Dept Cognitive Sciences Budapest Univ. Technology and Economics A látás számítástechnikai modelljei: Computational vision.

Az előadások a következő témára: "Dept Cognitive Sciences Budapest Univ. Technology and Economics A látás számítástechnikai modelljei: Computational vision."— Előadás másolata:

1 Dept Cognitive Sciences Budapest Univ. Technology and Economics A látás számítástechnikai modelljei: Computational vision

2 Dept Cognitive Sciences Budapest Univ. Technology and Economics A látás problematikája

3 Dept Cognitive Sciences Budapest Univ. Technology and Economics Bülthoff

4 Dept Cognitive Sciences Budapest Univ. Technology and Economics Miért? a világ 3D, a kép 2D a látott kép függ a kamerától (CCD, infra, BW) annak szenzitivitásától (felbontás, etc) megvilágítástól környezettől (köd, por etc) a tárgy anyagának visszaverő képességétől (textura, szin etc) Nagy mennyiségű info!

5 Dept Cognitive Sciences Budapest Univ. Technology and Economics Egy látórendszer feladatai A világ, a tárgyak, képek leírása Képfeldolgozás (fontos vonások kivonása) Szegmentálás (körvonalak, régiók, területek elkülönítése) Minta felismerés (egy tárgyat tartalmazó képek azonosítása) Kép megértése (több tárgy esetén 3D modell elkészítése a világról) Végső cél: a tárgyak, dolgok felismerése a képben

6 Dept Cognitive Sciences Budapest Univ. Technology and Economics Körvonal detekció Fontos jellemzők kivonása Textura analízis Szegmetálás Alak azonosítás Interpretáció, felismerés 3D

7 Dept Cognitive Sciences Budapest Univ. Technology and Economics V1 representation

8 Dept Cognitive Sciences Budapest Univ. Technology and Economics Do you really want to study vision?

9 Dept Cognitive Sciences Budapest Univ. Technology and Economics Parallel patways - monkey

10 Dept Cognitive Sciences Budapest Univ. Technology and Economics Computational Problems in Object Recognition How to account for shape-based encoding? Objects are more than the sum of their parts Properties of object constancy Viewing position distance, size orientation Illumination, contrast, color Occlusion defining cue

11 Dept Cognitive Sciences Budapest Univ. Technology and Economics Computational Problems in Object Recognition Frame of reference: object constancy across orientation View-dependent –Separate representation of an object for each viewpoint View-invariant –Critical properties/features used for object recognition »Major/minor axes, etc.

12 Dept Cognitive Sciences Budapest Univ. Technology and Economics Computational Problems in Object Recognition Hierarchical coding hypothesis –Object defined by Gnostic (or grandmother) cell? single neuron that represents"granny" activated by outputs from increasingly more complex detectors. NO!

13 Dept Cognitive Sciences Budapest Univ. Technology and Economics The inferotemporal cortex Unimodal „end-station of object recognition pathway

14 Dept Cognitive Sciences Budapest Univ. Technology and Economics Tamura and Tanaka, 2001 Complex shapes

15 Dept Cognitive Sciences Budapest Univ. Technology and Economics Sugase et al, 1999 Faces

16 Dept Cognitive Sciences Budapest Univ. Technology and Economics -viewpoint Logothetis, 1996, Booth and Rolls, 1998

17 Dept Cognitive Sciences Budapest Univ. Technology and Economics View-based recognition Recognition of 3-D objects depends upon multiple, stored views of objects Object recognition occurs when a current pattern matches a stored pattern

18 Dept Cognitive Sciences Budapest Univ. Technology and Economics FFA: „the one and only” Often referred to as Fusiform Face Area (FFA) Same location as face-specific cell responses Near areas involved with color vision & achromatopsia cooccurs with prosopagnosia A prosopagnosic showed no activity in FFA in response to faces.Autists neither...

19 Dept Cognitive Sciences Budapest Univ. Technology and Economics What activates the FFA? Kanwisher & her colleagues have probed the type of stimuli that activate this region Strong response: Frontal shots, profiles, cartoon faces, inverted faces?!, inverted cartoon faces, cat faces, faces with no eyes, & eyes alone. Weak response: Schematic faces, animal bodies, houses, back of head. FFA appears broadly tuned

20 Dept Cognitive Sciences Budapest Univ. Technology and Economics Is it a category? Simon says „no”

21 Dept Cognitive Sciences Budapest Univ. Technology and Economics Is it animate vs nonanimate? simon says „no” only front views? simon says „no”

22 Dept Cognitive Sciences Budapest Univ. Technology and Economics Expert activations in the FFA Greeble experts show activation in FFA whereas Greeble novices do not show significant activation Greebles may be a “face” Dog & Bird experts also show increased activity in the FFA. However, the activation is only half of that seen for faces Imaging data is equivocal.

23 Dept Cognitive Sciences Budapest Univ. Technology and Economics expertise leads to specialisation of a given area, previously thought to be face specific! Expertise and the FFA

24 Dept Cognitive Sciences Budapest Univ. Technology and Economics Unexpected ventral areas fMRI has revealed several areas that have no clear monkey homologues PPA Preferred Nonprefer. EBA Preferred Parahippocampal place area (PPA) – responds to places Extrastriate body area (EBA) – responds to bodies Epstein & Kanwisher, 1998 Downing et al., 2001

25 Dept Cognitive Sciences Budapest Univ. Technology and Economics EBA- human

26 Dept Cognitive Sciences Budapest Univ. Technology and Economics EBA/LOC FFA-PPA

27 Dept Cognitive Sciences Budapest Univ. Technology and Economics Another alternative James Haxby, NIMH, Bethesda, MD objectotopy one back repetition detection for other views of faces houses manmade objects

28 Dept Cognitive Sciences Budapest Univ. Technology and Economics distinct pattern of activations for each category overlapping, distributed.

29 Dept Cognitive Sciences Budapest Univ. Technology and Economics Distributed processing

30 Dept Cognitive Sciences Budapest Univ. Technology and Economics Conclusion- confusion THUS: 1. FFA- faces 2. FFA- expertise 3. What are you talking about? One ring above all-one area for all. VENTRAL OCCIPITO- TEMPORAL CORTEX

31 Dept Cognitive Sciences Budapest Univ. Technology and Economics A model summary

32 Dept Cognitive Sciences Budapest Univ. Technology and Economics Összefoglalás erős hierarchia növekvő komplexitás, RF egyre nagyobb „hasonlóság a percepcióhoz” (invarianciák) masszív parallellitás eltérő tulajdonságok - eltérő rendszerek szétosztott reprezentáció flexibilitás, plaszticitás

33 Dept Cognitive Sciences Budapest Univ. Technology and Economics If our brain was simple we would be too simple to understand it. Mario Puzo