Very Fast Template Matching

Schweitzer, Haim; Bell, John W.; Wu, Feng

doi:10.1007/3-540-47979-1_24

Cited by 78 publications

(41 citation statements)

References 8 publications

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“…These algorithms differ in terms of the appearance representation used, the number of objects tracked, and the method used to estimate the object motion. A. Yilmaz [3] divided these tracking methods into two subcategories based on the appearance representation used, namely, templates and density-based appearance models [11][12][13][14][15], and multi-view appearance models [16,17].…”

Section: Bii Kernel Trackingmentioning

confidence: 99%

Object Tracking Based on Machine Vision and improved SVDD Algorithm

Wang

Zhang

2015

International Journal on Smart Sensing and Intelligent Systems

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Section: Bii Kernel Trackingmentioning

confidence: 99%

Object Tracking Based on Machine Vision and improved SVDD Algorithm

Wang

Zhang

2015

International Journal on Smart Sensing and Intelligent Systems

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“…The sum-table is a precomputed data structure that acts as a lookup table, dramatically reducing the number of multiplications or additions required to evaluate a given expression. More specifically, the sum-table is a discrete version of an integral image [21,22].…”

Section: Fft and Sum-table For Denominator Of Nccmentioning

confidence: 99%

“…given by Equation (21). The results are summarized in Table 1 which compares the sum-table formulation to the FFT and direct formulation of the numerator of Equation (1).…”

Section: Computational Efficiency Examplementioning

confidence: 99%

Fast normalized cross correlation for motion tracking using basis functions

Hii

Hann

Chase

et al. 2006

Computer Methods and Programs in Biomedicine

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Digital Image-based Elasto-tomography (DIET) is an emerging method for noninvasive breast cancer screening. Effective clinical application of the DIET system requires highly accurate motion tracking of the surface of an actuated breast with minimal computation. Normalized cross correlation (NCC) is the most robust correlation measure for determining similarity between points in two or more images providing an accurate foundation for motion tracking. However, even using fast fourier transform (FFT) methods, it is too computationally intense for rapidly managing several large images. A significantly faster method of calculating the NCC is presented that uses rectangular approximations in place of randomly placed landmark points or the natural marks on the breast. These approximations serve as an optimal set of basis functions that are automatically detected, dramatically reducing computational requirements. To prove the concept, the method is shown to be 37-150 times faster than the FFT-based NCC with the same accuracy for simulated data, a visco-elastic breast phantom experiment and human skin. Clinically, this approach enables thousands of randomly placed points to be rapidly and accurately tracked providing high resolution for the DIET system.

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“…Instead if we approximate the image by a corresponding function the computation can be decreased. Schweitzer et al proposed an efficient template matching algorithm by introducing integral images and approximating the input image with second-or thirdorder polynomials [15].While Shinichiro and Masako Omachi [16] had proposed a fast template matching scheme using Legendre polynomial for approximating the template and then calculating the normalized cross correlation between the input image and sample templates of different sizes. We had considered the work of Shinichiro el al in particular for the task of template matching.…”

Section: Introductionmentioning

confidence: 99%

Computer vision based currency classification system

Singh¹,

Badoni²,

Verma³

2011

IJCA

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There are numerous problems associated with current system which are solving the problem of automatic currency classification. Some of the problems administered are like scaling, rotation and noise in the form of missing valuable data in printing or due to the wear and tear of currency notes. In our system we are first aligning the image horizontally along the x axis and after that foreground of the image is removed by detecting the location of edges, and once we have got the processed image we can apply any of the techniques for classification. Over here we are using fast template matching for recognizing the value of the currency. Once we get result after template matching we can classify the currency into different categories like 10, 50,100,500 and 1000. In our system we are aiming at the improvement on existing system by adding useful and robust pre-processing techniques which has been missing in most of the recent works done so far.

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Very Fast Template Matching

Cited by 78 publications

References 8 publications

Object Tracking Based on Machine Vision and improved SVDD Algorithm

Object Tracking Based on Machine Vision and improved SVDD Algorithm

Fast normalized cross correlation for motion tracking using basis functions

Computer vision based currency classification system

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