39 results about "Total graph" patented technology

The invention discloses a target detection sliding window scanning method based on scene geometric constraint. An existing total graph sliding window exhaustive search method based on a monocular camera has the problem that the real-time performance of a detection system can not be achieved caused by low search efficiency. The invention discloses the target detection sliding window scanning method based on the scene geometric constraint. According to the method, by using eight control points and a direct linear transformation algorithm, the homography matrix of a pre-calibrated vehicle-borne camera and the projection vector of a ground plane normal vector on an image are calculated, the upper boarder and the lower boarder of a sliding window scanning constraint area are calculated according to a constraint equation deduced from image imaging geometric constraint relation, and the image characteristics pyramid construction and sliding window scanning detection and classification are carried out only in the corresponding image constraint area. The scanning efficiency is high, and since only one camera is needed, a problem of high hardware cost of a multi-stereo detector is solved.

The invention discloses a phased array overall situation focusing system and a focusing method thereof. The phased array overall situation focusing system can transmit on a single energy converter unit by turns and all the energy converter units receive and focus transmission and receiving synthesis on echo line scanning signals of a plurality of sampling points of a space, so that the space imaging quality can be greatly improved. The phased array overall situation focusing system comprises an energy converter unit, a signal processing unit, a dynamic delay overall situation focusing compensation module and a signal overlapping module; the phased array overall situation focusing system can be used for transmitting the phased array and focusing the transmission and receiving synthesis on any one space point on a synthesis line behind a space initial focusing point; in an ultrasonic phased array control nondestructive testing application, the echo line scanning signals reflected by a pre-set sampling point with small imaging distortion and high image quality can be obtained; all depth focusing compensation requirements can also be met under refraction conditions. A traditional phased array technology can also be improved and distortion caused by the fact that defect depths are not in focusing depths is reduced; the total graph focusing effect of imaging an object to be detected can be realized.

The invention belongs to the field of data processing for large graphs and relates to a generation and search method for reachability chain list of a directed graph in the parallel environment. The method includes distributing the directed graph to every processor which stores nodes in the graph and sub-nodes corresponding to the nodes; compressing graph data split to the processors; calculating a backbone node reachability code of a backbone graph; building a chain index; building a skip list on the chain index; allowing data communication among the processors; allowing each processor to send skip list information to other processors; allowing each processor to upgrade own skip list information; and building a reachability index of a total graph. Through use of graph reachability compression technology in the parallel environment, the size of graph data is greatly reduced, system computing load is reduced, and a system can process the graph data on a larger scale. The method has the advantages that the speed of reading data from a disk is higher, search speed is indirectly increased, accuracy of search results is guaranteed, and network communication cost and search time are reduced greatly for a parallel computing system during searching.

The invention relates to a method for extracting depth continuity object images from an image. The method is particularly characterized by comprising steps that, 1, a depth image standing for a distance from an object to a camera is acquired; 2, a first total-graph scanning is carried out, depth continuity relation among pixel points are determined, first mark operation for marking depth continuity relation of the pixel points on a labeled graph is carried out; 3, during a process of first total-graph scanning, mark relation table operation on mark numbers of the depth image is carried out; 4, final mark number integration operation of a mark relation table is carried out; 5, second total-graph scanning on labeled graphs is carried out to realize merging mark, after second mark operation, the pixel points of a depth continuity object stand for same mark number in a mark graph; and 6, the depth continuity object mark graph is outputted. Through the method, stable and effective segmentation of the target object image can be realized through depth continuity of the target object in the depth image, and thereby image processing efficiency is greatly improved.

The invention discloses an intelligent graph adding method, a storage medium and an application server. The intelligent graph adding method includes the steps that an original graph corresponding to a graph adding instruction and newly added data needed by a newly added graph are obtained by receiving the graph adding instruction triggered by a user; the newly added graph is a new graph added on the basis of the original graph; according to the obtained original graph and the newly added data, whether the coordinate dimensionality in the original graph is consistent with that in the newly added graph is recognized; when it is recognized that the coordinate dimensionality in the original graph is consistent with that in the newly added graph, the newly added graph is added on the original graph according to the newly added data corresponding to the newly added graph to obtain a total graph composed of the original graph and the newly added graph after graph addition. The method has the advantages that the new graph can be added in the original graph just through one-step operation of the user, and convenience and intelligence of graph addition are improved.

The invention discloses a method for on-line monitoring of photoetching conditions, which comprises the following steps: (1) measuring the area of a graph to be monitored, and processing an image acquired by equipment detection; (2) measuring the area ratio of the effective graph region in the graph to be monitored in the acquired image to the total graph region; and (3) comparing whether the area ratio of the effective graph region in the graph to be monitored to the total graph region is greater than the set area ratio range value or not, wherein if the area ratio is greater than the set area ratio range value, judging that the photoetching conditions are qualified, and if the area ratio is not greater than the set area ratio range value, judging that the photoetching conditions are not qualified. By measuring the area ratio of the effective graph region to the marginal graph region, the invention can monitor the shape of the graph to be measured.

The invention discloses a method for dynamically sorting test cases based on program behavior network aggregation, which includes the following steps: Step S1: based on dynamic stub technology, monitor the execution process of all test cases executing the program to be tested; and obtain a general diagram of program calls TG; Step S2: Calculate the graph edit distance D between each two subgraphs using the graph edit distance measure method ij , and subsequently, use the graph edit distance to compute the graph similarity σ ij , to construct the graph similarity matrix M i,j ; Step S3: Use the K-medoids clustering algorithm to divide the test cases into n clusters; Step S4: Use the maximum-minimum distance sampling strategy to obtain the required test case sequence T' from the clustering result clusters. The invention achieves a certain balance in sorting performance and time overhead, and can obtain advantages in both performance and time overhead in large-scale software, and can effectively avoid the disadvantages of static mode by using dynamic mode to obtain test data.