期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：9
页码：538-545
出版社：Science and Information Society (SAI)
摘要：Until the last decades, researchers taught that
teaching a computer how to recognize a bunny, for example,
in a complex scene is almost impossible. Today, computer vision
system do it with a high score of accuracy. To bring the real world
to the computer vision system, real objects are represented as
3D models (point clouds, meshes), which adds extra constraints
that should be processed to ensure a good recognition, for
example the resolution of the mesh. In this work, based on the
state of the art method called Spin Image, we introduce our
contribution to recognize 3D objects. Our motivation is to ensure
a good recognition under different conditions such as rotation,
translation and mainly scaling, resolution changes, occlusions and
clutters. To that end we have analyzed the spin image algorithm
to propose an extended version robust to scale and resolution
changes, knowing that spin images fails to recognize 3D objects
in that case. The key idea is to approach the representation of
spin images of the same object under different conditions by the
mean of normalization, either these conditions result in linear or
non-linear correlation between images. Our contribution, unlike
spin image algorithm, allows to recognize objects with different
resolutions and scale. Plus it shows a good robustness to occlusions
up to 60% and clutters up to 50%, tested on two datasets:
Stanford and ArcheoZoo3D.
