From local motion estimates to global ones - physiology:

Slides:

Advertisements

Similar presentations

Land’s Retinex algorithm

Advertisements

Chapter 2: Marr’s theory of vision. Cognitive Science  José Luis Bermúdez / Cambridge University Press 2010 Overview Introduce Marr’s distinction between.

Motion Illusions As Optimal Percepts. What’s Special About Perception? Arguably, visual perception is better optimized by evolution than other cognitive.

Dynamic Occlusion Analysis in Optical Flow Fields

The perception of Shading and Reflectance E.H. Adelson, A.P. Pentland Presenter: Stefan Zickler.

Proportion Priors for Image Sequence Segmentation Claudia Nieuwenhuis, etc. ICCV 2013 Oral.

What happens when no correspondence is possible? Highly mismatched stereo-pairs lead to ‘binocular rivalry’ TANGENT ALERT! Open question: Can rivalry and.

December 5, 2013Computer Vision Lecture 20: Hidden Markov Models/Depth 1 Stereo Vision Due to the limited resolution of images, increasing the baseline.

Version 0.10 (c) 2007 CELEST VISI  N Star Light, Star Bright, Let’s Explore Light How You Perceive Light How many black dots can you count?

PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2004 Vision as Optimal Inference The problem of visual processing can be thought of as.

Perception of illumination and shadows Lavanya Sharan February 14th, 2011.

Optical Flow Brightness Constancy The Aperture problem Regularization Lucas-Kanade Coarse-to-fine Parametric motion models Direct depth SSD tracking.

Perception of illumination and shadows Lavanya Sharan February 14th, 2011.

Color, lightness & brightness Lavanya Sharan February 7, 2011.

Optical Flow Brightness Constancy The Aperture problem Regularization Lucas-Kanade Coarse-to-fine Parametric motion models Direct depth SSD tracking.

Human Visual System Lecture 3 Human Visual System – Recap

Deriving Intrinsic Images from Image Sequences Mohit Gupta Yair Weiss.

CS292 Computational Vision and Language Visual Features - Colour and Texture.

Visual motion Many slides adapted from S. Seitz, R. Szeliski, M. Pollefeys.

Motion Estimation Today’s Readings Trucco & Verri, 8.3 – 8.4 (skip 8.3.3, read only top half of p. 199) Numerical Recipes (Newton-Raphson), 9.4 (first.

3D Rigid/Nonrigid RegistrationRegistration 1)Known features, correspondences, transformation model – feature basedfeature based 2)Specific motion type,

Optical flow (motion vector) computation Course: Computer Graphics and Image Processing Semester:Fall 2002 Presenter:Nilesh Ghubade

Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm.

EE465: Introduction to Digital Image Processing

Shape from Shading and Texture. Lambertian Reflectance Model Diffuse surfaces appear equally bright from all directionsDiffuse surfaces appear equally.

1-1 Measuring image motion velocity field “local” motion detectors only measure component of motion perpendicular to moving edge “aperture problem” 2D.

December 4, 2014Computer Vision Lecture 22: Depth 1 Stereo Vision Comparing the similar triangles PMC l and p l LC l, we get: Similarly, for PNC r and.

1 Computational Vision CSCI 363, Fall 2012 Lecture 20 Stereo, Motion.

1 Perception, Illusion and VR HNRS 299, Spring 2008 Lecture 8 Seeing Depth.

Deriving Intrinsic Images from Image Sequences Mohit Gupta 04/21/2006 Advanced Perception Yair Weiss.

Visual motion Many slides adapted from S. Seitz, R. Szeliski, M. Pollefeys.

1 Computational Vision CSCI 363, Fall 2012 Lecture 28 Structure from motion.

December 9, 2014Computer Vision Lecture 23: Motion Analysis 1 Now we will talk about… Motion Analysis.

1 Computational Vision CSCI 363, Fall 2012 Lecture 21 Motion II.

CS332 Visual Processing Department of Computer Science Wellesley College Analysis of Motion Measuring image motion.

3D Imaging Motion.

1 Perception and VR MONT 104S, Fall 2008 Lecture 4 Lightness, Brightness and Edges.

CS332 Visual Processing Department of Computer Science Wellesley College Analysis of Motion Recovering 3-D structure from motion.

Motion Illusions As Optimal Percepts. What’s Special About Perception? Visual perception important for survival  Likely optimized by evolution  at least.

Multiple Light Source Optical Flow Multiple Light Source Optical Flow Robert J. Woodham ICCV’90.

Colour and Texture. Extract 3-D information Using Vision Extract 3-D information for performing certain tasks such as manipulation, navigation, and recognition.

True FOE Computed FOE Recovering the observer’s rotation Velocity component due to observer’s translation Velocity component due to observer’s rotation.

Perception and VR MONT 104S, Fall 2008 Lecture 8 Seeing Depth

By James J. Todd and Victor J. Perotti Presented by Samuel Crisanto THE VISUAL PERCEPTION OF SURFACE ORIENTATION FROM OPTICAL MOTION.

Tal Amir Advanced Topics in Computer Vision May 29 th, 2015 COUPLED MOTION- LIGHTING ANALYSIS.

A model for detecting illusory contours Caveat: Response decreases gradually with increasing intermeshing of gratings. This does not correspond with subjective.

CS332 Visual Processing Department of Computer Science Wellesley College CS 332 Visual Processing in Computer and Biological Vision Systems Analysis of.

Optical flow and keypoint tracking Many slides adapted from S. Seitz, R. Szeliski, M. Pollefeys.

1 Review and Summary We have covered a LOT of material, spending more time and more detail on 2D image segmentation and analysis, but hopefully giving.

1 Computational Vision CSCI 363, Fall 2012 Lecture 29 Structure from motion, Heading.

Image Motion. The Information from Image Motion 3D motion between observer and scene + structure of the scene –Wallach O’Connell (1953): Kinetic depth.

Think-Pair-Share What visual or physiological cues help us to perceive 3D shape and depth?

Intrinsic images and shape refinement

Jitendra Malik U.C. Berkeley

2D Motion is just the Beginning

Motion and Optical Flow

Measuring motion in biological vision systems

Common Classification Tasks

Range Imaging Through Triangulation

L Differentiate Threshold Integrate R.

Measuring motion in biological vision systems

Announcements Project 3b due Friday.

Pattern recognition (…and object perception).

?. ? White Fuzzy Color Oblong Texture Shape Most problems in vision are underconstrained White Color Most problems in vision are underconstrained.

Visual Motion and the Perception of Surface Material

CMSC 426: Image Processing (Computer Vision)

Announcements Questions on the project? New turn-in info online

Elements of Art Line Shape Form Texture Color Value Space.

Optical flow and keypoint tracking

Shape from Shading and Texture

Presentation transcript:

From local motion estimates to global ones - physiology:

Motion fields for more complex patterns: Hildreth (1985): Smoothness of velocity field along the contour True motion field Local motion estimates Smoothest Velocity field

Motion fields for more complex patterns (contd.):

Ellipse demo

Recovering 3D structure from motion: Kinetic Depth Effect [Wallach, 1953] Percept Another possible percept Inference: The human visual system has a preference for rigid interpretations

Ullman’s model for recovering 3D structure from motion: 1.Establish correspondence between features in different frames 2.Recover transformation matrix and z values of points Key result: For a rigid structure, 4 non-coplanar points in 3 frames are sufficient to solve for all the unknowns [Ullman, 1979] Open questions: 1. Do these bounds apply to human observers too? 2. Does the rigidity assumption always hold? 3. How do we recover the 3D structure of non-rigid dynamic objects? Video 1: NR rotating object Video 2: Johansson

Ullman’s incremental rigidity scheme: Allows structure recovery even for gradually deforming objects. However: 1. Humans are able to recover 3D structures even with just 2 frames. It is unclear how this is accomplished. 2. Correspondence is not an easy problem. Errors in correspondence lead to errors In structure recovery.

Processing Framework Proposed by Marr Recognition Shape From stereo Motion flow Shape From motion Color estimation Color estimation Shape From contour Shape From shading Shape From texture 3D structure; motion characteristics; surface properties Edge extraction Image

Color

Color Estimation: Goal: To recover the intrinsic surface reflectance of an object. And yet, we have good lightness constancy!

Lightness Constancy: The constancy in perceived surface reflectance regardless of differences in illumination. Goal: Given L, recover R. Clearly underconstrained. Assumptions are needed for unique solutions. Luminance (L) = Reflectance (R) * Illumination (I) Helmholtz’s theory: Observer ‘knows’ I through past experience. Hering, Wallach, Land & McCann: Observer computes luminance ratios across edges. (some important hidden assumptions here) Explain fig above

The perceptual importance of luminance ratios at edges: Cornsweet Illusion

Explaining simultaneous contrast illusions via edge ratios:

Are ratios taken with actual or perceived luminances? TANGENT ALERT!

Land and McCann’s Retinex theory: * I R L Given L, recover R

Land and McCann’s Retinex theory - Assumptions: 1.The world is flat and all sharp luminance variations are due to changes in reflectance. Reflectance always changes abruptly. 2.Illumination changes gradually across a scene. Basic idea: Preserve luminance ratios at edges and discard slow variations.