Samia BouchafaBertrand ZavidoviqueIEF University of Orsay FranceVito Di GesCesare ValentiDMA University of Palermo Italy

iAstro Workshop Granada 21-22 February 2002

Symmetry and perception

iAstro Workshop Granada 21-22 February 2002

iAstro Workshop Granada 21-22 February 2002

Computing Symmetry

iAstro Workshop Granada 21-22 February 2002

Edge Based ComputationSymmetry Axial Transform (SAT) (Blum, Nagel, 1978) Smoothed Local Symmetry (SLS) (Brady, Asada, 1984) Affine transformations and symmetry(Mukhergee, Zisserman, Brady, Chan, Cipolla, 1995) Partial occlusion (Sato, Cipolla, 1997)String oriented approach(Atallah, 1985), (Bruckstein and Shaked, 1995)

iAstro Workshop Granada 21-22 February 2002

Gray Levels ApproachesTexture analysis and symmetry measures (Cheterikov and Haralick, 1995)Measures based on the Radoms transform (Kiryati and Gofman, 1996)Context free attentional operators (Reisfeld, Wolfson and Yeshurun 1995)

iAstro Workshop Granada 21-22 February 2002

Symmetry TransformDi Ges, Valenti, 1994

iAstro Workshop Granada 21-22 February 2002

Discrete Symmetry Transform

iAstro Workshop Granada 21-22 February 2002

Points of interest

iAstro Workshop Granada 21-22 February 2002

Pyramid-DST(Di Ges,Valenti 1996)Discrete Fourier Transform of D0 andthen:

iAstro Workshop Granada 21-22 February 2002

iAstro Workshop Granada 21-22 February 2002

iAstro Workshop Granada 21-22 February 2002

Tracking problems

iAstro Workshop Granada 21-22 February 2002

Face analysisApplications:security systems, criminology. physical access control, man-machine interactions

iAstro Workshop Granada 21-22 February 2002

Expression analysisNeutral, Sadness, Disgust, Happiness, Fear, Anger, Surprise

iAstro Workshop Granada 21-22 February 2002

Neutral

Smiling

Neutral

92%

8%

Smiling

20%

80%

Object recognition systems Chella, Di Gesu, Infantino, Intravaia, Valenti 1997Object Recognition Using Multiple Views3D shape reconstruction from image sequences

iAstro Workshop Granada 21-22 February 2002

Iterated Object TransformDi Ges, Zavidovique, 2002The IOT computes the symmetry transform, T, on steadily intensity reduced versions of the input image

iAstro Workshop Granada 21-22 February 2002

Contrast change and level linesContrast change definitionNon-decreasing funtion gLevel set :Contrast change impactsome level sets disappearanceno geometric deformation

Motion impact (+ noise)some new level sets appearance Geometric deformation level lines crossing

iAstro Workshop Granada 21-22 February 2002

Detection criteria

How can we reconstruct the scene S ?

Possibilities for each line :1. The line is present no detection2. The line is not present Doubt :Is the reference complete ?Is the background uniform ?3. The line crosses another one detectionWeek Detection Strong Detection

iAstro Workshop Granada 21-22 February 2002

Motion detection algorithm

iAstro Workshop Granada 21-22 February 2002

Level line characterizationTwo possibilities :Global characterization surface, other moments of inertia, etc.Associated level lineOur choice : local characterization- Point detection- No level lines occlusions management

iAstro Workshop Granada 21-22 February 2002

The original sequence presents some contrast changes due the automatic gain control of the camera and to natural scene illumination changes. In the sequence, only points affected by motion are displayedThe result of the detection algorithm that is insensible towards contrast changes.

iAstro Workshop Granada 21-22 February 2002

iAstro Workshop Granada 21-22 February 2002

Fast Marching Methods and Level Set Methods are numerical techniques which can follow the evolution of interfaces. These interfaces can develop sharp corners, break apart, and merge together. The techniques have a wide range of applications, including problems in fluid mechanics, combustion, manufacturing of computer chips, computer animation, image processing, structure of snowflakes, and the shape of soap bubbles. These are two fundamentally different approaches to the problem of tracking moving interfaces, yet they share a common theory and numerical methodology.

iAstro Workshop Granada 21-22 February 2002

Edge Based ComputationSymmetry Axial Transform (SAT) (Blum, Nagel, 1978)

iAstro Workshop Granada 21-22 February 2002

Smoothed Local Symmetry (SLS) (Brady, Asada, 1984)

iAstro Workshop Granada 21-22 February 2002

DSTEdge based operatorYeshurunInput

iAstro Workshop Granada 21-22 February 2002

Face analysis and algorithmsCardaci, Di Gesu, Intravaia, 1998The algorithm is based on an attentive architecture.

local and global symmetry operators Reisfeld, Wolfson,Yeshurun (1995) Di Ges, Valenti, Strinati, (1997)

graph theoretical algorithms Zhan (1972)

facial anatomy (model driven) Russel, (1994)

iAstro Workshop Granada 21-22 February 2002

iAstro Workshop Granada 21-22 February 2002

Gelstat clustering (GC)Structural information are represented by a simple Internal Model (IM) based on psycho-visual correlation between components of face Chen, Yachida (1996) A relational graph (FG) is then built from the retrieved FC

iAstro Workshop Granada 21-22 February 2002

Neutral

Smiling

Neutral

92%

8%

Smiling

20%

80%

ResultsSquence initialeA sequence with global contrast changes

iAstro Workshop Granada 21-22 February 2002

ResultsThe same crossing junction but different lighting conditions

iAstro Workshop Granada 21-22 February 2002

ApplicationsRoad environment Vehicle/pedestrian detection and countingSubway environment Stationnary objects/human detection

iAstro Workshop Granada 21-22 February 2002

ComparisonsReference sequenceSix months beforeLevel linesGrey levelsLaplacianGradients orientation

iAstro Workshop Granada 21-22 February 2002

ComparisonsGradient orientationsProblems with stability and thresholding !

iAstro Workshop Granada 21-22 February 2002