Publication Date:
2018-11-06
Description:
The application environment with slightly diversity between template image and target image has been the mainstream in template matching over the past decade or so. This paper, however, will discuss template matching method in such scenarios with erratic weather, deformations, scaling etc. For the feature engineering, by choosing the appropriate layers in a CNN and pruning inefficient convolutional kernels in the layers we want, we construct a feature space that can be used to represent more complex features compared to the traditional computer vision feature engineering, such as corners, colours, edges etc. Meanwhile, the feature space and computational capacity can be greatly reduced by the pruning operation on convolutional kernels. For similarity measure, a distance penalty term on the feature of image patches will be added in the final score function to make our method robust to deformation and scaling. Furthermore, the key coefficients of penalty term have been opened so t...
Print ISSN:
1757-8981
Electronic ISSN:
1757-899X
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
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