Abstract book (pdf) - ICPR 2010

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is further performed to eliminate redundancy and obtain more representative sets. We also propose a simple and efficient strategy for quantizing the feature descriptors based on locality-sensitive hashing. By experiment, we show that our approach can efficiently discover objects against cluster and slight viewpoint variations. - 68 -
Technical Program for Tuesday August 24, <strong>2010</strong> - 69 -
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CONTENTS Organizing Committees 2 Tr
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Arun Ross West Virginia University
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Track IV: Biometrics and Human Comp
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Message from the Technical Program
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Page 19 and 20: Technical Program for Monday August
Page 21 and 22: 09:00-09.30, MoOT10 Anadolu Auditor
Page 23 and 24: 11:20-11:40, Paper MoAT2.2 2D Shape
Page 25 and 26: sponding feature points between the
Page 27 and 28: This paper concentrates on speech d
Page 29 and 30: 12:00-12:20, Paper MoAT6.4 AR-PCA-H
Page 31 and 32: tistical models fitted to large dat
Page 33 and 34: 16:10-16:30, Paper MoBT2.3 A Recurs
Page 35 and 36: MoBT4 Dolmabahçe Hall A Ocular Bio
Page 37 and 38: equation for estimating alphas of p
Page 39 and 40: 15:30-15:50, Paper MoBT7.1 Dyslexia
Page 41 and 42: 15:00-17:10, Paper MoBT8.3 Fast Odo
Page 43 and 44: 15:00-17:10, Paper MoBT8.11 Abnorma
Page 45 and 46: process. This method is an importan
Page 47 and 48: paper describes a general framework
Page 49 and 50: work machine environments, in which
Page 51 and 52: 15:00-17:10, Paper MoBT8.44 Road Ch
Page 53 and 54: the backgrounds with random landsca
Page 55 and 56: Research on complex shape recogniti
Page 57 and 58: image observations. Experimental re
Page 59 and 60: 15:00-17:10, Paper MoBT9.17 Possibi
Page 61 and 62: 15:00-17:10, Paper MoBT9.25 Gaussia
Page 63 and 64: 15:00-17:10, Paper MoBT9.34 CDP Mix
Page 65 and 66: 15:00-17:10, Paper MoBT9.42 Learnin
Page 67 and 68: This paper proposes a solution to t
Page 69: 15:00-17:10, Paper MoBT9.59 Tensor
Page 73 and 74: TuAT1 Marmara Hall Object Detection
Page 75 and 76: complexity of the co-association ma
Page 77 and 78: with Shape from Shading, we fully r
Page 79 and 80: 09:40-10:00, Paper TuAT5.3 Adding A
Page 81 and 82: is integrate the features at a desc
Page 83 and 84: TuAT8 Lower Foyer Structural Method
Page 85 and 86: 09:00-11:10, Paper TuAT8.8 Backgrou
Page 87 and 88: A Discrete Labelling Approach to At
Page 89 and 90: 09:00-11:10, Paper TuAT8.25 Backgro
Page 91 and 92: 09:00-11:10, Paper TuAT8.33 A Fast
Page 93 and 94: 09:00-11:10, Paper TuAT8.41 A Varia
Page 95 and 96: matching 3D CAD models, shape match
Page 97 and 98: TuAT9 Upper Foyer Biometrics Poster
Page 99 and 100: 09:00-11:10, Paper TuAT9.9 Robust R
Page 101 and 102: 09:00-11:10, Paper TuAT9.17 Probabi
Page 103 and 104: 09:00-11:10, Paper TuAT9.24 Attacki
Page 105 and 106: 09:00-11:10, Paper TuAT9.31 An Effi
Page 107 and 108: evaluation of micropattern represen
Page 109 and 110: signed a new encoding method. The e
Page 111 and 112: 09:00-11:10, Paper TuAT9.54 Face Re
Page 113 and 114: some applications such as stereo ma
Page 115 and 116: 14:50-15:10, Paper TuBT2.5 Compress
Page 117 and 118: 13:50-14:10, Paper TuBT4.2 Localize
Page 119 and 120: In this article, a new fragile, bli
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14:10-14:30, Paper TuBT7.3 Retinal
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similarities among face carvings ar
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16:00-16:20, Paper TuCT3.2 Level-Se
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A new method to extract dashed line
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16:00-16:20, Paper TuCT6.2 Modeling
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Ramos, Daniel, Univ. Autonoma de Ma
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paper, we proposes a one-shot scann
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13:30-16:30, Paper TuBCT8.16 Visual
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13:30-16:30, Paper TuBCT8.24 Discri
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13:30-16:30, Paper TuBCT8.32 Action
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13:30-16:30, Paper TuBCT8.40 Shape
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13:30-16:30, Paper TuBCT8.48 Accura
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13:30-16:30, Paper TuBCT9.3 Prototy
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13:30-16:30,Paper TuBCT9.11 Writing
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13:30-16:30,Paper TuBCT9.20 Unsuper
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13:30-16:30,Paper TuBCT9.28 Documen
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didate word of the document with wh
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string as an input to the transduce
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to previous pre-processing such as
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Technical Program for Wednesday Aug
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WeAT1 Marmara Hall Tracking and Sur
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ing that is constructed in a tangen
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09:20-09:40, Paper WeAT4.2 Von Mise
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10:00-10:20, Paper WeAT5.4 Face Hal
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09:00-09:20, Paper WeAT7.1 Multi-Vi
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Optimal sharpness differs from imag
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09:00-11:10, Paper WeAT8.14 Automat
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09:00-11:10, Paper WeAT8.22 On the
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09:00-11:10, Paper WeAT8.29 Multi-S
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09:00-11:10, Paper WeAT8.38 An Adap
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09:00-11:10, Paper WeAT8.47 Robust
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09:00-11:10, Paper WeAT8.56 Enhanci
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called 22q11.2 Deletion Syndrome. G
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cally presented simultaneously. Twe
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separate atlas, and identifying the
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09:00-11:10, Paper WeAT9.25 3D Reco
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eling the image background with a p
Page 195 and 196:
09:00-11:10, Paper WeAT9.41 Segment
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This paper presents a new approach
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13:50-14:10, Paper WeBT3.2 Gait Lea
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WeBT5 Topkapı Hall B Feature Extra
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14:10-14:30, Paper WeBT6.3 Script I
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14:50-15:10, Paper WeBT7.5 Gaussian
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16:00-16:20, Paper WeCT2.2 Direct P
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17:00-17:20, Paper WeCT3.5 Length I
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16:20-16:40, Paper WeCT5.3 Developm
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WeCT7 Dolmabahçe Hall C Handwritin
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13:30-16:30, Paper WeBCT8.3 Nonline
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We present a method of performing k
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Tefas, Anastasios, Aristotle Univ.
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For classifier ensembles, an effect
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native power more efficiently becau
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and iterations. To label object can
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13:30-16:30, Paper WeBCT8.53 Spike-
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dicates the possible locations of t
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In the off-line one, an alphabet, o
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image that it is currently displaye
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Answering to a query like when a pa
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13:30-16:30, Paper WeBCT9.30 A Mult
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13:30-16:30, Paper WeBCT9.38 Effici
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Technical Program for Thursday Augu
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ThAT1 Marmara Hall Object Detection
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10:00-10:20, Paper ThAT2.4 Rectifyi
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optimization problem which returns
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10:20-10:40, Paper ThAT5.5 On Selec
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09:20-09:40, Paper ThAT7.2 HMM-Base
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to obtain a background image. We ha
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sidering the fact that the parts in
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Jeon, Moongu, Gwangju Inst. of Scie
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fields of view. The nodes in the to
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09:00-11:10, Paper ThAT8.38 Robust
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We propose a scene classification m
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09:00-11:10, Paper ThAT8.53 Efficie
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letters that can be combined to cre
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Vector Machines. Results show that
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09:00-11:10, Paper ThAT9.19 EEG-Bas
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algorithm that translates brain sig
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09:00-11:10, Paper ThAT9.35 Off-Lin
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In this paper, we present a calibra
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curacy for imagined sign. Pairwise
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gaze estimation improved by 61:06%
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For the interpretation of a visual
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camera parameters by minimizing an
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combination of Gaussians (LCG), Mar
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and video frames were used to evalu
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Mezghani, Neila, Centre de Recherch
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ThCT2 Anadolu Auditorium Classifica
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16:40-17:00, Paper ThCT3.4 Boosted
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16:00-16:20, Paper ThCT5.2 Panorami
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extend this work by using functiona
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prediction of any one supposed expe
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portant characteristic of the image
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13:30-16:30, Paper ThBCT8.19 Word C
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This paper proposes an incremental
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13:30-16:30, Paper ThBCT8.34 Hetero
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13:30-16:30, Paper ThBCT8.43 Local
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13:30-16:30, Paper ThBCT8.52 Combin
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13:30-16:30, Paper ThBCT8.59 Discri
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In this paper, an anisotropic diffu
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13:30-16:30, Paper ThBCT9.12 Image
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of the inner product matrices into
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done on MIT-BIH Arrhythmia database
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13:30-16:30, Paper ThBCT9.36 Fence
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ness and quality of the tracking co
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13:30-16:30, Paper ThBCT9.52 An Emp
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