之前创建模板还是太慢了,修改了一下,效果还是比较明显的,应该没多大问题,测试后跟halcon的CreateShapeModel速度差不多了。 intDetector::addTemplate_rotate(shape_based_matching::shapeInfo_producer& shapes,conststd::string &class_id) {if(shapes.infos.size() <1)return-1; shapes.produce_infos();auto& f...
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gfit score estimates the geometric matching of point-based surfaces - it ranges between 0 and 1 hfit score estimates the matching of colored points representing pharmacophore features - it ranges between 0 and 1Thus, we calculate a hybrid score = gfit + hfit scores - gfit+hfit ranges between...
/* * shape_context.cpp -- Shape context demo for shape matching */#include "opencv2/shape.hpp"#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include "opencv2/imgproc.hpp"#include <opencv2/core/utility.hpp>#include <iostream>#include <string>using namespace std;using nam...
Shape-based alignmentmolecular surfacespoint cloudsregistrationmolecular similaritysensaas is a tool developed for aligning and comparing molecular shapes and sub-shapes. Alignment is obtained by registration of 3D point-based representations of the van der Waals surface. The method uses local properties ...
et al. Brain imaging of the cortex in ADHD: a coordinated analysis of large-scale clinical and population-based samples. Am. J. Psychiatry 176, 531–542 (2019). Article PubMed PubMed Central Google Scholar Assem, M., Blank, I. A., Mineroff, Z., Ademoglu, A. & Fedorenko, E. ...
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Unlike the fixed-sized window used in other sparse representation-based methods, the shape-adaptive regions have adaptive sizes and shapes, and conform to the spatial structure of the HSI as far as possible. Finally, the label of the test pixel is determined by applying the joint sparse ...
Similarly to BMI, ABSI and HI are based on the principle of allometry, which accounts for the expansion of the dimensions of individual body parts relative to the total body size with log-linear rather than linear models7. Similar to the scaling of log-transformed weight to log-...
from graphistry import is_in, gt # (a)-[{"v": 1, "type": "z"}]->(b) based on g g2b = g2.hop( source_node_match={g2._node: "a"}, edge_match={"v": 1, "type": "z"}, destination_node_match={g2._node: "b"}) g2b = g2.hop( source_node_query='n == "a"...