Fast Template Matching Method based Optimized Sum of Absolute Difference Algorithm for Face Localization

Nadir, Nourain Dawoud; Brahim, Belhaouari Samir; Janier, Josefina

doi:10.5120/2302-2912

Cited by 15 publications

(3 citation statements)

References 11 publications

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“…Correlation-based methods have been used extensively for a variety of applications such as motion analysis [5,12], object recognition [1], face detection [8,15], printed characters, industrial inspections of printed-circuit boards [14], surfacemounted devices, wafers [3], medical field [11], ceramic tiles etc. The conventional normalized correlation method does not meet speed requirements for industry applications, so always required desirable high speed method for better accuracy.…”

Section: Introductionmentioning

confidence: 99%

Counting Objects using Convolution based Pattern Matching Technique

K.Dharpure¹,

Potdar²,

Pandya³

2013

IJAIS

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In this paper, counting objects techniques are proposed for fast pattern matching algorithm based on normalized cross correlation and convolution technique which are widely used in image processing application. Pattern matching can be used to recognize and/or locate specific objects in an image. It is one of the emerging areas in computational object counting. In this paper, introduces a new pattern matching technique called convolution based on pattern matching algorithm. Many different pattern matching techniques have been developed but more efficient and robust methods are needed. The pattern matching algorithm is used to identify the patterns similar present in image. With the patterns, identify the similarity measures of the given pattern to count the object present in the given image. An experimental evaluation is carried out to estimate the performance of the proposed efficient pattern matching algorithm for remote sensing as well as common images in terms of estimation of execution times, efficiency and compared the results with an existing conventional methods.

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Section: Introductionmentioning

confidence: 99%

Counting Objects using Convolution based Pattern Matching Technique

K.Dharpure¹,

Potdar²,

Pandya³

2013

IJAIS

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“…The SAD(Sum Of Absolute Difference) is used to measure the similarity among template image T and sub-images within the source images S.It works via computing the absolute difference among every pixel in template image T and as well as subsequent pixel within the sub-images which is intended for comparision in the source image S.Then we find the summation of all the differences obtained to produce a straightforward metric of similarity.For instance,suppose a two-dimensional A×B template,T(a,b) which is used for matching inside a S(a,b) i.e the source image that has S×T wherever (S>A & T>B).For every pixel at location (a,b) within the figure,according to the method that we have described below [7] we can calculate the SAD distance. …”

mentioning

confidence: 99%

“…The results that we have obtained after summation povides SAD distance among the image window and template window.SAD (Sum Of Absolute Difference) is computed via the equation [7].…”

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confidence: 99%

An Overview of Various Template Matching Methodologies in Image Processing

Swaroop¹,

Sharma²

2016

IJCA

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The recognition and classification of objects in images is a emerging trend within the discipline of computer vision community. A general image processing problem is to decide the vicinity of an object by means of a template once the scale and rotation of the true target are unknown. Template is primarily a sub-part of an object that"s to be matched amongst entirely different objects. The techniques of template matching are flexible and generally easy to make use of, that makes it one amongst the most famous strategies of object localization. Template matching is carried out in versatile fields like image processing,signal processing, video compression and pattern recognition.This paper gives brief description of applications and methods where template matching methods were used.

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