首页
网站开发
桌面应用
管理软件
微信开发
App开发
嵌入式软件
工具软件
数据采集与分析
其他
首页
>
> 详细
代写data编程、代做Java/Python程序设计
项目预算:
开发周期:
发布时间:
要求地区:
Homework 7
Computer Vision, Spring 2024
Due Date: April 26, 2024
Total Points: 20
This homework contains two programming challenges. All submissions are due at
midnight on April 26, 2024, and should be submitted according to the instructions
in the document “Guidelines for Programming Assignments.pdf”.
runHw7.py will be your main interface for executing and testing your code.
Parameters for the different programs or unit tests can also be set in that file.
Before submission, make sure you can run all your programs with the command
python runHw7.py with no errors.
The numpy package is optimized for operations involving matrices and
vectors. Avoid using loops (e.g., for, while) whenever possible—looping can
result in long running code. Instead, you should “vectorize” loops to optimize
your code for performance. In many cases, vectorization also results in more
compact code (fewer lines to write!).
Challenge 1: In this challenge you are asked to develop an optical flow system. You
are given a sequence of 6 images (flow1.png – flow6.png) of a dynamic scene. Your
task is to develop an algorithm that computers optical flow estimates at each image
point using the 5 pairs (1&2, 2&3, 3&4, 4&5, 5&6) of consecutive images.
Optical flow estimates can be computed using the optical flow constraint equation
and Lucas-Kanade solution presented in class. For smooth motions, this algorithm
should produce robust flow estimates. However, given that the six images were
taken with fairly large time intervals in between consecutive images, the brightness
and temporal derivatives used by the algorithm are expected to be unreliable.
Therefore, you are advised to implement a different (and simpler) optical flow
algorithm. Given two consecutive images (say 1 and 2), establish correspondences
between points in the two images using template matching. For each image point in
the first image, take a small window (say 7x7) around the point and use it as the
template to find the same point in the second image. While searching for the
corresponding point in the second image, you can confine the search to a small
window around the pixel in the second image that has the same coordinates as the
2
one in the first image. The center of the 7x7 image window in the second image that
is maximally correlated with the 7x7 window in the first image is assumed to be the
corresponding point. The vector between two corresponding points is the optical
flow (u,v).
Write a program computeFlow that computes optical flow between two gray-level
images, and produces the optical flow vector field as a “needle map” of a given
resolution, overlaid on the first of the two images.
result = computeFlow(img1, img2, win_radius, template_radius,
grid_MN)
You need to choose a value for the grid spacing that gives good results without
taking excessively long to compute. (6 points)
For debugging purposes use the test case in debug1a. In this synthetic case, the flow
field consists of horizontal vectors of the same magnitude (translational motion
parallel to the image plane). Note that in the real case, foreshortening effects,
occlusions, and reflectance variations (as well as noise) complicate the result.
(2 point)
Challenge 2: Your task is to develop a vision system that tracks the location of an
object across video frames. Object tracking is a challenging problem since an
object’s appearance, pose and scale tend to change as time progresses. In class we
have discussed three popular tracking methods: template-based tracking,
histogram-based tracking and detection-based tracking. In this challenge, we will
assume the color distribution of an object stays relatively constant over time.
Therefore, we will track an object using its color histogram.
A color histogram describes the color distribution of a color image. The color
histogram that you will need to compute is defined as follows. Each bin of the color
histogram represents a range of colors, and the number of votes in each bin
indicates the number of pixels that have the colors within the corresponding color
range.
Be careful, in the initialization of your program, you should generate a color map
from the region of interest (ROI), and compute all subsequent color histograms
based on the same color map. It is only meaningful to compare two histograms
computed based on the same color map. Use the provided function chooseTarget
to drag a rectangle around a tracking target.
3
Write a program named trackingTester that estimates the location of an object in
video frames.
trackingTester(data_params, tracking_params)
trackingTester should draw a box around the target in each video frame, and
save all the annotated video frames as PNGs into a subfolder given in
data_params.out_dir.
After generating the annotated video frames, use the provided function
generateVideo to create a video file containing all the frames.
(12 points)
Include all the code you have written, as well as the resulting video files, but
DO NOT include the three tracking datasets and the individual output frames
in your submission.
软件开发、广告设计客服
QQ:99515681
邮箱:99515681@qq.com
工作时间:8:00-23:00
微信:codinghelp
热点项目
更多
cis432代做、代写python/java程...
2024-05-04
eeen3007j代写、c++程序设计代...
2024-05-04
代写data程序、代做c/c++, jav...
2024-05-04
comp2006代做、代写c++程序语言
2024-05-04
comp26020代做、java/c++设计编...
2024-05-04
csci251 advanced programming...
2024-05-03
cs 6290: high-performance co...
2024-05-03
assignment 2: executing and ...
2024-05-03
ecse427/comp310 programmin...
2024-05-03
cs 452 (fall 22): operating...
2024-05-03
comp9414 23t2 assignment 2 ...
2024-05-03
dpst1091 23t1 assignment 2 ...
2024-05-03
program代做、代写python设计编...
2024-05-03
热点标签
finm8007
comp2006
comp26020
comp1721
eeen3007j
cis432
csci251
comp5125m
com398sust
32022
mth6158
comp328
finn41615
2024
mec302
mgmt3004
mgt7158
com160
as.640.440
econ3016
finm7405
econ7021
fin600
infs4205/7205
mktg2510-
f27sb
csse2310/csse7231
rv32i
eecs 113
comp1117b
cs 412
comp 315
econ7300
comp2017
ecs 116
fit5046
com6511
comp30024
acs341
econ1020
isys3014
acc408
comp1047
csc 256
cs 6347
finm7008
comp34212
csmde21
estr2520
comp285/comp220
mds5130/iba6205
finc6010
is3s665
busi2194
125.785
iom209
msin0041
econ339
cmt218
mast10007
comp5349
ecx2953/ecx5953
bios706
comp3310
mth6150
comp30027
comp20005
eec286
busi2211
bff2401
fnce90046
visu2001
mang6554
finc6001
125785
data423-24s1
engi 1331
fint2100
(520|600).666
can202
cs 61b
mast20029
info20003
stat512
econ3208
cmpsc311
engg1340
ecmt1010
fit5216
basc0003
ee3121
acct2002
comp5313
busi2131
ise529
elec372/472
csit940/csit440
cenv6141
comp3027/comp3927
ftec5580
comp1433
msci223
mark203
en3098
eden1000
ece6483
econ4410
mats16302
cs 6476
com6521
comp222
comp3211
comp10002
csc1002
chc6186
cs 161
comp27112
comp282
swen20003
comm1190
elec9764
acfi3308
acct7101
fin6035
comp2048
geog0163
comp2013
coen 146
dts101tc
sehh2042
comp30023
comp4880/8880
cs 455
07
stat0045.
fil-30023
celen085
psyc40005
math40082
are271
comp9311
ee5311
imse2113
comp 2322
acct2102
fnd109
int102
is3s664
is6153
data4000
accfin5034
fit5212
cs536-s24
fit5225
ecos3006
mes202tc
finc5001
stat3061
csc171
cs1b
7ssmm712
bu.450.760
cs170
comp3411
swen90004
cpt206
comp5313/comp4313—large
bl5611
kxo206
comp532
elec207
kxo151
cs 2820
cpt108
math2319
dts204tc
qm222
comp2511
ccs599
infs1001
mat2355
eeee4123
25721
ifn647
pols0010
hpm 573
qbus6860
comp9417
csci 1100
stat0023
cse340
comp2003j
cs 2550
cs360
fin 3080
ierg 4080
cs6238
cit 594
finm7406
hw6
联系我们
- QQ: 9951568
© 2021
www.rj363.com
软件定制开发网!