Face detection based on color template and least square matching method

2017 ◽  
Author(s):  
Danmei Wang ◽  
Yi Ba ◽  
Liqun Nian ◽  
Duo Pei ◽  
Jiangmei Zhang
Keyword(s):  
Face Detection ◽  
Least Square ◽  
Matching Method ◽  
Least Square Matching

Digital Image Processing and Coordinate Transformation for Vic-3D System

2013 ◽  
Vol 333-335 ◽  
pp. 1002-1006
Author(s):  
Sha Jia Song ◽  
Neng He
Keyword(s):  
Image Processing ◽  
Correlation Analysis ◽  
Coordinate Transformation ◽  
Affine Transformation ◽  
Template Matching ◽  
Least Square Method ◽  
Least Square ◽  
3D Measurement ◽  
Matching Method ◽  
3D Measurement System

This paper discusses the problem about the processing of the images collected by Vic-3D measurement system and the coordinate transformation between the pixel coordinate and plane coordinate. Correlation analysis on image information is carried out by using Matlab. Template matching method is used to get the pixel coordinates of the marked part on the images. Based on affine transformation and least square method, I transform the pixel coordinates of the marked part on the images into the plane coordinates.

scholarly journals LEAST SQUARE MATCHING 1 TO COMBINE THE TWO IMAGES OF NCU AREA

Geoid ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. 117
Author(s):  
Hepi Hapsari Handayani
Keyword(s):  
Least Square ◽  
Least Square Matching

scholarly journals Automatic Extraction Of Interior Orientation Data In Aerial Photography Using Image Matching Method

2021 ◽  
Vol 5 (2) ◽  
pp. 510-514
Author(s):  
Helmy Mukti Wijaya ◽  
Teguh Hariyanto ◽  
Hepi Hapsari Handayani
Keyword(s):  
Image Matching ◽  
Cross Correlation ◽  
Focal Length ◽  
Least Square ◽  
Programming Algorithm ◽  
Matching Method ◽  
Orientation Data ◽  
Fiducial Mark ◽  
Normalized Cross Correlation ◽  
Matching Techniques

The Interior Orientation is a set of parameters that have been determined to transform the coordinates of the camera photo, that is the coordinates of the pixel leading to the coordinates of the image. This parameter is used to calibrate the camera before use so as to produce a precise measurement from an aerial photograph. This orientation parameter consists of a calibrated and equivalent camera focal length, lens distortion, principal point, fiducial mark location, camera resolution, and flatness of the focal plane. All of these parameters are attached to or contained on the camera sensor and the values of these parameters can usually be known from the camera's report page. In this work, the author wants to obtain pixel coordinates from the Fiducial Mark in the base image (Window Search) automatically, therefore a Fiducial Mark template was created which is formed from a piece of a photo image frame to determine the Fiducial Mark coordinate values from the base image ( Window Search), the basis of this programming is to use the concept of photogrammetry, which uses Image Matching techniques. The Image Matching process was developed from the C ++ Language programming algorithm platform, this was done in order to speed up computational results. There are a number of techniques for doing Image Matching, in this study the authors conducted using the Normalized Cross-Correlation Image Matching. In statistics Normalized Cross-Correlation is between two random variables by determining the size of how closely the two variables are different simultaneously. Similarly, Normalized Cross-Correlation in Image Matching is a measurement by calculating the degree of similarity between two images. This level of similarity is determined by Normalized Cross-Correlation (NCC). The Least Square Image Matching method is used to increase the accuracy of the coordinates of the conjugation points.

scholarly journals Particle Swarm Image Matching on Epipolar Line

2014 ◽  
Vol XL-4 ◽  
pp. 87-91
Author(s):  
J.-S. Hsia
Keyword(s):  
Image Matching ◽  
Particle Swarm ◽  
Window Size ◽  
Pso Algorithm ◽  
Least Square ◽  
Image Point ◽  
Reference Image ◽  
Epipolar Line ◽  
Matching Method ◽  
The Difference

This paper presents a method for determining the 3D position of an image point on a reference image using particle swarm optimization (PSO) to search the height (Z value) that gives the biggest Normalized Cross Correlation (NCC) coefficient. The searching area is in the surrounding of the height of the image point. The NCC coefficient evaluates the similarity with the image point and a corresponding point on an epipolar line in the search image. The position of corresponding image point on the epipolar line is determined by the height point on a sloping line locus. The PSO algorithm starts with a swarm of random particles. The position of each particle is a potential solution in the problem space. Each particle is given a randomized velocity and attracted toward the location of the best fitness. The position of each particle is iteratively modified by adding a newly computed velocity to its current position. The velocity is updated by three factors which are two attractions from local best position and global best position, two strengths of the attractions, and two uniform random numbers for each attraction. The iteration will stop when the current solution is convergent. The time of computation is highly related to the range of height and the interval of height enumeration when the approach to find a corresponding image point of an image point on a reference image is based on the height enumeration along sloping line locus. The precision of results can be improved by decreasing the interval of height enumeration. This shows the limitation of the enumeration method in the efficiency and accuracy. The issue is overcome by a method of using PSO algorithm. The proposed method using different parameters such as the size of image window, the number of particles, and the size of the height searching range has been applied to aerial stereo images. The accuracy of tested results is evaluated on the base of the comparison to the reference data from the results of least-square matching being manually given initial points. The evaluation result shows that tested results has given a solution to a level of less than 1 centimetre without using refined image matching method. The same level of accuracy can reach even when the searching range is bigger than 90 meters. But the difference of image window size may lead to the change of the matching result. And, without the procedures of both coarse-to-fine hierarchical solution and refined image matching method, the algorithm still can give the same accuracy level of least-square image matching resulting. This method also shows its ability to give reasonable matching results without manual assistance.

Geometric Error Compensation of Five-Axis Machining Centers Based on On-Machine Workpiece Measurement

2018 ◽  
Vol 12 (2) ◽  
pp. 230-237 ◽  
Author(s):  
Ryuta Sato ◽  
◽  
Keiichi Shirase
Keyword(s):  
Error Compensation ◽  
Geometric Error ◽  
Least Square ◽  
Geometric Errors ◽  
Rotary Axes ◽  
Cutting Test ◽  
Measurement Results ◽  
Simulation And Experiment ◽  
Five Axis ◽  
Least Square Matching

This study proposes an identification and compensation method for the geometric errors of the rotary axes in five-axis machining centers, based on the on-machine measurement results of the machined workpiece. Geometric errors can be identified from the shape geometry of the workpiece machined by five-axis motions because the influence of the errors appears on the shape geometry. An observation equation can be obtained based on the geometric error model and machined shape. The actual geometric errors can be identified by the least square matching of the measured and simulated machined shapes. In order to confirm the effectiveness of the proposed method, an actual cutting test and a simulation are performed. Based on their results, it is confirmed that the proposed method can successfully identify the geometric errors in the simulation. However, these errors cannot be identified in the experiments because a few of them do not have sufficient influences onto the machined shape. On the other hand, although the geometric errors cannot be correctly identified, it is confirmed that the they can be adequately compensated for based on the identified errors in both the simulation and experiment.

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