprises a target area, the target area represents the area where a to-be-labeled object is located, and three-dimensional model parameters of the to-be-labeled object in the target area are obtained; and a three-dimensional model is constructed based on the three-dimensional model parameters and comprises a three-dimensional outer contour point candidate area and a three-dimensional outer contour vertex. A three-dimensional outer contour point candidate region is projected to the to-be-labeled object to obtain a two-dimensional outer contour point candidate region of the to-be-labeled object. Based on a convex hull algorithm, screening is performed from the two-dimensional outer contour point candidate region to obtain a target outer contour point. Finally, the target outer contour point on the to-be-marked object is marked to obtain an outer contour point marking result. Thus, labeling of the two-dimensional human body outer contour can be rapidly and accurately achieved so that a large amount of training data can be obtained, and therefore the accuracy of the training result is improved.

A method and apparatus for determining a boundary of a work area are provided in an embodiment of that present application. The method include: acquiring a plurality of track points of a movable object when performing a work on the work area; generating an initial trajectory range according to the plurality of trajectory points; dividing the initial trajectory range into a plurality of grids according to a preset interval; determining a boundary of the work area according to a positional relationship between the plurality of track points and the plurality of grids. The embodiment directly determines the boundary through the collected information of the trajectory points, effectively improves the accuracy of the boundary, and solves the problem that the boundary error of extracting the workarea by the convex hull algorithm in the prior art is large.

The invention discloses a method for analyzing the collection range of a gravity pipeline pumping station, which includes such steps as modeling the data of drainage pipeline, connecting drainage pipeline to each other to obtain drainage pipeline network, carrying out topological inspection by suspension inspection and connection analysis after connection is completed, and carrying out topologicalinspection by connecting the drainage pipeline network to the gravity pipeline pumping station to obtain drainage pipeline network; analyzing the flow direction of the established drainage pipe network and marking the flow direction of the drainage pipe line with an arrow; starting from the pumping station, each branch of incoming water being tracked upstream in the direction of water flow according to the depth-first algorithm until all pipelines and pipe points at opposite junctions of water flow direction are found; carrying out spatial position analysis on the pipeline and the pipe point,respectively judging the relationship between the pipeline and the road surface, the district or the factory area; all the coordinates of the points in the result set being traversed in turn, and theouter contour being obtained by a Graham convex hull algorithm, which is the water collection range of the pumping station. Through the method, the water collection area of the backbone network can be known, and the corresponding drainage pump station of the pipeline can be quickly found.

The invention relates to a large-scale point cloud data-based large-scale high-speed rotary equipment error hybrid evaluation method. The method is a flatness error evaluation method according to a minimum area criterion. Updating the speed and the position of the planeness error solving particle swarm algorithm; simulating an annealing algorithm mechanism, and determining a transition probabilityfunction according to Gibbs criterion regular distribution; optimizing calculation is carried out, falling into a local optimal solution is avoided, and the solving precision is improved; removing invalid data from an optimization calculation result by adopting a convex hull algorithm to obtain effective measurement data points, and solving the planeness by utilizing a particle swarm intelligentoptimization algorithm. The flatness shape error evaluation value of large-scale point cloud data can be accurately obtained, and the method is suitable for measurement and evaluation of geometrical shape errors of large-scale rotary products. The method is simple and convenient in calculation and high in solving precision, is used for evaluating the shape error of the rotor connecting surface ofthe aero-engine, and can improve the final measurement and evaluation precision, thereby improving the assembly precision.

In view of how to analyze the operating state of a transformer and perform fault diagnosis based on a vibration method, the embodiment of the invention discloses an online abnormality detection methodfor transformer vibration. The method comprises: obtaining new data samples about a vibration signal of the transformer operating state within a preset time period; extracting the characteristic parameters of the new data samples based on wavelet packet analysis; completing the learning of the new data samples based on a fast convex hull algorithm, training and updating a single-class abnormalitydetector model; and calling the updated single-class abnormality detector model to perform online abnormality detection of the current transformer vibration. According to the online abnormality detection method for transformer vibration, since the incremental learning algorithm is used to learn only the new data samples within the preset time period, the new data can be processed in time and effectively, the new training samples can be learned online in real time, and rapid upgrade of detection models can be achieved. At the same time, time and space requirements of the model update can be reduced.