'Weak classifiers' diaporamas de présentation

Lecture 25: More on Lightness Recovery

Lecture 25: More on Lightness Recovery. CAP 5415. Announcements. Project write-ups due December 2. Goal: Find the albedo. Take this image. Observation. Shading Information. Albedo Information. By splitting it into two images that represent the shading and albedo of the scene.

899 views • 82 slides

Face Detection and Head Tracking

Face Detection and Head Tracking. Ying Wu yingwu@ece.northwestern.edu Electrical Engineering & Computer Science Northwestern University, Evanston, IL http://www.ece.northwestern.edu/~yingwu. Face Detection: The Problem. The Goal: Identify and locate faces in an image The Challenges:

831 views • 67 slides

Machine Learning

Machine Learning. Ensemble Models. ? ? ? ? ? ? ? ?. Training Data. Data1. Data m. Data2. ? ? ? ? ? ? ? ?. Learner m. Learner2. Learner1. ? ? ? ? ? ? ? ?. Model1. Model2. Model m. Final Model. Model Combiner. Learning Ensembles.

554 views • 55 slides

Tutorial : Learning Topics in Game -Theoretic Decision Making Michael Littman - Rutgers University

Tutorial : Learning Topics in Game -Theoretic Decision Making Michael Littman - Rutgers University *Thanks to Michael Kearns, Manfred Warmuth, Rob Schapire also Michael Bowling, Gerry Tesauro Why Game Theory at COLT? Multiple choice: MK & MW are interested and got to pick the topic.

931 views • 54 slides

Ensemble Learning

Ensemble Learning. CS 6243 Machine Learning Modified from the slides by Dr. Raymond J. Mooney http://www.cs.utexas.edu/~mooney/cs391L And by Dr. Carla P. Gomes http://www.cs.cornell.edu/Courses/cs4700/2008fa/. Ensemble Learning.

497 views • 47 slides

Adaboost

762 views • 46 slides

Adaboost

638 views • 46 slides

Longin Jan Latecki Temple University latecki@temple

475 views • 44 slides

Object recognition

Object recognition. Methods for classification and image representation. Credits. Slides by Pete Barnum Slides by Fei-Fei Li Paul Viola, Michael Jones, Robust Real-time Object Detection, IJCV 04 Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

682 views • 42 slides

610 views • 42 slides

597 views • 42 slides

Taxonomic classification for web-based videos

Taxonomic classification for web-based videos. Author: Yang Song et al. (Google) Presenters: Phuc Bui & Rahul Dhamecha. 1. Introduction. Taxonomic classification for web-based videos. Web-based Video Classification. Web-based Video (e.g. Youtube )

453 views • 28 slides

Random Forest

389 views • 28 slides

Automatic Car Detection

Automatic Car Detection. Using Statistic-based Boosting Cascade. Weilong Yang, Wei Song, Zhigang Qiao, Michael Fang. System Overview. Haar -like feature generation Integral image AdaBoost (feature selection) Limitations Statistic-based boosting ( StatBoost ) Cascade structure

453 views • 26 slides

Statistical Learning of Multi-View Face Detection

Statistical Learning of Multi-View Face Detection. Microsoft Research Asia Stan Li, Long Zhu, Zhen Qiu Zhang, Andrew Blake, Hong Jiang Zhang, Harry Shum Presented by Derek Hoiem. Overview. Viola-Jones AdaBoost FloatBoost Approach Multi-View Face Detection FloatBoost Results

445 views • 25 slides

Statistical Learning of Multi-View Face Detection

Statistical Learning of Multi-View Face Detection. Microsoft Research Asia Stan Li, Long Zhu, Zhen Qiu Zhang, Andrew Blake, Hong Jiang Zhang, Harry Shum Presented by Derek Hoiem. Overview. Viola-Jones AdaBoost FloatBoost Approach Multi-View Face Detection FloatBoost Results

397 views • 25 slides

Friday, 23 May 2003 William H. Hsu Department of Computing and Information Sciences, KSU

312 views • 18 slides

Unified Crowd Segmentation

Unified Crowd Segmentation. P . Tu , T. Sebastian, G. Doreto , N. Krahnstoever , J. Rittscher , T. Yu ECCV 2008. Presenter: Ramin Mehran. Goal: Segmentation. Crowd Analysis Conventional Approach Segmentation Object Detection Tracking Analysis. Ideas: a Robust Algorithm. Approaches.

310 views • 17 slides

A New Boosting Algorithm Using Input-Dependent Regularizer

A New Boosting Algorithm Using Input-Dependent Regularizer. Rong Jin 1 , Yan Liu 2 , Luo Si 2 , Jamie Carbonell 2 , Alex G. Hauptmann 2 1. Michigan State University, 2. Carnegie Mellon University. Outline. Introduction of AdaBoost algorithm Problems with AdaBoost

306 views • 17 slides

References. Acknowledgement. Future Work. Introduction. Data Preprocessing/Visualization.

575 views • 1 slides