matching

There are three separate approaches to pattern matching provided by PostgreSQL : the traditional SQL LIKE operator, the more recent SIMILAR TO operator (added in SQL:1999), and POSIX-style regular expressions. Aside from the basic "does this string match this pattern?" operators, functions are available to extract or replace matching substrings and to split a string at matching locations. Tip: If you have pattern matching needs that go beyond this, consider writing a user-defined function in Perl or Tcl. 9.7.1. LIKE stringLIKEpattern[ ESCAPEescape-character ]stringNOT LIKEpattern[ ESCAPEescape

imagine that we use pagination to split and display a mysql result like this, sorted on auto inceremental ID and date: SELECT name FROM members ORDER BY id DESC, date DESC LIMIT $start, $len we display that result and page navigation links below it using php. how we can find that record ID number x is in which page of that result so we set page number to that page and display that page and end-user do not need to click navigation and find it? First get total number of records. select count(*) as total from members; Find the number of the row member 'x' is found in the record list select count(

I am currently working on a project to pictorially explain the Hopcroft-Karp algorithm. I am using the pseudo-code from the Wikipedia article . I have also seen this algorithm implemented on Stack Overflow in Python This would do fantastically if only I didn't have to understand the algorithm completely to use it. My questions are as follows: What is the meaning of the Dist[] array in the pseudo-code, and how is the layering of the graph done in the Breadth-First-Search. I have a grasp on the workings of the DFS. Thanks in advance. The standard BFS creates layers such that nodes in successive

I have a database of companies. My application receives data that references a company by name, but the name may not exactly match the value in the database. I need to match the incoming data to the company it refers to. For instance, my database might contain a company with name "A. B. Widgets & Co Ltd." while my incoming data might reference "AB Widgets Limited", "A.B. Widgets and Co", or "A B Widgets". Some words in the company name (A B Widgets) are more important for matching than others (Co, Ltd, Inc, etc). It's important to avoid false matches. The number of companies is small enough

I seem to have found an algorithm but am having trouble understanding it, I was wondering if any of you knew the generic outline of the algorithm. Here is the link to the algorithm I found on page 2 http://www.cse.iitb.ac.in/~sundar/cs435/lecture23.pdf Luixo Algorithm is simple as that: Find unmatched vertex, mark it as not included in minimum vertex cover. Mark all matched neighbours of this vertex as included in minimum vertex cover. Treat all mates of vertexes from previous step as unmatched vertexes and repeat step 1. If recursion ended, repeat from step 1 (that is case of several

I've implement a Robust matcher found on the internet based on differents tests : symmetry test, Ratio Test and RANSAC test. It works well. I used then findHomography in order to have good matches. Here the code : RobustMatcher::RobustMatcher() : ratio(0.65f), refineF(true),confidence(0.99), distance(3.0) { detector = new cv::SurfFeatureDetector(400); //Better than ORB //detector = new cv::SiftFeatureDetector; //Better than ORB //extractor= new cv::OrbDescriptorExtractor(); //extractor= new cv::SiftDescriptorExtractor; extractor= new cv::SurfDescriptorExtractor; // matcher= new cv:

I was reading the docs about template matching with opencv and python and in the last part about template matching with multiple objects, the code detect the 19 coins on the mario image but, is it possible to count the number of objects detected with some function on python like len() or any opencv method? Here is the code showed on the tutorial: http://docs.opencv.org/3.1.0/d4/dc6/tutorial_py_template_matching.html Template Matching Code: import cv2 import numpy as np from matplotlib import pyplot as plt img_rgb = cv2.imread('mario.png') img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)