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Multiscale Integral Invariants For Facial Landmark Detection in 2.5D Data

Multiscale Integral Invariants For Facial Landmark Detection in 2.5D Data
Multiscale Integral Invariants For Facial Landmark Detection in 2.5D Data

Multiscale Integral Invariants For Facial Landmark Detection in 2.5D Data

Adam Slater, Yu Hen Hu* Department of Electrical & Computer Engineering University of Wisconsin - Madison

Madison, WI, USA

{ajslater, yhhu}@https://www.wendangku.net/doc/4011323434.html,

Nigel Boston* Department of Mathematics & ECE University of Wisconsin - Madison Madison, WI, USA

boston@https://www.wendangku.net/doc/4011323434.html,

Abstract— In this paper, we introduce a novel 3D surface landmark detection method using a 3D integral invariant feature extended from that proposed by Manay et al. for 2D contours. We apply this new feature to detect the nose tips of 2.5D range images of human faces. Using the Face Recognition Grand Challenge 2.0 dataset, our method compares favorably with a recently proposed competing method.*

I.I NTRODUCTION

The area of face recognition is a well-researched field, with many thoroughly studied approaches and algorithms. However, the vast majority of this research has been in the area of two-dimensional images. With the relatively recent availability of three-dimensional (3D) face databases and scanning equipment, 3D face recognition has begun to receive much more attention. While there have been a number of proposals in recent years [19] for various systems for face recognition using this newly-available three-dimensional data, very few papers have focused on the initial registration and landmark detection stage intrinsic to the operation of most of these algorithms. This paper seeks to explicitly address this particular aspect of 3D face recognition and presents a novel algorithm for the detection of 3D facial landmark points and the registration of 3D facial range images.

This area of facial feature detection and location has been receiving increased attention lately, with several differing approaches proposed. One of the earliest and most common methods has been the use of local mean and Gaussian curvature information [2,3,4,5,6]. These approaches either use the curvature to segment the face into regions for recognition or attempt to use local curvature to locate feature points for registration. Unfortunately, methods based on local curvature information can be unstable due to noisy data and are fairly prone to giving false positives.

A more recent method used by Medioni et al. [7] used the ICP (Iterative Closest Point) algorithm for registration. This is a general purpose algorithm for the iterative minimization of mean square error when aligning arbitrary point clouds. This method, while guaranteed to converge, may converge to local minima and is dependent on the assumption that one of the datasets to be aligned is a subset of the other and both are free from outliers, in the sense that that each point in at least

*Yu Hen Hu and Nigel Boston are supported by the National Science Foundation under Grant No. CCF-0434355one of the datasets has a valid corresponding point in the other [17].

The Point Signature[1,8,9] method proposed by Chua and Jarvis proposed a method of extracting local features by projecting local points onto a plane orthogonal to a local approximation of the surface normal vector. This technique was extended by the Spin Image representation [10,11], which modified the original technique to include invariance to rotations and translations. A further modification of this technique was proposed by Wang et al. [15,16], whose Local Shape Map scheme mitigated problems with data loss and ambiguousness in the Spin Map calculation. All of these methods, however, rely on the accurate approximation of surface normals, which are sensitive to expression variation and noise, and all suffer from a relatively poor tradeoff between storage required and performance.

A somewhat similar method, proposed by Xu et al. [12] is used as a basis for comparison with the algorithm proposed in this paper. This work relies on calculations of the angle of intersection between point normals and vectors to points in a local area. The method reduces the storage requirements of the previously discussed algorithms by only retaining the mean and variance of each set of intersection angles and by using an early filtration method to reduce its number of candidate points.

In this paper, we propose a 3D integral invariant feature for 3D surface and apply it to detect the nose tip landmark for a given range image of human face. Our method is inspired by the work of integral invariant signatures by Manay et al. [13], which approximate contour features based on integration, rather than differentiation, of local areas. Our approach is also influenced by Mortara et al.'s technique of tracing the contours of intersection of 3D spheres, similar to a Gaussian curvature calculation [14].

Most prior methods of 3D landmark detection or local feature extraction have been very sensitive to small-scale variations in the data which can be caused by expression variation or noisy data acquisition and suffer from a poor storage to performance tradeoff. Our method aims to rectify these difficulties while retaining the discriminative power of local feature methods like the point signature or spin image.

Specific contributions of this work include:

x Formulation of an integral invariant feature over 3D surfaces

x Development of an efficient, incremental feature extraction method of the proposed 3D integral invariant.

x An empirical feature dimension selection method using linear discriminant analysis.

x

A hierarchical multi-modal 3D surface landmark detection method for locating nose tip using both 3D range image and corresponding 2D color image.

In the rest of this paper, the proposed 3D surface integral invariant will be presented in section II. An efficient incremental feature extraction method is discussed in section III. Multi-modal, multi-stage pattern classification for nose tip landmark detection is presented in section IV.

II.I NTEGRAL I NVARIANTS

Our method is inspired by the work of Manay et al.[13], specifically. However, whereas this prior work focused on one-dimensional contours in R 2, we extend this method to two-dimensional surfaces in R 3.

The method approximates the value of the integral invariant signature

()(,)()I p h p x d x J P 3

where p is a point of interest, x is a point in the local neighborhood of p , and d ?(x ) represents an infinitesimal geodesic distance on the surface. ? represents the surface itself, and J represents the volume enclosed by the surface. h (p,x ) is a kernel function which satisfies

(,)()(,)()g h p x d x h gp x d x g G P P 33

where G is a group and gp is the image of p under the group action of g in G . It has been shown that any function I J (p ) which satisfies this relationship is invariant with respect to the group G.

In this work, we begin with the three-dimensional analog to the special Euclidean invariant kernel proposed in [13],

(,)(())r h p x B p x F J

which represents the indicator function of the intersection of a sphere B r (p ) of radius r centered at the point p with the volume enclosed by the surface ?. This kernel is invariant with respect to the special Euclidean group, which includes any rigid transformations of the data, such as translations or rotations. This gives rise to the corresponding integral invariant:

3

J )()(p B r dx p I

This integral represents the volume of intersection of a sphere of radius r , centered at point p , and volume enclosed by the surface J . This new kernel also remains invariant under special Euclidean group.

III.F EATURE E XTRACTION

The single parameter of this invariant is the radius of sphere of intersection, r . To obtain a complete representation of each point’s local region, it’s essential to perform this calculation for a variety of radii. However, performing this calculation over a scale-space will result in redundant

information, since each larger scale also contains the information from each smaller scale. In order to minimize the amount of redundant information contained by the local surface representation, it’s necessary to minimize the number of repeated calculations between scale levels. To this end, we redefine our kernel function h (p,x ) as:

[][1](,)(()))(()))()r k r k h p x B p x B p x F J F J

where r [k 1] < r [k ]. This results in an integral invariant equivalent to the volume of intersection of the surface J and a spherical shell with interior radius r [k -1] and exterior radius r [k ]:

[][1]()()()r k r k B p B p I p dx dx J J

J

3

3

This function is invariant under the special Euclidean group. As a discrete approximation to this integral, we used the middle Riemann sum of the intersection volume. For each point (x p ,y p ,z p ), we approximated this integral as:

||

|

z

y x p p p

z

y x p p p

p p p k r y y x x S z

z f k r y y x x S z

z f k r z y x I ,,,,]))

1[,,(,(]))[,,(,(])[,,,(J 2

121212212122(,)()0Az z z f z z A z z z z z z z !-°

d ?°d ˉ

222

222222

(,,)0

r x y x y r S x y r x y r - d ?

where A is the area of each point’s local region. In our experiments, the data was sampled uniformly over the x-y plane, so A was constant. Although better approximations are possible and an area for future research, this was sufficient for our implementation.

The local shape of a 3D surface can be represented by a feature vector that consists of I J for increasing values of r [k ]. The extent of k determines the dimension of the feature vector. Obviously, the feature dimension is dependent on particular type of 3D surfaces to be represented. Hence, it is appropriate to determine the feature dimension empirically based on the avaiable 3D surface data.

Specifically, a subset of 100 loosely registered face scans taken from face recognition grand challenge V.2.0 data set [18] are used as the training data. For each face scan, we choose a subsample of the facial surface points including the nose tip to compute the 3D features. At each point, we calculate I J for r [k ] varying from 4mm to 100mm at 2mm increments. We assume the feature vectors corresponding to the nose tip and those not at the nose tip will form two K dimensional probability distributions where K is the feature dimension.

We then compute the Mahalanobis distance similar to that used in Fisher’s linear discriminant analysis between these two distributions for each feature dimension K :

11212T

J m m S m m

where m 1 and m 2 respectively are the mean vectors of the nose and non-nose distribution, and S is the covariance matrix corresponding to the non-nose distribution. The larger this distance, the more likely the feature vector is able to discriminate nose tip from other regions on the facial surface. A plot of this distance as a function of the radii used for computing the 3D feature is depicted in Figure 1. In the current implementation, we chose to retain features of radii less than 60mm, because features greater than 60mm appear to

give little benefit, as shown in Fig. 1.

Figure 1: Class separability based on Mahalanobis distance for feature radii from 4mm to 100mm, averaged over a training set of 100 faces

IV.M ULTI -MODAL , MULTI -STAGE NOSE TIP DETECTION

Once the feature dimension is determined, a simple quadratic discriminant analysis linear classifier is applied to classify the feature vectors into one of two classes: nose tip versus non-nose tip. While there are more sophisticated classifiers that potentially would yield better performance, due to space limitation, we present only linear classification results in this paper.

Since the FRGC database facial range images also include hair and clothes, initial tests of the nose-tip detection algorithm yielded excessive false positive classifications. Fortunately, for each range image, the database also provides an aligned color 2D image. Therefore, we employ skin color segmentation to restrict the search within the skin-colored facial region. This is accomplished with a simple Bayesian lookup-table based skin color classifier on the chrominance components of the YCbCr color-space. This classifier outputs a map of the probability that each point on our surface is human skin.

Secondly, we used a more complex classifier using other readily recognizable points on the face. In developing this second classifier, we first determined the average uniqueness of each point on several of our training samples using the Mahalanobis distance from the mean of the face as a criterion.

This measurement is shown in Fig. 2. It became evident that the medial canthus (inside eye corner) is an easily classified point on the face using our 3-dimensional invariant, due largely to its strongly positive curvature and relative invariance to expression variation. Our second classifier uses the same local feature discussed earlier to detect the medial canthi, then computes the distance from each canthus to each nose candidate point. These distances are then classified using

a quadratic discriminant to give another probability map.

Figure 2: Average Mahalanobis distance of facial points from the mean, evaluated on 100 loosely-registered face scans with feature radii from 4mm to 100mm in increments of 2mm

After computing these three measurements, the integral invariant, the Bayesian color probability map, and the medial canthus distance probability map, the three are multiplied together to give the final conditional probability of each point

being a nose tip.

Figure 3: Comparison of our classifier and a baseline algorithm

V.E XPERIMENTAL R ESULTS

For our experiments, we used the FRGC 2.0 database of 4007 2.5D range scans and associated 2D color images of human faces. These scans, although all of the head region, are relatively unconstrained, in that many include hair and clothing as well as pose and expression variation. Of these range scans, 101 were used as a training set, and the algorithm

was evaluated on the remaining 3906. In this test, we used 10 radii for our algorithm, ranging from 4mm to 60mm. A small amount of preprocessing was performed on the data before the algorithm was applied, consisting of a median filter and removal of very large outlier points, as well as resampling on a 1mm square rectangular grid. For the sake of comparison, we also implemented an earlier algorithm proposed by Xu et al.[12], chosen for its thorough algorithm description. The parameters used for this algorithm were the same given in the paper.

Figure 3 summarizes our experimental results as a cumulative distribution of distances from the manually selected nose tip on each range image. As an illustration of the performance of each method, we examined the percentage of noses which were detected within 1cm of the manually selected position. Using only the integral invariant, 98.08% of these were detected to this tolerance. With skin color segmentation, this improved to 98.52%. Using the canthus distance further improved classification to 99.08%. Using all three classifiers slightly degraded performance to 99.03%, which we believe is due to overfitting. Our baseline method determined 45.3% of the noses to this degree of accuracy. This was due in part to a high number of false positives due to hair and clothing variations and early culling of important feature points, possibly due to the density of our range scans or a small amount of surface noise.

VI.C ONCLUSION &F UTURE W ORK

This paper presents a novel method for landmark detection in 2.5D facial range scans. This method is based on the Integral Invariants of Manay et al., but extends their algorithm to 3 spatial dimensions and performs classification over multiple scales. This strategy outperforms other common techniques in its resistance to false positives and its low space requirements.

The method presented in this work lends itself very well to some future algorithmic optimizations. First, a cascaded classifier seems a logical direction for this work, and would improve the efficiency of our feature extraction. Also, there are some implementation details that could benefit from future algorithm refinements; for example, at present, our feature extraction process iterates over each pixel in a local area multiple times for each point extracted. This should be possible in only a single pass, and is an area for future improvement.

Another area for improvement is the algorithm used for our discrete approximation to the integral invariant signature. Further extensions to this work could include the investigation of alternative methods for combining the classifier results and choosing feature radii. A final area of future research would be an unconstrained face detection problem on 2.5D data, although we know of no appropriate dataset yet in existence.

VII.R EFERENCES

[1] C. S. Chua and R. Jarvis, “Point Signatures: A New Representation for 3D Object Recognition,” International Journal of Computer Vision, vol. 25(1), pp. 63 – 85, October 1997 [2] G. Gordon, “Face recognition based on depth maps and surface curvature. Geometric Methods in Computer Vision,” SPIE vol. 1570, pp. 1–12, July 1991.

[3] J. C. Lee and E. Milios, “Matching range images of human faces,” In the Proceedings of Int’l Conf. on Comp. Vision, pp. 722–726, 1990.

[4] T. Nagamine, T. Uemura, and I. Masuda, “3D facial image analysis for human identification. International Conference on Pattern Recognition,” ICPR, pp. 324–327, 1992.

[5] H. T. Tanaka, M. Ikeda, and H. Chiaki, “Curvature-based face surface recognition using spherical correlation-principal directions for curved object recognition,” Third International Conference on Automated Face and Gesture Recognition, pp. 372–377, 1998.

[6] AB Moreno, á Sánchez, JF Vélez, and FJ Díaz, “Face recognition using 3D surface-extracted descriptors,” Irish Machine Vision and Image Processing Conference, September 2003.

[7] G. Medioni and R. Waupotitsch, “Face recognition and modeling in 3D,” IEEE Int’l Workshop on Analysis and Modeling of Faces and Gestures, pp. 232-233, October 2003.

[8] Y. Wang, C. Chua, and Y. Ho, “Facial feature detection and face recognition from 2D and 3D images,” Pattern Recognition Letters, vol 23, pp. 1191–1202, 2002.

[9] C.-S. Chua, F. Han, and Y.-K. Ho, “3-D human face recognition using point signature,” in Proc. 4th IEEE Int. Conf. Automatic Face Gesture Recognition, pp. 233-238, 2000.

[10] Andrew E. Johnson , Martial Hebert, “Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21(5), pp. 433-449, May 1999. [11] Y. Li and W.A.P. Smith, “Face Recognition using Patch-based Spin Images,” International Conference on Pattern Recognition, vol. 1(20-24) , pp. 408- 411, Aug. 2006

[12] C. Xu, Y. Wang, T. Tan, and L. Quan, “Robust nose detection in 3D facial data using local characteristics,” International Conference on Image Processing, vol. 3(24-27), pp. 1995- 1998, Oct. 2004.

[13] S. Manay and B.-W. Hong, “Integral Invariants For Shape Matching.,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28(10), pp. 1602-1618, Oct. 2006.

[14] M. Mortara, Gi. Patané, M. Spagnuolo, B. Falcidieno, J. Rossignac, “Blowing Bubbles for Multi-Scale Analysis and Decomposition of Triangle Meshes,” Algorithmica, vol. 38(1), pp. 227-248, October 2003.

[15] Y. Wang, G. Pan, Z. Wu, and S. Han, “Sphere-spin-image: A viewpoint-invariant surface representation for 3d face recognition,” in International Conference on Computational Science, pp. 427–434, 2004.

[16] Z. Wu, Y. Wang, and G. Pan, “3d face recognition using local shape map,” ICIP, pp. 2003–2006, 2004.

[17] D. Chetverikov, D. Svirko, D. Stepanov, and P. Krsek, “The Trimmed Iterative Closest Point algorithm,” in the proceedings of International Conference on Pattern Recognition. vol. 3(11-15), pp. 545-548, Aug. 2002. [18] P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, K. Jin Chang Hoffman, J. Marques, W. Jaesik Min Worek, “Overview of the face recognition grand challenge,” in the proceedings of IEEE Computer Society Conference on Compuer Vision and Pattern Recognition, vol. 1, pp. 947-954, June 2005.

[19] K.W. Bowyer, K. Chang, P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,” Computer Vision and Image Understanding, vol. 101(1), pp. 1-15, 2006.

to与for的用法和区别

to与for的用法和区别 一般情况下, to后面常接对象; for后面表示原因与目的为多。 Thank you for helping me. Thanks to all of you. to sb.表示对某人有直接影响比如,食物对某人好或者不好就用to; for表示从意义、价值等间接角度来说,例如对某人而言是重要的,就用for. for和to这两个介词,意义丰富,用法复杂。这里仅就它们主要用法进行比较。 1. 表示各种“目的” 1. What do you study English for? 你为什么要学英语? 2. She went to france for holiday. 她到法国度假去了。 3. These books are written for pupils. 这些书是为学生些的。 4. hope for the best, prepare for the worst. 作最好的打算,作最坏的准备。 2.对于 1.She has a liking for painting. 她爱好绘画。 2.She had a natural gift for teaching. 她对教学有天赋/ 3.表示赞成同情,用for不用to. 1. Are you for the idea or against it? 你是支持还是反对这个想法? 2. He expresses sympathy for the common people.. 他表现了对普通老百姓的同情。 3. I felt deeply sorry for my friend who was very ill. 4 for表示因为,由于(常有较活译法) 1 Thank you for coming. 谢谢你来。 2. France is famous for its wines. 法国因酒而出名。 5.当事人对某事的主观看法,对于(某人),对…来说(多和形容词连用)用介词to,不用for.. He said that money was not important to him. 他说钱对他并不重要。 To her it was rather unusual. 对她来说这是相当不寻常的。 They are cruel to animals. 他们对动物很残忍。 6.for和fit, good, bad, useful, suitable 等形容词连用,表示适宜,适合。 Some training will make them fit for the job. 经过一段训练,他们会胜任这项工作的。 Exercises are good for health. 锻炼有益于健康。 Smoking and drinking are bad for health. 抽烟喝酒对健康有害。 You are not suited for the kind of work you are doing. 7. for表示不定式逻辑上的主语,可以用在主语、表语、状语、定语中。 1.It would be best for you to write to him. 2.The simple thing is for him to resign at once. 3.There was nowhere else for me to go. 4.He opened a door and stood aside for her to pass.

江南营_江南深度研学之旅(1)

诗梦江南,入画寻踪 ——长清区实验小学江南深度研学实践之旅 【课程简介】 一道水,一架桥,一支橹声,隽秀婉约的聚合了太多的历史文化。此次研学活动旨在让同学们了解祖国江南,同时感受一场从远古传说,到春秋的吴越文化,到南北朝的文人风骨,再到明清以及近代的大儒伟人的历史盛宴。活动中,同学们将一起寻访王羲之、蔡元培、鲁迅、周恩来等名人伟人故里,穿越历史,冶爱国之志,体悟文化魅力;一起走进园,欣赏宋代江南私家园林的秀美景观,探寻园林蕴含的文化涵;一起游历西湖,领略“淡妆浓抹总相宜”的如画美景;一起走进综合性人文科学博物馆博物馆、中国黄酒博物馆,全面了解历史文化。 【课程特色】 ●文化名镇江南风采 ●穿越时空触摸历史 【行程简表】

上午探访安昌古镇漫游小桥流水梦回江南水乡游历江南小镇,画笔描绘 第五天 下午乘坐高铁前往:车次G60东-西 15:22-19:48辅导员送站一次相聚一生情谊备注:因天气交通等原因,组委会保留调整活动顺序及个别项目的权力,保证活动总量不变。 【活动费用】 2900/人;包含火车(往返高铁)及活动期间所有的费用。 ?【人文积淀-理性思维】·第一天下午·钱塘江·六和塔 钱塘江潮被誉为“天下第一潮”,是世界一大自然奇观,它是天体引力和地球自转的离心作用,加上湾喇叭口的特殊地形所造成的特大涌潮。六和塔位于省市西湖之南,钱塘江畔 月轮山上,是中国现存最完好的砖木结构古塔之一。 小任务1:学生面对浩渺的钱塘江,接受审美教育,并结合手册提示,探究钱塘江大潮的在科学原理; 小任务2:学生走进六和塔,收集关于六和塔的传说故事,留下自己与六和塔最美的合照; ?【审美情趣-人文积淀】·第二天上午·西湖·省博物馆 西湖,是一首诗,一幅天然图画,一个美丽动人的故事,不论是多年居住在这里的人还是匆匆而过的旅人,无不为这天下无双的美景所倾倒。平湖秋月、断桥残雪、柳浪闻莺、花 港观鱼、雷峰夕照、双峰插云、南屏晚钟、三潭印月,西湖十景个擅其胜。省博物馆是省规 模最大的综合性人文科学博物馆,文物品类丰富,年代序列完整。 小任务1:集体创绘,全体学生齐动手,集体协作,面对美景,协作创作最美的西湖; 小任务2:走进博物馆,寻访国宝,找一找最能代表江南文化的文物,向小组同学分享并交流;

延时子程序计算方法

学习MCS-51单片机,如果用软件延时实现时钟,会接触到如下形式的延时子程序:delay:mov R5,#data1 d1:mov R6,#data2 d2:mov R7,#data3 d3:djnz R7,d3 djnz R6,d2 djnz R5,d1 Ret 其精确延时时间公式:t=(2*R5*R6*R7+3*R5*R6+3*R5+3)*T (“*”表示乘法,T表示一个机器周期的时间)近似延时时间公式:t=2*R5*R6*R7 *T 假如data1,data2,data3分别为50,40,248,并假定单片机晶振为12M,一个机器周期为10-6S,则10分钟后,时钟超前量超过1.11秒,24小时后时钟超前159.876秒(约2分40秒)。这都是data1,data2,data3三个数字造成的,精度比较差,建议C描述。

上表中e=-1的行(共11行)满足(2*R5*R6*R7+3*R5*R6+3*R5+3)=999,999 e=1的行(共2行)满足(2*R5*R6*R7+3*R5*R6+3*R5+3)=1,000,001 假如单片机晶振为12M,一个机器周期为10-6S,若要得到精确的延时一秒的子程序,则可以在之程序的Ret返回指令之前加一个机器周期为1的指令(比如nop指令), data1,data2,data3选择e=-1的行。比如选择第一个e=-1行,则精确的延时一秒的子程序可以写成: delay:mov R5,#167 d1:mov R6,#171 d2:mov R7,#16 d3:djnz R7,d3 djnz R6,d2

djnz R5,d1 nop ;注意不要遗漏这一句 Ret 附: #include"iostReam.h" #include"math.h" int x=1,y=1,z=1,a,b,c,d,e(999989),f(0),g(0),i,j,k; void main() { foR(i=1;i<255;i++) { foR(j=1;j<255;j++) { foR(k=1;k<255;k++) { d=x*y*z*2+3*x*y+3*x+3-1000000; if(d==-1) { e=d;a=x;b=y;c=z; f++; cout<<"e="<

常用介词用法(for to with of)

For的用法 1. 表示“当作、作为”。如: I like some bread and milk for breakfast. 我喜欢把面包和牛奶作为早餐。 What will we have for supper? 我们晚餐吃什么? 2. 表示理由或原因,意为“因为、由于”。如: Thank you for helping me with my English. 谢谢你帮我学习英语。 3. 表示动作的对象或接受者,意为“给……”、“对…… (而言)”。如: Let me pick it up for you. 让我为你捡起来。 Watching TV too much is bad for your health. 看电视太多有害于你的健康。 4. 表示时间、距离,意为“计、达”。如: I usually do the running for an hour in the morning. 我早晨通常跑步一小时。 We will stay there for two days. 我们将在那里逗留两天。 5. 表示去向、目的,意为“向、往、取、买”等。如: Let’s go for a walk. 我们出去散步吧。 I came here for my schoolbag.我来这儿取书包。 I paid twenty yuan for the dictionary. 我花了20元买这本词典。 6. 表示所属关系或用途,意为“为、适于……的”。如: It’s time for school. 到上学的时间了。 Here is a letter for you. 这儿有你的一封信。 7. 表示“支持、赞成”。如: Are you for this plan or against it? 你是支持还是反对这个计划? 8. 用于一些固定搭配中。如: Who are you waiting for? 你在等谁? For example, Mr Green is a kind teacher. 比如,格林先生是一位心地善良的老师。 尽管for 的用法较多,但记住常用的几个就可以了。 to的用法: 一:表示相对,针对 be strange (common, new, familiar, peculiar) to This injection will make you immune to infection. 二:表示对比,比较 1:以-ior结尾的形容词,后接介词to表示比较,如:superior ,inferior,prior,senior,junior 2: 一些本身就含有比较或比拟意思的形容词,如equal,similar,equivalent,analogous A is similar to B in many ways.

研学方案

“研学旅行”实施方案 一、项目实施背景 从2013年发布《国民休闲旅游纲要》到2016年的《关于推进中小学生研学旅行的意见》,国家教育部等多部门发文要求大力推进研学旅行。研学旅行有利于促进学生培育和践行社会主义核心价值观,激发学生对党、对国家、对人民的热爱之情;有利于推动全面实施素质教育,创新人才培养模式,引导学生主动适应社会,促进书本知识和生活经验的深度融合;有利于加快提高人民生活质量,满足学生日益增长的旅游需求,从小培养学生文明旅游意识,养成文明旅游行为习惯。近年来,各地积极探索开展研学旅行,部分试点地区取得显著成效,在促进学生健康成长和全面发展等方面发挥了重要作用。二、定位与宗旨 目前大多数研学旅行还处在研究开发状态,良莠不齐,市场认可度不够,家长热度不高(尤其省内)。这是我们的机遇,也是挑战,我们的定位是要打造出一个学校认可、家长认可、学生认可的研学品牌,让学生在研学中学到东西。 三、具体实施 (一)方案A:纯旅游研学 本方案以若干旅游景点为研学地点,前期采取跟旅行社合作的方式(合作方式有待探讨),研学的核心(课件+“内容”)内容采取跟大学历史系或者旅游系的老师合作。 该方案的优点:该方案采用跟旅行社合作,研学路线可以借用

旅行社的优势,资源充分整合,老师和家长的路线选择多,可以极大丰富学生的课外知识,并且可以开展夏令营和冬令营活动。缺点是要综合考虑各个年龄段的学生,路线过多,会导致前期工作准备不够充足。 方案细节初步安排如下: 1、前期工作(3月20日-3月30日): (1)与某个旅行社达成合作关系(目前有合作意向的有康辉旅行社); (2)与某个大学的历史或者旅游系老师达成合作关系,负责研学核心内容的开发,包括路线的选择和内容的开发 (3)完成计划的策划和确定具体实施细节。 2、中期工作(4月1日-5月30日) (1)4月1日-4月15日与旅行社和老师确定最终的研学路线; (2)4月15日-5月30日一个半月的时间根据最终具体的研学路线,来做具体的研学课件和研学内容,研究出研学到底应该让学生学到什么,怎么保证学生能学到这些; (3)同时根据最终确定的研学方案做好定价方案,在这个过程中要充分进行调研,进学校、访家长,做到收费合理; (4)根据做好的方案做好线上推广,把做好的资料全部上传到线上,可以参考北京世纪明德。

单片机C延时时间怎样计算

C程序中可使用不同类型的变量来进行延时设计。经实验测试,使用unsigned char类型具有比unsigned int更优化的代码,在使用时 应该使用unsigned char作为延时变量。以某晶振为12MHz的单片 机为例,晶振为12M H z即一个机器周期为1u s。一. 500ms延时子程序 程序: void delay500ms(void) { unsigned char i,j,k; for(i=15;i>0;i--) for(j=202;j>0;j--) for(k=81;k>0;k--); } 计算分析: 程序共有三层循环 一层循环n:R5*2 = 81*2 = 162us DJNZ 2us 二层循环m:R6*(n+3) = 202*165 = 33330us DJNZ 2us + R5赋值 1us = 3us 三层循环: R7*(m+3) = 15*33333 = 499995us DJNZ 2us + R6赋值 1us = 3us

循环外: 5us 子程序调用 2us + 子程序返回2us + R7赋值 1us = 5us 延时总时间 = 三层循环 + 循环外 = 499995+5 = 500000us =500ms 计算公式:延时时间=[(2*R5+3)*R6+3]*R7+5 二. 200ms延时子程序 程序: void delay200ms(void) { unsigned char i,j,k; for(i=5;i>0;i--) for(j=132;j>0;j--) for(k=150;k>0;k--); } 三. 10ms延时子程序 程序: void delay10ms(void) { unsigned char i,j,k; for(i=5;i>0;i--) for(j=4;j>0;j--) for(k=248;k>0;k--);

中职生网络安全教育教案

中职生网络安全教育教案 15406 一、教学目的 为了贯彻落实国家信息部安全使用网络的倡议,继续进一步搞好我校的网络使用安全教育,减少因网络的使用而带来的负面影响,杜绝网络潜在危险。 二、重点内容 1、加强网络使用安全知识的学习,加强自身使用网络的遵守意识。 2、组织各种安全教育活动,增强遵守网络使用安全的意识。 三、教育内容 1、导入语 当我们步入网络社会,发现青年与网络之间存在众多的契合点,正是这些契合点使青年对互联网“一网情深”。青年在网络影响向社会展示了其众多绚丽之处,令人倍感惊喜。但不少青年网民的失色表现却不能给社会增辉。 2、网络的影响 ①网络的正面影响 1)利用网络进行德育教育工作,教育者可以以网友的身份和青少年在网上“毫无顾忌”地进行真实心态的平等交流,这对于德育工作者摸清、摸准青少年的思想并开展正面引导和全方位沟通提供了新的快捷的方法。此外,网络信息具有可下载性、可储存性等延时性特点,网络信息的传播具有实时性和交互性的特点。 2)提供了求知学习的新渠道。目前在我国教育资源不能满足需求的情况下,网络提供了求知学习的广阔校园,学习者在任何时间、任何地点都能接受高等教育。这对于处在应试教育体制下的青少年来说无疑是一种最好的解脱,它不但有利于其身心的健康发展,而且有利于家庭乃至社会的稳定。 3)开拓青少年全球视野,提高青少年综合素质。上网使青少年的政治视野、知识范畴更加开阔,可以培养他们和各式各样的人交流的能力;通过在网上阅览各类有益图书,触类旁通,提高自身文化素养。 ②网络的负面影响

1)对于青少年“三观”形成构成潜在威胁。青少年很容易在网络上接触到资本主义的宣传论调、文化思想等,思想处于极度矛盾、混乱中,其人生观、价值观极易发生倾斜。 2)网络改变了青少年在学习和生活中的人际关系及生活方式。同时,上网使青少年容易形成一种以自我为中心的生存方式,集体意识淡薄,个人自由主义思潮泛滥。 3)信息垃圾弱化青少年思想道德意识,污染青少年心灵,误导青少年行为。 4)网络的隐蔽性导致青少年不道德行为和违法犯罪行为增多。 3、充分认识网络发展中的“青年问题”,积极寻求对策 1)充分认识网上思想渗透问题,强化对青少年的教育引导。必须加强对青少年 的思想政治教育。 2)切实加强网上文明行为规范的建设。广泛开展以宣传《青少年网络文明公约》为主题的各项活动,积极引导青少年遵守网络道德。 3)构建网络和社会互动的青少年教育体系。着重加强对青少年的社会化教育,提高青少年适应现代社会的能力,使他们勇敢地直面现实世界,积极投入到改造社会的实践中去。 4)开辟更多的更好的青年网站,积极占领网络阵地。加强青少年教育软件的开发制作,利用法律和技术上的可行性打击网上违法犯罪现象,走“以法治网”的良性发展轨道。 2017.3.2

for和to区别

1.表示各种“目的”,用for (1)What do you study English for 你为什么要学英语? (2)went to france for holiday. 她到法国度假去了。 (3)These books are written for pupils. 这些书是为学生些的。 (4)hope for the best, prepare for the worst. 作最好的打算,作最坏的准备。 2.“对于”用for (1)She has a liking for painting. 她爱好绘画。 (2)She had a natural gift for teaching. 她对教学有天赋/ 3.表示“赞成、同情”,用for (1)Are you for the idea or against it 你是支持还是反对这个想法? (2)He expresses sympathy for the common people.. 他表现了对普通老百姓的同情。 (3)I felt deeply sorry for my friend who was very ill. 4. 表示“因为,由于”(常有较活译法),用for (1)Thank you for coming. 谢谢你来。

(2)France is famous for its wines. 法国因酒而出名。 5.当事人对某事的主观看法,“对于(某人),对…来说”,(多和形容词连用),用介词to,不用for. (1)He said that money was not important to him. 他说钱对他并不重要。 (2)To her it was rather unusual. 对她来说这是相当不寻常的。 (3)They are cruel to animals. 他们对动物很残忍。 6.和fit, good, bad, useful, suitable 等形容词连用,表示“适宜,适合”,用for。(1)Some training will make them fit for the job. 经过一段训练,他们会胜任这项工作的。 (2)Exercises are good for health. 锻炼有益于健康。 (3)Smoking and drinking are bad for health. 抽烟喝酒对健康有害。 (4)You are not suited for the kind of work you are doing. 7. 表示不定式逻辑上的主语,可以用在主语、表语、状语、定语中。 (1)It would be best for you to write to him. (2) The simple thing is for him to resign at once.

江南营江南深度研学之旅1

江南营-江南深度研学之旅(1)

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诗梦江南,入画寻踪 ——长清区实验小学江南深度研学实践 之旅 【课程简介】 一道水,一架桥,一支橹声,隽秀婉约的杭州绍兴聚合了太多的历史文化。此次研学活动旨在让同学们了解祖国江南,同时感受一场从远古传说,到春秋的吴越文化,到南北朝的文人风骨,再到明清以及近代的大儒伟人的历史盛宴。活动中,同学们将一起寻访王羲之、蔡元培、鲁迅、周恩来等名人伟人故里,穿越历史,陶冶爱国之志,体悟文化魅力;一起走进沈园,欣赏宋代江南私家园林的秀美景观,探寻园林蕴含的文化内涵;一起游历西湖,领略“淡妆浓抹总相宜”的如画美景;一起走进综合性人文科学博物馆浙江博物馆、中国黄酒博物馆,全面了解浙江历史文化。 【课程特色】 ●文化名镇江南风采 ●穿越时空触摸历史 【行程简表】 时间课程安排课程主题课程链接 第一天上午乘坐高铁前往杭州:车次G63 济南-杭州东 07:23-11:53辅导员接站读万卷书行万里路下午参观钱塘江、六和塔看天下第一潮登镇潮六和塔追寻江畔的历史故事 晚上研学课程指导分组讨论课程,研学收获分享 实践-辅导员指导学生完成课程手 册 第二天上午 游历杭州西湖置身如画美景感受西湖柔情参观苏堤、孤山、曲院风荷 浙江博物馆参观历史展品考察浙江文化感受历史文化的沉淀 下午灵隐寺、飞来峰登山览胜景寺宇悟佛心登山参观庙宇,了解佛教文化 晚上研学课程指导分组讨论课程,研学收获分享实践-辅导员指导学生完成课程手册 第三天上午探访鲁迅故里探寻书中世界亲访三味书屋追寻鲁迅先生的足迹 下午 游览沈园漫步江南园林,探寻文化内涵 人文-体味江南风情/建筑-江南园林建 筑风格 参观黄酒博物馆参观历史文物体悟江南魅力历史-绍兴历史文化 晚上 大善塔 仓桥直街 漫步古城小道欣赏绍兴夜色实践-实地感受,见景抒情 第四天上午书圣故里历史街区历游文人旧地感受文化魅力人文-文人旧所、大家荟萃

51单片机延时时间计算和延时程序设计

一、关于单片机周期的几个概念 ●时钟周期 时钟周期也称为振荡周期,定义为时钟脉冲的倒数(可以这样来理解,时钟周期就是单片机外接晶振的倒数,例如12MHz的晶振,它的时间周期就是1/12 us),是计算机中最基本的、最小的时间单位。 在一个时钟周期内,CPU仅完成一个最基本的动作。 ●机器周期 完成一个基本操作所需要的时间称为机器周期。 以51为例,晶振12M,时钟周期(晶振周期)就是(1/12)μs,一个机器周期包 执行一条指令所需要的时间,一般由若干个机器周期组成。指令不同,所需的机器周期也不同。 对于一些简单的的单字节指令,在取指令周期中,指令取出到指令寄存器后,立即译码执行,不再需要其它的机器周期。对于一些比较复杂的指令,例如转移指令、乘法指令,则需要两个或者两个以上的机器周期。 1.指令含义 DJNZ:减1条件转移指令 这是一组把减1与条件转移两种功能结合在一起的指令,共2条。 DJNZ Rn,rel ;Rn←(Rn)-1 ;若(Rn)=0,则PC←(PC)+2 ;顺序执行 ;若(Rn)≠0,则PC←(PC)+2+rel,转移到rel所在位置DJNZ direct,rel ;direct←(direct)-1 ;若(direct)= 0,则PC←(PC)+3;顺序执行 ;若(direct)≠0,则PC←(PC)+3+rel,转移到rel 所在位置 2.DJNZ Rn,rel指令详解 例:

MOV R7,#5 DEL:DJNZ R7,DEL; rel在本例中指标号DEL 1.单层循环 由上例可知,当Rn赋值为几,循环就执行几次,上例执行5次,因此本例执行的机器周期个数=1(MOV R7,#5)+2(DJNZ R7,DEL)×5=11,以12MHz的晶振为例,执行时间(延时时间)=机器周期个数×1μs=11μs,当设定立即数为0时,循环程序最多执行256次,即延时时间最多256μs。 2.双层循环 1)格式: DELL:MOV R7,#bb DELL1:MOV R6,#aa DELL2:DJNZ R6,DELL2; rel在本句中指标号DELL2 DJNZ R7,DELL1; rel在本句中指标号DELL1 注意:循环的格式,写错很容易变成死循环,格式中的Rn和标号可随意指定。 2)执行过程

双宾语 to for的用法

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