This document summarizes the block matching based motion estimation algorithm. The algorithm estimates motion between two frames by comparing blocks in the reference frame to blocks in the target frame. It discusses key parameters like block size, search window, and matching criteria. Experimental results on example images demonstrate how varying these parameters affects the motion fields and computation time. The best results were found with a block size of 16x16, search window of 16, and threshold of 1.7.

1. 1
ECE 565 Computer Vision and Image Processing
Spring 2010
Computer Assignment # 2
Block Matching Based
Motion Prediction Method
By
Dhawal Subodh Wazalwar
A20249257
ECE Dept
Illinois Institute of Technology
Due date 30th April 2010
1

2. Block Matching Based Motion Estimation
1. Introduction
The main aim of the Block Matching(BM) Motion Estimation is to compare images taken at two
different time frames and estimate the direction of motion taken place between the two frames. Here, the challenge
is to get the best motion vector by using a pixel domain search method and proper manipulation of BM parameters.
Fig 1.1 tries to demonstrate the basic concept of block matching motion estimation. Here, two blocks
Reference and Target Block are shown and the Target block is the translated version of Reference block. The key
aspect in this method is how accurately can we estimate motion direction with appropriate Motion Vectors
Column
i,j
i+v1,j+v2
Row
Reference Block at i,j
Target Block at i+v1,j+v2
Direction of Motion Vector
v1,v2 Motion Vector Magnitude
Fig 1.1 Basic Understanding of Block Matching
Any BM Motion Estimation method needs following parameters to be selected beforehand :
• The Matching Criteria
Various metrics like Sum of Absolute Differences(SAD),Mean Square Error(MSE). Mean Absolute
Difference(MAD) can be used for quantitatively proving that the two blocks in comparison are matching.
Here, in this implementation SAD is used which is represented as
where,
It+1(i,j) -----> Target Frame
It (i,j) -----> Reference Frame
k,l -----> The block location
N -----> Block size
[v1;v2] ----> Motion Vector for that block
• Search Strategy
The search strategy influences the quality of Motion Field and also the overall computation time as
demonstrated in later parts. There are various search strategies like Full Exhaustive Search, 3-point
Search, Cross Search Method and many more versions of it. In this implementation, we have used full
exhaustive search method.
2

3. • Search Window and Block Size
The Block size(N) is the size of the block of target and reference frame to be compared for estimating the
motion vectors. On the other hand, Search Window(M) is the resolution for which the search over the block
is done.
2. Algorithm
2.1 Implementation Steps
Flow chart in Fig 2.1 shows the important steps in the Full Exhaustive Search Block Matching Motion
Estimation Method.
Reference Image Target Image
Initialize N,M,th
Select the appropriate
Target block It+1(i,j) of NXN
Yes No Reference
Calculate Checked Calculate
for all Block
SAD(0) Motion SAD
vectors It(i+v1,j+v2)
Is No Is
SAD(v)>SAD(0) SAD
-th*N*N min
No
Yes Yes
Motion Store
Vectors =0 Motion Vectors
Display Assign block
Determine
Motion In the
DFD,FD
Field Predicted Image
Fig 2.1 Flow Chart showing the Implementation steps for Full Exhaustive BM Method
3

4. 2.2 Key Aspects and Findings in the Algorithm Implementation
• If the block size is not multiples of the Frame size,we get the problem of addressing pixels outside the
frame. In such cases, here, we restrict the block size to the the last boundary( i.e row and col value) when 'v'
addresses outside the frame boundary. On the other hand, when it addresses pixels before the boundary, it is
restricted to the start position i.e '1'.
• Also, in some cases the motion vector v=[v1;v2] can also cause the same addressing problem. In this
situation, we put a condition and check for such cases. If this situation is detected, we ignore that motion
vector and carry on for the next motion vector. The reason for doing this is, we can estimate motion
happening inside the frame, not outside the frame. So, there is no harm in ignoring the motion vectors
addressing outside the frame.
• Displaced Frame Difference(DFD) and Frame Difference(FD) is obtained by
DFD= ∑(abs(Estimated Frame – Target Frame)),
FD = ∑(abs(Reference Frame – Target Frame))
• We get negative values for DFD and FD. So, for proper display we need to map those negative values in the
positive domain. So, we find the minimum value for DFD and FD frame and simply add the absolute of that
value respectively to get the proper DFD and FD frame. Another way of doing this is converting both the
frames in 'uint8' form but in this case all the negative values become '0' and we lose the exact pattern in
DFD and FD frames. So, the former method of adding the absolute of minimum value serves the purpose
and helps getting better display results.
• The threshold condition used for detecting the most appropriate motion vector is
SAD(v')<SAD(0) – th*N*N
where N is block size, th is the threshold value given initially
• The Total SAD is calculated by adding the minimum SAD values for each block.
• A check for getting the number of iterations performed and also the time required for the entire analysis is
also added.
3. Experimental Results
The algorithm determines the motion vectors, predicted image and displays motion vector field, Displaced
Frame Difference(DFD),Frame Difference(FD) along with the predicted Image.
3.1 Analysis on Example Image 1:
Case 1: Block size NXN=16X16, Search Window M=16,Threhold=0
The results are shown below in Figure 3.1 to 3.4
Fig 3.1 Reference Image
4

5. Fig 3.2 Target and Predicted Image for Example 1 with N=16,M=16,th=0
Fig 3.3 FD and DFD for the Image 1 with N=16,M=16,th=0
Now, finally the most important of all Motion Vector Field is shown below in Fig 3.4
Fig 3.4 Motion Vector Field showing the estimated motion direction for N=16,M=16,th=0
Here, in this motion vector field diagram, we can see that it clearly shows the motion for the displacement
of face and shoulder part, while the random fields near the boundary are for the background part which has Camera
5

6. effect and is not a part of actual motion displacement.
Note: The Direction shown is for the motion from Predicted ie Target Image to the Reference Image since
the algorithm tries to find the target block in the reference block translated by the appropriate motion vector.
Also, if we see the reference and the target image, we can see that actual information is approximately in
the following region:
Face: Row 15:100, Column 50:150
Shoulders: Row 95:144, Column 1:176
So ideally, we should have motion vectors at the following coordinate range shown in the Figure 3.5
Fig 3.5 Range of Ideal Motion Vector Field
So by changing the threshold value, we can neglect the unwanted motion vectors which came because of the
background random motion, which we need not consider.
Case 2: Block size NXN=16X16, Search Window M=16,Threshold=0.5
The results for Case 2 are shown in Fig 3.6 and 3.7
Fig 3.6 Predicted and DFD for N=16,M=16,th=0.5
6

7. Motion Vector Field
Columns
1 2 3 4 5 6 7 8 9 10 11
0
1
2
3
4
Rows
5
6
7
8
9
Fig 3.7 Motion Vector Field for Image 1 with N=16,M=16,th=0.5
We can see in Fig 3.7, we get better Motion Field Representation than obtained in Fig 3.4 for Case 1.
Also, after comparing the Reference and Target Image in Fig 3.1 and 3.2 , it was found that the max displacement
in any direction is not more than 5-6 pixels approximately. This was determined by using 'pixval' command in
Matlab and finding the displacement at key positions in Image like Person's Face, Eyes and other such features
visible. Therefore, in optimal Motion field matrix, we can expect max absolute value to be not more than 5-6 in this
case. So here in Case 2 with th=0.5, although we see almost expected Motion Vector, after checking matrix values,
we find that max absolute value here is 8. So, th=0.5 is not the optimal threshold.
Note: Here, one important observation can be made regarding 'Search Range' M. As mentioned above, max
displacement was approximately found to be 5-6. So, if we give search range less than this value(say M=4), it
would be interesting to see the Motion Field, shown in Fig 3.8
Fig 3.8 Motion Vector Field for N=16,M=4,th=0.5
7

8. If we compare direction of motion vectors in Fig 3.7 and Fig 3.8, we can see the difference, in that for th=4, we
don't get the expected direction. Also in this case, Total SAD=86609 as compared to 67616 for N=16,M=16,th=0.5.
Therefore, we should always keep Search Range M greater than the absolute of maximum displacement
between Reference and Target Image to get the accurate motion estimation direction.
So, after continuing the changes in the threshold value, it was found that we get better possible motion
vector field when threshold value is in the range of 1.5 to 2, and it was found that the best results were seen at
th=1.7, after which there was not much change in the motion field.
Case 3: N=16,M=16,th=1.7
Predicted ,DFD images and the corresponding Motion Vector Field are shown in Fig 3.9 and Fig 3.10
Fig 3.9 Predicted and DFD Image for N=16, M=16, th=1.7
Motion Vector Field
Columns
1 2 3 4 5 6 7 8 9 10 11
1
2
3
4
Rows
5
6
7
8
9
Fig 3.10 Example 1 Motion Vector Field for N=16,M=16,th=1.7 (Close to desired results)
Here, after the absolute value of maximum displacement shown by Motion vectors is 6. So, visually as well
as theoretically its is proved that Optimum threshold for Example 1 Image is '1.7'
Now, we will change the block size N=8, keeping same M=16 and Threshold th=1.7, and Fig 3.11 and Fig
3.12 show the obtained results.
8

9. Case 4: Image 1 with N=8,M=16,th=1.7
Here if we compare DFD in Fig 3.11 and Fig 3.9 for N=16, we can see that for N=8, we seem to be getting
results because almost see very sketchy outline of shoulder area compared to that in Fig 3.9 for N=16. This is also
demonstrated by that Total SAD value has reduced from 70343 to 60450, which is testimony of the fact we have
obtained better Prediction Image. This is true because smaller the block of Target Image used for comparison, more
is the possibility of detecting it in the Reference Image, meaning when we reduce the block size, we start getting
minor details which were missed for N=16.
Fig 3.11 Example 1 Predicted and DFD Image with N=8,M=16,th=1.7
Fig 3.12 Motion Field for N=8,M=16, th=1.7
On the other hand, if we compare Motion vector fields in Fig 3.12 for N=8 and Fig 3.10 at N=16, we
actually the reverse effect. In that, yes we are able to get finer details but missing out on few border motion vectors
for same threshold value(here th=1.7).That means, the same threshold value is not optimal in all case but only for
that particular block size. The quality of Motion Vector depends on the condition expressed below
SAD(V')<SAD(0) - th*N*N
Now here, if all factors reduce by same amount then the same threshold value will give optimal results.
However, it was found that Total SAD value reduces by different amount(here from 70343 to 60450) compared to
the 'th*N*N' which becomes 1/4th. The total SAD(0) value remains same. This shows that all factors have different
reduction factors and so results depends on the particular block situation. Therefore, we see mismatch in the
9

10. Motion Vector Field results for N=8 and N=16 for same Threshold value. So, every time we change the block size,
we should freshly determine the optimal threshold value for that block size.
Now, here one more thing to be taken into consideration is the computational time. For example for
N=16,M=16 the number of iterations/computations were found to be 107812, while for N=8, M=16, there number
was 431245. in terms of time the later analysis took almost 25 seconds more. This can be explained by reducing the
block size, we have to get motion vectors for more number of blocks and so the increase in computation time. Also,
if we decrease the search window ( say M=8), the computation time would be less. Fig 3.13 and fig 3.14 shows
results for N=8,M=8,th=1.7.
Fig 3.13 Predicted and DFD for N=8,M=8 and th=1.7
Fig 3.14 Motion Field Vector for N=8,M=8,th=1.7
If we compare the DFD for N=8,M=8 in Fig 3.14 and N=8,M=16 in Fig 3.12, we don't see much changes due to
the fact that actual displacement between Reference and Target frame is not more than 6 pixels approximately. So
for fixed N, if we change M above 6-7, we should not find much difference. This is also demonstrated by the fact
that Total SAD value doesn't change much as in the Tabular Summary for all cases applied on Image1 shown in
Tab 3.1
10

11. Block Size(N) Search Threshold(th) Number of Time in secs Total SAD Total SAD(0)
Range(M) Iterations
16 16 0 107812 10.13 65986 231549
16 16 0.5 107812 9.43 67616 231549
16 16 1.7 107812 16 70343 231549
8 16 1.7 431245 30.98 60450 231549
8 8 1.7 114445 4.92 60491 231549
Tab 3.1 Overall Summary of all cases analyzed
3.2 Analysis on Example Image 2
Similar Analysis was done for the Example Image 2 and the results for all the cases are shown in Fig 3.15 to
Fig 3.24
Case 1: N=16,M=16,th=0
Fig 3.15 Reference Example Image 2
Fig 3.16 Target and Predicted Image for Example 2 with N=16,M=16,th=0
11

12. Fig 3.17 DFD and FD Image for Example 2 with N=16,M=16,th=0
Motion Vector Field
Columns
0 5 10 15 20 25
0
2
4
6
Rows
8
10
12
14
16
Fig 3.18 Motion vector Field for Image 2 with N=16,M=16 and th=0
If we compare Reference and Target Image, we can see there are some regions which because of motion get
occluded, for example the surrounding region behind the Vehicle. So, we can't estimate motion for such areas, even
though we see some motion vectors in that region. This problem is called “Occlusion” Problem and is one of the
limitations of Basic Block Matching method.
Another limitation is “Aperture Problem”, in which we have blocks of same intensity values multiple times
in the regions successively and so we don't get any motion estimation for such cases, since in actual
mathematically there is no motion. However, if the region is sub block of bigger block, we need to have motion
vectors since there is a motion. Although not exactly, but we see a similar case at Vehicles Door region at
approximately Block(10,5) in Motion Vector field. In Fig 3.18 we still see some motion vector of small magnitude
because actually we don't have same intesnity throughout that region, but it has similar texture, which can be
proved by the fact that the region at Block(10,5) is not much visible even in Frame Difference(FD).
Now, for N=16,M=16 and th=0, we are almost getting the desired Motion vector field indicating motion at
all places, which is actually true if we compare the Reference and Target Image. This is unlike Example image 1
where we were concerned in estimating motion in particular region.
12

13. Now, if here th=0 will be the best possible Threshold Value. Now, if we change the threshold we expect
little degraded results, since some required motion vectors will not satisfy the threshold condition.
Case 2: Image 2 with N=16,M=16, th=1
Fig 3.19 to Fig 3.20 shows the results for Case 2
Fig 3.19 Predicted and DFD Image for N=16,M=16,th=1
Fig 3.20 Motion Vector Field for N=16,M=16,th=1
Here, if we compare the DFD for th=1 in Fig 3.19 and for th=0 in Fig 3.17, we can't see much of the
difference visually. However, if we compare Total SAD value for both cases, we find that it has increased from
692509 to 694021. Also, if we compare Motion Vector Field in Fig 3.20 and Fig 3.18, as expected we see some
required motion vectors missing. This proves the point made earlier that th=0 is the most optimal threshold value.
Case 3: N=8,M=16,th=0
Fig 3.21 and Fig 3.22 shows the Predicted, DFD and Motion Vector Field for N=8,M=16,th=0
13

14. Fig 3.21 Predicted and DFD Image for N=8,M=16,th=0
If we compare DFD image for N=8 in Fig 3.21 and for N=16 in Fig 3.17 with th=0, we can clearly see for
N=8, the bright spot on the vehicle is missing, and thus giving more accurate predicted Image. This is because for
smaller block size 8X8, the bright spot couldn't be a part of matching block in the Target Frame, so we don't see it
in the predicted image. Thus, with smaller block size, we start getting finer details in the Target Image.
If we compare Motion Vector field in Fig 3.22 and Fig 3.16, we can see that for same threshold value i.e
th=0, we get the optimal results. So, in this case, the quality of Motion Field is actually improved for N=8,M=16
and th=0. This is unlike the Example 1 Image where the situation was reverse and we had to track motion at some
part of whole Image.
Finally, for N=8,M=8,th=0, results are shown in Fig 3.23 and Fig 3.24 and then all the cases analyzed are
tabulated in Table 3.2.
Fig 3.22 Motion vector Field for N=8,M=16,th=0
Fig 3.23 Predicted and DFD Image with N=8,M=8,th=0;
14

15. Motion Vector Field
Columns
0 5 10 15 20 25 30 35 40 45
0
5
10
Rows
15
20
25
30
Fig 3.24 Motion Vector Field for N=8,M=8,th=0
Block Size(N) Search Threshold(th) Number of Time in secs Total SAD Total SAD(0)
Range(M) Iterations
16 16 0 375706 35.98 692509 1714664
16 16 0.5 375706 35.57 692812 1714664
16 16 1 375706 36.39 694021 1714664
8 16 0 1470151 114.9 619854 1714664
8 8 0 390151 17.11 623930 1714664
Tab 3.2 Summary for All case analyzed For Example Image 2
3.3 Changing the Search Method
From the analysis shown in Tab 3.1 and Tab 3.2, we can see that Full exhaustive search method
gives very good motion estimation prediction. The main reason for getting such good results is because
we try searching over entire Search Window Resolution. On the flip side, this method takes a lot of
computational effort. So, there are various methods as mentioned in the introduction part for getting
faster and almost close to Full Exhaustive Method Results.
As regarding the quality of Motion Vector Field, we may miss some Object motion vector fields,
since we scan at few specific points rather than at each and every location in Search Range M as
done in Full Exhaustive Search Method. Thus, the quality of Motion vector field depends on the
search method's accuracy.
For example, here in the same implementation if we put the Threshold Condition ( SAD(v') <
SAD(0) -th*N*N) inside the loop of Motion Vector Search Range and if the condition is not
satisfied, we break out of the loop and assume that there is no motion and assign '0' value to the
motion vector, we get following Motion Vector Field for N=16,M=16,th=0 shown in Fig 3.25.
We can easily see that many required motion vectors are missing but still we get a rough
estimate of the motion estimation. The number of iterations/computations performed reduces
to 46547 as compared to 107812 in Full Search Method. If we use methods like 3 point,
Cross Search Methods, we can get better accuracy in Motion Vector Field results.
15

16. Fig 3.25 Motion Vector Field for Modified Search Method with N=16,M=16,th=0
4. Conclusion
Based on summary obtained in Table 3.1 for Example Image 1 and Table 3.2 for Example Image 2,
following conclusions can be drawn:
• Effect of Changing BM parameters on Motion Estimation Accuracy
◦ Block Size(N): Smaller the Block size, we get finer details of the target Image in the predicted image i.e
lower Total SAD value. However, the computational effort increases.
◦ Search Window(M): Theoretically, greater the value of M, better the quality of predicted image, lower
total SAD value and more accurate Motion Vector Field Representation. Practically, we need to ensure
that the value of M is greater than the absolute maximum displacement between Reference and Target
image in any direction. Also, higher the value, more is the computational effort.
◦ Threshold(th): The optimal value of 'th' depends on the images. If we need to estimate for entire
Image, optimal value will be independent of N and is equal to zero. However, if we have to consider
only a particular region in image for motion estimation, every time we change N and M, we need to get
optimal threshold value. In general , increasing 'th' results in increased total SAD value.
• Full Exhaustive Search Method gives a very accurate Motion vector Field Representation, however the
computational effort required is more. Using other search methods like 3 point search method, can help
getting faster results.
• Block Matching based motion estimation method cannot estimate motion for the part when it is occluded in
between the frames or if it has Aperture problem.
5. References
[1] 'Digital Video Processing', A. Murat Tekalp, Chapter 6 Block Based Methods
[2] 'ECE 565 Computer Vision and Image Processing' class notes, Professor Jovan Brankov
16

17. 6. Appendix
6.1 MATLAB Code for Block Matching based Motion Estimation
function [f_e dfd fd tot_sad tot_sado comp]=blockmatching(f_r,f_t,N,M,th)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%%%%%%%%%%Full Exhaustive Block Matching Motion Estimation Method%%%
% Inputs
% f_r ---> Reference Image
% f_t ---> Target Image
% N ---> Block Size
% M ---> Search Window
% th ---> Threshold Value
% Outputs
% f_e ---> Estimated Image
% dfd ---> Displaced Frame Difference
% fd ---> Frame Difference
% tot_sad ---> Total SAD value
% tot_sado ---> Total SAD value at v=0
% comp --> Number of Iterations/Computations Performed
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%
%Converting the images from uint8 to Double Format
f_r=double(f_r);
f_t=double(f_t);
%Determining the Image Size
[row col]=size(f_t);
%Initializing a Estimated Image
f_e=zeros(row,col);
f_e=double(f_e);
a=1;
b=1;
%Initiating the Total SAD variable
tot_sad=0;
tot_sado=0;
it=1;
%Number of Computations
comp=1;
tic
for i=1:N:row
for j=1:N:col
%Initializing Motion Vectors and SAD matrices
v1_min=0;
v2_min=0;
sad=[];
v1_mat=[];
v2_mat=[];
it=1;
17

18. %Searching for the optimal motion vector over the entire search range M
for v1=-M:M
for v2=-M:M
%Incrementing the number of Computations
comp=comp+1;
%Determining the Row start points for Target and Reference Blocks to be compared for Motion
Estimation
start_t_i=i;
start_r_i=v1+i;
%Determining the Row end points for Target and Reference Blocks
if(start_t_i+N>row)
end_t_i=row;
end_r_i=v1+row;
else
end_t_i=start_t_i+N-1;
end_r_i=start_r_i+N-1;
end;
%Determining the Column Start Points for Target and Reference Blocks
start_t_j=j;
start_r_j=j+v2;
%Determining the Column end points for Target and Reference Blocks
if(start_t_j+N>col)
end_t_j=col;
end_r_j=v2+col;
else
end_t_j=start_t_j+N-1;
end_r_j=start_r_j+N-1;
end;
if(end_r_i<row && end_r_j<col && start_r_i>0 && start_r_j>0)
%Calculating the SAD value for the current motion vector
dif=f_t(start_t_i:end_t_i,start_t_j:end_t_j)-f_r(start_r_i:end_r_i,start_r_j:end_r_j);
dif=abs(dif);
sadx=sum(sum(dif));
if(it==1)
sad=sadx;
v1_mat=v1;
v2_mat=v2;
it=it+1;
else
sad=[sad ;sadx];
v1_mat=[v1_mat; v1];
v2_mat=[v2_mat; v2];
it=it+1;
18

19. end;
end;
end;
end;
% Finding the Motion vectors for Minimum SAD value
if(isempty(sad)==0)
pos=find(sad==min(sad));
v1_min=v1_mat(pos(1),1);
v2_min=v2_mat(pos(1),1);
end;
%Calculating SAD(0)
sado=abs(f_t(start_t_i:end_t_i,start_t_j:end_t_j)-f_r(start_t_i:end_t_i,start_t_j:end_t_j));
sado=sum(sum(sado));
tot_sado=tot_sado+sado;
%Checking for the Threshold Condition
if(min(sad)>=sado-th*N*N)
v1_min=0;
v2_min=0;
tot_sad=tot_sad+sado;
else
tot_sad=tot_sad+min(sad);
end;
%Storing the determined motion vectors in an array
V1(a,b)=v1_min;
V2(a,b)=v2_min;
b=b+1;
%Calculating the start and end points in the Predicted and to be
%assigned Reference Block
start_r_j=j+v2_min;
start_r_i=v1_min+i;
if(start_r_j+N-1>col && start_t_j+N-1<=col)
end_r_j=2*start_r_j+N-col -1;
end_t_j=col;
end;
if(start_t_j+N-1>col&&start_r_j+N-1<=col)
end_r_j=start_r_j+start_t_j+N-col -2;
end_t_j=col;
end;
if(start_t_j+N-1>col && start_r_j+N-1>col )
end_t_j=col;
end_r_j=start_r_j+end_t_j-start_t_j;
end;
if(start_t_j+N-1<=col )
end_r_j=start_r_j+N-1;
end_t_j=start_t_j+N-1;
end;
if(start_r_i+N-1>row || start_t_i+N-1<=row)
19

20. end_r_i=2*start_r_i+N-row -1;
end_t_i=row;
end;
if(start_t_i+N-1>row || start_r_i+N-1<=row)
end_r_i=start_r_i+start_t_i+N-row -2;
end_t_i=row;
end;
if(start_t_i+N-1>row && start_r_i+N-1>row )
end_t_i=row
end_r_i=start_r_i+end_t_i-start_t_i;
end;
if(start_t_i+N-1<=row )
end_t_i=start_t_i+N-1;
end_r_i=start_r_i+N-1;
end;
f_e(start_t_i:end_t_i,start_t_j:end_t_j)=f_r(start_r_i:end_r_i,start_r_j:end_r_j);
end;
b=1;
a=a+1;
end;
%Getting the Displaced Difference Frame Image
dfd=imsubtract(f_e,f_t);
%Getting the minimum value so as to properly match -ve values for better
%display
min_dfd=min(min(dfd));
dfd=dfd+abs(min_dfd);
%Getting the Frame Difference
fd=imsubtract(f_r,f_t);
%Getting the minimum value so as to properly match -ve values for better
%display
min_fd=min(min(fd));
fd=fd+abs(min_fd);
figure,imshow(mat2gray(f_r));
title('Reference Image');
figure,subplot(1,2,1),imshow(mat2gray(f_t));
title('Target Image');
subplot(1,2,2),imshow(mat2gray(f_e));
title('Predicted Image');
figure,subplot(1,2,1),imshow(mat2gray(dfd));
title('Displaced Frame Difference');
subplot(1,2,2),imshow(mat2gray(fd));
title('Frame Difference');
figure,quiver(V2,V1);
set(gca,'xaxislocation','top','yaxislocation','left','xdir','default','ydir','reverse');
title('Motion Vector Field');
xlabel('Columns');
ylabel('Rows');
toc
20