-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathresearch.html
379 lines (347 loc) · 16.9 KB
/
research.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
<!DOCTYPE html>
<!--
// WEBSITE: https://themefisher.com
// TWITTER: https://twitter.com/themefisher
// FACEBOOK: https://www.facebook.com/themefisher
// GITHUB: https://github.com/themefisher/
-->
<html lang="en">
<head>
<!-- Basic Page Needs
================================================== -->
<meta charset="utf-8">
<title>Machine Vision and Learning Lab</title>
<!-- Mobile Specific Metas
================================================== -->
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=5.0">
<!-- Favicon -->
<link rel="shortcut icon" type="image/x-icon" href="images/logo.png" />
<!-- CSS
================================================== -->
<!-- Fontawesome Icon font -->
<link rel="stylesheet" href="plugins/themefisher-font/style.css">
<!-- bootstrap.min css -->
<link rel="stylesheet" href="plugins/bootstrap/bootstrap.min.css">
<!-- Animate.css -->
<link rel="stylesheet" href="plugins/animate-css/animate.css">
<!-- Magnific popup css -->
<link rel="stylesheet" href="plugins/magnific-popup/dist/magnific-popup.css">
<!-- Slick Carousel -->
<link rel="stylesheet" href="plugins/slick-carousel/slick.css">
<link rel="stylesheet" href="plugins/slick-carousel/slick-theme.css">
<!-- Main Stylesheet -->
<link rel="stylesheet" href="css/style.css">
</head>
<body id="home" data-spy="scroll" data-target=".navbar-nav" data-offset="80">
<!--
Start Preloader
==================================== -->
<div class="preloader">
<div class="sk-cube-grid">
<div class="sk-cube sk-cube1"></div>
<div class="sk-cube sk-cube2"></div>
<div class="sk-cube sk-cube3"></div>
<div class="sk-cube sk-cube4"></div>
<div class="sk-cube sk-cube5"></div>
<div class="sk-cube sk-cube6"></div>
<div class="sk-cube sk-cube7"></div>
<div class="sk-cube sk-cube8"></div>
<div class="sk-cube sk-cube9"></div>
</div>
</div>
<!-- End Preloader
==================================== -->
<!--
Fixed Navigation
==================================== -->
<header id="navigation" class="navigation">
<div class="container">
<div class="navbar-header w-100">
<nav class="navbar navbar-expand-lg navbar-dark px-0">
<!-- logo -->
<a class="navbar-brand logo" href="index.html">
<img src="images/logo.png" alt="Website Logo" />
</a>
<!-- /logo -->
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbar01"
aria-controls="navbarSupportedContent" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse" id="navbar01" data-toggle="collapse" data-target=".navbar-collapse">
<ul class="navbar-nav navigation-menu ml-auto">
<li class="nav-item">
<a class="nav-link" href="index.html#home">Home</a>
</li>
<li class="nav-item">
<a class="nav-link" href="index.html#research">Research</a>
</li>
<li class="nav-item">
<a class="nav-link" href="index.html#news">News</a>
</li>
<li class="nav-item">
<a class="nav-link" href="index.html#members">Members</a>
</li>
<li class="nav-item">
<a class="nav-link" href="index.html#contact-us">Contact</a>
</li>
<li class="nav-item">
<a class="nav-link" href="https://ccumvllab.github.io/site/index.html">
Old Website</a>
</li>
</ul>
</div>
</nav>
</div>
</div>
</header>
<!--
End Fixed Navigation
==================================== -->
<section id="team-research" class="parallax-section section section-bg overly">
<div class="container">
<div class="row ">
<div class="col-lg-12">
<!-- section title -->
<div class="title text-center">
<h2>Our <span class="color">Researches</span></h2>
<h3>The research direction and results of our laboratory in recent years.</h3>
<div class="border"></div>
</div>
<!-- /section title -->
</div> <!-- /end col-lg-12 -->
</div> <!-- end row -->
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/calligraphy.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Writing calligraphy on obot</h2>
<p>
The research includes three areas of artistic creation, robotic automation, and
artificial intelligence(AI). The AI technology is used to construct writing style of
famous calligrapher, and writing with the arm.
<br>
<strong>Calligraphy style transfer:</strong>
<br>
 In the process of calligraphy style transfer, the method is based on CycleGAN. With a
improvement of adding embedding layers to overcome that a single model can only convert
a different style limit. By collecting the wrist movements during writing, the robot can
simulate the calligrapher's writing. After the calligraphy are written.
<br>
<strong>Generating stroke orders and robot trajectory:</strong><br>
 Thining the transferred calligraphy lets the robotic arm simulate the calligrapher's
writing action to write the calligraphy characters, we need to convert the coordinates
of the thinned images to get the six-axis data. The six-axis sequence data of the
calligraphy is provided to the robot arm for writing the calligraphy characters.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/makeup-attack-628x279.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Incorporating attack information into makeup to attack deep learning models</h2>
<p>
Machine learning has evolved very rapidly, with good results in both computer vision and
natural language processing. There are many deep learning techniques that are used in
everyday life of humans such as autonomous vehicles and face recognition systems.
Nowadays, the gradual dependence of human daily life on deep neural networks can lead to
serious consequences, so the security of neural networks becomes very important.
Therefore, the deep neural network has obvious weaknesses. We propose a method based on
generating a confrontation network to generate a facial makeup picture that can deceive
the face recognition system. We hide the perturbation of the attack in the results of
the abnormal makeup photos that humans can’t detect. The experimental results show that
we can not only generate high-quality facial makeup images, but also our attack results
have a high attack success rate in the face recognition system.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/music-game-628x396.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Using the Generative Adversarial Network(GAN) to generate music rhythm games</h2>
<p>
The music rhythm game is currently a very popular game, and we propose to generate a
music rhythm game spectrum based on the method of Generative Adversarial Network. The
music is separated into two parts: the vocal and the soundtrack, which makes the
generated spectrum closer to the real spectrum. The model consists of two concepts of
Generative Adversarial Network: Conditional Generative Adversarial Nets (CGANs) for
music information and Improved Wasserstein GAN (WGAN-GP) for better convergence of the
model.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/scribble-lines-to-painting-520x237.jpg" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Be an Artist! Scribble Lines to Painting.</h2>
<p>
We propose a fully automated system that converts random graffiti into a painting.
However, this is a serious challenge because the input graffiti can be very messy and
hide multiple objects, so finding the correlation between these repeated lines and
multiple objects is not a simple matter. In the system, we use selective search, sparse
coding and Convolutional Neural Network (CNN), in which we use selective search to find
the part of the object that may be the object of the graffiti; then use sparse coding to
find the corresponding element; CNN sets the style to be converted. The final
experimental results show that the methods we use have superior performance and produce
artistic works.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/full-model-2-844x484.jpg" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Clothing style analysis and popular element capture</h2>
<p>
With more and more styles of clothing and accessories, regardless of the physical or online store, consumers
will spend a lot of time looking for their favorite styles in many styles, so if consumers
can give some photos of their favorite costumes, systematic analysis Find out the
relevant information in the photo (such as the store address, matching related
accessories, etc.). For the store, if you can collect the relevant clothing styles of
the customers, you can adjust the purchase styles and the furnishings in the store
according to this information, further recommend the related accessories to consumers
according to the preferences of consumers and save consumers to find matching
accessories. time. For garment manufacturers, they can analyze the data collected by
various stores to know which styles are popular and those styles are unpopular, and thus
become the next batch of new style design references.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/60359706-2364327760514781-6025843136377389056-n-641x285.png"
alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Deep Learning for Sensor-Based Rehabilitation</h2>
<p>
In this work, we aimed to
evaluate four kinds of rehabilitation exercises at three levels: good, average, and bad.
We propose a novel evaluation method by learning the best feature of each class.
The idea was to design an evaluation matrix where each entry corresponded to one level
of one exercise. By setting the largest number in one entry, the evaluation matrix could
be used along with the output layer of the deep learning model to infer the best feature of that exercise at
a
particular level.
The evaluation score is obtained by examining the distance measure of the current feature and the best
feature of that class.
We also collect a new rehabilitation
exercise dataset for the rehabilitation exercise evaluation. It contains four different
rehabilitation
actions at three levels, defined by rehabilitation physicians.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/outdoor-low-resolution-face-recognition-554x261.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Outdoor low resolution face recognition</h2>
<p>
The goal of this project is to compare low-resolution face images to verify that they
are the same person. In today’s unrestricted environment, the effectiveness of face
recognition often decreases due to posture factors, so we establish a normalization
method to restore any face angle, thereby returning the face angle of any state to
increase The effectiveness of face recognition. The project uses two Caffe model
architectures: Matching-Convolutional NeuralNetwork (M-CNN) and Siamese Neural Network
(SNN). Finally, the accuracy of the SNN model is more than 90%, which is higher than
that of M-CNN.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/image-multi-label-classification-628x461.jpg" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Multiple attributes image classification</h2>
<p>
When sorting face images, there are inevitably some accessories in the images to be
identified, such as sunglasses, scarves, earrings, etc., or external environmental
factors such as light, angle, etc. These accessories or environmental factors are in
people. The face image is called multiple attributes. We uses the existing Local
Discriminant Embedding (LDE) algorithm as an extension to achieve multiple attribute
classification purposes.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/sparse-coding-628x463.jpg" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Sparse Coding</h2>
<p>
In recent years, sparse coding has been very popular in the field of computer vision and
image processing. Sparse coding consists of a linear combination of input data,
dictionary and input data. Sparse coding can be used for image denoising, restoration,
and classification. The laboratory focuses on two research directions based on sparse
coding: multiple attribute image classification and sparse coding of huge amounts of
data.
</p>
</div>
</div>
</div>
</section>
<!-- end Contact Area
========================================== -->
<footer id="footer" class="bg-one">
<div class="container">
<div class="row wow fadeInUp" data-wow-duration="500ms">
<div class="col-lg-12">
<!-- Footer Social Links -->
<div class="social-icon">
<ul class="list-inline">
<li class="list-inline-item"><a href="https://www.youtube.com/channel/UCJr6UDkcy_d3Ox_WBJibdNg"><i
class="tf-ion-social-youtube"></i></a></li>
</ul>
</div>
<!--/. End Footer Social Links -->
<!-- copyright -->
<div class="copyright text-center">
<a href="index.html">
<img src="images/logo.png" width="40px" height="40px" />
</a>
<p class="mt-3">Copyright
©
<script>
document.write(new Date().getFullYear())
</script> CCU MVL Lab. All Rights Reserved. <br> Designed & Developed by JeffLin.
</p>
</div>
<!-- /copyright -->
</div> <!-- end col lg 12 -->
</div> <!-- end row -->
</div> <!-- end container -->
</footer> <!-- end footer -->
<!--
Essential Scripts
=====================================-->
<!-- Main jQuery -->
<script src="plugins/jquery/jquery.min.js"></script>
<!-- Bootstrap 3.1 -->
<script src="plugins/bootstrap/bootstrap.min.js"></script>
<!-- Slick Carousel -->
<script src="plugins/slick-carousel/slick.min.js"></script>
<!-- Portfolio Filtering -->
<script src="plugins/filterzr/jquery.filterizr.min.js"></script>
<!-- Magnific popup -->
<script src="plugins/magnific-popup/dist/jquery.magnific-popup.min.js"></script>
<!-- Google Map API -->
<script src="https://maps.googleapis.com/maps/api/js?key=AIzaSyAgeuuDfRlweIs7D6uo4wdIHVvJ0LonQ6g"></script>
<script src="plugins/google-map/gmap.js"></script>
<!-- wow.min Script -->
<script src="plugins/wow/wow.min.js"></script>
<!-- Custom js -->
<script src="js/script.js"></script>
</body>
</html>