'Optic flow' presentation slideshows

Computer and Robot Vision II

Computer and Robot Vision II. Chapter 15 Motion and Surface Structure from Time Varying Image Sequences. Presented by: ??? & ??? 0917 533843 r94922081@ntu.edu.tw ???? : ??? ??. 15.1 Introduction.

1.94k views • 177 slides

862 views • 67 slides

Cosc 6326/Psych6750X

Cosc 6326/Psych6750X. Movement, Tracking. Moving about virtual and real environments. Simulating motion through the environment visual (vection) vestibular stimulation auditory surround motion/vibration tactile surround motion/vibration

678 views • 55 slides

Chapter 7: Taking Action

Chapter 7: Taking Action. The Ecological Approach to Perception. Approach developed by J. J. Gibson (began in late 1950s) Gibson felt that traditional laboratory research on perception was: too artificial - observers were not allowed to move their heads.

675 views • 49 slides

Lecture 7

Lecture 7. Moving past vision, to understand motion and video. Grounding image. Optic flow is 2d vector on image (u,v) Assuming: intensity only changes due to the motion. The derivitives are smooth Then we get a constraint: I x u + I y v + I t = 0 Defines line in velocity space

466 views • 40 slides

3D Space Perception (aka Depth Perception)

890 views • 39 slides

Chapter 7: Taking Action

Chapter 7: Taking Action. The Ecological Approach to Perception. Approach developed by J. J. Gibson (began in late 1950s) Gibson felt that traditional laboratory research on perception was: too artificial - observers were not allowed to move their heads.

477 views • 39 slides

Chapter 7: Taking Action

Chapter 7: Taking Action. The Ecological Approach to Perception. Approach developed by J. J. Gibson (began in late 1950s) Gibson felt that traditional laboratory research on perception was: too artificial - observers were not allowed to move their heads.

514 views • 31 slides

521 views • 28 slides

Chapter 10: Perception and Action

419 views • 26 slides

Contours and Optical Flow: Cues for Capturing Human Motion in Videos

Contours and Optical Flow: Cues for Capturing Human Motion in Videos. Thomas Brox Computer Vision and Pattern Recognition Group University of Bonn Research partially funded by the German Research Foundation (DFG). Human pose tracking from video. Tracking of markers attached to the body

415 views • 26 slides

Segmentation of Vehicles in Traffic Video

Segmentation of Vehicles in Traffic Video. Tun-Yu Chiang Wilson Lau. Introduction. Motivation In CA alone, >400 road monitoring cameras, plans to install more Reduce video bit-rate by object coding

375 views • 25 slides

Visually-Induced Postural Sway in Children Aged 7-12: Effect of Frequency and Surface Support

Visually-Induced Postural Sway in Children Aged 7-12: Effect of Frequency and Surface Support Sparto PJ, Redfern MS, Furman JM, Mandel EM, Casselbrant ML Depts of Physical Therapy, Otolaryngology, Bioengineering University of Pittsburgh, PA, USA. Sponsors: Eye and Ear Foundation,

264 views • 17 slides

Linear Subspace Transforms

Linear Subspace Transforms. PCA, Karhunen-Loeve, Hotelling csc610, 2001. Next: Biological vision. Please look at eye book and pathways readings on www page. Image dependent basis. Other transforms, (Fourier and Cosine) uses a fixed basis.

256 views • 17 slides

Extra-Striate Visual Processing

253 views • 16 slides

749 views • 7 slides

NTC ACTIVITIES 2005 Outline

NTC ACTIVITIES 2005 Outline. Activities Joint w/ MIT ONR STTR Phase II Final Demonstration AFOSR STTR - Autonomy w/ Imperfect Comms XS-Series Helicopter Development New flight systems Aerial photography + other apps Vision in Autonomous Aircraft HILsim for Optic Flow Sensors

145 views • 4 slides

MODELING MST OPTIC FLOW RESPONSES USING RECEPTIVE FIELD SEGMENTAL INTERACTIONS

Recorded Data Model Training. Recorded Data Model Training. 819R64. 40. Dual Gaussian Model of Singles Stimuli. Fit to Single Segment Local Motion Responses. 20. Neuron 819R64. Recorded Data Model Fit. 35. 15. Firing Rate (sp/s). 30. 10. 5. 25. 0. 20. Stimulus Condition. 15.

183 views • 1 slides

Department of Computer Science & Engineering

88 views • 1 slides

MODELING THE RECPTIVE FIELD ORGANIZATION OF OPTIC FLOW SELECTIVE MST NEURONS

2 Site Data Transforms 1 Site Model for Each Hot Spot Direction. INTRODUCTION. Dual Gaussian Model of MST Response Field Can Fit Optic Flow Data. Local Motion Composition Of The Global Pattern In Optic Flow. Dual Gaussian Model (derived from single site, local motion data).

142 views • 1 slides