英语

Photogrammetric and Lidar Data Registration

Using Linear Features

Ayman Habib, Mwafag Ghanma, Michel Morgan, and Rami Al-Ruzouq

Abstract

The enormous increase in the volume of datasets acquired

by lidar systems is leading towards their extensive exploita-tion in a variety of applications, such as, surface reconstruc-tion, city modeling, and generation of perspective views. Though being a fairly new technology, lidar has been influ-enced by and had a significant impact on photogrammetry. Such an influence or impact can be attributed to the com-plementary nature of the information provided by the two systems. For example, photogrammetric processing of imagery produces accurate information regarding object space break lines (discontinuities). On the other hand, lidar provides accurate information describing homogeneous physical surfaces. Hence, it proves logical to combine data from the two sensors to arrive at a more robust and com-plete reconstruction of 3D objects. This paper introduces alternative approaches for the registration of data captured

by photogrammetric and lidar systems to a common refer-ence frame. The first approach incorporates lidar features as control for establishing the datum in the photogrammetric bundle adjustment. The second approach starts by manipu-lating the photogrammetric imagery to produce a 3D model, including a set of linear features along object space disconti-nuities, relative to an arbitrarily chosen coordinate system. Afterwards, conjugate photogrammetric and lidar straight-line features are used to establish the transformation be-tween the arbitrarily chosen photogrammetric coordinate system and the lidar reference frame. The second approach (bundle adjustment, followed by similarity transformation) is general enough to be applied for the co-registration of mul-tiple three-dimensional datasets regardless of their origin (e.g., adjacent lidar strips, surfaces in GIS databases, and temporal elevation data). The registration procedure would allow for the identification of inconsistencies between the surfaces in question. Such inconsistencies might arise from changes taking place within the object space or inaccurate calibration of the internal characteristics of the lidar and the photogrammetric systems. Therefore, the proposed method-ology is useful for change detection and system calibration applications. Experimental results from aerial and terrestrial datasets proved the feasibility of the suggested methodologies.

Introduction

Currently, a variety of applications demand fast and reliable collection of data about physical objects (e.g., automatic DEM generation, city modeling, and object recognition). Such applications require the availability of information pertain-ing to the geometric and semantic characteristics of such objects in which surfaces play an important role (Habib and Schenk, 1999). Photogrammetry is the conventional method for surface reconstruction. However, lidar systems, whether ground based, airborne, or space borne, have recently emerged as a new technology with a promising potential towards dense and accurate data capture on physical surfaces (Schenk and Csathó, 2002). Photogrammetry and lidar have unique characteristics that make either technology preferable in specific applications. For example, photogrammetry is more suited for mapping heavily populated areas, while lidar is preferable in mapping Polar Regions. However, one can observe that a negative aspect in one technology is con-trasted by a complementary strength in the other. Therefore, integrating the two systems would prove extremely benefi-cial (Schenk and Csathó, 2002).

Photogrammetric object space reconstruction starts with identifying features of interest in overlapping images. Con-jugate features and the exterior orientation parameters of the involved images are then used in an intersection procedure yielding corresponding object features. Surfaces derived from terrestrial and aerial imagery possess a rich body of scene information. Moreover, derived object space features are very accurate, especially if they appear in more than two images as a result of the high redundancy. The weakness of photogram-metry is the “matching problem” (i.e., finding corresponding features in overlapping images). The success of automatic surface reconstruction from imagery is contingent on the reliability of the matching process. Manual or automatic matching is only possible when features with unique gray-scale value distribution function are used. As a result, im-plemented features usually correspond to locations along discontinuities in one or more directions within the images (e.g., edges and interest points). Such features usually pertain to discontinuities and break lines in the object space. There-fore, photogrammetric surfaces provide a rich set of informa-tion along object space break lines and almost no information along homogeneous surfaces with uniform texture.

Lidar has been conceived as a method to directly and accurately capture digital elevation data. However, in order

to reach the high accuracy potential, the lidar system must

be well calibrated and equipped with a high end DGPS/INS navigation unit (Filin and Csathó, 1999). An appealing feature in the lidar output is the direct availability of 3D coordinates of points in the object space. The surface recon-struction process is simply formulated as a three-dimensional rigid body transformation of points from scanner space to object space. One should note that there is no inherent redundancy in the computation of lidar points. Moreover,

Department of Geomatics Engineering, University of Calgary, 2500, University Drive, NW., Calgary, Alberta, Canada T2N 1N4 (habib@geomatics.ucalgary.ca; mghanma@geomatics. ucalgary.ca; mfmorgan@ucalgary.ca; rialruzo@ucalgary.ca). PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING Photogrammetric Engineering & Remote Sensing

Vol. 71, No. 6, June 2005, pp. 699–707.

0099-1112/05/7106–0699/$3.00/0? 2005 American Society for Photogrammetry

and Remote Sensing

June 2005699

lidar surfaces are mainly positional, and there is no addi-tional semantic or scene information available except when the intensity of the reflected signal is recorded. Since lidar provides a discrete set of irregularly distributed object points, the acquired surfaces possess rich information along homo-geneous physical surfaces, and almost no information along object space discontinuities.

It should be clear from the previous discussion that the integration of photogrammetric and lidar data would be extremely beneficial. For example, lidar surfaces could be used to constrain and resolve ambiguities in the photogram-metric matching process. Moreover, photogrammetric data will enrich lidar surfaces by providing more semantic attri-butes. Also, photogrammetric and lidar surfaces can be inspected for inconsistencies, which has to be justified (e.g., changes in the object space or inaccurate calibration of the internal characteristics of either system). Therefore, success-ful integration will facilitate subsequent processing activities such as system calibration, object recognition, and genera-tion of 3D textured models. However, achieving the full potential of the synergism between the two technologies is contingent on accurate and reliable co-registration of the respective surfaces relative to the same reference frame (Habib and Schenk, 1999; Schenk, 1999; Postolov et al., 1999). This should not be surprising, since any registration process aims at combining data and information from mul-tiple sensors in order to achieve improved accuracies and better inference about the environment than could be attained through the use of a single sensor (Brown, 1992).

The most common methods for solving the registration problem between two datasets are based on the identifica-tion of common points. Such methods are not applicable when dealing with lidar surfaces, since they correspond to laser footprints rather than distinct points that could be identified in the imagery (Baltsavias, 1999). Traditionally, surface-to-surface registration or comparisons have been achieved by interpolating both datasets into a regular grid. The comparison is then reduced to estimating the necessary shifts by analyzing the elevations at corresponding grid posts (Ebner and Strunz, 1988; Ebner and Ohlhof, 1994; Kilian et al., 1996). There are several problems with this approach. First, the interpolation to a grid will introduce errors especially when dealing with captured surfaces over urban areas. Moreover, minimizing the differences between the surfaces along the z-direction is only valid when dealing with horizontal planar surfaces (Habib and Schenk, 1999). Postolov et al. (1999) introduced another approach, which does not require initial interpolation of the data. However, the implementation procedure involves an interpolation of one surface at the location of conjugate points on the other surface. Furthermore, the registration is based on minimiz-ing the differences between the two surfaces along the

z-direction. Schenk (1999) devised an alternative approach, where distances between points of one surface along surface normals to locally interpolated patches of the other surface are minimized. Habib and Schenk (1999) and Habib et al. (2001b) implemented this methodology within a comprehen-sive automatic registration procedure. Such an approach is based on the manipulation of photogrammetric data to pro-duce object space planar patches. This might not be always possible, since photogrammetric surfaces provide accurate information along object space discontinuities while supply-ing almost no information along homogeneous surfaces with uniform texture.

This paper introduces alternative methodologies for the registration of photogrammetric and lidar data using three-dimensional, straight-line features. The following section outlines the main components of an effective registration paradigm. Afterwards, the methodology for extracting the 700June 2005registration primitives from photogrammetric and lidar data

is explained. Then, the details of the mathematical model for

establishing the transformation parameters between the two

datasets are introduced. The last two sections cover experi-

mental results (using terrestrial and airborne datasets), as

well as, conclusions and recommendations for future work.

Registration Paradigm

In general, any registration process aims at combining data

and information from multiple sensors in order to achieve an

improved accuracy and better inference about the environ-

ment than could be attained through the use of a single

sensor. Due to the enormous increase in the volume of spa-

tial data that is being acquired by an ever-growing number

of sensors, there is a pressing need for the development of

accurate and robust registration procedures that can handle

spatial data with varying formats. An effective registration

procedure must address the following issues (Brown, 1992):

Registration Primitives

The first step in the registration procedure is to decide upon

the primitives to use for establishing the transformation be-

tween the datasets in question. The primitive choice influ-

ences subsequent registration steps. In this research, straight-

line features have been used as the registration primitives.

This choice is motivated by the fact that such primitives can

be reliably, accurately, and automatically extracted from

photogrammetric and lidar datasets.

Similarity Measure

The next step in the registration paradigm is the selection of

the similarity measure, which mathematically expresses the

relationship between the attributes of conjugate primitives in

overlapping surfaces. The similarity measure formulation

depends on the selected registration primitives and their

respective attributes. In this work, the similarity measure

formulation has been incorporated in mathematical con-

straints ensuring the coincidence of conjugate linear features

after establishing the proper co-registration between invol-

ved surfaces.

Registration Transformation Function

The most fundamental characteristic of any registration

technique is the type of spatial transformation or mapping

function needed to properly overlay the two datasets. In this

research, a 3D similarity transformation is used as the regis-

tration transformation function, Equation 1. Such transfor-

mation assumes the absence of systematic biases in both

photogrammetric and lidar surfaces (Filin, 2001). However,

the quality of fit between conjugate primitives can be ana-

lyzed to investigate the presence of such behavior.

X A

￡ Y A §

Z A

X T

￡ Y T §

Z T

X a

, K ) ￡ Y a §

Z a

S R( ,(1)

where S is the scale factor, (X T Y T Z T)T is the translation

vector between the origins of the photogrammetric and lidar

coordinate systems, R( , ,K) is the 3D orthogonal rotation

matrix between the two coordinate systems, (X a Y a Z a)T are

the photogrammetric point coordinates, and (X A Y A Z A)T are

the coordinates of the corresponding point relative to the

lidar reference frame.

Matching Strategy

To automate the solution of the registration problem, a

controlling framework that utilizes the primitives, the

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

similarity measure, and the transformation function must be established. Such framework is usually referred to as the matching strategy. In the current research, the correspon-dence between conjugate entities has been solved manually.

A comprehensive matching strategy will be the focus of future work.

The presented research in this paper is concerned with the first two issues of the abovementioned registration para-digm. The following two sections describe the extraction of the registration primitives from photogrammetric and lidar datasets as well as the manipulation of these primitives to derive an estimate of the transformation parameters between the involved datasets.

Extraction of Registration Primitives

In this research, overlapping surfaces are co-registered by the virtue of conjugate linear features extracted from pho-togrammetric and lidar data. The next subsection explains the manipulation of photogrammetric data for the purpose of producing a 3D model of the object space, relative to an arbitrarily chosen reference frame, including a set of linear

features along objects’ discontinuities. Afterwards, the gen-eration of corresponding linear features from the lidar data-set will be discussed. Photogrammetric and lidar discontinu-ities will then be used to establish the transformation between the arbitrarily chosen photogrammetric coordinate system and the lidar reference frame.

Photogrammetric Linear Features

The methodology for producing 3D straight-line features

from photogrammetric datasets depends on the representa-tion scheme of such features in the object and image space. Prior research in this area concluded that representing object space straight-lines using two points along the line is the most convenient representation from a photogrammetric point of view since it yields well-defined line segments (Habib, 1999; Habib et al., 2002). On the other hand, image space lines will be represented by a sequence of 2D coordi-nates of intermediate points along the feature. This represen-tation is attractive since it can handle image space linear features in the presence of distortions, as they will cause deviations from straightness. Moreover, it will allow for the incorporation of linear features in scenes captured by line cameras since perturbations in the flight trajectory would lead to deviations from straightness in image space linear features corresponding to object space straight lines (Habib

et al., 2001a).

The suggested procedure starts by identifying two points in one (Figure 1a) or two images (Figure 1b) along the line under consideration, Figure 1. These points will be used to define the corresponding object space line segment. One should note that these points can be selected in any of the images within which this line appears. Moreover, they need not be identifiable or even visible in other images. Interme-diate points along the line are measured in all the overlap-ping images. Similar to the end points, the intermediate points need not be conjugate, Figure 1.

For the end points, the relationship between the mea-sured image coordinates {(x1, y1), (x2, y2)} and the corre-sponding ground coordinates {(X1, Y1, Z1), (X2, Y2, Z2)} is established through the collinearity equations. Only four equations will be written for each line. The incorporation

of intermediate points into the adjustment procedure is achieved through a mathematical constraint. The underlying principle in this constraint is that the vector from the per-spective center to any intermediate image point along the

line is contained within the plane defined by the perspec-

tive center of that image and the two points defining the PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Figure 1. End points defining the object line are either

measured in (a) one image or (b) two images.

straight line in the object space, Figure 2. In other words, for

the intermediate points a constraint, which indicates that

the points {(X1, Y1, Z1), (X2, Y2, Z2), (X Oi, Y Oi, Z Oi) and (x i, y i,

0)} are coplanar is introduced. This can be mathematically

described through Equation 2:

(V1V2) V30.(2) In the above equation, V1 is the vector connecting the

perspective center to the first end point along the object

space line, V2 is the vector connecting the perspective center

to the second end point along the object space line, and V3

is the vector connecting the perspective center to an inter-

mediate point along the corresponding image line. It should

be noted that the three vectors should be represented rela-

tive to a common coordinate system (e.g., the ground co-

ordinate system). The constraint in Equation 2 incorporates

Figure 2. Perspective transformation between image

and object space straight lines and the coplanarity

constraint for intermediate points along the line.

June 2005701

the image coordinates of the intermediate point, the Exterior Orientation Parameters (EOP), the Interior Orientation Para-meters (IOP) including distortion parameters, as well as, the ground coordinates of the points defining the object space line. Such a constraint does not introduce any new parame-ters and can be written for all intermediate points along the line in the imagery. The number of constraints is equal to

the number of intermediate points measured along the image line. In general, Equation 2 and four collinearity equations are used to estimate the EOP as well as the object coordi-nates of the end points along the linear feature in question. However, for self-calibration exercises, the IOP can be solved for as well.

The manipulation of tie straight-lines appearing in a group of overlapping images can be summarized as follows: first, two points that define the object line are measured in one or two images. For these points four collinearity equa-tions are formulated. Then, intermediate points are meas-ured along the image line in overlapping images. Each inter-mediate point provides one additional constraint of the form in Equation 2. Such a methodology can be easily incorpo-rated in an existing point-based bundle adjustment proce-dure. The treatment of a control linear feature (with known object coordinates of its end points) will be slightly differ-ent. Since the control line already provides the end points, they need not be measured in any of the images. Therefore, the image space linear features will be represented by a group of intermediate points in all the images. Each interme-diate point provides one constraint of the form in Equation 2. For single photo resection, a minimum of three control lines per image is required. One should note that the above discussion outlines one approach, which can be utilized for the extraction of linear features from photogrammetric data. However, other approaches might be used to achieve this objective (e.g., Baillard et al., 1999; Habib et al., 2003). Lidar Linear Features

The growing acceptance of lidar as an efficient data acquisi-tion system by researchers in the photogrammetric commu-nity led to a number of studies aiming at preprocessing lidar data. The purpose of such studies ranges from simple pri-mitive detection and extraction to more complicated tasks such as segmentation and perceptual organization (Maas and Vosselman, 1999; Csathó et al., 1999; Lee and Schenk, 2001; Vosselman and Dijkman, 2001; Filin, 2002; Rottensteiner and Briese, 2003). Lidar data segmentation into a group of homo-geneous patches can be carried out using an accumulator array that keeps track of the frequency of points along pre-defined analytical surfaces involving unknown parameters (Maas and Vosselman, 1999). Another alternative for surface segmentation starts by identifying seed points, which can be augmented by neighboring points that fit the behavior of the sought-after surfaces (Lee and Schenk, 2001). Identified point clouds belonging to the selected patches from either approach are then used within a least squares adjustment to determine the encompassing plane. The quality of fit should be ana-lyzed to check whether the segmented patch is planar, or not. Also, a blunder detection procedure has to be imple-mented to remove points outside the segmented planar patch. Neighboring planar patches are finally intersected to produce lidar straight-line features, which are defined by their end points. Filin (2002) introduced an alternative surface clustering technique, which starts by identifying patterns in the data based on a set of attributes that catego-rize the sought-after information and produce the best sepa-ration among classes. A grouping process is then applied to classify areas with homogeneous attributes.

This research aims at investigating the possibility of using linear features for the registration of photogrammetric 702June 2005and lidar data. Therefore, suspected planar patches in lidar

dataset are manually identified with the help of correspon-

ding optical imagery, Figure 3. The selected patches are

then checked using a least squares adjustment to determine

whether they are planar or not and to remove blunders.

Finally, neighboring planar patches with different orientation

are intersected to determine the end points along object space

discontinuities between the patches under consideration.

The extraction of linear features from ranging data can

be simplified by using the intensity image provided by

newly available lidar systems. In such a case, image process-

ing or edge detection techniques can be applied to the

intensity image to identify the linear features, which could

be then related to photogrammetric linear features.

Photogrammetric to Lidar Data Registration Using Linear Features: Similarity Measure

This section discusses the manipulation of conjugate linear

features for the purpose of determining the parameters of the

Figure 3. Manually identified planar patches within the

(a) lidar data guided by the (b) corresponding optical

image.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

registration transformation function relating the surfaces in question. Lidar linear features can be integrated with the photogrammetric data in two different ways. The first and most straightforward alternative is to use the lidar linear features as control for the photogrammetric bundle adjust-ment. In this scenario, the lidar features will establish the datum for the photogrammetric model. This alternative is restrictive since it is not general enough to allow for any surface-to-surface registration exercise regardless of their origin. For example, it cannot be used to establish the regis-tration between two overlapping lidar surfaces. Moreover, direct incorporation of the lidar features in the photogram-metric adjustment will not allow for an explicit inspection

of the compatibility or the discrepancy between the involved surfaces. Such a discrepancy might take place due to im-proper system calibration, measurement blunders, or physi-cal changes in the object space.

The other alternative is to separately process the pho-togrammetric and lidar datasets to generate the linear fea-tures. It has to be mentioned that the datum for the pho-togrammetric bundle adjustments will be established by choosing an arbitrary reference frame. For example, seven out of the nine coordinates of three well-distributed tie points can be arbitrarily fixed. Afterwards, conjugate features will be manipulated to establish the parameters of the 3D similarity transformation relating the photogrammetric co-ordinate system to the lidar reference frame. Determining the parameters of the registration transformation function will be carried out using a similarity measure that involves the attri-butes of the linear features. The derivation of the similarity measure is based on the fact that the photogrammetric line segment should coincide with the corresponding lidar seg-ment after applying the registration transformation function, Figure 4.

The formulation of the similarity measure depends on the choice of the selected parameters to represent the regis-tration primitives (3D straight lines). As mentioned before, representing the line segments using two points along the line is the most convenient representation alternative. In this regard, it is worth mentioning that the end points represent-ing corresponding lidar and photogrammetric line segments need not be conjugate. Other means for representing straight-line features will entail several problems such as failure to represent finite line segments, singularities, variant error measures, and complicated models relating corresponding lines (Schwermann, 1994; Habib, 1999; Habib et al., 2002).

Referring to Figure 4, one can see that one of the end points of the photogrammetric line segment (e.g., point 1) should lie along the vector connecting the end points defi-ning the lidar line segment (e.g., points A and B). Such coincidence will only take place after applying the necessary registration transformation function relating the two surfaces

(assumed to be 3D similarity transformation in this applica-

tion). Such a constraint can be mathematically described by

Equation 3:

X T

￡ Y T §

Z T

S R(

X1

,K) ￡ Y1 §

Z1

X A

￡ Y A §

Z A

X B

l1 ￡ Y B

Z B

X A

Y A §

Z A

(3)

,

where (X T Y T Z T)T is the translation vector between the

origins of the photogrammetric and lidar coordinate systems,

R( , ,K) is the required rotation matrix to make the photo-

grammetric coordinate system parallel to the lidar reference

frame, and S is the scale factor.

Another constraint of the form in Equation 3 can be

introduced for the second point along the photogrammetric

model (e. g., point 2), Equation 4:

X T

￡ Y T §

Z T

S R(

X2

,K) ￡ Y2 §

Z2

X A

￡ Y A §

Z A

X B

l2 ￡ Y B

Z B

X A

Y A § .

Z A

(4)

,

Subtracting Equations 3 and 4 yields the following mathe-

matical expression:

(l2

Substituting

follows:

X B

l1) ￡ Y B

Z B

for S/(

X A

Y A §

Z A

X A

Y A §

Z A

2

S R(

1),

,

X2

,K) ￡ Y2

Z2

X1

Y1 § .

Z1

(5)

Equation 5 can be rewritten as

X2

,K) ￡ Y2

Z2

X1

Y1 § .

Z1

X B

￡ Y B

Z B

lR(,(6)

Equation 6 should come as no surprise, since it mathe-

matically formulates the concept that the photogrammet-

ric line segment (1-2) should be parallel to the lidar line

segment (A-B) after applying the rotation matrix (see Figure 4).

Dividing the first two rows of Equation 6 by the third

one would lead to the elimination of the scale factor ( ),

Equation 7:

(X B

(Z B

(Y B

(Z B

X A)

Z A)

Y A)

Z A)

R11 (X2

R31 (X2

R21 (X2

R31 (X2

X1)

X1)

X1)

X1)

R12 (Y2

R32 (Y2

R22 (Y2

R32 (Y2

Y1)

Y1)

Y1)

Y1)

R13 (Z2

R33 (Z2

R23 (Z2

R33 (Z2

Z1)

Z1)

Z1) .

Z1)

(7)

Equation 7 can be written for each pair of conjugate line

segments yielding two equations which contribute towards

the estimation of two rotation angles, the azimuth, and pitch

angle along the line. On the other hand, the roll angle across

the line cannot be estimated. Hence, a minimum of two non-

parallel lines is needed to recover the three elements of the

rotation matrix ( , , K).

Having estimated the rotation angles, one can proceed

with the recovery of the scale factor and the shift compo-

nents of the registration transformation function. To derive

these parameters, the rotation matrix is first applied to the

coordinates of the points defining the photogrammetric line

segment, Equation 8:

Figure 4. Similarity measure between photogrammetric and lidar linear features.

X T

￡ Y T §

Z T

U1

S ￡ V1 §

W1

X A

￡ Y A §

Z A

X B

l1 ￡ Y B

Z B

X A

Y A §

Z A

June 2005

(8)

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING703

where,

U1

￡ V1 §W1R(

X1

￡ Y1 § .

Z1

, ,K)

Rearranging the terms in Equation 8 yields the following equation:

X B l1 ￡ Y B

Z B X A

Y A §

Z A

X T

￡ Y T

Z T

S U1

S V1

S W1

X A

Y A § .

Z A

(9)

In Equation 9, one can eliminate 1 by dividing the first and second rows by the third one, Equation 10:

(X B (Z B (Y B (Z B X A)

Z A)

Y A)

Z A)

(X T

(Z T

(Y T

(Z T

SU1

SW1

SV1

SW1

X A)

Z A)

Y A) .

.

Z A)

(10)

A similar set of Equation 10 can be written for the second point along the photogrammetric line segment. The resulting four equations from both points are then used to derive two independent constraints, which could be utilized to estimate the shift components and the scale factor of the registration function, Equation 11:

(X T (Z T (Y T (Z T S U1

S W1

S V1

S W1

X A)

Z A)

Y A)

Z A)

(X T

(Z T

(Y T

(Z T

S U2

S W2

S V2

S W2

X A)

Z A)

Y A)

.

Z A)

(11)

Thus, a single pair of line segments would yield two constraints of the form in Equation 11 towards the estima-tion of the scale and shift components. Therefore, at least two lines are needed to estimate the involved four parame-ters (X T, Y T, Z T, and S). Through Eigenvalue analysis, it has been established that these lines should not be coplanar. In summary, a minimum of two non-coplanar line segments is needed to recover the seven elements of the 3D similarity transformation. One should note the parameters can be solved for in a sequential manner (i.e., using Equation 7 for estimating the rotation angles followed by the estimation of

the shift components and the scale factor through Equation 11) or simultaneously through concurrent consideration of the constraints in Equations 7 and 11.Figure 5. (a) Terrestrial photogrammetric dataset, and (b) extracted linear features.

Experimental Results

To illustrate the feasibility of using linear features to estab-lish the registration between two three-dimensional datasets, two experiments involving data captured by terrestrial,

as well as, aerial platforms were conducted. For the terres-trial dataset, a scene rich with planar surfaces and linear

features was prepared as depicted in Figure 5a. A SONY DSC-F707 camera with a five mega-pixel grid and a maximum resolution of 2560 pixels1960 pixels was used to cap-

ture twelve overlapping images from different locations.

A bundle adjustment procedure incorporating tie points

as well as linear features was carried out according to

the methodology outlined earlier. The datum of the photogrammetric model had been arbitrarily chosen by randomly fixing seven coordinates of three, well-distributed tie points. The output of the bundle adjustment procedure included the ground coordinates of tie points in addition to

704June 2005the ground coordinates of points defining the object space

line segments. Figure 5b shows the arrangement of linear

features extracted from imagery.

A Cyrax 2400 ground-based laser scanner was used to

capture 3D point cloud over the same scene, Figure 6a.

Homogeneous patches have been manually identified in the

lidar data and then used in a least squares adjustment to fit

planar surfaces. Neighboring planar surfaces were finally

intersected to produce object space straight-line segments,

Figure 6b. Using special targets, the Cyrax system provided

3D coordinates of selected points, which have been used as

tie points within the photogrammetric bundle adjustment

procedure. The lidar coordinates of these points will be

used to check the quality of the registration by comparing

them to the corresponding photogrammetric coordinates

after applying the estimated transformation function.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Figure 6. (a) Terrestrial lidar dataset, and (b) extracted linear features.

Least squares adjustment was then used to solve for the parameters of the 3D similarity transformation function using the similarity measure described by Equations 7 and 11. As mentioned before, a minimum of two, non-coplanar line segments are needed for the estimation of the seven transformation parameters. The estimated parameters are shown in Table 1. Afterwards, the lidar coordinates of the special targets were used to verify the compatibility between photogrammetric and lidar coordinates. The estimated trans- formation parameters were applied to the derived photo- grammetric coordinates of the special targets relative to the arbitrarily chosen reference frame. The transformed photo- grammetric coordinates were finally compared to the corre- sponding lidar measurements. The comparison results are summarized in Table 2. The comparison shows that the two sets of coordinates are compatible within the range of 2.65 mm to 4.47 mm, which is commensurate with the specification of the Cyrax and implemented digital camera. Therefore, it is concluded that the photogrammetric and lidar surfaces are compatible, and there are no systematic biases in either system that have not been accounted for.

Another experiment had been conducted with a dataset captured using an aerial platform over heavily populated area, Figure 3b. In this experiment, twenty-three overlapping images in three flight lines were used in a bundle adjust- ment, which incorporated linear features, mainly building boundaries, as well as some tie points. Similar to the terres- trial dataset, the datum had been arbitrarily chosen by fixing seven coordinates of three, well-distributed tie points. The output of the bundle adjustment procedure included the ground coordinates of tie as well as the ground coordinates of points defining the object space line segments. In the lidar data, homogeneous patches had been manually identi- fied to correspond to selected features in imagery. Planar surfaces were then fitted through the selected patches, from which neighboring planar surfaces were intersected to pro- duce object space line segments. A total of twenty-three 3D edges had been identified along ten buildings from three lidar strips (well-distributed within the area of interest). Once again, the developed similarity measures (Equa- tions 7 and 11) were utilized in a least squares adjustment procedure, involving the identified linear features in the photogrammetric and lidar datasets, to solve for the transfor- mation parameters relating the two surfaces. The estimated parameters of the 3D similarity transformation function are shown in Table 1. These parameters were then used to superimpose the lidar features onto the transformed photogrammetric ones, Figure 7. The mean normal distance between conjugate lidar and transformed photogrammetric line segments turned out to be 3.27 m (mainly in the Z-direction). This was a surprising result considering the camera, flight mission, and lidar specifications. The compat- ibility between the two surfaces was expected to be in the sub-meter range. Gazing at the discrepancy between the PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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Figure 7. (a) Top view, and (b) side view of the

deviation between the lidar and aerial photogrammetric features.

Figure 8. A typical pattern of the effect of uncompen- sated radial lens distortion on the reconstructed object space.

The mean normal distance between conjugate lidar and transformed photogrammetric line segments turned out to be 0.58 m, which lies within the expected accuracy range. One should also note the significant improvement in the quality of the estimated registration transformation parameters as depicted by the respective standard deviation. This improve- ment in the spatial discrepancies and the quality of the transformation parameters after introducing the radial lens distortion verifies its existence. It is worth mentioning that independent processing of the photogrammetric and lidar data allowed for: (a) the identification of unexpected dis- crepancies between the extracted features (in the range of 3.27 m), and (b) the justification of the origin of such a discrepancy (i.e., a pattern resembling that of uncompen- sated radial lens distortion). These findings would not be possible by simultaneous incorporation of the lidar features in the photogrammetric adjustment, where one would only observe a poor quality of fit.

This paper introduced two methodologies for establishing the co-registration between three-dimensional datasets, such as these generated from lidar and photogrammetric data using conjugate straight-line segments. The first approach directly incorporates the lidar lines in the photogrammetric bundle adjustment procedure (i.e., one step procedure). The second approach is characterized by a two-step procedure. It starts with deriving a photogrammetric model relative to an arbitrary datum. Then, the lidar lines are used to establish the datum for the photogrammetric model through absolute orientation. The second approach is general enough for deal- ing with photogrammetric-to-lidar and lidar-to-lidar registra- tion problems.

The involved line segments were represented by their end points while assuming the end points of corresponding line segments might not be conjugate. Analyzing the forego- ing results from the terrestrial and aerial experiments, the proposed methodology proved the feasibility of using linear features to establish a common reference frame for datasets acquired by photogrammetric and lidar systems. The limited number of required primitives (a minimum of two, non- coplanar line segments) to implement the procedure added to its practicality and convenience. The registration process allows for the detection, as well as, the justification of the origin of any discrepancy between the involved datasets (e.g., sensor calibration errors as explained by the experi- mental results from the aerial dataset, measurement errors, and physical changes) through a closer look at the discrep-

Conclusions and Recommendations for Future Research

Baillard, C., C. Schmid, A. Zisserman, and A. Fitzgibbon, 1999.

Automatic line matching and 3D reconstruction of buildings

from multiple views, International Archives of Photogrammetry

and Remote Sensing, 32(3–2W5):69–80.

Baltsavias, E., 1999. A comparison between photogrammetry and

laser scanning, ISPRS Journal of Photogrammetry and Remote Sensing, 54(1):83–94.

Brown, L. G., 1992. A survey of image registration techniques, ACM

Computing Surveys, 24(4):325–376.

Csathó, B.M., K. Boyer, and S. Filin, 1999. Segmentation of laser

surfaces, International Archives of Photogrammetry and Remote

Sensing, 32(3W14):73–80.

Ebner, H., and G. Strunz, 1988. Combined point determination using

digital terrain models as control information, International

Archives of Photogrammetry and Remote Sensing,

27(B11/3):578–587.

Ebner, H., and T. Ohlhof, 1994. Utilization of ground control points for image orientation without point identification in image

space, International Archives of Photogrammetry and Remote

Sensing, 30(3/1):206–211.

Filin, S., and B. Csathó, 1999. A novel approach for calibrating satellite laser altimeter systems, International Archives of Photogrammetry and Remote Sensing, 32(3W14):47–54.

References

Filin, S., 2001. Recovery of systematic biases in laser altimeters

using natural surfaces, International Archives of Photogramme-

try and Remote Sensing, 34(3-W4):85–91.

Filin, S., 2002. Surface clustering from airborne laser scanning data,

International Archives of Photogrammetry and Remote Sensing,

32(3A):119–124.

Habib, A., 1999. Aerial triangulation using point and linear features,

International Archives of Photogrammetry and remote Sensing,

32(3–2W5):137–141.

Habib, A., and T. Schenk, 1999. New approach for matching surfaces

from laser scanners and optical sensors, International Archives

of Photogrammetry and Remote Sensing, 32(3W14):55–61.

Habib, A., Y. Lee, and M. Morgan, 2001a. Bundle adjustment with

self-calibration of line cameras using straight lines, Joint Work-

shop of ISPRS WG I/2, I/5 and IV/7: High Resolution Mapping

from Space 2001, University of Hanover, 19–21 September,

unpaginated CD-ROM.

Habib, A., Y. Lee, and M. Morgan, 2001b. Surface matching and

change detection using the modified Hough transform for robust

parameter estimation, The Photogrammetric Record, 17(98):

303–315.

Habib, A., M. Morgan, and Y. Lee, 2002. Bundle adjustment with

self calibration using linear features, The Photogrammetric

Record, 17(100):635–650.

Habib, A., Y. Lee, and M. Morgan, 2003. Automatic matching and

three-dimensional reconstruction of free-form linear features

from stereo images, Journal of Photogrammetric Engineering &

Remote Sensing, 69(2):189–197.

Kilian, J., N. Haala, and M. Englich, 1996. Capture and evaluation of

airborne laser scanner data, International Archives of Photo-

grammetry and Remote Sensing, 31(B3):383–388.

Lee, I., and T. Schenk, 2001. 3D perceptual organization of laser

altimetry data, International Archives of Photogrammetry and

Remote Sensing, 34(3W4):57–65.

Maas, H., and G. Vosselman, 1999. Two algorithms for extracting

building model from raw laser altimetry data, ISPRS Journal of

Photogrammetry and Remote Sensing, 54(2–3):153–163.

Postolov, Y., A. Krupnik, and K. McIntosh, 1999. Registration of

airborne laser data to surfaces generated by photogrammetric

means, International Archives of Photogrammetry and Remote

Sensing, 32(3W14):95–99.

Rottensteiner, F., and C. Briese, 2003. Automatic generation of

building models from lidar data and the integration of aerial

images, International Archives of Photogrammetry and Remote

Sensing, 34(3W13):174–180.

Schenk, T., 1999. Determining transformation parameters between

surfaces without identical points, Technical Report Photogram-

metry No. 15, Department of Civil and Environmental Engineer-

ing and Geodetic Science, Ohio State University, 22 p.

Schenk, T., and B. Csathó, 2002. Fusion of lidar data and aerial

imagery for a more complete surface description, International

Archives of Photogrammetry and Remote Sensing, 34(3A):

310–317.

Schwermann, R., 1994. Automatic image orientation and object

reconstruction using straight lines in close range photogramme-

try, International Archives of Photogrammetry and Remote

Sensing, Vol. 30, Commission V Symposium, Melbourne,

pp. 349–356.

Vosselman, G., and S. Dijkman, 2001. 3D building model recon-

struction from point clouds and ground plans, International

Archives of Photogrammetry and Remote Sensing, 34(3W4):

37–43.

(Received 02 December 2003; accepted 08 March 2004; revised 31

March 2004)

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING 706June 2005

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从英汉思维差异看英语写作的培育模式 摘要汉语式表达一直以来是困扰学生英语写作的一大难题。然而，在传统的语言教学中，由于教师过分强调语言的准确性，词汇的积累量和语法规则，学生虽能写出基本句型，但实际有效的句子写作不尽如人意。如何在写作过程中避免出现汉语式表达，培养学生写出正确而有效的英语句子，本文从英汉思维差异粗浅地探讨学生英语写作的培养基本模式。 关键词翻译适应选择论中医术语英译 中图分类号：H315 文献标识码：A 1英语写作中的中式表达 中国学生在写英语作文时，受汉语表达习惯的影响，很容易写出汉语化的英语。这是因为，在写作过程中，学生的头脑往往先呈现的是中文符号，之后将其不假思索地转换为英文，虽然有时符合语法规则，不影响理解，但是，在表达方式上与标准的英语习惯不符，从而影响语言的地道性。例如，表达中文“有”这一概念时，有这样的句子“The chief reason for the change have five points.”就属于汉式词汇在英语表达中的的生搬硬套。此外，常见的中式表达错误还有汉式的无主语句，英汉语序机械式对等，汉式的多动词连用等，如“People think go to a movie will cost

a lot of money”出现谓语动词使用混乱的表达，不妨将其改为“People think going to a movie will cost a lot of money”，这样句子的层次才更明显；再比如“Watching TV is convenient and won’t suffer from traffic jams”句中，逻辑主语跟后半句的谓语搭配不当；还有一些习惯表达及搭配的误用，如接电话很容易被误写为“receive the phone”……究其原因，是学生不注意英汉两种语言和文化背景的差异导致的错误。 2英汉思维差异 英文写作和中文写作其实是不同的东西方语篇思维模式的体现。西方人的思维方式以逻辑和直线性为特点，在遣词造句谋篇上遵循着从一般（genera1）到具体（specific），从概括（summarize）到举例（exemplify），从整体（whole）到个体（respective）的原则，即单刀直入先表达主要思想，然后对其加以说明或论证，一旦一点被论证结束，就不应该再回过头来重复讨论，因此语段展开的过程中，每个句子、每个段落都自然连贯，具有一种行云流水般的流动感；而东方人的思维方式以直觉的整体性与和谐的辨证性著称，是螺旋式思维模式，有种文章之妙，无过婉转曲折和只可意会不可言传的感觉，即对于所要表达的主题可以洋洋洒洒，下笔千言，给人形散而神聚的感官享受。从语言组织方式上又可以分为以句法和词汇为衔接手段的形

英语思维

一、英语思维方式的培养应该从模仿开始。 “学习语言的主要手段是模仿，这种模仿是从听觉定向活动开始的，经过大脑分析器的作用，然后由心理活动器官的操练而完成的。”心理语言学家认为，语言是从听开始的，当一个婴儿生下来就学说话时，完全是靠听，模仿（imitate）母亲的声音。如果一个婴儿生下来就是一个聋子，他就听不到声音，也谈不上什么成功的模仿者。一个不足10岁的儿童，如果他一直生活在第一语言环境中，他就能学到一种漂亮的母语。如果想学好外语，必须下大功夫模仿，采取多种方式，利用一切机会进行模仿。埃克斯利（Eckersley,C.E.1974）说过，毫无疑问，模仿是成功的钥匙，也许是把金钥匙。（There is no doubt that imitation is one of the keys, perhaps the golden key ,to success.）有人认为模仿很简单，好学，其实不然。养成一个好的模仿习惯并不容易，这种模仿只有像学母语那样，方可学好。不下功夫，以为轻而易举可以模仿好外语语音是不可能的。 因此，要想学好外语就要在模仿上下功夫，因为外语语言能否学好，在很大程度上决定于听准外语老师发音的能力和学习者的模仿能力以及反复模仿的耐心。如果跟着外语老师念一遍，过后一劳永逸，那是学不好外语的。所以，一定要持之以恒地模仿、重复、练习。“听别人怎样说，就照样跟着说。”这是学习语言的必由之路。 二、英语思维模式的培养应该培养自己摆脱母语的影响，用英语想英语。 用英语想英语，指的是在使用英语时用英语想（think in English），而不是用本族语想。用英语想，也可以说成用英语思考。学英语而不学用英语思考，一定学不好。用英语思考，就是在使用英语进行表达和理解时，没有本族语思考的介入，没有“心译”的介入，或者说本族语思考的介入被压缩到了极不明显的程度，自己也感觉不到“心译”的负担。这才是真正流利，熟练的境界和标志。 用英语思考并不神秘，也非高不可攀。初学时，“心译”的介入很明显，但时间一长，反复运用的次数越来越多，“心译”的程度就会越来越小，以至接近于消失。可见，培养英语思考的基本途径是系统的大量的反复使用，是实践练习。语言是工具。使用任何工具都有一个从不熟练到熟练的过程，在不熟练的阶段，多余的动作很明显，总要一边做一边考虑。初学者使用外语时，“心译”就是这种多余的活动，是一边用一边考虑的表现。这里所说的考虑实际上是在大脑里进行的对将要表现出来的外部活动的一种检验。用本族语交际时，也有考虑考虑再说的情况，可以说是在心里把原来要说的话转成或翻译为另外一些说法进行掂量。但由于习以为常，所以不会给人造成负担和精神紧张。而在用英语交际时，由于怕错，所以想了又想，而由于英语不熟，语汇不多，所以就求助于本族语，产生“心译”。因此，培养用英语思考，消除“心译”，主要消除学生怕错的紧张心理。 学习英语、使用英语都要用思想。思想要有逻辑性。逻辑指思维的规律性。思想的逻辑性，条理性在很大程度上取决于人的大脑对客观事物反映的系统性和所掌握语言的系统程度。语言问题与逻辑问题是密切联系的。学生使用英语进行表达或理解别人用英语表达的思想时，所遇到的困难虽然表现为语言上的困难，但实质上有相当一部分，或在相当程度上乃是逻辑上的困难。表达不好，常常是思路不清，理解不好，则常常是推理能力差。因此，为了培养用英语思考，就要加强英语练习的逻辑性，注意按照英语所反映的客观事物的多种联系，从性质、属性、层次、因果等各方面的关系，对练习的形式和内容进行组织，训练学生成套地表达和理解，形成以英语为外壳的思维定势，相应的英语材料则以连锁反应的方式在大脑里源源不断地涌现。 摆脱母语影响的教育，用英语想英语应表现课堂上的每一分钟。 （一）营造良好的思维环境，激活学生学习思维。 1．融洽师生关系，激发用英语思考的兴趣。 2．培养学生的独立学习能力，让他们有更多的用英语想英语，独立思维的时间和空间。

E英语教程的翻译题答案精选

U n i t 1 1、他实现了自己成为一名歌手的愿望 His wish to become a singer has come true 2、这趟火车可能要晚点了 This train is likely to be late 3、我的建议是演讲的时候要充满信心 My advice is to make a speech with confidence Unit2 1、她因为没有立刻回答而向我表示歉意 She apologized to me for not answering immediately 2、如果那个男孩不逗那条狗，就不会被咬 If the boy had left the dog alone,he wouldn’t have been bitten 3、他一直在这个小镇上过着宁静的生活 He has been leading a peaceful life in this small town Unit3 1、我们有必要权衡工作和家庭生活两者的需求 There is a need for us to balance the demands of the work with those of family life 2、有了因特网，全世界的信息都可以为你所用 With the Internet you can tap into all information of the world 3、我想借这个机会，对你们的帮助表示感谢 I’d like to draw upon this opportunity to express my thinks for your help Unit4 1、工厂散发出难闻的气味 The factory gives off a terrible smell 2、经费的减少使这个研究项目延迟了好几年 The spending cuts set back the project for several years 3、研究表明女性的寿命要比男性长 The study indicates that women live longer than men Unit5 1、我目前承担不了更多的工作 I can’t take on more work 2、这笔钱可以使他们实现自己的梦想 The money enabled them to live out their dreams 3、我们的公司准备购买更多的生产性设备 Our company is going to buy more productive equipment Unit6 1、在舞蹈方面，我们谁也比不上她 None of us can equal her in dancing

上海华育中学初三英语一模冲刺复习试卷(附答案)

上海华育中学初三英语一模冲刺复习试卷(附答案)

46 life. Probably people will never think about it. However, TV——the most pervasive (无处不在) and persuasive modern technologies, 47 by rapid change and growth——is moving into a new era, an era of extraordinary sophistication (复杂而精密) and versatility(多用途), which 48 to reshape our lives and our world. It is an electronic revolution, made possible by the 49 of television and computer The word “television”, derived from its Greek (tele distant) and Latin (vision sight) roots, can literally be interpreted as sight from a distance. Very 50 put, it works in this way through a sophisticated system of electronics, television 51 the capability of converting an image into electronic impulses, which can be sent through a wire or cable. These impulses, when fed into a receiver (a television set), can then be electronically reconstituted into that same image. Television is more than just an electronic system, however. It is a 52 of expression, as well as a vehicle for communication, and becomes a powerful tool for reaching other human beings. The field of television can be 53 into two categories determined by its means of transmission. First, there is broadcast television. And second, there is non-broadcast television. Ⅳ. Complete the sentences with the given words in their proper forms（用括号中所给单词的适当形式完成下列句子，每空格限填一词) (共8分) 54. The policeman held up his hand so that children could cross the road in _______ (safe). 55. He said that he loved this ___________ (imagine) film very much 56. He was promoted to a post of great ____________ (responsible). 57. The government has set up a health care network to heal the _________ (wound) and rescue the dying. 58. Cooperation is more important than ____________ (compete) 59. The ____________ you walk, t he sooner you’ll get there.(fast) 60. It is hard to learn the _____________ (operate) of this complicated machine. 61. The book written by this Nobel Prize winner is really worth _________ (read). V. Rewrite the following sentences as required（根据所给要求，改写下列句子。62-67题,每空格限填一词。68题注意句首大写) (共14分) 62.The new flat cost the family all their savings. (改为一般疑问句) _______ the new flat _________ the family all their savings? 63. The new chemistry lab can’t be used for the time being.（保持句意基本不变） The new chemistry lab can’t be used ___________ ___________. 64. You need no more help from us.(改为反意疑问句) You need no more help from us, ________ ___________? 65. The students must sweep the floor at once.(改为被动语态) The floor must ________ ________ by the students at once.

英语思维

课程笔记：Introduction 一、美语思维课程的目的： 学会用美国英语来思维，学会用英语来表达自己 学会将已经了解的语言知识按美国人的方式组织起来，表达出去 一旦你可以用英语去思考，那么你在英语使用上就会处于一种自由状态 Let’s go to get something. （书面语，语法正确） Let’s go get something. (口语，不一定符合语法) 美国英语口语中并不一定符合英语语法。 Let’s go see a movie之类的句子，书面写出来是错误的，但在口语中则是没有问题的，反而接近美国人口语的风格。 口语和我们所学的书面英语是有很大的区别的。这就决定了学习口语的思路和我们以往学习书面英语的方法是不一样的。 二、学习目标： 明白什么叫口语（Spoken English） 我们课程的目标是让大家做到心口如一，即让同学们能够将心里想的东西准确的用英语表达初来。 三、两个基本概念： 1、语言是什么 语言不是词汇的堆积，也不是词汇+语法的组合，语言和文化有着密切的关系。 例如：当我们听不懂别人的讲话时，中国人常会问“什么”，一激动就会用what [hw?t]，这会让外国人感到反感。 “What” in American English is very strong and challengeable word. (很强挑衅意味) 如果大家真的没听懂，可以用I beg your pardon. 或者Excuse me. 或者变调的what[弱hw?t]。 2、个性（personality），个性是口语中不可忽略的一个因素 在动态的个性的表现中平衡人们之间的个性差别，把语言说得更有人性化，就是个性的含义。语言里必须加上个性、加上人性，离开这一点是不能说好一门语言的。 个性分为两种： 1）positive; 2）negative

e英语教程册unit课文翻译

When i grow up Crayons danced across sheets of paper to illustrate our dream jobs. 彩色蜡笔在纸上飞舞，描绘着我们梦想的工作。 Our drawings were hung in the hall way for our parents to see at Back to School Night. 我们的画被挂在走廊里，好让我们的父母在“返校之夜”可以看到。 I remember looking down the line and seeing pictures of ballet dancers dancing, firefighters putting out a big fire, and spacemen leaping across the moon—jobs that were seen as typical dreams of five-year-olds. 我记得放眼望去，有的画上是正在跳舞的芭蕾舞演员，有的是正在扑灭大火的消防员，有的是正在月球上跳跃行走的宇航员，这些工作都是五岁孩子梦想中的工作。 My picture showed a stick figure with brown hair holding a bottle of orange juice over something like a counter. 我画的是一个留着棕色头发的人物线条，她站在柜台后，手里拿着一瓶橙汁。Underneath was my hardly readable handwriting: When I grow up, I want to work at the Market Basket because it would be fun to swipe orange juice across the scanner at the checkout counter.

上海市华育中学2015学年第一学期九年级英语学科月考试卷资料

华育中学2015学年第一学期月考试卷 Part 1 Listening (第一部分听力) I. Listening Comprehension (听力理解) (共30分) A. Listen and choose the right picture (根据你听到的内容，选出相应的图片): (共6分) 1. ________ 2. ________ 3. ________ 4. ________ 5. ________ 6. ________ B. Listen to the dialogue and choose the best answer to the question you hear (根据你听到的对话和问题，选出最恰当的答案) (8分) 7. A. In March B. In April C. In May D. In June 8. A. Harry B. Jack C. Mary D. Tom 9. A. A librarian B. A shop assistant C. A secretary D. A tour guide 10. A. They probably love the rainy days. B. They are not interested in each other. C. Neither of them will be free next week. D. They will probably go on a picnic soon. 11. A. By bus B. By bike C. By underground D. By taxi 12. A. Mother and son B. Teacher and student C. Customer and waitress D. Taxi driver and passenger 13. A. He doesn’t like the film B. He has no money at all. C. He has to go with his Mum. D. He has to do the homework first. 14. A. At a post office B. At a library C. At a hospital D. At a KFC restaurant C. Listen to the passage and tell whether the following statements are true or false (判断下列 句子是否符合你听到的内容, 符合的用“T”表示，不符合的用“F”表示) (6分) 1

学好英语重在英语思维方式的培养

学好英语重在英语思维方式的培养 学好英语重在英语思维的培养。直接用英语思考将有助于你在英语交流的过程中更快地回应对方，说英语的时候也更加流利，同时，还会减少词汇错误问题的出现机率。 那么，正确的英语思维模式应该是怎样的呢？如下图所示： 可能有些同学会反映：“老师，我的词汇量很少，英语水平不怎么样，操作起来比较难啊！” 即使你是英语初学者，按照以下几个步骤，英语思考模式的培养就会慢慢形成！小伙伴们，让我们一起努力吧！ 首先，单词联想 例如在家时我们可以主动联想以下这些单词：door、book、read、tale、 chair、sofa、go、window、kitchen、bedroom等等，去学校时又可以联想到teacher、student、notebook、pen、friend、class、pencil、blackboard、lesson等等。大家千万不能小看这些单词，不信，那麻烦你们打开自己的包包看一看，所有物品的名称都能够用英语表达出来吗？

单词练习相对简单，但这种方法对于你词汇量的积累是很有帮助的，同时为进一步的练习做准备。 接着，是短句表达的训练。 如果你可以很熟练地直接说出大量的英语词汇，那么就可以进入到第二个步骤，开始着手组织简短的句子。 例如，在听音乐时，可以尝试用英文表达自己的感受，可以从简单的描述开始练习： ?I am listening to music. ?This piece of music is beautiful. ?I like classical music. 又如，观看比赛时，你可以这样表达： ?The match is interesting. ?I think team A will win this match. ?The player number 8 is the best one.

用美国人的思维方式学习英语

用美国人的思维方式学习英语 用美国人的思维方式学习英语 一、如何用英文简单界定一个东西的技巧 美国人和美国人交谈80%是想告诉对方这个事物是什么。我们的课本尽管词汇难度不断加深，但思维逻辑结构却只停留在一个水平上。中国人常说Whereisthebook(这本书在哪儿)?很少有人说Whatisabook(书是什么)?而美国的小学生就开始问：Whatisthebook?这种Whereisthebook只是思维的描述阶段。但是我想连大学生也很难回答Whatisabook?因为中国传统英语教学模式没有教会学生表达思想的技巧。 二、如果已经学会界定，但理解还有偏差，那就要训练 Howtoexplainthingsindifferentways(用不同的.方式解释同一事物)。一种表达式对方不懂，美国人会寻找另一种表达式最终让对方明白。因为事物就一个，但表达它的语言符号可能会很多。这就 要多做替换练习。传统的教学方法也做替换练习，但这种替换不是 真替换，只是语言层面的替换，而不是思维层面的替换。 比如，Iloveyou(我爱你)。按我们教学的替换方法就把you换成her，mymother等，这种替换和小学生练描红没有什么区别。这种 替换没有对智力构成挑战，没有启动思维。这种替换句子的基本结 构没变，我听不懂Iloveyou，肯定也听不懂Iloveher。如果替换为Iwanttokissyou，Iwanttohugyou，Iwillshowmyhearttoyou等，或者给对方讲电影《泰坦尼克》，告诉对方那就是爱，这样一来对方 可能就明白了。这才叫真正的替换。也就是说用一种不同的方式表 达同一个意思，或者一个表达式对方听不清楚，举一个简单易懂的 例子来表达，直到对方明白。 三、我们必须学会美国人怎样描述东西

上海华育中学初三英语一模冲刺复习试卷(附答案)(完整资料)

【最新整理，下载后即可编辑】 华育中学一模考冲刺训练(一) （满分150分，考试时间100分钟） Part 2 Phonetics, Vocabulary and Grammar （第二部分语音、词汇和语法） Ⅱ. Choose the best answer (选择最恰当的答案) (共20分) ( ) 26.Tu You you was awarded 2015 Nobel Prize for her 【最新整理，下载后即可编辑】

contribution to health of mankind. Which of the following is correct for the underlined word? A) /priz/ B) /praiz/ C) /pris/ D) /prais/ ( )27. The fisherman’s wife said that she wanted to be _________ mayor of the city ? A) a B) an C) / D) the ( ) 28. “Do you want to see my ID card or my driver’s license?” “_____ will do.” A)Every B)Each C)Either D)Neither ( ) 29. You will find _______ important to learn a second foreign language. A) those B) that C) it D)this ( ) 30. A healthy diet is essential _______ everybody. You should care more about what you daily eat. A) for B)on C) in D) over ( ) 31. A report says that about ________ of the English teachers in Shanghai are under the age of 35. A) three-fifth B) third-fifths C) thirds-fifth D)three-fifths ( ) 32. The old woman can’t see the message on the mobile phone _________. A) clear enough B) enough clear C) clearly enough D) enough clearly ( ) 33. We thought the idea sounded __________, yet common sense told us it wouldn’t work. A) well B) bad C) good D) badly ( ) 34. It’s raining so hard that the w ater in the river can be seen ________ 【最新整理，下载后即可编辑】