KEYWORDS: Clouds, 3D modeling, Fractal analysis, 3D acquisition, Target recognition, Statistical analysis, Data modeling, 3D metrology, Soil science, Information technology
The traditional discernible criteria for a 2D target are mostly based on Johnson criterion, to overcome the limitations of the Johnson criterion and fill the gap in a 3D point cloud, a novel discernible criterion has been proposed for the 3D point cloud. Based on the multifractal spectrum, the spatial distribution of the 3D point cloud is described. By analyzing the multifractal spectra at different resolutions, feature trend and the final discernible resolution are concluded. The experimental results show that the limiting resolution of T90, F15C is 585mm, the limiting resolution of T90 and Rexton is 517mm, and the limiting resolution of F15C and Rexton is 541mm. The proposed discernible criteria can provide theoretical support for limit identification resolution of 3D point cloud target.
Image dehazing is one of the most important image processing methods and it is widely used in daily application. A large number of dehazing algorithms have been proposed in recent years. In order to solve the problem that the dehazing performances of the classic dark channel prior methods for hazy images with the sun are bad, we propose a dehazing method for removing mist interference in images with the sun in the sky. An atmospheric scattering model is proposed to estimate the scattered light of the sun in the hazy circumstance. We also propose a gradient-based transmission map estimation method to estimate the refined transmission map accurately while reducing the computational complexity. The effectiveness of our method is confirmed by extensive experiments over a wide variety of images.
In this paper, we proposed a novel three-dimension local surface descriptor named RPBS for point cloud representation.
First, points cropped form the query point within a predefined radius is regard as a local surface patch. Then pose
normalization is done to the local surface to equip our descriptor with the invariance to rotation transformation. To
obtain more information about the cropped surface, multi-view representation is formed by successively rotating it along
the coordinate axis. Further, orthogonal projections to the three coordinate plane are adopted to construct two-dimension
distribution matrixes, and binarization is applied to each matrix by following the rule that whether the grid is occupied, if
yes, set the grid one, otherwise zero. We calculate the binary maps from all the viewpoints and concatenate them
together as the final descriptor. Comparative experiments for evaluating our proposed descriptor is conducted on the
standard dataset named Bologna with several state-of-the-art 3D descriptors, and results show that our descriptor
achieves the best performance on feature matching experiments.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.