Detecting Moving Shadows Algorithms and Evaluation

Detecting Moving Shadows:

Algorithms and Evaluation

Andrea Prati,Member,IEEE,

Ivana Mikic,Member,IEEE,

Mohan M.Trivedi,Member,IEEE,and

Rita Cucchiara,Member,IEEE

Abstract—Moving shadows need careful consideration in the development of robust dynamic scene analysis systems.Moving shadow detection is critical for accurate object detection in video streams since shadow points are often misclassified as object points,causing errors in segmentation and tracking.Many algorithms have been proposed in the literature that deal with shadows.However, a comparative evaluation of the existing approaches is still lacking.In this paper, we present a comprehensive survey of moving shadow detection approaches.We organize contributions reported in the literature in four classes two of them are statistical and two are deterministic.We also present a comparative empirical evaluation of representative algorithms selected from these four classes.Novel quantitative(detection and discrimination rate)and qualitative metrics(scene and object independence,flexibility to shadow situations,and robustness to noise)are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences.These video sequences and associated“ground-truth”data are made available at https://www.wendangku.net/doc/3415263723.html,/aton/shadow to allow for others in the community to experiment with new algorithms and metrics.

Index Terms—Shadow detection,performance evaluation,object detection, segmentation,traffic scene analysis,visual surveillance.

?

1I NTRODUCTION

D ETECTION and tracking of moving objects is at the core of many applications dealing with image sequences.One of the main challenges in these applications is identifying shadows which objects cast and which move along with them in the scene. Shadows cause serious problems while segmenting and extracting moving objects due to the misclassification of shadow points as foreground.Shadows can cause object merging,object shape distortion,and even object losses(due to the shadow cast over another object).The difficulties associated with shadow detection arise since shadows and objects share two important visual features.First,shadow points are detectable as foreground points since they typically differ significantly from the background. Second,shadows have the same motion as the objects casting them. For this reason,the shadow identification is critical both for still images and for image sequences(video)and has become an active research area,especially in the recent past.It should be noted that, while the main concepts utilized for shadow analysis in still and video images are similar,typically,the purpose behind shadow extraction is somewhat different.In the case of still images, shadows are often analyzed and exploited to infer geometric properties of the objects causing the shadow(“shape from shadow”approaches)as well as to enhance object localization and measurements.Examples of this can be found in aerial image analysis for recognizing buildings[1],[2],for obtaining 3D reconstruction of the scene[3],or even for detecting clouds and their shadows[4].Another important application domain for shadow detection in still images is for the3D analysis of objects to extract surface orientations[5]and light source direction[6].

Shadow analysis,considered in the context of video data,is typically performed for enhancing the quality of segmentation results instead of deducing some imaging or object parameters.In the literature,shadow detection algorithms are normally associated with techniques for moving object segmentation.In this paper,we present a comprehensive survey of moving shadow detection approaches.We organize contributions reported in the literature in four classes and present a comparative empirical evaluation of representative algorithms selected from these four classes.This comparison takes into account both the advantages and the drawbacks of each proposal and provides a quantitative and qualitative evaluation of them.Novel quantitative(detection and discrimination rate)and qualitative metrics(scene and object independence,flexibility to shadow situations,and robustness to noise)are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences.These video sequences and associated“ground-truth”data are made available at https://www.wendangku.net/doc/3415263723.html,/aton/shadow to allow for others in the community to experiment with new algorithms and metrics. This availability follows the idea of data-sharing embodied in Call for Comparison,like the project of European COST211Group(see http://www.iva.cs.tut.fi/COST211/for further details).

In the next section,we develop a two layer taxonomy for surveying various algorithms presented in the literature.Each approach class is detailed and discussed to emphasize its strengths and its limitations.In Section3,we develop a set of evaluation metrics to compare the shadow detection algorithms.This is followed by Section4,where we present a results of empirical evaluation of four selected algorithms on a set of five video sequences.The final section presents concluding remarks.

2T AXONOMY OF S HADOW D ETECTION A LGORITHMS Most of the proposed approaches take into account the shadow model described in[7].To account for their differences,we have organized the various algorithms in a two-layer taxonomy.The first layer classification considers whether the decision process introduces and exploits uncertainty.Deterministic approaches use an on/off decision process,whereas statistical approaches use prob-abilistic functions to describe the class membership.Introducing uncertainty to the class membership assignment can reduce noise sensitivity.In the statistical methods(see[8],[9],[10],[11],[12]), the parameter selection is a critical issue.Thus,we further divide the statistical approaches in parametric and nonparametric methods. The study reported in[8]is an example of the parametric approach,whereas[10],[11]are examples of the nonparametric approach.The deterministic class(see[6],[7],[13],[14])can be further subdivided.Subclassification can be based on whether the on/off decision can be supported by model-based knowledge or not.Choosing a model-based approach undoubtedly achieves the best results,but is,most of the time,too complex and time consuming compared to the nonmodel-based.Moreover,the number and the complexity of the models increase rapidly if the aim is to deal with complex and cluttered environments with different lighting conditions,object classes,and perspective views.

It is also important to recognize the types of“features”utilized for shadow detection.Basically,these features are extracted from three domains:spectral,spatial,and temporal.Approaches can exploit differently spectral features,i.e.,using gray level or color

. A.Prati and R.Cucchiara are with the Dipartimento di Ingegneria dell’Informazione,Universita`di Modena e Reggio Emilia,Via Vignolese, 905/b,Modena,Italy.E-mail:{prati.andrea,cucchiara.rita}@unimore.it. .I.Mikic is with Q3DM Inc.,10110Sorrento Valley Road,Suite B,San Diego,CA92121.E-mail:imikic@https://www.wendangku.net/doc/3415263723.html,.

.M.M.Trivedi is with the Computer Vision and Robotics Research Laboratory,Department of Electrical and Computer Engineering,Uni-versity of California,San Diego,9500Gilman Drive,La Jolla,CA92037.

E-mail:trivedi@https://www.wendangku.net/doc/3415263723.html,.

Manuscript received7June2001;revised19Aug.2002;accepted18Jan. 2003.

Recommended for acceptance by P.Anandan.

For information on obtaining reprints of this article,please send e-mail to: tpami@https://www.wendangku.net/doc/3415263723.html,,and reference IEEECS Log Number114319.

information.Some approaches improve results by using spatial information working at a region level or at a frame level instead of pixel level.This is a classification similar to that used in[15]for the background maintenance algorithms.Finally,some methods exploit temporal redundancy to integrate and improve results.

In Table1,we have classified21papers dealing with shadow detection in four classes.We highlight spectral,spatial,and temporal features used by these algorithms.In this paper,we focus our attention on four algorithms(reported in bold in Table1) representative of three of the above-mentioned classes.For the statistical parametric class,we choose the algorithm proposed in[8] since this utilizes features from all three domains.The approach reported in[11]can be considered to be a very good representative of the statistical nonparametric class and is also cited and used in [17].Within the deterministic nonmodel-based class,we choose to compare the algorithm described in[13]because it is the only one that uses HSV color space for shadow detection.Finally,the algorithm reported in[7]has been selected for its unique capability of coping with penumbra.The deterministic model-based class has not been considered due to its complexity and due to its reliance on very specific task domain assumptions.For instance,the approach used in[14]models shadows using a simple illumination model: Assuming parallel incoming light,they compute the projection of the3D object model onto the ground,exploiting two parameters for the illumination direction set offline and assumed to be constant during the entire sequence.However,in an outdoor scene,the projection of the shadow is unlikely to be perspective since the light source cannot be assumed to be a point light source.Therefore,the need for object models and the illumination position’s manual setting make this approach difficult to implement in a general-purpose framework.

In the next sections,we describe briefly the selected ap-proaches.For more details,refer to the corresponding papers or see the detailed description that we reported in[27].

2.1Statistical Nonparametric(SNP)Approach

As an example of the statistical nonparametric(SNP)approach,we choose the one described in[28],and detailed in[11].This work considers the color constancy ability of human eyes and exploits the Lambertian hypothesis to consider color as a product of irradiance and reflectance.The distortion of the brightness i and the distortion of the chrominance CD i of the difference between the expected color of a pixel and its value in the current image are computed and normalized with regard to their root mean square of pixel i.The values b i and d

CD i obtained are used to classify a pixel in four categories:

CeiT?

F oreground:d

CD i>(CD or b i<( lo;else

Background:b i<( 1and b i>( 2;else Shadowed backg::b i<0;else

Highlighted backg::otherwise

8

>><

>>:

e1TThe rationale used is that shadows have similar chromaticity, but lower brightness than the background model.A statistical learning procedure is used to automatically determine the appropriate thresholds.

2.2Statistical Parametric(SP)Approach

The algorithm described in[8]for traffic scene shadow detection is an example of statistical parametric(SP)approach. This algorithm claims to use two sources of information:local (based on the appearance of the pixel)and spatial(based on the assumption that the objects and the shadows are compact regions).The a posteriori probabilities of belonging to back-ground,foreground,and shadow classes are maximized.The a priori probabilities of a pixel belonging to shadow are computed by assuming that v??R;G;B T is the value of the pixel not shadowed and by using an approximated linear transformation"v?Dv(where D?diaged R;d G;d BTis a diag-onal matrix obtained by experimental evaluation)to estimate the color of the point covered by a shadow.The D matrix is assumed approximately constant over flat surfaces.If the background is not flat over the entire image,different D matrices must be computed for each flat subregion.The spatial information is exploited by performing an iterative probabilistic relaxation to propagate neighborhood information.In this statistical parametric approach,the main drawback is the difficult process necessary to select the parameters.Manual segmentation of a certain number of frames has to be done to collect statistics and to compute the values of matrix D.An

TABLE1

Classification of the Literature on Shadow Detection

(G=Gray-Level,C=Color,L=Local/Pixel-Level,R=Region-Level,F=Frame-Level,S=Static,and D=Dynamic.)

1This paper considers only still images.

2This paper is not properly a deterministic model approach.It uses an innovative approach based on inverse perspective mapping in which the assumption is that the shadow and the object that casts it are overlapped if projected on the ground plane.Since a model of the scene is necessary,we classify this paper in this class.

3This paper has the unique characteristic of using the DCT to remove shadow.For this reason,we can say that this paper works on frequency-level.The rationale used by the authors is that a shadow has,in the frequency domain,a large DC component,whereas the moving object has a large AC component.

4Since this paper uses a fuzzy neural network to classify points as belonging or not to a shadow,it can be considered a statistical approach.However,how much the parameter setting is automated is not clear in this paper.

expectation maximization(EM)approach could be used to automate this process,as in[12].

2.3Deterministic Nonmodel-Based(DNM1)Approach The system described in[13]is an example of the deterministic nonmodel-based approach(and we call it DNM1).This algorithm works in the HSV color space.The main reasons are that the HSV color space corresponds closely to the human perception of color [29]and it has revealed more accuracy in distinguishing shadows. In fact,a shadow cast on a background does not change its hue significantly[30].Moreover,the authors exploit saturation in-formation since they note that shadows often lower the saturation of the points.The resulting decision process is reported in the following equation:

SP kex;yT?

1if I V kex;yT

V

k

^eI S kex;yTàB S kex;yTT(S^j I H kex;yTàB H kex;yTj(H

0otherwise;

8

><

>:

e2T

where I kex;yTand B kex;yTare the pixel values at coordinateex;yTin the input image(frame k)and in the background model (computed at frame k),respectively.The use of prevents the identification as shadows those points where the background was slightly changed by noise,whereas takes into account the “power”of the shadow,i.e.,how strong the light source is with regard to the reflectance and irradiance of the objects.Thus,the stronger and higher the sun(in the outdoor scenes),the lower should be chosen.

2.4Deterministic Nonmodel-Based(DNM2)Approach Finally,we compare the approach presented in[7].This is also a deterministic nonmodel-based approach,but we have included it because of its completeness(it is the only work in the literature that deals with penumbra in moving cast shadows).The shadow detection is provided by verifying three criteria:the presence of a ”darker”uniform region,by assuming that the ratio between the actual value and reference value of a pixel is locally constant in presence of cast shadows,the presence of a high difference in luminance with regard to reference frame,and the presence of static and moving edges.Static edges hint at a static background and can be exploited to detect nonmoving regions inside the frame difference.Moreover,to detect penumbra,the authors propose computing the width of each edge in the difference image.Since penumbra cause a soft luminance step at the contour of a shadow, they claim that the edge width is the more reliable way to distinguish between objects contours and shadows contours (characterized by a width greater than a threshold).

This approach is one of the most complete and robust proposed in the literature.Nevertheless,in this case,the assumptions and the corresponding approximations introduced are strong and they could lack in generality.Also,the penumbra criterion is not explicitly exploited to add penumbra points as shadow points,but it is only used to remove the points that do not fit this criterion. Moreover,the proposed algorithm uses the previous frame (instead of the background)as a reference frame.This choice exhibits some limitations in moving region detection since it is influenced by object speed and it is too noise sensitive.Thus,to make the comparison of these approaches as fair as possible, limited to the shadow detection part of the system,we implemen-ted the DNM2approach using a background image as a reference, as the other three approaches do.3P ERFORMANCE E VALUATION M ETRICS

In this section,the methodology used to compare the four approaches is presented.In order to systematically evaluate various shadow detectors,it is useful to identify the following two important quality measures:good detection(low probability of misclassifying a shadow point)and good discrimination(the probability of classifying nonshadow points as shadow should be low,i.e.,low false alarm rate).The first one corresponds to minimizing the false negatives(FN),i.e.,the shadow points classified as background/foreground,while,for good discrimina-tion,the false positives(FP),i.e.,the foreground/background points detected as shadows,are minimized.

A reliable and objective way to evaluate this type of visual-based detection is still lacking in the literature.A very good work on how to evaluate objectively the segmentation masks in video sequences is presented in[31].The authors proposed a metric based on spatial accuracy and temporal stability that aims at evaluating information differently than the FPs and FNs,depend-ing on their distance from the borders of the mask,and at taking into account the shifting(instability)of the mask along the time.In [22],the authors proposed two metrics for moving object detection evaluation:the Detection Rate(DR)and the False Alarm Rate(FAR). Assuming T P as the number of true positives(i.e.,the shadow points correctly identified),these two metrics are defined as follows:

DR?

T P

T PtF N

;F AR?

F P

T PtF P

:e3TThe Detection Rate is often called true positive rate or also recall in the classification literature and the FAR corresponds to1àp, where p is the so-called precision in the classification theory.These figures are not selective enough for shadow detection evaluation since they do not take into account whether a point detected as shadow belongs to a foreground object or to the background.If shadow detection is used to improve moving object detection,only the first case is problematic since false positives belonging to the background do not affect neither the object detection nor the object shape.

To account for this,we have modified the metrics of(3), defining the shadow detection rate and the shadow discrimination rate$as follows:

?

T P S

T P StF N S

;$?

TP F

T P FtF N F

;e4T

where the subscript S stands for shadow and F for foreground.The T P F is the number of ground-truth points of the foreground objects minus the number of points detected as shadows,but belonging to foreground objects.

In addition to the above quantitative metrics,we also consider the following qualitative measures in our evaluation:robustness to noise,flexibility to shadow strength,width and shape,object indepen-dence,scene independence,computational load,and detection of indirect cast shadows and penumbra.Indirect cast shadows are the shadows cast by a moving object over another moving object and their effect is to decrease the intensity of the moving object covered,probably affecting the object detection,but not the shadow detection.

4E MPIRICAL C OMPARATIVE E VALUATION

In this section,the experimental results and the quantitative and qualitative comparison of the four approaches are presented.First, a set of sequences to test the algorithms was chosen to form a complete and nontrivial benchmark suite.We select the sequences reported in Table2,where both indoor and outdoor sequences are present,where shadows range from dark and small to light and large,and where the object type,size,and speed vary considerably.

The Highway I and the Highway II sequences show a traffic environment (at two different lighting conditions),where the shadow suppression is very important to avoid misclassification and erroneous counting of vehicles on the road.The Campus sequence is a noisy sequence from an outdoor campus site where cars approach an entrance barrier and students are walking around.The two indoor sequences report two laboratory rooms in two different perspectives and lighting conditions.In the Laboratory sequence,besides walking people,a chair is moved in order to detect its shadow.

4.1Quantitative Comparison

To compute the evaluation metrics described in Section 3,the ground truth for each frame is necessary.We obtained it by segmenting the images with an accurate manual classification of points in foreground,background,and shadow regions.We prepared ground truth on tens of frames for each video sequence representative of different situations (dark/light objects,multiple objects or single object,occlusions or not).

All four approaches,but the DNM2,have been faithfully and completely implemented.In the case of DNM2,some simplifica-tions have been introduced:The memory MEM used in [7]to avoid infinite error propagation in the change detection masks (CDMs)has not been implemented since it is computationally very heavy and not necessary (in the sequences considered there is no error propagation);some minor tricks (like that of the closure of small edge fragments)have not been included due to the lack of details in the paper.However,these missing parts of the algorithm do not influence shadow detection at all.In conclusion,the comparison has been set up as fairly as possible.

Results are reported in Table 3.To establish a fair comparison,algorithms do not implement any background updating process (since each tested algorithm proposes a different approach).

Instead,we compute the reference image and other parameters from the first N frames (with N varying with the sequence considered).The first N frames can be considered as the training set and the remaining frames as the testing set for our experimental framework.Note that the calculated parameters remain constant for the whole sequence.The visual results on a subset of the Intelligent Room and of the Highway I sequences are available at https://www.wendangku.net/doc/3415263723.html,/aton/shadow.Fig.1shows an example of visual results from the indoor sequence Intelligent Room .

The SNP algorithm is very effective in most of the cases,but with very variable performances.It achieves the best detection performance and high discrimination rate $in the indoor sequence Laboratory ,with percentages up to 92percent.However,the discrimination rate is quite low in the Highway I and Campus sequences.This can be explained by the dark (similar to shadows)appearance of objects in the Highway I sequence and by the strong noise component in the Campus sequence.

The SP approach achieves good discrimination rate in most of the cases.Nevertheless,its detection rate is relatively poor in all the cases,but the Intelligent room sequence.This is mainly due to the approximation of constant D matrix on the entire image.Since the background can be rarely assumed as flat on the entire image,this approach lacks in generality.Nevertheless,good accuracy in the case of the Intelligent room test shows how this approach can deal with indoor sequences where the assumption of constant D matrix is valid.

The DNM1algorithm is the one with the most stable performance,even with totally different video sequences.It achieves good accuracy in almost all the sequences.It outperforms the other algorithms in the Campus and in the Intelligent room sequences.

The DNM2algorithm suffers mainly due to the assumption of planar background.This assumption fails in the case of the

TABLE 2

The Sequence Benchmark Used

TABLE 3

Experimental Results

Laboratory sequence where the shadows are cast both on the floor and on the cabinet.The low detection performance in the Campus sequence is mainly due to noise and this algorithm has proven low robustness to strong noise.Finally,this algorithm achieves the worst discrimination result in all the cases but the Intelligent room sequence.This is due to its assumption of textured objects:If the object appearance is not textured(or seems not textured due to the distance and the quality of the acquisition system),the probability that parts of the object are classified as shadow rises.In fact,in the Intelligent room sequence,the clothes of the person in the scene are textured and the discrimination rate is higher.This approach outperforms the others in the more difficult sequence(Highway II).

The statistical approaches perform robustly in noisy data due to statistical modeling of noise.On the other hand,deterministic approaches(in particular,if pixel-based and almost unconstrained as DNM1)exhibit a good flexibility to different situations.Difficult sequences like Highway II,require,however,a more specialized and complete approach to achieve good accuracy.To help evaluating the approaches,the results on the Highway I outdoor sequence and on the Intelligent room indoor sequence are available at https://www.wendangku.net/doc/3415263723.html,/aton/shadow.

4.2Qualitative Comparison

To evaluate the behavior of the four algorithms with respect to the qualitative issues presented in Section3,we vote them ranging from“very low”to“very high”(see Table4).The DNM1method is the most robust to noise,thanks to its pre and postprocessing algorithms[13].The capacity to deal with different shadow size and strength is high in both the SNP and the DNM1.However,the higher flexibility is achieved by the DNM2algorithm,which is able to detect even the penumbra in an effective way.Nevertheless,this algorithm is very object-dependent in the sense that,as already stated,the assumption on textured objects strongly affects the results.Also,the two frame difference approach proposed in[7]is weak as soon as the object speeds increase.The hypothesis of a planar background makes the DNM2and,especially,the SP approaches more scene-dependent than the other two.Although we cannot claim to have implemented these algorithms in the most efficient way,the DNM2seems the more time consuming due to the amount of processing necessary.On the other hand,the SNP is very fast.

Finally,we evaluated the behavior of the algorithms in the presence of indirect cast shadows(see Section3).The DNM2 approach is able to detect both the penumbra and the indirect cast shadow in a very effective way.The SP and the DNM1methods failed in detecting indirect cast shadows.The pixel-based decision cannot distinguish correctly between this type of moving shadows and those shadows cast on the background.However,the SP approach is able to detect relatively narrow penumbra.

5C ONCLUDING R EMARKS

Development of practical dynamic scene analysis systems for real-world applications needs careful consideration of the moving shadows.The research community has recognized this and serious,substantive efforts in this area are being reported.The main motivator for this paper is to provide a general framework to discuss such contributions in the field and also to provide a systematic empirical evaluation of a selected representative class of shadow detection algorithms.Papers dealing with shadows are classified in a two-layer taxonomy and four representative algorithms are described in detail.A set of novel quantitative and qualitative metrics has been adopted to evaluate the approaches.

The main conclusion of the empirical study can be described as follows:For a general-purpose shadow detection system with minimal assumptions,a pixel-based deterministic nonmodel-based approach(DNM1)assures best results.On the other hand,to detect shadows efficiently in one specific environment,more assumptions yield better results and the deterministic model-based approach should be applied.In this situation,if the object classes are numerous to allow modeling of every class,a complete deterministic approach,like the DNM2,should be selected.If the environment is indoor,the statistical approaches are the more reliable since the scene is constant and a statistical description is very effective.If there are different planes onto which the shadows can be cast,an approach like SNP is the best choice.If the shadows are scattered,narrow,or particularly “blended”to the environment,a region-based dynamic approach, typically deterministic,is the best choice(as DNM2in the Highway II scene reported in this paper).Finally,if the scene is

Fig.1.Results of in the Intelligent room sequence.Gray pixels identify foreground points and dark pixels indicate shadow points.(a)Raw image,(b)SNP result,(c)SP result,(d)DNM1result,and(e)DNM2result.

TABLE4

Qualitative

Evaluation

noisy,a statistical approach or a deterministic approach with effective pre and postprocessing steps should be used.Finally,we want to remark that all the evaluated approaches exploit a large set of assumptions to limit complexity,and to avoid being unduly constrained to a specific scene model.This limits their shadow detection accuracies. This,in fact,points to the limitations of using only image-derived information in shadow detection.Further improvements would require feedback of specific task/scene domain knowledge.

A very interesting future direction has been suggested by an unknown reviewer.He/she suggested considering the physically important independent variables to evaluate the algorithms.If we can consider as parameters of the scene,for example,the type of illumination for indoor scene or the surface type upon which the shadows are cast in outdoor environments,we can build up a benchmark on which to test the different approaches.Results on accuracy on this benchmark would be more useful to future reserachers/developers of shadow detection(and motion detec-tion)algorithms since they are more physically linked to the considered scene.

A CKNOWLEDGMENTS

This research was supported in part by the California Digital Media Innovation Program(DiMI)in partnership with the California Department of Transportation(Caltrans),Sony Electro-nics,and Compaq Computers.The authors wish to thank the collaborators from the Caltrans(TCFI)in Santa Barbara for their support and interactions.They extend special thanks to their colleagues in the CVRR Laboratory for their efforts in acquiring the video data sets utilized in their studies.They also wish to thank all the reviewers for their thoughtful comments that help to improve the paper.Ivana Mikic was with the Computer Vision and Robotics Department of Electrical and Computer Engineering,University of California,San Diego.

R EFERENCES

[1]R.B.Irvin and D.M.McKeown Jr.,“Methods for Exploiting the Relationship

between Buildings and Their Shadows in Aerial Imagery,”IEEE Trans.

Systems,Man,and Cybernetics,vol.19,pp.1564-1575,1989.

[2]Y.Liow and T.Pavlidis,“Use of Shadows for Extracting Buildings in Aerial

Images,”Computer Vision,Graphics,and Image Processing,vol.49,no.2, pp.242-277,Feb.1990.

[3]G.Medioni,“Obtaining3-D from Shadows in Aerial Images,”Proc.IEEE

Int’l https://www.wendangku.net/doc/3415263723.html,puter Vision and Pattern Recognition,pp.73-76,1983.

[4] C.Wang,L.Huang,and A.Rosenfeld,“Detecting Clouds and Cloud

Shadows on Aerial Photographs,”Pattern Recognition Letters,vol.12,no.1, pp.55-64,Jan.1991.

[5]S.A.Shafer and T.Kanade,“Using Shadows in Finding Surface Orienta-

tions,”Computer Vision,Graphics,and Image Processing,vol.22,no.1,pp.145-176,Apr.1983.

[6] C.Jiang and M.O.Ward,“Shadow Identification,”Proc.IEEE Int’l Conf.

Computer Vision and Pattern Recognition,pp.606-612,1992.

[7]J.Stauder,R.Mech,and J.Ostermann,“Detection of Moving Cast Shadows

for Object Segmentation,”IEEE Trans.Multimedia,vol.1,no.1,pp.65-76, Mar.1999.

[8]I.Mikic,P.Cosman,G.Kogut,and M.M.Trivedi,“Moving Shadow and

Object Detection in Traffic Scenes,”Proc.Int’l Conf.Pattern Recognition, vol.1,pp.321-324,Sept.2000.

[9]M.M.Trivedi,I.Mikic,and G.Kogut,“Distributed Video Networks for

Incident Detection and Management,”Proc.IEEE Int’l Conf.Intelligent Transportation Systems,pp.155-160,Oct.2000.

[10] A.Elgammal,D.Harwood,and L.S.Davis,“Non-Parametric Model for

Background Subtraction,”Proc.IEEE Int’l https://www.wendangku.net/doc/3415263723.html,puter Vision’99 FRAME-RATE Workshop,1999.

[11]T.Horprasert,D.Harwood,and L.S.Davis,“A Statistical Approach for

Real-Time Robust Background Subtraction and Shadow Detection,”Proc.

IEEE Int’l https://www.wendangku.net/doc/3415263723.html,puter Vision’99FRAME-RATE Workshop,1999.

[12]N.Friedman and S.Russell,“Image Segmentation in Video Sequences:A

Probabilistic Approach,”Proc.13th Conf.Uncertainty in Artificial Intelligence, 1997.

[13]R.Cucchiara,C.Grana,G.Neri,M.Piccardi,and A.Prati,“The Sakbot

System for Moving Object Detection and Tracking,”Video-Based Surveillance Systems—Computer Vision and Distributed Processing,pp.145-157,2001.[14] D.Koller,K.Daniilidis,and H.H.Nagel,“Model-Based Object Tracking in

Monocular Image Sequences of Road Traffic Scenes,”Int’l https://www.wendangku.net/doc/3415263723.html,puter Vision,vol.10,pp.257-281,1993.

[15]K.Toyama,J.Krumm,B.Brumitt,and B.Meyers,“Wallflower:Principles

and Practice of Background Maintenance,”Proc.IEEE Int’l https://www.wendangku.net/doc/3415263723.html,puter Vision,vol.1,pp.255-261,1999.

[16]X.Tao,M.Guo,and B.Zhang,“A Neural Network Approach to the

Elimination of Road Shadow for Outdoor Mobile Robot,”Proc.IEEE Int’l Conf.Intelligent Processing Systems,vol.2,pp.1302-1306,1997.

[17]S.J.McKenna,S.Jabri,Z.Duric,A.Rosenfeld,and H.Wechsler,“Tracking

Groups of People,”Computer Vision and Image Understanding,vol.80,no.1, pp.42-56,Oct.2000.

[18]J.M.Scanlan,D.M.Chabries,and R.W.Christiansen,“A Shadow Detection

and Removal Algorithm for2-D Images,”Proc.Int’l Conf.Acoustics,Speech, and Signal Processing,vol.4,pp.2057-2060,1990.

[19]M.Kilger,“A Shadow Handler in a Video-Based Real-Time Traffic

Monitoring System,”Proc.IEEE Workshop Applications of Computer Vision, pp.11-18,1992.

[20]N.M.Charkari and H.Mori,“A New Approach for Real Time Moving

Vehicle Detection,”Proc.IEEE/RSJ Int’l Conf.Intelligent Robots and Systems, pp.273-278,1993.

[21]G.G.Sexton and X.Zhang,“Suppression of Shadows for Improved Object

Discrimination,”Proc.IEE Colloquium Image Processing for Transport Applications,pp.1-6,Dec.1993.

[22]K.Onoguchi,“Shadow Elimination Method for Moving Object Detection,”

Proc.Int’l Conf.Pattern Recognition,vol.1,pp.583-587,1998.

[23]G.Funka-Lea and R.Bajcsy,“Combining Color and Geometry for the

Active,Visual Recognition of Shadows,”Proc.IEEE Int’l https://www.wendangku.net/doc/3415263723.html,puter Vision,pp.203-209,1995.

[24]Y.Sonoda and T.Ogata,“Separation of Moving Objects and Their

Shadows,and Application to Tracking of Loci in the Monitoring Images,”

Proc.Int’l Conf.Signal Processing,pp.1261-1264,1998.

[25] C.Tzomakas and W.von Seelen,“Vehicle Detection in Traffic Scenes Using

Shadows,”Technical Report98-06,IR-INI,Institut fur Nueroinformatik, Ruhr-Universitat Bochum,FRG,Germany,Aug.1998.

[26]N.Amamoto and A.Fujii,“Detecting Obstructions and Tracking Moving

Objects by Image Processing Technique,”Electronics and Comm.in Japan, Part3,vol.82,no.11,pp.28-37,1999.

[27] A.Prati,R.Cucchiara,I.Mikic,and M.M.Trivedi,“Analysis and Detection

of Shadows in Video Streams:A Comparative Evaluation,”Proc.Third Workshop Empirical Evaluation Methods in Computer Vision—IEEE Int’l Conf.

Computer Vision and Pattern Recognition,2001.

[28]I.Haritaoglu,D.Harwood,and L.S.Davis,“W4:Real-Time Surveillance of

People and Their Activities,”IEEE Trans.Pattern Analysis and Machine Intelligence,vol.22,no.8,pp.809-830,Aug.2000.

[29]N.Herodotou,K.N.Plataniotis,and A.N.Venetsanopoulos,“A Color

Segmentation Scheme for Object-Based Video Coding,”Proc.IEEE Symp.

Advances in Digital Filtering and Signal Processing,pp.25-29,1998.

[30]R.Cucchiara,C.Grana,M.Piccardi,A.Prati,and S.Sirotti,“Improving

Shadow Suppression in Moving Object Detection with HSV Color Information,”Proc.IEEE Int’l Conf.Intelligent Transportation Systems, pp.334-339,Aug.2001.

[31]P.Villegas,X.Marichal,and A.Salcedo,“Objective Evaluation of

Segmentation Masks in Video Sequences,”Proc.Workshop Image Analysis for Multimedia Interactive Services,pp.85-88,May1999.

[32]G.Medioni,“Detecting and Tracking Moving Objects for Video Surveil-

lance,”Proc.IEEE Int’l https://www.wendangku.net/doc/3415263723.html,puter Vision and Pattern Recognition,vol.2, pp.319-325,1999.

.For more information on this or any other computing topic,please visit our Digital Library at https://www.wendangku.net/doc/3415263723.html,/publications/dlib.

scifinder使用介绍

6.6.1 内容简介 SciFinder Scholar是美国化学学会所属的化学文摘服务社CAS（Chemical Abstract Service）出版的化学资料电子数据库学术版。它是全世界最大、最全面的化学和科学信息数据库。 《化学文摘》(CA)是涉及学科领域最广、收集文献类型最全、提供检索途径最多、部卷也最为庞大的一部著名的世界性检索工具。CA报道了世界上150多个国家、56种文字出版的20000多种科技期刊、科技报告、会议论文、学位论文、资料汇编、技术报告、新书及视听资料，摘录了世界范围约98%的化学化工文献，所报道的内容几乎涉及化学家感兴趣的所有领域。 CA网络版SciFinder Scholar，整合了Medline医学数据库、欧洲和美国等30几家专利机构的全文专利资料、以及化学文摘1907年至今的所有内容。涵盖的学科包括应用化学、化学工程、普通化学、物理、生物学、生命科学、医学、聚合体学、材料学、地质学、食品科学和农学等诸多领域。 SciFinder Scholar 收集由CAS 出版的数据库的内容以及MEDLINE?数据库，所有的记录都为英文（但如果MEDLINE 没有英文标题的则以出版的文字显示）。 6.6.2 通过 SciFinder Scholar 可以得到的信息：

6.6.3 SciFinder? Scholar? 使用的简单介绍 主要分为Explore 和Browse。如图6.6.1 一、Explore Explore Tool 可获取化学相关的所有信息及结构等，有如下： 1、Chemical Substance or Reaction – Retrieve the corresponding literature 2、By chemical structure 3、By substance identifier 4、By molecular formula

儿童歌谣大全

儿童歌谣大全 各位读友大家好，此文档由网络收集而来，欢迎您下载，谢谢 篇一：儿童歌谣歌词大全 [小螺号]儿歌歌词 小螺号，嘀嘀嘀吹海鸥听了展翅飞小螺号，嘀嘀嘀吹浪花听了笑微微小螺号，嘀嘀嘀吹 声声唤船归啰 小螺号，嘀嘀嘀吹 阿爸听了快快回啰。 茫茫的海洋，蓝蓝的海水 吹起了小螺号，心里美吔 [小鸟小鸟] 蓝天里有阳光，树林里有花香 小鸟小鸟，你自由地飞翔 在田野，在草地，在湖边，在山冈 小鸟小鸟迎着春天歌唱 啦啦啦啦啦。爱春天，爱阳光，爱湖水，爱花香小鸟小鸟，我的好朋友让我们一起飞翔歌唱

一起飞翔歌唱 啦啦啦啦啦 [让我们荡起双桨] 让我们荡起双桨，小船儿推开波浪海面倒映着白塔，四周环绕着绿树红墙小船儿轻轻飘荡在水中 迎面吹来了凉爽的风 红领巾迎着太阳，阳光洒在海面上 水中鱼儿望着我们 悄悄听我们愉快歌唱 小船儿轻轻飘荡在水中 迎面吹来了凉爽的风 做完了一天的功课，我们来尽情欢乐 我问你亲爱的伙伴 谁给我们安排下幸福的生活？ 小船儿轻轻飘荡在谁中 迎面吹来了凉爽的风 [小红花]儿歌歌词 金波词尚疾曲 花园里，篱笆下，我种下一朵小红花

春天的太阳当头照，春天的小雨沙沙下 啦啦啦啦啦，啦啦啦啦啦 小红花张嘴笑哈哈 花园里，篱笆下，我种下一朵小红花 春天的太阳当头照，春天的小雨沙沙下 啦啦啦啦啦，啦啦啦啦啦 小红花张嘴笑哈哈[我家几口] 金苗苓词曲我家有几口？ 让我扳指头 爸爸，妈妈，还有我 再加一个布娃娃 哟！有四口我家有几口？ 让我扳指头 爸爸，妈妈，还有我 再加一个布娃娃 哟！有四口 [谁会这样] 少数民族儿歌潘振声曲 谁会飞呀，鸟会飞

鸟儿鸟儿怎样飞？ 拍拍翅膀飞呀飞 谁会游呀，鱼会游 鱼儿鱼儿怎样游？ 摇摇尾巴点点头 谁会跑呀，马会跑马儿马儿怎样跑？四脚离地身不摇。[我叫轻轻] 张友珊词汪玲曲 走路轻轻轻轻 上夜班的阿姨还没醒呀 敲门轻轻轻轻 给邻居叔叔送呀送封信 说话轻轻轻轻 姐姐灯下看书多用心呀 大家夸我是好孩子 给我取个名字叫呀叫轻轻 走路轻轻轻轻 上夜班的阿姨还没醒呀 敲门轻轻轻轻 给邻居叔叔送呀送封信 说话轻轻轻轻 姐姐灯下看书多用心呀

儿歌歌词大全

《樱桃小丸子》 小小年纪谈起理想一串串 想当专家、想做博士、想出唱片老爸老妈老师老友都夸赞 想来容易，说来简单，做做就难要数一百，先数一二三 要过明天先过好今天 瞄准目标看齐 噼里啪啦，噼里啪啦 做事不偷懒 噼里啪啦，噼里啪啦

学习不怕难 我们脚踏实地地干 瞄准目标看齐 噼里啪啦，噼里啪啦 读书真勤快 噼里啪啦，噼里啪啦 今天学得好 噼里啪啦，噼里啪啦 明天理想能实现 《多啦A梦》 如果我有仙女棒变大变小变漂亮

还要变个都是漫画巧克力和玩具的家 如果我有机器猫我要叫他小叮当 竹蜻蜓和时光隧道能去任何的地方 让小孩大人坏人都变成好人 (hi 大家好,我是小叮当) ang ang ang小叮当帮我实现所有的愿望 躺在草地上幻想想动想西想玩耍 想到老师还有考试一个头就变成两个大好在我有小叮当困难时候求求他 万能笔和时间机器能做任何的事情 让我的好朋友一齐分享他 (啊!救命啊!有老鼠!) ang ang ang 小叮当帮我实现所有的愿望 躺在草地上幻想想动想西想玩耍 想到老师还有考试一个头就变成两个大好在我有小叮当困难时候求求他 万能笔和时间机器能做任何的事情 让我的好朋友一齐分享他 (小叮当永远是你们的好朋友喔!) ang ang ang 小叮当帮我实现所有的愿望

ang ang ang 小叮当帮我实现所有的愿望 《铁臂阿童木》 越过辽阔天空，啦啦啦飞向遥远群星，来吧！阿童木，爱科学的好少年。善良勇敢的啦啦啦铁臂阿童木，十万马力七大神力，无私无畏的阿童木。穿过广阔大地，啦啦啦潜入深深海洋，来吧！阿童木，爱和平的好少年。善良勇敢的啦啦啦铁臂阿童木，我们的好朋友啊， 无私无畏的阿童木。 《小龙人之歌》 天上有，无数颗星星，那颗最小的就是我，我不知道我从哪里来，也不知道我在哪里生。地上有，无数个龙人，那个最小的就是我，我不知道我从哪里来，也不知道我在哪里生。啊----这是我将在妈妈怀 抱里，啊寻遍天涯，去找他 《我是一条小青龙》我头上有只角，我身后有尾巴，谁也不知道，我有多少秘密？我头上有只角，我身后有尾巴，谁也不知道，我有多少秘密。我是一条小青龙（小青龙，小青龙）我有许多小秘密（小秘

56首经典儿歌歌词大全

56首经典儿歌歌词大全 1、做早操 早上空气真叫好，我们都来做早操。 伸伸臂，弯弯腰，踢踢腿，蹦蹦跳，天天锻炼身体好。 2、饭前要洗手 小脸盆，水清请，小朋友们笑盈盈，小手儿，伸出来， 洗一洗，白又净，吃饭前，先洗手，讲卫生，不得病。 3、小手绢 小手绢，四方方，天天带在我身上。 又擦鼻涕又擦汗，干干净净真好看。 4、搬鸡蛋 小老鼠，搬鸡蛋，鸡蛋太大怎么办?一只老鼠地上躺， 紧紧抱住大鸡蛋。一只老鼠拉尾巴，拉呀拉呀拉回家。 5、大骆驼 骆驼骆驼志气大，风吹日晒都不怕。 走沙漠，运盐巴，再苦再累不讲话。 6、螳螂 螳螂哥，螳螂哥，肚儿大，吃得多。飞飞能把粉蝶捕， 跳跳能把蝗虫捉。两把大刀舞起来，一只害虫不放过 7、大蜻蜓 大蜻蜓，绿眼睛，一对眼睛亮晶晶， 飞一飞，停一停，飞来飞去捉蚊蝇。 8、小鸭子 小鸭子，一身黄，扁扁嘴巴红脚掌。 嘎嘎嘎嘎高声唱，一摇一摆下池塘。 9、拍手歌 你拍一，我拍一，天天早起练身体。 你拍二，我拍二，天天都要带手绢。 你拍三，我拍三，洗澡以后换衬衫。 你拍四，我拍四，消灭苍蝇和蚊子。 你拍五，我拍五，有痰不要随地吐。 你拍六，我拍六，瓜皮果核不乱丢。 你拍七，我拍七，吃饭细嚼别着急。 你拍八，我拍八，勤剪指甲常刷牙。 你拍九，我拍九，吃饭以前要洗手。

你拍十，我拍十，脏的东西不要吃。 10 、小螃蟹 小螃蟹，真骄傲，横着身子到处跑， 吓跑鱼，撞倒虾，一点也不懂礼貌 11 、庆六一 儿童节，是六一，小朋友们真欢喜。 又唱歌来又跳舞，高高兴兴庆六一。 12、花猫照镜子 小花猫，喵喵叫，不洗脸，把镜照， 左边照，右边照，埋怨镜子脏，气得胡子翘。 13、蚂蚁搬虫虫 小蚂蚁，搬虫虫，一个搬，搬不动，两个搬，掀条缝， 三个搬，动一动，四个五个六七个，大家一起搬进洞。 14、小青蛙 小青蛙，呱呱呱，水里游，岸上爬， 吃害虫，保庄稼，人人都要保护它。 15、花儿好看我不摘 公园里，花儿开，红的红，白的白， 花儿好看我不摘，人人都说我真乖。 16 、红绿灯 大马路，宽又宽，警察叔叔站中间， 红灯亮，停一停，绿灯亮，往前行。 17 、七个果果 一二三四五六七，七六五四三二一。 七个阿姨来摘果，七个篮子手中提。七个果子摆七样。 苹果、桃儿、石榴、柿子、李子、栗子、梨。 18、睡午觉 枕头放放平，花被盖盖好。 小枕头，小花被，跟我一起睡午觉，看谁先睡着。 19 、吃荸荠 荸荠有皮，皮上有泥。洗掉荸荠皮上的泥，削去荸荠外面的皮，荸荠没了皮和泥，干干净净吃荸荠。 20 、小云骑牛去打油 小云骑牛去打油，遇着小友踢皮球，皮球飞来吓了牛，摔下小云撒了油。 21 、盆和瓶 车上有个盆，盆里有个瓶，乒乒乒，乓乓乓，不知是瓶碰盆，还是盆碰瓶。

SciFinder使用说明

SciFinder使用说明 SciFinder简介 SciFinder?由美国化学会（American Chemical Society, ACS）旗下的美国化学文摘社（Chemical Abstracts Service, CAS）出品，是一个研发应用平台，提供全球最大、最权威的化学及相关学科文献、物质和反应信息。SciFinder涵盖了化学及相关领域如化学、生物、医药、工程、农学、物理等多学科、跨学科的科技信息。SciFinder收录的文献类型包括期刊、专利、会议论文、学位论文、图书、技术报告、评论和网络资源等。 通过SciFinder，可以： ?访问由CAS全球科学家构建的全球最大并每日更新的化学物质、反应、专利和期刊数据库，帮助您做出更加明智的决策。 ?获取一系列检索和筛选选项，便于检索、筛选、分析和规划，迅速获得您研究所需的最佳结果，从而节省宝贵的研究时间。 无需担心遗漏关键研究信息，SciFinder收录所有已公开披露、高质量且来自可靠信息源的信息。 通过SciFinder可以获得、检索以下数据库信息：CAplus SM（文献数据库）、CAS REGISTRY SM (物质信息数据库)、CASREACT? （化学反应数据库）、MARPAT?（马库什结构专利信息数据库）、CHEMLIST? (管控化学品信息数据库)、CHEMCATS?(化学品商业信息数据库)、MEDLINE?(美国国家医学图书馆数据库)。 专利工作流程解决方案PatentPak TM已在SciFinder上线，帮助用户在专利全文中快速定位难以查找的化学信息。 SciFinder 注册须知： 读者在使用SciFinder之前必须用学校的email邮箱地址注册，注册后系统将自动发送一个链接到您所填写的email邮箱中，激活此链接即可完成注册。参考“SciFinder注册说明”。

舌尖上的中国分集简介

舌尖上的中国分集简介 第一集自然的馈赠 本集导入作为一个美食家，食物的美妙味感固然值得玩味，但是食物是从哪里来的？毫无疑问，我们从大自然中获得所有的食物，在我们走进厨房，走向餐桌之前，先让我们回归自然，看看她给我们的最初的馈赠。本集将选取生活在中国境内截然不同的地理环境（如海洋，草原，山林，盆地，湖泊）中的具有代表性的个人、家庭和群落为故事主角，以及由于自然环境的巨大差异（如干旱，潮湿，酷热，严寒）所带来的截然不同的饮食习惯和生活方式为故事背景，展现大自然是以怎样不同的方式赋予中国人食物，我们又是如何与自然和谐相处，从而了解在世代相传的传统生活方式中，通过各种不同的途径获取食物的故事。 美食介绍 烤松茸油焖春笋雪菜冬笋豆腐汤 腊味飘香腌笃鲜排骨莲藕汤椒盐藕夹酸辣藕丁煎焖鱼头泡饼煎焗马鲛鱼酸菜鱼松鼠桂鱼侉炖鱼本集部分旁白中国拥有世界上最富戏剧性的自然景观，高原， 第一集: 自然的馈赠 山林，湖泊，海岸线。这种地理跨度有助于物种的形成和保存，任何一个国家都没有这样多潜在的食物原材料。为了得到这份自然的馈赠，人们采集，捡拾，挖掘，捕捞。穿越四季，本集将展现美味背后人和自然的故事。香格里拉，松树和栎树自然杂交林中，卓玛寻找着一种精灵般的食物——松茸。松茸保鲜期只有短短的两天，商人们以最快的速度对松茸进行精致的加工，这样一只松茸24小时之后就会出现在东京的市场中。松茸产地的凌晨3点，单珍卓玛和妈妈坐着爸爸开的摩托车出发。穿过村庄，母女俩要步行走进30公里之外的原始森林。雨让各种野生菌疯长，但每一个藏民都有识别松茸的慧眼。松茸出土后，

卓玛立刻用地上的松针把菌坑掩盖好，只有这样，菌丝才可以不被破坏，为了延续自然的馈赠，藏民们小心翼翼地遵守着山林的规矩。为期两个月的松茸季节，卓玛和妈妈挣到了5000元，这个收入是对她们辛苦付出的回报。 舌尖上的中国DVD 老包是浙江人，他的毛竹林里，长出过遂昌最大的一个冬笋。冬笋藏在土层的下面，从竹林的表面上看，什么也没有，老包只需要看一下竹梢的叶子颜色，就能知道笋的准确位置，这完全有赖于他丰富的经验。笋的保鲜从来都是个很大的麻烦，笋只是一个芽，是整个植物机体活动最旺盛的部分。聪明的老包保护冬笋的方法很简单，扒开松松的泥土，把笋重新埋起来，保湿，这样的埋藏方式就地利用自然，可以保鲜两周以上。在中国的四大菜系里，都能见到冬笋。厨师偏爱它，也是因为笋的材质单纯，极易吸收配搭食物的滋味。老包正用冬笋制作一道家常笋汤，腌笃鲜主角本来应该是春笋，但是老包却使用价格高出20倍的遂昌冬笋。因为在老包眼里，这些不过是自家毛竹林里的一个小菜而已。在云南大理北部山区，醒目的红色砂岩中间，散布着不少天然的盐井，这些盐成就了云南山里人特殊的美味。老黄和他的儿子在树江小溪边搭建一个炉灶，土灶每年冬天的工作就是熬盐。云龙县的冬季市场，老黄和儿子赶到集市上挑选制作火腿的猪肉，火腿的腌制在老屋的院子里开始。诺邓火腿的腌制过程很简单，老黄把多余的皮肉去除，加工成一个圆润的火腿，洒上白酒除菌，再把自制的诺盐均匀的抹上，不施锥针，只用揉、压，以免破坏纤维。即使用现代的标准来判断，诺邓井盐仍然是食盐中的极品，虽然在这个古老的产盐地，盐业生产已经停止，但我们仍然相信诺邓盐是自然赐给山里人的一个珍贵礼物。圣武和茂荣是兄弟俩，每年9月，他们都会来到湖北的嘉鱼县，来采挖一种自然的美味。这种植物生长在湖水下面的深深的淤泥之中，茂荣挖到的植物的根茎叫做莲藕，是一种湖泊中高产的蔬菜——藕。作为职业挖藕人，每年茂荣和圣武要只身出门7个月，采藕的季节，他们就从老家安徽赶到有藕的地方。较高的人工报酬使得圣武和茂荣愿意从事这个艰苦的工作。挖藕的人喜欢天气寒冷，这不是因为天冷好挖藕，而是天气冷买藕吃藕汤的人就多一些，藕的价格就会涨。整整一湖的莲藕还要采摘5个月的时间，在嘉鱼县的珍湖上，300个职业挖藕人，每天从日出延续到日落，在中国遍布淡水湖的大省，这样场面年年上演。今天当我们有权远离自然，享受美食的时候，最应该感谢的是这些通过劳动和智慧成就餐桌美味的人们。 第二集主食的故事 本集导入主食是餐桌上的主要食物，是人们所需能量的主要来源。从远古时代赖以充饥的自然谷物到如今人们餐桌上丰盛的、让人垂涎欲滴的美食，一个异彩纷呈、变化多端的主食世界呈现在你面前。本集着重描绘不同地域、不同民族、不同风貌的有关主食的故事，展现人们对主食的样貌、口感的追求，处理和加工主食的智慧，以及中国人对主食的深

幼儿儿歌歌词大全

幼儿儿歌歌词大全 1、小白兔 小白兔乖乖，把门开开，快点开开，我要进来，不开不开就不开，妈妈没回来，谁叫也不开。小白兔乖乖，把门开开，妈妈回来，我要进来，快开快开快快开，妈妈回来了，我来把门开。 2、小花狗 小花狗，真叫脏，不洗脚丫就上床。问它为什么？它说忘，忘，忘！ 3、吃饭 小宝宝，坐坐好，妈妈盛饭喂宝宝。细细嚼，慢慢咽，宝宝吃得直叫好。 4、干净 鼻涕擦干净，指甲剪干净，脸儿洗干净，妈妈和和亲一亲。 5、乒乓球 乒乓球,两人打,他给我,我给他,谁都不愿落在自己家. 6、踢毽子 小毽子,小毽子,飞上天, 落下地，我们都来踢踢它,踢不好儿没关系. 7、走夜路 夜色黑,星星闪,小朋友,把家还,突然窜出一只兔,吓得它,满处窜. 8、小盒子 小盒子,作用大,铅笔橡皮装的下, 还有一支小钢笔,装在里面心欢喜, 9、大马路 马路长又宽,我站在中间, 老师过来拜拜手,让我赶快走,说是有危险. 10、木头人

三三三，我们都是木头人， 不许哭来不许笑，还有一个不许动。 11、小皮球,小小篮,落地开花二十一,二五六,二五七, 二八二九三十一;三五六,三五七,三八三九四十一…… 12、小蜜蜂 小蜜蜂，嗡嗡嗡，嗡嗡嗡，大家一起勤劳动， 来匆匆，去匆匆，走得兴味浓，春暖花开不做工， 将来哪里好过冬？嗡嗡嗡，嗡嗡嗡，不学懒惰虫。 13、请你唱个歌吧 小杜鹃,小杜鹃，我们请你唱个歌，快来呀,大家来呀， 我们静听你的歌，咕咕!咕咕!歌声使我们快乐 14、谁会飞 谁会飞呀，鸟会飞，鸟儿鸟儿怎样飞？拍拍翅膀飞呀飞 谁会游呀，鱼会游，鱼儿鱼儿怎样游？摇摇尾巴点点头 谁会跑呀，马会跑，马儿马儿怎样跑？四脚离地身不摇。 15、好朋友 你帮我来梳梳头，我帮你来扣纽扣 团结友爱手拉手，我们都是好朋友 嘿嘿！ 16、数鸭子 门前大桥下，游过一群鸭， 快来快来数一数，二四六七八。 17、小雪花 小雪花，小雪花，飘在空中像朵花，小雪花，小雪花 飘在窗上变窗花，小雪花，小雪花，飘在手上不见了

舌尖上的中国解说词全文

《舌尖上的中国》解说词 第一集《自然的馈赠》 中国拥有世界上最富戏剧性的自然景观，高原，山林，湖泊，海岸线。这种地理跨度有助于物种的形成和保存，任何一个国家都没有这样多潜在的食物原材料。为了得到这份自然的馈赠，人们采集，捡拾，挖掘，捕捞。穿越四季，本集将展现美味背后人和自然的故事。 香格里拉，松树和栎树自然杂交林中，卓玛寻找着一种精灵般的食物——松茸。松茸保鲜期只有短短的两天，商人们以最快的速度对松茸的进行精致的加工，这样一只松茸24小时之后就会出现在东京的市场中。 松茸产地的凌晨3点，单珍卓玛和妈妈坐着爸爸开的摩托车出发。穿过村庄，母女俩要步行走进30公里之外的原始森林。雨让各种野生菌疯长，但每一个藏民都有识别松茸的慧眼。松茸出土后，卓玛立刻用地上的松针把菌坑掩盖好，只有这样，菌丝才可以不被破坏，为了延续自然的馈赠，藏民们小心翼翼地遵守着山林的规矩。 为期两个月的松茸季节，卓玛和妈妈挣到了5000元，这个收入是对她们辛苦付出的回报。 老包是浙江人，他的毛竹林里，长出过遂昌最大的一个冬笋。冬笋藏在土层的下面，从竹林的表面上看，什么也没有，老包只需要看一下竹梢的叶子颜色，就能知道笋的准确位置，这完全有赖于他丰富的经验。 笋的保鲜从来都是个很大的麻烦，笋只是一个芽，是整个植物机体活动最旺盛的部分。聪明的老包保护冬笋的方法很简单，扒开松松的泥土，把笋重新埋起来，保湿，这样的埋藏方式就地利用自然，可以保鲜两周以上。 在中国的四大菜系里，都能见到冬笋。厨师偏爱它，也是因为笋的材质单纯，极易吸收配搭食物的滋味。老包正用冬笋制作一道家常笋汤，腌笃鲜主角本来应该是春笋，但是老包却使用价格高出20倍的遂昌冬笋。因为在老包眼里，这些不过是自家毛竹林里的一个小菜而已。 在云南大理北部山区，醒目的红色砂岩中间，散布着不少天然的盐井，这些盐成就了云南山里人特殊的美味。老黄和他的儿子树江小溪边搭建一个炉灶，土灶每年冬天的工作就是熬盐。 云龙县的冬季市场，老黄和儿子赶到集市上挑选制作火腿的猪肉，火腿的腌制在老屋的院子里开始。诺邓火腿的腌制过程很简单，老黄把多余的皮肉去除，加工成一个圆润的火腿，洒上白酒除菌，再把自制的诺盐均匀的抹上，不施锥针，只用揉、压，以免破坏纤维。 即使用现代的标准来判断，诺邓井盐仍然是食盐中的极品，虽然在这个古老的产盐地，盐业生产已经停止，但我们仍然相信诺邓盐是自然赐给山里人的一个珍贵礼物。 圣武和茂荣是兄弟俩，每年9月，他们都会来到湖北的嘉鱼县，来采挖一种自然的美味。这种植物生长在湖水下面的深深的淤泥之中，茂荣挖到的植物的根茎叫做莲藕，是一种湖泊中高产的蔬菜——藕。

浙江大学scifinder使用教程

浙江大学scifinder使用教程 1、输入网址：https://www.wendangku.net/doc/3415263723.html,/ 如图1，点击继续浏览 图1 2、进入浙大的入口，输入用户名密码（卖家提供） 图2 3、登陆进去是图3这个页面。注意此时会自动安装插件，切记要一路放行。登陆成功的标志是屏幕右上角有个蓝框绿蓝S的LOGO！ 如果未出现S，那么请根据图2的手动安装组件，下载安装组件！

图3 4、登陆页面不要覆盖，新标签页打开浙江大学图书馆 https://www.wendangku.net/doc/3415263723.html,/libweb/点数据库导航，找到scifinder页面，进入 图4

5、点击图5红框中的链接https://www.wendangku.net/doc/3415263723.html,/cgi-bin/casip，看下IP是不是浙大的IP，一般是61或者210开头，确定是，那就可以输入链接https://https://www.wendangku.net/doc/3415263723.html,/登陆了 图5 6、scifinder登陆页面输入用户名密码（卖家提供），您就可以使用scifinder啦 图6

常见问题以及解决方法 1.浏览器不支持JavaScript，提示“您的浏览器不支持JavaScript（或它被 禁止了）请确认您的浏览器能支持JavaScript”，请启用“工具- >Internet选项->安全->自定义级别->活动脚本”选项。 2.浏览器不支持Cookie，提示“你的浏览器禁止了Cookie，必须设置为允许 才可以继续使用”，请在“工具->Internet选项->隐私->高级”启用 Cookie支持 3.浏览不支持BHO，提示:"您的浏览器没有启用第三方扩展"，关闭IE时无法 自动注销用户。请在 "工具->Internet选项->高级->启用第三方浏览器扩展”前打勾启用! 4.APP服务不可用,可能是控件不是最新的，请您关闭IE，重新登陆VPN 5.IP服务不可用,可能你安装的IP服务控件不是最新的。请点击“程序- >SINFOR SSL VPN->卸载CS应用支持”和“程序->SINFOR SSL VPN->卸载SSL VNIC”，手动卸载IP服务控件，然后再重新登陆VPN。 6.IP服务可能与某些杀毒软件冲突。请在杀毒软件中放行IP服务的客户端程 序，或者在使用时暂时禁用杀毒软件。

舌尖上的中国栏目策划

《舌尖上的中国》电视节目策划书 一、背景浅析 改革开放以来，国人物质生活更加富足，在吃方面也变得更加讲究。食客、美食家、吃货等的出现，则从另一方面体现国人口味的变化。衣食住行为人之根本，古人读万卷书、行万里路已是满足，今人在此之外加上品万种美食，才称得上是真正的享受生活。而中国幅员辽阔，有八大菜系，淮扬菜、粤菜、鲁菜、湘菜，每种菜系都有其特色美食，而且烹调方法各有不同，可以说，国人够有口福的了。在《舌尖上的中国》这部纪录片中，导演和摄制组带着观众探访祖国各地美食，了解各式各样与美食有关的人和事，总共七集、每集50分钟的容量，节奏紧凑、制作精良，尝遍美食的同时又能遍览祖国风物，纪录片煞是好。而随着中国经济的飞速发展，人们的物质生活水平也得到了极大地提高，已经不再局限于吃饱穿暖这样基本的要求。民以食为天，在物质文明丰富至如斯的今天，人们开始重视对于生活品质更高层次的追求。对于吃，食不厌精、烩不厌细，不只吃味道，还要吃环境、吃服务、吃文化，吃健康。 二、企划动机 《舌尖上的中国》这部以美食为主题的纪录片，前些日子风头力压各种电视连续剧，成为连日来的微博“刷屏利器”并高居话题榜榜首，甚至让许多早已抛弃了电视的80后“吃货”们，纷纷锁定夜间的央视坐等这部“吃货指南”。人们为美食流口水，为美食的故事流眼泪，为美食的文化而感动自豪，自豪于中国饮食文化的博大精深，甚至有人高调地赞美“这才是最好的爱国主义教育片”。看要应对新的发展形势，就必须有新的态度与措施。饮食行业发展到今日，大街小巷、五花八门，各种价位、各种地域乃至跨国界的美食比比皆是。如何从

千篇一律的店面经营中脱颖而出？不仅仅是商家绞尽脑汁，着力打开销售额与知名度。美食老饕们也寻寻觅觅，力图享受到有特色、有内涵的精彩美食。《舌尖上的中国》这档美食资讯节目，是继《舌尖上的中国》这个记录片之后的后续美食资讯类节目，更实现其为商家和消费者两者搭建一个交流的平台，优秀的商家可以尽展己之所长，以此为基础展示自身特色美食功夫。而美食爱好者们则可以通过节目的推介，节省大量的时间、精力，接触到平时踏破铁鞋无觅处，只能通过口口相传难辨优劣，隐藏在街头巷尾的各种美食。大可以凭此慕名而来、亦可尽兴而去。 一、节目名称——《舌尖上的中国》 二、节目类别——继纪录片《舌尖上的中国》之后，观众参与度高的一档各地美食资讯节目 三、节目主旨——让爱美食的人走进节目让看节目的人走近美食 四、节目目标 借助最近《舌尖上的中国》的热点，重点打造一档精品的美食资讯节目，不仅仅做美食，还囊括了相关的综合资讯。力图达到“美食主动靠过来，观众自然看进去”的效果，打造口碑，铸就精品。 五、节目定位 这是一档集继纪录片《舌尖上的中国》之后的观众参与度高的一档各地美食资讯节目。将美食推介节目与菜肴烹饪节目于一身，力图更好的开发各地特色美食资源的服务资讯类美食节目，兼顾一定的娱乐性质。采用立体全面的推介方式，为现场和电视机前的观众提供优质精选的美食信息，商家现场展示、与美食爱好者们现场沟通交流。

幼儿园儿歌歌词大全

《一闪一闪亮晶晶》 一闪一闪亮晶晶。漫天都是小星星。挂在天空放光明。好像千万小眼睛。太阳慢慢向西沉。乌鸦回家一群群。星星眨着小眼睛。光辉照耀到天明。一闪一闪亮晶晶。满天都是小星星。 《春天在哪里》 春天在哪里呀。春天在哪里。春天在那青翠的山林里。这里有红花呀这里有绿草。还有那会唱歌的小黄鹂。嘀哩哩嘀哩嘀哩哩。嘀哩哩嘀哩嘀哩哩。春天在青翠的山林里。还有那会唱歌的小黄鹂。春天在哪里呀。春天在哪里。春天在那湖水的倒影里。映出红的花呀映出绿的草。还有那会唱歌的小黄鹂。 《卖报歌》 卖报歌。啦啦啦，啦啦啦，我是卖报的小行家，不等天明去等派报，一面走一面叫，今天的新闻真正好，七个铜板就买两份报。啦啦啦，啦啦啦，我是卖报的小行家，大风大雨里满街跑，走不好滑一跤，满身的泥水惹人笑，饥饿寒冷只有我知道。 《两只老虎》 两只老虎。两只老虎。跑的块跑的快。一只没有耳朵。一只没有尾巴。真奇怪。真奇怪。两只老虎。两只老虎。跑的块跑的快。一只没有鼻子。一只没有头发。真奇怪。真奇怪。 《葫芦娃》 葫芦娃葫芦娃。一根藤上七朵花。风吹雨打都不怕。啦啦啦啦。叮当咚咚当当。葫芦娃。叮当咚咚当当。本领大啦啦啦啦。葫芦娃葫芦娃。本领大。葫芦娃葫芦娃。一根藤上七朵花。风吹雨打都不怕。啦啦啦啦。叮当咚咚当当。葫芦娃。叮当咚咚当当。本领大啦啦啦啦。 《歌声与微笑》 请把我的歌带回你的家。请把你的微笑留下。请把我的歌带回你的家。请把你的微笑留下。明天明天这歌声飞遍海角天涯。飞遍海角天涯。明天明天这微笑将是遍野春花。将是遍野春花。 《小螺号》 小螺号嘀嘀嘀吹。海鸥听了展翅飞。小螺号嘀嘀嘀吹。浪花听了笑微微。小螺号嘀嘀嘀吹。声声唤船归啰。小螺号嘀嘀嘀吹。阿爸听了快快回啰。茫茫的海滩。蓝蓝的海水。吹起了螺号。心里美吔。 《小毛驴》 我有一只小毛驴我从来也不骑。有一天我心血来潮骑着去赶集。我手里拿着小皮鞭我心里正得意。不知怎么哗拉拉拉拉我摔了一身泥。我有一只小毛驴我从来也不骑。有一天我心血来潮骑着去赶集。我手里拿着小皮鞭我心里正得意。不知怎么哗拉拉拉拉我摔了一身泥。 《数鸭子》 门前大桥下游过一群鸭。快来快来数一数。二四六七八。嘎嘎嘎嘎。真呀真多呀。数不清到底多少鸭。数不清到底多少鸭。赶鸭老爷爷胡子白花花。唱呀唱着家乡戏。还会说笑话。小孩小孩。快快上学校。别考个鸭蛋抱回家。别考个鸭蛋抱回家。 《一只哈巴狗》 一只哈巴狗。站在大门口。眼睛黑油油。想吃肉骨头。一只哈巴狗。吃完肉骨头。尾巴摇一摇。向我点点头。

《舌尖上的中国》简介和解说词1-7集

《舌尖上的中国》解说词1-7集 第一季：1.自然的馈赠 作为一个美食家，食物的美妙味感固然值得玩味，但是食物是从哪里来的？毫无疑问，我们从大自然中获得所有的食物，在我们走进厨房，走向餐桌之前，先让我们回归自然，看看她给我们的最初的馈赠。 本集将选取生活在中国境内截然不同的地理环境(如海洋,草原,山林,盆地,湖泊)中的具有代表性的个人、家庭和群落为故事主角，以及由于自然环境的巨大差异(如干旱，潮湿，酷热，严寒)所带来的截然不同的饮食习惯和生活方式为故事背景，展现大自然是以怎样不同的方式赋予中国人食物，我们又是如何与自然和谐相处，从而了解在世代相传的传统生活方式中，通过各种不同的途径获取食物的故事。 第一季：2.主食的故事 主食是餐桌上的主要食物，是人们所需能量的主要来源。从远古时代赖以充饥的自然谷物到如今人们餐桌上丰盛的、让人垂涎欲滴的美食，一个异彩纷呈、变化多端的主食世界呈现在你面前。本集着重描绘不同地域、不同民族、不同风貌的有关主食的故事，展现人们对主食的样貌、口感的追求，处理和加工主食的智慧，以及中国人对主食的深厚情感。 第一季：3.转化的灵感 腐乳、豆豉、黄酒、泡菜，都有一个共同点，它们都具有一种芳香浓郁的特殊风味。这种味道是人与微生物携手贡献的成果。而这种手法被称作“发酵”。中国人的老祖宗，用一些坛坛罐罐，加上敏锐的直觉，打造了一个食物的新境界。要达到让食物转化成美食的境界，这其中要逾越障碍，要营造条件，要把握机缘，要经历挫败，从而由“吃”激发出最大的智慧。 第一季：4.时间的味道 腌制食品，风干晾晒的干货，以及酱泡、冷冻等是中国历史最为久远的食物保存方式。时至今日，中国人依然对此类食品喜爱有加。 本集涉及的美食主要有腊肉，火腿，烧腊，咸鱼(腌鱼)，腌菜，泡菜，渍菜，以及盐渍，糖渍，油浸，晾晒，风干，冷冻等不同食物保存方法，展现以此为基础和原材料的各种中国美食。贮藏食物从早先的保存食物 方便携带发展到人们对食物滋味的不断追求，保鲜的技术中蕴涵了中国人的智慧，呈现着中国人的生活，同时“腌制发酵保鲜”也蕴含有中国人的情感与文化意象，如对故乡的思念，内心长时间蕴含的某种情感等等。第一季：5.厨房的秘密 与西方“菜生而鲜，食分而餐”的饮食传统文化相比，中国的菜肴更讲究色、香、味、形、器。而在这一系列意境的追逐中，中国的厨师个个都像魔术大师，都能把“水火交攻”的把戏玩到如火纯青的地步，这是 8000年来的修炼。我们也在这漫长的过程中经历了煮、蒸、炒三次重要 的飞跃，他们共同的本质无非是水火关系的调控，而至今世界上懂得蒸菜和炒菜的民族也仅此一家。本集将主要透过与具有精湛美食技艺的人有关的故事，一展中国人在厨房中的绝技。 第一季：6.五味的调和

幼儿园儿歌歌词大全

幼儿园儿歌歌词 1《花仙子之歌》 lu lu lu lu lu lu lu lu lu lu lu lu lu lu lu lu lu 能给人们带来幸福的花儿啊。你在哪里悄悄地开放。我到处把你找。脚下的路伸向远方。大波斯菊是我的帽子。蒲公英在我在我枕边飘荡。穿过那阴森的针槐林。奋勇向前向前。幸福的花仙子就是我。名字叫Lulu不寻常。说不定说不定有那么一天。就来到来到你身旁。lu lu lu lu lu。lu lu lu lu lu。lu lu lu lu lu lu lu。大波斯菊是我的帽子。蒲公英在我在我枕边飘荡。穿过那阴森的针槐林。奋勇向前向前。幸福的花仙子就是我。名字叫Lulu不寻常。说不定说不定有那么一天。就来到来到你身旁。lu lu lu lu lu。lu lu lu lu lu。lu lu lu lu lu lu lu。 2《哪咤传奇》 是他。是他。就是他。我们的朋友。小哪咤。就是他。是他。就是他。少年英雄。小哪咤上天他比天要高。下海他比海更大。智斗妖魔勇降鬼怪。少年英雄就是小哪咤。有时他也聪明有时他也犯傻。他的个头和我一般高。有时他很努力有时他也贪玩。他的年纪和我一般大。上天他比天要高。下海他比海更大。智斗妖魔勇降鬼怪。少年英雄就是小哪咤。 3《两个小娃娃打电话》 两个小娃娃呀正在打电话呀。喂喂喂你在哪里呀。诶诶诶我在幼儿园。两个小娃娃呀正在打电话呀。喂喂喂你在做什么。诶诶诶我在学唱歌。两个小娃娃呀正在打电话呀。喂喂喂你在哪里呀。诶诶诶我在幼儿园。两个小娃娃呀正在打电话呀。喂喂喂你在做什么。诶诶诶我在学唱歌。两个小娃娃呀正在打电话呀。喂喂喂你在哪里呀。诶诶诶我在幼儿园。两个小娃娃呀正在打电话呀。喂喂喂你在做什么。诶诶诶我在学唱歌。 4《山猫和吉米》 蓝蓝的天空，青青的草地 快乐的山猫，快乐的吉咪 来到了大自然，呼吸新空气 快乐成长，健康成长， 这里是快乐的，快乐的天地 来吧。。来吧来吧。。。来吧。。来吧来吧。。。来吧。。来到我的身边 来吧。。来吧来吧。。。来吧。。来吧来吧。。。和我一起唱歌跳舞 蓝蓝的天空，青青的草地 帅气的山猫，漂亮的吉咪 来到了我身边，快乐无极限快乐成长，健康成长 这里是快乐的，快乐的天地 5《大头儿子小头爸爸》 大头儿子,小头爸爸.。一对好朋友,快乐父子俩.。儿子的头大手儿小,。爸爸的头小手儿大.。大手牵小手,走路不怕滑.。走着走着走走走走,转眼儿子就长大.。 啦啦啦啦啦啦拉啦,转眼儿子就长大! 6《小羊儿》 小羊咩咩叫妈妈。母羊咩咩也叫他。跟着妈妈一道去。吃饱早回家。小羊咩咩叫妈妈。 母羊咩咩也叫他。跟着妈妈一道去。吃饱早回家。小羊咩咩叫妈妈。母羊咩咩也叫他。 跟着妈妈一道去。吃饱早回家。小羊咩咩叫妈妈。母羊咩咩也叫他。跟着妈妈一道去。 吃饱早回家。跟着妈妈一道去。吃饱早回家。啦......啦......

幼儿儿歌歌词20首

幼儿儿歌歌词20首 1、小白兔 小白兔乖乖，把门开开，快点开开，我要进来，不开不开就不开，妈妈没回来，谁叫也不开。 小白兔乖乖，把门开开，妈妈回来，我要进来，快开快开快快开，妈妈回来了，我来把门开。 2、小花狗 小花狗，真叫脏，不洗脚丫就上床。问它为什么？它说忘，忘，忘！ 3、吃饭 小宝宝，坐坐好，妈妈盛饭喂宝宝。细细嚼，慢慢咽，宝宝吃得直叫好。 4、干净 鼻涕擦干净，指甲剪干净，脸儿洗干净，妈妈和和亲一亲。 5、乒乓球 乒乓球,两人打,他给我,我给他, 谁都不愿落在自己家. 6、踢毽子

小毽子,小毽子,飞上天, 落下地，我们都来踢踢它,踢不好儿没关系. 7、走夜路 夜色黑,星星闪,小朋友,把家还, 突然窜出一只兔,吓得它,满处窜. 8、小盒子 小盒子,作用大,铅笔橡皮装的下, 还有一支小钢笔,装在里面心欢喜, 9、大马路 马路长又宽,我站在中间, 老师过来拜拜手, 让我赶快走,说是有危险. 10、木头人 三三三，我们都是木头人， 不许哭来不许笑，还有一个不许动。 11、小皮球,小小篮,落地开花二十一,二五六,二五七, 二八二九三十一;三五六,三五七,三八三九四十一……

12、小蜜蜂 小蜜蜂，嗡嗡嗡，嗡嗡嗡，大家一起勤劳动， 来匆匆，去匆匆，走得兴味浓，春暖花开不做工， 将来哪里好过冬？嗡嗡嗡，嗡嗡嗡，不学懒惰虫。 13、请你唱个歌吧 小杜鹃,小杜鹃，我们请你唱个歌，快来呀,大家来呀， 我们静听你的歌，咕咕!咕咕!歌声使我们快乐 14、谁会飞 谁会飞呀，鸟会飞，鸟儿鸟儿怎样飞？拍拍翅膀飞呀飞 谁会游呀，鱼会游，鱼儿鱼儿怎样游？摇摇尾巴点点头 谁会跑呀，马会跑，马儿马儿怎样跑？四脚离地身不摇。 15、好朋友 你帮我来梳梳头，我帮你来扣纽扣 团结友爱手拉手，我们都是好朋友 嘿嘿！ 16、数鸭子 门前大桥下，游过一群鸭，

舌尖上的中国(1-7)全集解说词

《舌尖上的中国》7集（全）解说词 《舌尖上的中国》是纪录频道推出的第一部高端美食类系列纪录片，从2011年3月开始大规模拍摄，是国内第一次使用高清设备拍摄的大型美食类纪录片。共在国内拍摄60个地点方，涵盖了包括港澳台在内的中国各个地域，它全方位展示博大精深的中华美食文化。向观众，尤其是海外观众展示中国的日常饮食流变，千差万别的饮食习惯和独特的味觉审美，以及上升到生存智慧层面的东方生活价值观，让观众从饮食文化的侧面认识和理解传统和变化着的中国。 而台词优美而清新，听着解说看着画面，让人心醉··· 第一集《自然的馈赠》 中国拥有世界上最富戏剧性的自然景观，高原，山林，湖泊，海岸线。这种地理跨度有助于物种的形成和保存，任何一个国家都没有这样多潜在的食物原材料。为了得到这份自然的馈赠，人们采集，捡拾，挖掘，捕捞。穿越四季，本集将展现美味背后人和自然的故事。 香格里拉，松树和栎树自然杂交林中，卓玛寻找着一种精灵般的食物——松茸。松茸保鲜期只有短短的两天，商人们以最快的速度对松茸的进行精致的加工，这样一只松茸24小时之后就会出现在东京的市场中。 松茸产地的凌晨3点，单珍卓玛和妈妈坐着爸爸开的摩托车出发。穿过村庄，母女俩要步行走进30公里之外的原始森林。雨让各种野生菌疯长，但每一个藏民都有识别松茸的慧眼。松茸出土后，卓玛立刻用地上的松针把菌坑掩盖好，只有这样，菌丝才可以不被破坏，为了延续自然的馈赠，藏民们小心翼翼地遵守着山林的规矩。 为期两个月的松茸季节，卓玛和妈妈挣到了5000元，这个收入是对她们辛苦付出的回报。老包是浙江人，他的毛竹林里，长出过遂昌最大的一个冬笋。冬笋藏在土层的下面，从竹林的表面上看，什么也没有，老包只需要看一下竹梢的叶子颜色，就能知道笋的准确位置，这完全有赖于他丰富的经验。 笋的保鲜从来都是个很大的麻烦，笋只是一个芽，是整个植物机体活动最旺盛的部分。聪明的老包保护冬笋的方法很简单，扒开松松的泥土，把笋重新埋起来，保湿，这样的埋藏方式就地利用自然，可以保鲜两周以上。 在中国的四大菜系里，都能见到冬笋。厨师偏爱它，也是因为笋的材质单纯，极易吸收配搭食物的滋味。老包正用冬笋制作一道家常笋汤，腌笃鲜主角本来应该是春笋，但是老包却使用价格高出20倍的遂昌冬笋。因为在老包眼里，这些不过是自家毛竹林里的一个小菜而已。在云南大理北部山区，醒目的红色砂岩中间，散布着不少天然的盐井，这些盐成就了云南山里人特殊的美味。老黄和他的儿子树江小溪边搭建一个炉灶，土灶每年冬天的工作就是熬盐。云龙县的冬季市场，老黄和儿子赶到集市上挑选制作火腿的猪肉，火腿的腌制在老屋的院子里开始。诺邓火腿的腌制过程很简单，老黄把多余的皮肉去除，加工成一个圆润的火腿，洒上白酒除菌，再把自制的诺盐均匀的抹上，不施锥针，只用揉、压，以免破坏纤维。 即使用现代的标准来判断，诺邓井盐仍然是食盐中的极品，虽然在这个古老的产盐地，盐业生产已经停止，但我们仍然相信诺邓盐是自然赐给山里人的一个珍贵礼物。 圣武和茂荣是兄弟俩，每年9月，他们都会来到湖北的嘉鱼县，来采挖一种自然的美味。这

百首儿歌童谣歌词大全(可自编手指操)

百首儿歌童谣歌词大全 1、做早操 ?早上空气真叫好，我们都来做早操。 伸伸臂，弯弯腰，踢踢腿，蹦蹦跳，天天锻炼身体好。 2、饭前要洗手 小脸盆，水清请，小朋友们笑盈盈，小手儿，伸出来，洗一洗，白又净，吃饭前，先洗手，讲卫生，不得病。 3、小手绢 小手绢，四方方，天天带在我身上。 又擦鼻涕又擦汗，干干净净真好看。 4、搬鸡蛋 小老鼠，搬鸡蛋，鸡蛋太大怎么办？一只老鼠地上躺，紧紧抱住大鸡蛋。一只老鼠拉尾巴，拉呀拉呀拉回家。 5、大骆驼 骆驼骆驼志气大，风吹日晒都不怕。 走沙漠，运盐巴，再苦再累不讲话。 6、螳螂 螳螂哥，螳螂哥，肚儿大，吃得多。飞飞能把粉蝶捕，跳跳能把蝗虫捉。两把大刀舞起来，一只害虫不放过7、大蜻蜓 大蜻蜓，绿眼睛，一对眼睛亮晶晶， 飞一飞，停一停，飞来飞去捉蚊蝇。 8、小鸭子 小鸭子，一身黄，扁扁嘴巴红脚掌。 嘎嘎嘎嘎高声唱，一摇一摆下池塘。 9、拍手歌 你拍一，我拍一，天天早起练身体。 你拍二，我拍二，天天都要带手绢。 你拍三，我拍三，洗澡以后换衬衫。 你拍四，我拍四，消灭苍蝇和蚊子。 你拍五，我拍五，有痰不要随地吐。 你拍六，我拍六，瓜皮果核不乱丢。 你拍七，我拍七，吃饭细嚼别着急。 你拍八，我拍八，勤剪指甲常刷牙。 你拍九，我拍九，吃饭以前要洗手。 你拍十，我拍十，脏的东西不要吃。 10 、小螃蟹 小螃蟹，真骄傲，横着身子到处跑， 吓跑鱼，撞倒虾，一点也不懂礼貌

11 、庆六一 儿童节，是六一，小朋友们真欢喜。 又唱歌来又跳舞，高高兴兴庆六一。 12、花猫照镜子 小花猫，喵喵叫，不洗脸，把镜照， 左边照，右边照，埋怨镜子脏，气得胡子翘。 13、蚂蚁搬虫虫 小蚂蚁，搬虫虫，一个搬，搬不动，两个搬，掀条缝，三个搬，动一动，四个五个六七个，大家一起搬进洞。 14、小青蛙 小青蛙，呱呱呱，水里游，岸上爬， 吃害虫，保庄稼，人人都要保护它。 15、花儿好看我不摘 公园里，花儿开，红的红，白的白， 花儿好看我不摘，人人都说我真乖。 16 、红绿灯 大马路，宽又宽，警察叔叔站中间， 红灯亮，停一停，绿灯亮，往前行。 17 、七个果果 一二三四五六七，七六五四三二一。 七个阿姨来摘果，七个篮子手中提。七个果子摆七样。苹果、桃儿、石榴、柿子、李子、栗子、梨。 18、睡午觉 枕头放放平，花被盖盖好。 小枕头，小花被，跟我一起睡午觉，看谁先睡着。 19 、吃荸荠 荸荠有皮，皮上有泥。 洗掉荸荠皮上的泥，削去荸荠外面的皮， 荸荠没了皮和泥，干干净净吃荸荠。 20 、小云骑牛去打油 小云骑牛去打油，遇着小友踢皮球， 皮球飞来吓了牛，摔下小云撒了油。 21 、盆和瓶 车上有个盆，盆里有个瓶，乒乒乒，乓乓乓， 不知是瓶碰盆，还是盆碰瓶。 22、小脏手，长指甲

scifinder使用手册

研发工具Scifinder 《使用说明书》 第一章 Scifinder数据库介绍 1.1 Scifinder数据库 1.1.1 检索系统 支持的检索方式：主题检索、物质名称检索、分子式检索、结构式检索、亚结构式检索、相似结构式检索、反应式检索、类反应式检索、分析与二次检索。作者、机构名称、杂志名称、专利号、CA号 检索结果后处理系统：Scifinder不仅仅包含有分子式、反应式和结构式（包括亚结构检索）等多种检索功能，而且在得到初步检索结果的基础上还可以进一步利用超过20个选项的后处理功能以最快的速度找到最精确的答案。它已经超越了检索工具的范畴，而成为研发人员不可或缺的研发工具。 包含的信息量：大于2600万条文章记录；3000多万个有机无机物质，5800多万生物序列，1100多万条单步多步反应。 信息时效：1907年－至今 1.1.2 原文下载系统 Scifinder具有强大的期刊和专利链接，收录了全世界9500多种主要期刊和50多家合法专利发行机构的专利文献中公布的研究成果，占全球化工信息的98%。其中能直接链接原文的化工类核心期刊1365种，约占全球英文版电子化工类期刊总数的90%，几乎包含了所有高影响因子期刊。 1.2 Scifinder的化学学科覆盖 事实上，Scifinder数据库囊括了自20世纪以来所有与化学相关的资料，以及大量生命科学及及其它科学学科方面的信息。

1.2.1 生命科学领域 遗传学、基因组学、酶学、蛋白质组学、实验内科学、生物化学、生物技术学、分子生物学、微生物学、细胞生物学、药理学等 能够保证将9大主要专利组织2日内发布的与生物加工方法、蛋白质组技术、新遗传学工艺，及其它对现代药品开发至关重要的课题等相关的专利收录进来； 通过Scifinder访问遗传学、酶学、生物化学、生物技术学及更多科学信息。 用户可查找：摘自细胞、科学、核酸研究、现代药品开发资料及其它知名期刊的科学信息。 1.2.2 传统化学领域 有机化学 高分子——聚合物、塑料、纺织、橡胶粘合剂等 应用化学和化学工程——加工、工业化学、金属/合金、陶瓷、环境等 物理化学、无机化学和分析化学