PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia.

Slides:



Advertisements
Similar presentations
Istanbul Forum Country Exercise: Standard slides for country delegations.
Advertisements

CVPR2013 Poster Modeling Actions through State Changes.
Motion.
Fire Detection & Assessment Practical work E. Chuvieco (Univ. of Alcalá, Spain)
Analysis of Contour Motions Ce Liu William T. Freeman Edward H. Adelson Computer Science and Artificial Intelligence Laboratory Massachusetts Institute.
Víctor Ponce Miguel Reyes Xavier Baró Mario Gorga Sergio Escalera Two-level GMM Clustering of Human Poses for Automatic Human Behavior Analysis Departament.
1 Approximated tracking of multiple non-rigid objects using adaptive quantization and resampling techniques. J. M. Sotoca 1, F.J. Ferri 1, J. Gutierrez.
2 s 2.org Qiuling Zhu, Navjot Garg, Yun-Ta Tsai, Kari Pulli NVIDIA An Energy Efficient Time-sharing Pyramid Pipeline for Multi-resolution.
National Center for Supercomputing Applications University of Illinois at Urbana-Champaign MAEviz: Seismic Risk Assessment System October 31, 2008.
Tennessee Center for Performance Excellence Section 3 – Evaluating Results.
Probability-based Dynamic Time Warping for Gesture Recognition on RGB-D data All rights reserved HuBPA© Human Pose Recovery and Behavior Analysis Group.
Tracking Features with Large Motion. Abstract Problem: When frame-to-frame motion is too large, KLT feature tracker does not work. Solution: Estimate.
Optical Flow Methods 2007/8/9.
Discriminative Training of Kalman Filters P. Abbeel, A. Coates, M
Lecture#6: segmentation Anat Levin Introduction to Computer Vision Class Fall 2009 Department of Computer Science and App math, Weizmann Institute of Science.
1 Formation et Analyse d’Images Session 7 Daniela Hall 7 November 2005.
Multiple Organ detection in CT Volumes - Week 2 Daniel Donenfeld.
A Scale and Rotation Invariant Approach to Tracking Human Body Part Regions in Videos Yihang BoHao Jiang Institute of Automation, CAS Boston College.
Vectorial Distortion For Performance Evaluation Current investigations …. Mathieu Delalandre and Ernest Valveny Meeting of Document Analysis Group Computer.
Parallel implementation of RAndom SAmple Consensus (RANSAC) Adarsh Kowdle.
Measuring the GRH and the cosmological parameters with MAGIC Oscar Blanch IFAE, Universitat Autònoma de Barcelona ISCRA 2002 June 2002.
Chapter 6 Control Using Wireless Throttling Valves.
Prakash Chockalingam Clemson University Non-Rigid Multi-Modal Object Tracking Using Gaussian Mixture Models Committee Members Dr Stan Birchfield (chair)
C HU H AI C OLLEGE O F H IGHER E DUCATION D EPARTMENT O F C OMPUTER S CIENCE Preparation of Final Year Project Report Bachelor of Science in Computer Science.
Motion Object Segmentation, Recognition and Tracking Huiqiong Chen; Yun Zhang; Derek Rivait Faculty of Computer Science Dalhousie University.

Miguel Reyes 1,2, Gabriel Dominguez 2, Sergio Escalera 1,2 Computer Vision Center (CVC) 1, University of Barcelona (UB) 2
Background Subtraction for Temporally Irregular Dynamic Textures Gerald Dalley, Joshua Migdal, and W. Eric L. Grimson Workshop on Applications of Computer.
The Research Design. Experimental Design Definition A description of what a researcher would like to find out and how to find it out. Pre-requisites 1.Identification.
DETECTION AND CLASSIFICATION OF VEHICLES FROM A VIDEO USING TIME-SPATIAL IMAGE NAFI UR RASHID, NILUTHPOL CHOWDHURY, BHADHAN ROY JOY S. M. MAHBUBUR RAHMAN.
#MOTION ESTIMATION AND OCCLUSION DETECTION #BLURRED VIDEO WITH LAYERS
COBXXXX EXPERIMENTAL FRAMEWORK FOR EVALUATION OF GUIDANCE AND CONTROL ALGORITHMS FOR UAVS Sérgio Ronaldo Barros dos Santos,
QoS Evaluation Model for a Campus-Wide Network: an alternative approach Juan Antonio Martínez Comunicacions - Servei d’Informàtica.
1 University of Texas at Austin Machine Learning Group 图像与视频处理 计算机学院 Motion Detection and Estimation.
עיבוד תמונות Image Processing. Images and Data Image Image Processing Image Image Computer Vision Data Data Computer Graphics Image.
Bo QIN, Zongshun MA, Zhenghua FANG, Shengke WANG Computer-Aided Design and Computer Graphics, th IEEE International Conference on, p Presenter.
Limitations of Cotemporary Classification Algorithms Major limitations of classification algorithms like Adaboost, SVMs, or Naïve Bayes include, Requirement.
PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia.
MODELING MST OPTIC FLOW RESPONSES USING RECEPTIVE FIELD SEGMENTAL INTERACTIONS Chen-Ping Yu +, William K. Page*, Roger Gaborski +, and Charles J. Duffy.
Joint Tracking of Features and Edges STAN BIRCHFIELD AND SHRINIVAS PUNDLIK CLEMSON UNIVERSITY ABSTRACT LUCAS-KANADE AND HORN-SCHUNCK JOINT TRACKING OF.
 (Worse) Joint cooperation between the two organizations depends on whether the available resources are adequate enough.  (Better) Cooperation between.
Segmentation of Vehicles in Traffic Video Tun-Yu Chiang Wilson Lau.
Monitoring and Enhancing Visual Features (movement, color) as a Method for Predicting Brain Activity Level -in Terms of the Perception of Pain Sensation.
Urban Watershed Restoration: Putting Plans Into Action Tanis Douglas Bowker Creek Initiative Coordinator Capital Regional District, Victoria BC.
CT333/CT433 Image Processing and Computer Vision.
Progress presentation
C HU H AI C OLLEGE O F H IGHER E DUCATION D EPARTMENT O F C OMPUTER S CIENCE Preparation of Final Year Project Report Bachelor of Science in Computer Science.
By Muhammad Shahid Iqbal Module No. 09 Future Worth Method Engineering Economics.
A Free Software tool for Automatic Tuning of Segmentation Parameters SPT 3.0 Pedro Achanccaray, Victor Ayma, Luis Jimenez, Sergio Garcia, Patrick Happ,
MASTER’S IN ENVIRONMENTAL CHANGE: ANALYSIS AND MANAGEMENT.
1 Exercise 1. (a) Find all optimal sequences for the scheduling problem 1 ||  w j C j with the following jobs. (b) Determine the effect of a change in.

Dynamically Computing Fastest Paths for Intelligent Transportation Systems - ADITI BHAUMICK ab3585.
May 2003 SUT Color image segmentation – an innovative approach Amin Fazel May 2003 Sharif University of Technology Course Presentation base on a paper.
PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia.
April 21, 2016Introduction to Artificial Intelligence Lecture 22: Computer Vision II 1 Canny Edge Detector The Canny edge detector is a good approximation.
HUMAN MEDIA INTERACTION CREATIVE TECHNOLOGY FOR PLAY AND
Electrical Engineering
F. Orga (2), R. M. Alsina-Pagès (2), F. Alías (2), J. C. Socoró (2),
Figure 11.1 Linear system model for a signal s[n].
INTRODUCTION OBJECTIVE METHODS RESULTS CONCLUSIONS
Innovation and Score Reporting
MODELING MST OPTIC FLOW RESPONSES
Presented by: Yang Yu Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy Mingliang Chen, Xing Wei, Qingxiong.
MODELING MST OPTIC FLOW RESPONSES
Institution Name.
Analysis of Contour Motions
Image and Video Processing
Proposal 2 for 2018: Crowdsourcing parking availability
Initial Progress Report
Presentation transcript:

PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia Artificial (Universitat Autònoma de Barcelona) Assignment 0X (dd/mm/yyyy) JohnSmith, JohnSmith, JohnSmith, JohnSmith, JohnSmith. Assignment description Color Depolarization of super-pixel artifacts Multifiltering of spectral Gaussians Decoloration of traffic lipids

PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia Artificial (Universitat Autònoma de Barcelona) Exercise 1. LK and TV-L1 comparison kjlkjlk Propose an explanation of the performance observed for each method in each of the 4 regions. Why the methods perform as they do ? Results obtained, plots,... Assignment 0X (dd/mm/yyyy) JohnSmith, JohnSmith, JohnSmith, JohnSmith, JohnSmith.

PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia Artificial (Universitat Autònoma de Barcelona) Exercise 2. Pyramid Levels Propose a criterion to establish the minimal number of levels of the pyramid for a given sequence. Analyse the performance of LK and TV-L1 for pyramids of 1,2,4,8 and 16 levels: Tables, plots, Assignment 0X (dd/mm/yyyy) JohnSmith, JohnSmith, JohnSmith, JohnSmith, JohnSmith.

PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia Artificial (Universitat Autònoma de Barcelona) Exercise 3. ρ Parameter Study how the parameter rho affects the performance of the LK method. Tables, plots, … Assignment 0X (dd/mm/yyyy) JohnSmith, JohnSmith, JohnSmith, JohnSmith, JohnSmith. How ρ alters the optical flow computed ? Why ?

PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia Artificial (Universitat Autònoma de Barcelona) Exercise 4. Tune the parameters of TV-L1 to obtain a good performance in the Enpeda sequence Assignment 0X (dd/mm/yyyy) JohnSmith, JohnSmith, JohnSmith, JohnSmith, JohnSmith. Detail the tuning process and the results achieved.

PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia Artificial (Universitat Autònoma de Barcelona) Exercise 4. Propose a way to segment the vehicles from the computed optical flow vectors. Assignment 0X (dd/mm/yyyy) JohnSmith, JohnSmith, JohnSmith, JohnSmith, JohnSmith. Detail your approach

PROGRESS PRESENTATION Master in Computer Vision and Artificial Intelligence mcvai.uab.es Computer Vision Center and Institut d’Investigació en Ingel·ligencia Artificial (Universitat Autònoma de Barcelona) Optional Exercise. Proposal of Segmentation Detail the implementation of your proposal. Evaluate the results obtained Assignment 0X (dd/mm/yyyy) JohnSmith, JohnSmith, JohnSmith, JohnSmith, JohnSmith.