Image registration based on feature point sets

Zhang, Quanbing; Luo, Bin; Wei, Sui; Yang, Shangjun

doi:10.1117/12.539975

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Here t x and t y are translation parameters, S denotes the scale factor and θ is the angle of rotation. The transformation can be described as the following equation [2]:…”

Section: Principle and Problemmentioning

confidence: 99%

A Mutual Information Based Image Measurement for Large Workpiece

Zhou

Yang

2010

AMM

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Image measurement based on machine vision is a promising method for the precise measurements of machine parts. As for a large sized workpiece that exceeds the FOV (field of view) of the camera, image mosaic must be performed to implement measurement. The problematic behavior of image mosaic is the contradiction between the precision of mosaic and the complex of algorithm adopted. Mutual information as a similarity metric has been explained and introduced into the mosaic operation in this paper. To obtain a satisfactory mosaic effect and measuring precision, a machine vision based configuration that integrates the illumination setup and motion control schemes was built to ensure the accuracy. The precise motion control system was utilized to limit the search space and speed up the operation. Experimental results show that the precision of the image mosaic with mutual information as a similarity metric meets the requirements.

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“…Here t x and t y are translation parameters, S denotes the scale factor and θ is the angle of rotation. The transformation can be described as the following equation [2]:…”

Section: Principle and Problemmentioning

confidence: 99%

A Mutual Information Based Image Measurement for Large Workpiece

Zhou

Yang

2010

AMM

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show abstract

“…In the area-based approach, there is a classic algorithm which is called registration algorithm based on a template. The method of based on gray correlation is proposed by literature [2], however, due to the limitations of time-consuming affected, and the algorithm's practical is constrained, this method's errors is large. Featurebased approach is matched by the characteristics which derived from the pixel values.…”

Section: Introductionmentioning

confidence: 99%

Image Mosaic Method Based on Gaussian Second-order Difference Feature Operator

Chen¹,

Hao²,

Di³

2015

TIJEE

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<p><em>To compose the wide visual angle and high resolution image from the sequence of images which have overlapping region in the same scene quickly and correctly, an improved SIFT algorithm which is based on D2oG interest point detector was proposed. It extracted the image feature points and generated corresponding feature descriptors by improved SIFT algorithm. Then, using the random consistency (RANSAC) algorithm purified feature point matching pairs and calculating the transformation matrix H. Last, complete the seamless mosaic of images by using the image fusion algorithm of slipping into and out. It respectively process the images which had the four typical transformations with the traditional SIFT and the proposed method. The result indicated that the number of feature pairs is fewer than SIFT algorithm and the mosaic time is shorter, and then the matching efficiency is higher than the later. This proposed method reduces the complexity of operation and improves real-time of image mosaic simultaneously.</em></p>

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Image registration based on feature point sets

Cited by 2 publications

References 12 publications

A Mutual Information Based Image Measurement for Large Workpiece

A Mutual Information Based Image Measurement for Large Workpiece

Image Mosaic Method Based on Gaussian Second-order Difference Feature Operator

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