Abstract
This paper proposes a 2-dimensional (2D) maximum entropy threshold segmentation (2DMETS) based speeded-up robust features (SURF) approach for image target matching. First of all, based on the gray level of each pixel and the average gray level of its neighboring pixels, we construct a 2D gray histogram. Second, by the target and background segmentation, we localize the feature points at the interest points which have the local extremum of box filter responses. Third, from the 2D Haar wavelet responses, we generate the 64-dimensional (64D) feature point descriptor vectors. Finally, we perform the target matching according to the comparisons of the 64D feature point descriptor vectors. Experimental results show that our proposed approach can effectively enhance the target matching performance, as well as preserving the real-time capacity.
Highlights
In recent decades, the image target matching plays a significant role in many research fields, like the computer vision and digital image processing [1], and has been widely used in a variety of military and civil applications [2], such as the image target detection, autonomous navigation, 3-dimensional reconstruction, target and scene recognition, and visual positioning and tracking
By setting L = 16, we have 256 pairs of gray levels, as represented at horizontal coordinates in gray histogram, while the vertical coordinates stand for the frequencies of gray level pairs
A novel 2DMETS based speeded-up robust features (SURF) proposed in this paper is proved to perform well in accuracy and computation cost for image target matching
Summary
The image target matching plays a significant role in many research fields, like the computer vision and digital image processing [1], and has been widely used in a variety of military and civil applications [2], such as the image target detection, autonomous navigation, 3-dimensional reconstruction, target and scene recognition, and visual positioning and tracking. Gray correlation-based algorithm is based on the calculation of image similarities and the searching for the extreme values of similarities by using the optimal parameters in transformation model. Feature-based algorithm mainly relies on the matching of the feature parameters extracted from images (e.g., the points, lines, and surfaces in images). In the condition of slight distortion of gray and geometry, a large amount of computation cost is required by gray correlation-based algorithm, it normally outperforms feature-based algorithm, in terms of accuracy, robustness, and antinoise ability. In the serious distortion condition, feature-based algorithm is much preferred due to the lower false matching rates and better robustness for gray changes, image deformation, and occlusion
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
