'Shape model' diaporamas de présentation

Statistical Shape Modelling

Statistical Shape Modelling. Multimodal Interaction Dr. Mike Spann m.spann@bham.ac.uk http://www.eee.bham.ac.uk/spannm. Contents . Introduction Point distribution models Principal component analysis Shape modelling using PCA Case study. Face image analysis

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Part 2: part-based models

Part 2: part-based models. by Rob Fergus (MIT). Problem with bag-of-words. All have equal probability for bag-of-words methods Location information is important. Overview of section. Representation Computational complexity Location Appearance Occlusion, Background clutter Recognition

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Part 2: part-based models

Part 2: part-based models. by Rob Fergus (MIT). Problem with bag-of-words. All have equal probability for bag-of-words methods Location information is important. Overview of section. Representation Computational complexity Design choices Recognition Learning Automated methods.

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Face and Facial feature tracking ASM, AAM , CLM

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Face alignment using Boosted Appearance Model (BAM)

Face alignment using Boosted Appearance Model (BAM). Satya mahesh Muddamsetty Supervisor : Tommaso Gritti. Video processing & Analysis group. Examiner : Mikael Nilsson, Department of Signal processing, BTH September 30 , 2009. Outlines. Introduction. Brief summary of Previous methods

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Minimum Description Length Shape Modelling

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Lecture and laboratory No. 0 1 Models of cooperating systems

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FEATURE Overview & Update

FEATURE Overview & Update. Brian Carcich Peter Thomas Cornell University. Based on CIRCLE.PRO by Ernest Bowman-Cisneros. FEATURE Agenda. Why FEATURE? Status of FEATURE What FEATURE does (demo) FEATURE output, input, environment How FEATURE models data Geometry; Shape; SPICE Kernels

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Asteroid Occultations & Light-curve Inversion models

Asteroid Occultations & Light-curve Inversion models. This is based on a paper published in ICARUS this year, with the following authors:

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Active Appearance Models for Face Detection

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Outline

Outline. Overview Integrating Vision Models CCM: Cascaded Classification Models Learning Spatial Context TAS: Things and Stuff Descriptive Querying of Images LOOPS: Localizing Object Outlines using Probabilistic Shape Future Directions. [Heitz et al. NIPS 2008b]. Image Queries on Objects.

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AAM based Face Tracking with Temporal Matching and Face Segmentation

CVPR 2010. AAM based Face Tracking with Temporal Matching and Face Segmentation. Mingcai Zhou 1 ? Lin Liang 2 ? Jian Sun 2 ? Yangsheng Wang 1. 1 Institute of Automation Chinese Academy of Sciences, Beijing, China. 2 Microsoft Research Asia Beijing, China. Outline. AAM Introduction

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Transfer Learning of Object Classes: From Cartoons to Photographs

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Slides for Sampling from Posterior of Shape

Slides for Sampling from Posterior of Shape. MURI Kickoff Meeting Randolph L. Moses November 3, 2008. Topics. Shape Analysis (Fan, Fisher & Willsky) MCMC sampling from shape priors/posteriors. MCMC Sampling from Shape Posteriors. MCMC Shape Sampling. Key contributions

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Leveraging the Crowd for Complex Analytics Koushik Sinha HP Labs India koushik.sinha@hp

Continuous medial representation in image shape analysis and classification By : Leonid Mestetskiy Professor, Department of Computational Mathematics and Cybernetics , Moscow State University, Moscow, Russia. Email : mestlm@mail.ru.

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Proposed Dissimilarity Measure

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Rodent Behavior Analysis

Rodent Behavior Analysis. Tom Henderson Vision Based Behavior Analysis Universitaet Karlsruhe (TH) 12 November 2003. 1/9. How papers fit in survey framework.

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July 3 Burst D. Lindler Sept. 6 2005

July 3 Burst D. Lindler Sept. 6 2005. Data Sets. All are 60 second Clear 6 observations taken on July 3. Outbust at 8:37. 8600023 7:47:02 (pre-burst) 8600032 8:51:12 8600041 9:52:52 8600050 10:47:12 8600059 11:57:42.

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3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images

3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images. Ryo Kurazume, Kaori Nakamura, Yumi Iwashita, Tsutomu Hasegawa Graduate School of ISEE, Kyushu University, Japan Toshiyuki Okada, Yoshinobu Sato, Nobuhiko Sugano, Tsuyoshi Koyama

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Implicit Active Shape Models for 3D Segmentation in MR Imaging

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