Abstract book (pdf) - ICPR 2010

More documents

Recommendations

Info

CONTENTS Organizing Committees 2 Tracks and Co-Chairs 4 Message from the General Chair 6 Message from the Technical Program Chairs 7 Technical Program Overview 8 Technical Program for Monday 17 Technical Program for Tuesday 69 Technical Program for Wednesday 157 Technical Program for Thursday 239 - 1 -
Page 4 and 5: Organizing Committees Conference Ch
Page 6 and 7: Tracks and Co-Chairs Track I: Compu
Page 8 and 9: Message from the General Chair It i
Page 10 and 11: - 8 -
Page 12 and 13: - 10 -
Page 14 and 15: - 12 -
Page 16 and 17: - 14 -
Page 18 and 19: - 16 -
Page 20 and 21: - 18 -
Page 22 and 23: to the background and which distrib
Page 24 and 25: MoAT3 Topkapı Hall A Motion and Mu
Page 26 and 27: 11:40-12:00, Paper MoAT4.3 A Score
Page 28 and 29: MoAT6 Topkapı Hall B Human Compute
Page 30 and 31: 11:40-12:00, Paper MoAT7.3 A New Sy
Page 32 and 33: In this paper, we propose a visual
Page 34 and 35: 15:50-16:10, Paper MoBT3.2 Estimati
Page 36 and 37: 16:30-16:50, Paper MoBT4.4 Entropy
Page 38 and 39: 15:50-16:10, Paper MoBT6.2 Document
Page 40 and 41: Gurcan, Metin, The Ohio State Univ.
Page 42 and 43: 15:00-17:10, Paper MoBT8.7 Adaptive
Page 44 and 45: 15:00-17:10, Paper MoBT8.15 Context
Page 46 and 47: 15:00-17:10, Paper MoBT8.24 CANCELE
Page 48 and 49: tracking. The proposed learning met
Page 50 and 51: A novel wire detection algorithm fo
Page 52 and 53:
15:00-17:10, Paper MoBT8.48 A Unifi
Page 54 and 55:
15:00-17:10, Paper MoBT8.55 Task-Or
Page 56 and 57:
15:00-17:10, Paper MoBT9.5 Lipreadi
Page 58 and 59:
15:00-17:10, Paper MoBT9.13 Baby-Po
Page 60 and 61:
15:00-17:10, Paper MoBT9.21 Verific
Page 62 and 63:
Most of existing dimensionality red
Page 64 and 65:
15:00-17:10, Paper MoBT9.38 Manifol
Page 66 and 67:
Feature weighting plays an importan
Page 68 and 69:
We report performance evaluation of
Page 70 and 71:
is further performed to eliminate r
Page 72 and 73:
- 70 -
Page 74 and 75:
transformed into a polar histogram.
Page 76 and 77:
09:20-09:40, Paper TuAT3.2 A Color
Page 78 and 79:
10:00-10:20, Paper TuAT4.4 Single C
Page 80 and 81:
TuAT6 Dolmabahçe Hall B Texture Re
Page 82 and 83:
09:40-10:00, Paper TuAT7.3 Tokenles
Page 84 and 85:
09:00-11:10, Paper TuAT8.4 Hierarch
Page 86 and 87:
09:00-11:10, Paper TuAT8.12 Differe
Page 88 and 89:
to improve its estimation when the
Page 90 and 91:
In this paper, we propose a new alg
Page 92 and 93:
09:00-11:10, Paper TuAT8.37 View-In
Page 94 and 95:
A multi-scale approach is proposed
Page 96 and 97:
09:00-11:10, Paper TuAT8.54 Hierarc
Page 98 and 99:
spectral palm print data (420nm~110
Page 100 and 101:
Vandermeulen, Dirk Suetens, Paul, K
Page 102 and 103:
accuracy degradation when comparing
Page 104 and 105:
eters of the expert systems (classi
Page 106 and 107:
09:00-11:10, Paper TuAT9.35 Cancela
Page 108 and 109:
paper, we propose to analyse pores
Page 110 and 111:
in a watch list or checking for dup
Page 112 and 113:
09:00-11:10, Paper TuAT9.58 Profile
Page 114 and 115:
13:30-13:50, Paper TuBT2.1 Local Ro
Page 116 and 117:
This work contributes to part-based
Page 118 and 119:
TuBT5 Dolmabahçe Hall B Watermarki
Page 120 and 121:
14:30-14:50, Paper TuBT6.4 Intensit
Page 122 and 123:
information in different scales of
Page 124 and 125:
ally optimal feature sets. The new
Page 126 and 127:
17:00-17:20, Paper TuCT3.5 Multi-Vi
Page 128 and 129:
Müller, Henning, Univ. of Applied
Page 130 and 131:
TuCT7 Dolmabahçe Hall C Fingerprin
Page 132 and 133:
Kawanaka, Haruki, Aichi Prefectural
Page 134 and 135:
13:30-16:30, Paper TuBCT8.12 Eigenb
Page 136 and 137:
13:30-16:30, Paper TuBCT8.20 Slip a
Page 138 and 139:
13:30-16:30, Paper TuBCT8.28 Increm
Page 140 and 141:
13:30-16:30, Paper TuBCT8.36 Multib
Page 142 and 143:
method of selecting good basis func
Page 144 and 145:
13:30-16:30, Paper TuBCT8.53 Segmen
Page 146 and 147:
13:30-16:30, Paper TuBCT9.7 Scribe
Page 148 and 149:
13:30-16:30,Paper TuBCT9.16 Offline
Page 150 and 151:
opportunities in literary analyses
Page 152 and 153:
13:30-16:30,Paper TuBCT9.32 A Basel
Page 154 and 155:
Automatic identification of an indi
Page 156 and 157:
features widely used in existing DS
Page 158 and 159:
- 156 -
Page 160 and 161:
- 158 -
Page 162 and 163:
10:20-10:40, Paper WeAT1.5 A Combin
Page 164 and 165:
cannot be convincingly synthesised
Page 166 and 167:
WeAT5 Topkapı Hall B Face Analysis
Page 168 and 169:
86.7M training samples which shows
Page 170 and 171:
Equal Error Rate (EER), and the com
Page 172 and 173:
09:00-11:10, Paper WeAT8.10 Geodesi
Page 174 and 175:
09:00-11:10, Paper WeAT8.18 Active
Page 176 and 177:
09:00-11:10, Paper WeAT8.25 An Impr
Page 178 and 179:
09:00-11:10, Paper WeAT8.33 Paralle
Page 180 and 181:
09:00-11:10, Paper WeAT8.43 Transit
Page 182 and 183:
ectional and can be calculated from
Page 184 and 185:
coefficients for embedding the wate
Page 186 and 187:
09:00-11:10, Paper WeAT9.6 Human St
Page 188 and 189:
09:00-11:10, Paper WeAT9.13 Statist
Page 190 and 191:
09:00-11:10, Paper WeAT9.21 Unsuper
Page 192 and 193:
ments. The method proposed improves
Page 194 and 195:
Functional neuroimaging consists in
Page 196 and 197:
WeBT1 Marmara Hall Tracking and Sur
Page 198 and 199:
This contribution presents a method
Page 200 and 201:
The goal of this paper is the devel
Page 202 and 203:
Lei, Huang, Chinese Acad. of Scienc
Page 204 and 205:
in specific brain regions. Moreover
Page 206 and 207:
Liu, Cheng-Lin, Chinese Acad. of Sc
Page 208 and 209:
WeCT3 Dolmabahçe Hall A Active Con
Page 210 and 211:
effective annealing procedure that
Page 212 and 213:
scale analysis of iris images to en
Page 214 and 215:
This paper addresses the problem of
Page 216 and 217:
In this paper we propose a new mult
Page 218 and 219:
13:30-16:30, Paper WeBCT8.15 Spatia
Page 220 and 221:
In this paper, we address the probl
Page 222 and 223:
13:30-16:30, Paper WeBCT8.32 A Samp
Page 224 and 225:
13:30-16:30, Paper WeBCT8.40 Image
Page 226 and 227:
PASCAL VOC 2007 image benchmark sho
Page 228 and 229:
to enhance HoG to detect walking pe
Page 230 and 231:
We propose a method that analyzes t
Page 232 and 233:
13:30-16:30, Paper WeBCT9.11 Tertia
Page 234 and 235:
Perdoch, Michal, Czech Tech. Univ.
Page 236 and 237:
Huang, Qingming, Chinese Acad. of S
Page 238 and 239:
13:30-16:30, Paper WeBCT9.34 Compar
Page 240 and 241:
Cooharojananone, Nagul, Chulalongko
Page 242 and 243:
- 240 -
Page 244 and 245:
10:20-10:40, Paper ThAT1.5 A Re-Eva
Page 246 and 247:
09:40-10:00, Paper ThAT3.3 Crowd Mo
Page 248 and 249:
ThAT5 Dolmabahçe Hall B Image Segm
Page 250 and 251:
siderable variations in camera sett
Page 252 and 253:
ThAT8 Upper Foyer Image Analysis; S
Page 254 and 255:
orientation histograms computed on
Page 256 and 257:
Choi, Jin Young, Automation and Sys
Page 258 and 259:
fectively captured by supervised tr
Page 260 and 261:
Our approach uses simple geometric
Page 262 and 263:
foregrounds in multi-views based on
Page 264 and 265:
09:00-11:10, Paper ThAT8.49 Visibil
Page 266 and 267:
ance cues. We demonstrate that inco
Page 268 and 269:
09:00-11:10, Paper ThAT9.7 A Vision
Page 270 and 271:
In this paper, we propose a new alg
Page 272 and 273:
(ii) multiple kernel Support Vector
Page 274 and 275:
of the target human subject constru
Page 276 and 277:
simulation study and demonstrated t
Page 278 and 279:
09:00-11:10, Paper ThAT9.46 On-Line
Page 280 and 281:
The advance of falsification techno
Page 282 and 283:
In this paper we provide a framewor
Page 284 and 285:
14:30-14:50, Paper ThBT2.4 A Consta
Page 286 and 287:
Lovell, Brian Carrington, The Univ.
Page 288 and 289:
the idea of cross-dimensional compa
Page 290 and 291:
and non text pixels. Our previous L
Page 292 and 293:
16:00-16:20, Paper ThCT1.2 Combinin
Page 294 and 295:
Quiniou, Solen, Ec. de Tech. Supér
Page 296 and 297:
16:20-16:40, Paper ThCT4.3 Connecte
Page 298 and 299:
15:40-16:00, Paper ThCT6.1 Regressi
Page 300 and 301:
16:20-16:40, Paper ThCT7.3 MONORAIL
Page 302 and 303:
elations, whereas neighboring data
Page 304 and 305:
13:30-16:30, Paper ThBCT8.15 Invisi
Page 306 and 307:
correcting output code matrix. In t
Page 308 and 309:
13:30-16:30, Paper ThBCT8.30 User A
Page 310 and 311:
tection data indicate that combinin
Page 312 and 313:
Biswas, Jit, Inst. for Infocomm Res
Page 314 and 315:
the results show an improvement com
Page 316 and 317:
13:30-16:30, Paper ThBCT8.63 The Pr
Page 318 and 319:
13:30-16:30, Paper ThBCT9.7 Image R
Page 320 and 321:
clean conditions and are most robus
Page 322 and 323:
13:30-16:30, Paper ThBCT9.24 Emotio
Page 324 and 325:
them to discriminate between cognit
Page 326 and 327:
direction of the original center pi
Page 328 and 329:
13:30-16:30, Paper ThBCT9.47 Compar
Page 330:
13:30-16:30, Paper ThBCT9.57 Detect
show all

Abstract book (pdf) - ICPR 2010

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?