同义词
attain强调获得某种结果和到达某种程度
attain a degree
achieve一般指完成某种壮举，成就某种事业
achieve the goal
implement是指履行某种义务
implement the proposal
accomplish是指完成某种（军事等)任务
意思介于achieve与complete之间,既指完成了某事又有成就感的感情色彩
accomplish the task
complete一般指完成项目或者作业等
complete the homework
satisfying令人满意的 修饰东西
satisfied使人满意的 修饰人
当要表达“在某人的帮助下”时用with one's help.
当要表达借助某人/某物时用with the help of sb/sth.
With one's help意思是“在某人的帮助下”,其中的one's是形容词性物主代词或名词所有格.
With the help of则是加名词性物主代语或名词.
with the help of sb. == with one’s help 在某人的帮助下
连接词
first(ly),
second(ly),
third(ly),.
漂亮的说法：
Above all/First of all/
Furthermore,/What's more,/in addition,/moreover/Meanwhile/Thus,
Last but not least/Finally
To the best of our knowlege
Specifically/More specifically/Therefore/Along this way/Most recently/For example/In this work/With the above definition/As LIOP and other LBP-like methods/Obviously/As expected/In order to alleviate this problem/In order to deal with the problem of /Due to this observation/To investigate/ For this purpose/Among the propsed one/Generally speaking
句型
引言
1.Our work is fundamentally different from the previous one.
2.Our proposed two descriptors have the following characteristics, making them both distinctive and robust to many image transformations:
3.Many literatures can be found on this topic.
4.A fall event usually lasts for a short period of time.
算法
1.Recently, to alleviate this problem, some researcher have proposed to use
2.These methods work well on on monotonic illumination changes, but they do not make significant improvements on overall performance.
3.To further explore the effectiveness of orninal information, two intensity order patterns are proposed in this paper, namely LIOP and OIOP.
4.It is worth noting that different from other methods such as , we
5.To overcome these problems, here we proposea LIOP for feature extraction.
6.Before the formal definition of LIOP, some basic mappings are firtly introduced.
7.From the intensity order based patch division, we can observe that it actually quantizes the intensity order of pixel.
8.To give a better understanding of our method, we visualize the OIOP compuation in Figure3.
9.To further study the performance of intensity order based descriptors for dealing with complex illumination changes,we captured tow additional image sequences....
10. In this section, we evaluate all the tested descriptors on the Patch dataset.
11.Local descriptors n the literature can be roughly divided into two categories concerning rotation invariance:
12.In order to give more insight into the influence of the estimated orientatin on the matching performance of local descriptor, we conducted some image matching experiments.
实验部分
1.All the parameters are listed in Table 1.
2.To investigate the effect of these parameters, we performed image matching experiments on 90 paris of images provided by MM with different parameter settings listed by the 3-rd column of Table 1.
3.It can be found that in figure d that the performance of MIOP slightly degrades along with teh decrease of D.
4. Fore a compromise between discriminability and dimensionality, we select D=128 to make MIOP have the same dimension as SIFT.
5.The results are shown in Figure 7.
6.For all parameter settings, OIOP consitently outperforms *OIOP.
7. This sufficiently demonstrates the effectiveness of the proposed learing based quantization.
8.The detailed results on Harris-Affine regin are show in figure 9.
9.For OIOP, it is better or at least comparable to LIOP in most tested cases.
10. For a comprehensive study, we also conduct the experiments on other four popular affine covariant regions: ………。
11.Beside the Oxford dataset and Pathc dataset, we have also evaluated our descriptors on teh 3D objects datast proposed by M and P.
12.As our experiments show, one single support regon is not enough to distinguish incorrect matches from correct ones in general.
13. As shown in figure, we choose support regions as the N nested regins centered at the interest point with an equal increment of radius.
14.It can be seen from fig 9 that the performances of MROGH and MRRID are improved when the number of support regions is ncreased.
15.To evaluate the performance of the proposed descriptors, we conducted extensive experiments on image matching.
16.We further conducted experiments on object recognition to show the effectiveness of the proposed descriptors.
图片部分
Fig.13 shows the comparative results.
结论
1.This paper intensively explores ordinal information for feature description.
2.The key contribution include the following aspects:
3.Our main contributions include:
4.It has the following important properties:
