Fast Template Matching Based on Normalized Cross Correlation With Adaptive Multilevel Winner Update

Wei, Shou-Der; Lai, Shang-Hong

doi:10.1109/tip.2008.2004615

Cited by 140 publications

(54 citation statements)

References 16 publications

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“…Although the conventional template matching, which is usually based on the normalized cross-correlation (NCC), has been proven to be a reliable method in many applications, the matching accuracy is only one pixel. [22][23][24][25] It is easy to obtain integer displacements with one-pixel accuracy by the NCC method. However, most of the time, it needs higher measuring accuracy.…”

Section: A Experimental Setup and Measuring Methodsmentioning

confidence: 99%

Displacement measurement of the compliant positioning stage based on a computer micro-vision method

Zhang

Wang

et al. 2016

AIP Advances

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We propose a practical computer micro-vision-based method for displacement measurements of the compliant positioning stage. The algorithm of the proposed method is based on a template matching approach composed of an integer-pixel search and a sub-pixel search. By combining with an optical microscopy, a high resolution CCD camera and the proposed algorithm, an extremely high measuring precision is achieved. Various simulations and experiments are conducted. The simulation results demonstrate that the matching precision can reach to 0.01 pixel when the noise interference is low. A laser interferometer measurement system (LIMS) is established for comparison. The experimental results indicate that the proposed method possesses the same performance as the LIMS but exhibits a greater flexibility and operability. The measuring precision can theoretically attain to 2.83 nm/pixel.

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Section: A Experimental Setup and Measuring Methodsmentioning

confidence: 99%

Displacement measurement of the compliant positioning stage based on a computer micro-vision method

Zhang

Wang

et al. 2016

AIP Advances

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“…Aspect ratio and shape are then used to filter out false positives. Pierrard et al [4] proposed a face mole detection method using normalized cross correlation (NCC) [5] and 3D morph model that are more resistant to lighting and face gesture. However, only prominent moles are found and the recognition rate is 87%.…”

Section: Related Workmentioning

confidence: 99%

Face Mole Detection, Classification and Application

Hsieh¹,

Lai²

2015

JCP

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Do you have moles on face? Are the moles good or bad? We could treat this problem from medical, cosmetic, or even fortune applications viewpoint. Here, image processing techniques are investigated to detect and verify the goodness of face mole from fortune telling point of view. The process includes Voronoi diagram setup for sample moles, face mole detection, and mole recognition. In the first part, Voronoi diagram is used to partition the given sample mole-face into regions. In the second part, face detection and Laplacian of Gaussian is used to get the prominent features on face. Aspect ratio and area are two features for mole verification. At last, an algorithm is developed to warp the detected user moles to the sample mole-face by Thin-plate Spline Analysis for mole recognition. By using Voronoi diagram, the mole recognition spends only O(logn), where n is the number of given sample moles. The goodness of the recognized mole could be retrieved according to the stored information for fortune telling. The system was tested with 20 people and the mole recognition rate reached 91.25% which demonstrated the feasibility of the proposed system.

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“…For the similarity measure, the sum of absolute difference (SAD) and the sum of squared differences (SSD) are widely used to various applications [4] [5]. SAD and SSD are defined as follows:…”

Section: Template Matching (Tm)mentioning

confidence: 99%

“…NCC is more robust than SAD and SSD under illumination changes [4] [6]. Especially, the FFT-based method calculates the cross correlation in the frequency domain [4]. NCC is defined as follows: (4) …”

Section: Template Matching (Tm)mentioning

confidence: 99%

Cognition System of Bolt Hole Using Template Matching

Baek¹,

Mo²,

Jeong³

et al. 2011

28th International Symposium on Automation and Robotics in Construction (ISARC 2011)

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ABSTRACT:In the construction field, the bolting robot provides convenience and reduces a process time of assembling steel frames. When assembling steel frames, the bolting robot finds a bolt hole with the help of camera. If the light conditions are either too bright or too dark, however, it is hard to find a bolt hole in the input image because the contrast between bolt hole and background is vague. In order to improve detection accuracy in such environment, this paper proposes the image processing algorithm using template matching (TM). First, the input image is converted from RGB to YCbCr in order to reduce influence of shadow. Then we separate Y channel of image in darkness or extract edges from Cb and Cr channel of image in brightness. Template image is created with the radius which can be calculated with the distance to a steel frame. We used TM to create the normalized cross correlation (NCC) image which shows correlation between processed image and template. Finally, bolt holes are extracted from the high-correlation part of NCC image. Experimental results show that the proposed method is robust to detect bolt holes under various illumination conditions.

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Fast Template Matching Based on Normalized Cross Correlation With Adaptive Multilevel Winner Update

Cited by 140 publications

References 16 publications

Displacement measurement of the compliant positioning stage based on a computer micro-vision method

Displacement measurement of the compliant positioning stage based on a computer micro-vision method

Face Mole Detection, Classification and Application

Cognition System of Bolt Hole Using Template Matching

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