GCAPS Status Report Ryan Weiss Nick Hebner Kooper Fram.

Slides:



Advertisements
Similar presentations
Active Shape Models Suppose we have a statistical shape model –Trained from sets of examples How do we use it to interpret new images? Use an “Active Shape.
Advertisements

Face Recognition and Biometric Systems Eigenfaces (2)
Detecting Faces in Images: A Survey
Slide Ruler. ? X 5" On today’s menu...  What happened with Gravity  Noise  The tool today  Fundamental Limitations  Magical Christmas Land  (Where.
Digital Photography with Flash and No-Flash Image Pairs By: Georg PetschniggManeesh Agrawala Hugues HoppeRichard Szeliski Michael CohenKentaro Toyama,
Face Alignment with Part-Based Modeling
Position and Attitude Determination using Digital Image Processing Sean VandenAvond Mentors: Brian Taylor, Dr. Demoz Gebre-Egziabher A UROP sponsored research.
Face Recognition Method of OpenCV
1 Video Processing Lecture on the image part (8+9) Automatic Perception Volker Krüger Aalborg Media Lab Aalborg University Copenhagen
Object Recognition & Model Based Tracking © Danica Kragic Tracking system.
3D M otion D etermination U sing µ IMU A nd V isual T racking 14 May 2010 Centre for Micro and Nano Systems The Chinese University of Hong Kong Supervised.
Kinect Case Study CSE P 576 Larry Zitnick
Multi video camera calibration and synchronization.
Ubiquitous Navigation
Automatic Face Recognition Using Color Based Segmentation and Intelligent Energy Detection Michael Padilla and Zihong Fan Group 16 EE368, Spring
Research Update and Future Work Directions – Jan 18, 2006 – Ognjen Arandjelović Roberto Cipolla.
Rodent Behavior Analysis Tom Henderson Vision Based Behavior Analysis Universitaet Karlsruhe (TH) 12 November /9.
Face Recognition using PCA (Eigenfaces) and LDA (Fisherfaces)
Tracking using the Kalman Filter. Point Tracking Estimate the location of a given point along a sequence of images. (x 0,y 0 ) (x n,y n )
Highlights Lecture on the image part (10) Automatic Perception 16
METEOR Guidance System P07106 Nov 2006 – May 2007 Project Review.
PCA Channel Student: Fangming JI u Supervisor: Professor Tom Geoden.
Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP.
Multiple Object Class Detection with a Generative Model K. Mikolajczyk, B. Leibe and B. Schiele Carolina Galleguillos.
Panorama Stitching and Augmented Reality. Local feature matching with large datasets n Examples: l Identify all panoramas and objects in an image set.
PhD Thesis. Biometrics Science studying measurements and statistics of biological data Most relevant application: id. recognition 2.
Vision-Based Biometric Authentication System by Padraic o hIarnain Final Year Project Presentation.
Face Recognition Using Neural Networks Presented By: Hadis Mohseni Leila Taghavi Atefeh Mirsafian.
Augmented Reality and 3D modelling Done by Stafford Joemat Supervised by Mr James Connan and Mr Mehrdad Ghaziasgar.
GCAPS Team Design Review CPE 450 Section 1 January 21, 2008 Nick Hebner Kooper Frahm Ryan Weiss.
Multimodal Interaction Dr. Mike Spann
September 23, 2014Computer Vision Lecture 5: Binary Image Processing 1 Binary Images Binary images are grayscale images with only two possible levels of.
Gili Werner. Motivation Detecting text in a natural scene is an important part of many Computer Vision tasks.
Joon Hyung Shim, Jinkyu Yang, and Inseong Kim
Learning to perceive how hand-written digits were drawn Geoffrey Hinton Canadian Institute for Advanced Research and University of Toronto.
Video Segmentation Prepared By M. Alburbar Supervised By: Mr. Nael Abu Ras University of Palestine Interactive Multimedia Application Development.
Face Recognition: An Introduction
CVPR 2003 Tutorial Recognition and Matching Based on Local Invariant Features David Lowe Computer Science Department University of British Columbia.
Video Eyewear for Augmented Reality Presenter: Manjul Sharma Supervisor: Paul Calder.
Vision-based human motion analysis: An overview Computer Vision and Image Understanding(2007)
School of Engineering and Computer Science Victoria University of Wellington Copyright: Peter Andreae, VUW Image Recognition COMP # 18.
Update September 21, 2011 Adrian Fletcher, Jacob Schreiver, Justin Clark, & Nathan Armentrout.
1 Research Question  Can a vision-based mobile robot  with limited computation and memory,  and rapidly varying camera positions,  operate autonomously.
Real-Time Simultaneous Localization and Mapping with a Single Camera (Mono SLAM) Young Ki Baik Computer Vision Lab. Seoul National University.
1 Motion estimation from image and inertial measurements Dennis Strelow and Sanjiv Singh.
Face Alignment at 3000fps via Regressing Local Binary Features CVPR14 Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun Presented by Sung Sil Kim.
Multimodal Interaction Dr. Mike Spann
Face Image-Based Gender Recognition Using Complex-Valued Neural Network Instructor :Dr. Dong-Chul Kim Indrani Gorripati.
Face Detection Using Skin Color and Gabor Wavelet Representation Information and Communication Theory Group Faculty of Information Technology and System.
Underwater Vehicle Navigation Techniques Chris Barngrover CSE 237D.
Team Members Ming-Chun Chang Lungisa Matshoba Steven Preston Supervisors Dr James Gain Dr Patrick Marais.
MRP Deep Dive Presented by: Bryan Dehler Implementation Consultant November 10 th, 2015.
GCAPS Team Design Review CPE 450 Section 1 January 22, 2008 Nick Hebner Kooper Frahm Ryan Weiss.
Current Works Determined drift during constant velocity test caused by slight rotation which results in gravity affecting accelerometers Analyzed data.
IEEE International Conference on Multimedia and Expo.
Augmented Reality and 3D modelling Done by Stafford Joemat Supervised by Mr James Connan.
CSSE463: Image Recognition Day 29 This week This week Today: Surveillance and finding motion vectors Today: Surveillance and finding motion vectors Tomorrow:
1 Long-term image-based motion estimation Dennis Strelow and Sanjiv Singh.
SIFT.
Over the recent years, computer vision has started to play a significant role in the Human Computer Interaction (HCI). With efficient object tracking.
Things about pattern recognition OGD. Pattern recognition ● Simplify the input ● Extract features ● Process ● Learn? ● Output results.
Paper – Stephen Se, David Lowe, Jim Little
+ SLAM with SIFT Se, Lowe, and Little Presented by Matt Loper
Robustness Evaluation of Perceptual Watermarks
Simultaneous Localization and Mapping
Context-based vision system for place and object recognition
Computer Vision Lecture 5: Binary Image Processing
Parking Spot Recognition from Video Footage
Outline H. Murase, and S. K. Nayar, “Visual learning and recognition of 3-D objects from appearance,” International Journal of Computer Vision, vol. 14,
Simultaneous Localization and Mapping
Presentation transcript:

GCAPS Status Report Ryan Weiss Nick Hebner Kooper Fram

General Status PCA Testing PCA Testing Good recognition of landmark existence Good recognition of landmark existence Need to experiment with sensitivity threshold, number of dimensions, number of training images, camera settings, separate eigenspaces, averaging over time Need to experiment with sensitivity threshold, number of dimensions, number of training images, camera settings, separate eigenspaces, averaging over time IMU Testing IMU Testing Position drifting Position drifting Bias compensation Bias compensation Noise filtering Noise filtering

Schedule

PCA Live Environment Testing Increasing size of image set Increasing size of image set Improves pose estimation Improves pose estimation Little impact on object recognition accuracy Little impact on object recognition accuracy More sensitive to position of object that appearance of individual landmark More sensitive to position of object that appearance of individual landmark Decreasing image brightness Decreasing image brightness Slight improvement in object recognition Slight improvement in object recognition Details of surroundings increase uniqueness of images Details of surroundings increase uniqueness of images

Landmark Information Simple tool to associate info with landmark object Simple tool to associate info with landmark object OpenGL or OpenCV utility OpenGL or OpenCV utility Associates position/orientation with manifold point Associates position/orientation with manifold point Automatic information generation based on minimal manual input Automatic information generation based on minimal manual input Computes world coordinates from pixel coordinates Computes world coordinates from pixel coordinates A

IMU Testing Acceleration Acceleration Velocity Velocity Position Position

Other Status Updates Procurement Procurement Golf-cart on order – will stay with cart for test and demo Golf-cart on order – will stay with cart for test and demo Otherwise, complete Otherwise, complete