Bernal S D_2010.pdf - University of Plymouth

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2.3. ILLUSORY AND OCCLUDED CONTOURS 4 ^ Figuif 2.12: The bipole applied lo illusory contour formaiiori. The bipole is used to evaluate the effect of" a contour elernetn at a given location on the likelihood of perceiving a ctmtour ai a second location. Ii is typically represcnied by a characierisiic iigureeighi shape which describes the coupling strength between the centre unit and surrounding units according to their relative position and orientation. A neural iniplcnienlalion of this concept gives rise to biptilc cells, dclined as nonlinear grouping operators which receive inpul from real edges
2.3. ILLUSORY AND OCCLUDED CONTOURS implement bipole units. A similar model which also falls into die base-grouping category focuses on the feed-forward integration of end-stopped responses (Heitger ct al, 1998). This approach extends the classi cal simple and complex cell nuxiel to include end-stopped neurons, cells which decrease iheir response when the stimulus size exceeds the classical receptive held, The response of the ori ented end-stopped cells is important as it is associated with figure-ground segregation, lividencc shows that partial occlusion typically generates abrupt terminations at the side of the occluded surface, which can be accounted for by ligure-ground segregation mechanisms. Furthermore. the model end-slopped neurons also distinguish and encode the direction of contrast between figure and ground surfaces. It was found that neurons in V2 that respond to illusory contours are sensitive to the direction of contrast, and that this usually matches the occlusion direc tion. Thus, the model performs contour interpolation of subjective contours by integrating over die end-stopped cells according to the bipole weighting function, and taking into accouni the contrast-direction selectivity of these cells. This feedforward processing stage is sometimes referred to as the preattenlive phase, and al though some highlight the exclusive involvement of feedl'orward as an advantage, the general consensus is that feedforward processing by itself is insuflicient to perform contour completion, specially across large areas of the visual field. For example, the size of receptive fields in lower level regions, where contour completion effects are observed, is insuflicient lii cover the visual field distance (> lO'') between the inducer line segments of large illusory figures (Angelucci and Bullier 2003. Sterzer et al, 2006). However, as pointed out al the end of this section, preallentive processing can also be underslotxJ as an important initial stage in the more global completion process, which also involves recurrent processing. Horizontal and feedback models A second category of models, also denoted as incremental grouping models (Roelfsema 2UU6), lake into account the context of the elcmenls involved by making use of horizonial and feedback connectivity. Under this network scheme, neurons stimulated directly from luminance-defined contours provide facilitalive interactions to neurons which do not receive direct retinal stimulation. It is usually considered an attentive, and thus 56
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A cortical model of object percepti
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A cortical model of object percepti
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Contents Ab.stract V AcknowlcdRcnie
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4.7 Original contrihutions in this
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3.9 Example of belief propagation i
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5.19 SI and CI model responses to a
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XVI
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2009 Durabernal S. Wennekers T, Den
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J.I. OVERVIEW retinal stimulation (
Page 21 and 22: J.2. MAIN CONTRfBUTlONS • A revie
Page 23 and 24: 2,1. OBJECT RECOGNITION ihe princip
Page 25 and 26: 2.1. OBJECT REC(JGNmON The dorsal s
Page 27 and 28: 2.1. OBJECT RECOGNJTION properties
Page 29 and 30: 2.1. OBJECT RECOGNITION of input em
Page 31 and 32: 2.1. OBJEOTRBCXiGNrnON ta) u I/I ^.
Page 33 and 34: 2.1. OBJECT RECOGNITION 2.1.2.1 HMA
Page 35 and 36: 2.1. OBJECT RECnCNmON unit will be
Page 37 and 38: 2.1. OBJECT REC(X}NIT!ON tivity, st
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Page 41 and 42: 2.2. HSGH-LEVEL FEEDBACK feature an
Page 43 and 44: 2.2. HIGH-LEVEL FEEDBACK Thirdly, p
Page 45 and 46: 2.2. mCH-LBVEL FEEDBACK Kandom 2 LO
Page 47 and 48: 1.2. HIGH-LEVEL FEEDBACK « 70 U M
Page 49 and 50: 2.2. HIGH-LEVEL FEEDBACK D Receptiv
Page 51 and 52: 1.1. HIGH-LEVEL FEEDBACK context of
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Page 55 and 56: 2.2. HIGH-LEVEL FEEDBACK activity g
Page 57 and 58: 2.2. HIGH-LEVEL FEEDBACK task, such
Page 59 and 60: 2.2. HIGH'lM^im,_WEDBACK 2004), and
Page 61 and 62: 2.2. HIGH-LEVEL FEEDBACK However, t
Page 63 and 64: 2.2. HIGH-LEVEL FEEDBACK sizes that
Page 65 and 66: 2.3. ILLUSORY AND OCCLUDED CONTOURS
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Page 79 and 80: 2.3. a.LVSORY AND OCCLUDED CONTnURS
Page 81 and 82: 2.4. ORIGINAL CONTRIBUTIONS IN THIS
Page 83 and 84: 3.1. THE BAYBSIAN BRAIN HYPOTHESIS
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Page 87 and 88: 3.1. THE BAYESJAN BRAIN HYPOmESIS T
Page 89 and 90: 3.2. EVIDENCE FROM THE BRAIN ity in
Page 91 and 92: 3.3. DEFINITION AND MATHEMATlCACPOR
Page 93 and 94: 3.3. DEFINITION AND MATHEMATICAL FO
Page 95 and 96: XX DEFINITION AND MATHEMATICAL FORM
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Page 101 and 102: 3.3. DEFINITION AND MATHFMAnCAL FOR
Page 103 and 104: .1.3. DERNrnON AmMAnWMATtCAL FORMUL
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Page 115 and 116: 3.3. OmNmON AND MATHEMATSOALFORMVLA
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Page 119 and 120: 3 J. DEFINITION AND MATHEMATICAL f-
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3.3. D^JmnON AND MATHEMATICAL F0RMU
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.1.3. DEFI^anON AND MATHFMATICAL FO
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3.3. DEFINITION AND MATHEMATICAl. F
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3J. DEFINITION AND MATHEMATICAL FOR
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3.4. EXISTING MODELS is on models t
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.1.4. EXISTING MODELS Figure J. 10:
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3.4. EXISTING MODELS the node encod
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3.4. EXISTING MOOm^ Type of f>raph
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3.4. EXISTING MODELS The model comp
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X4. EXISTING MODELS ;v 1,1 gi (8>^8
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3A. EXISTING MODELS proposes a triv
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3.4. EXISTING MODELS by the higher
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3.4. EXISTING MODELS aleni lo findi
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3.4. EXISTING MODELS Model Epshtein
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3.4. EXISTING MODELS Fristonelal. 2
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3.4. EXISTING MODELS Oulgoing teedl
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3.5. ORIGINAL CONTRIBUTIONS IN THIS
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4.1 HMAX AS A BAYESIAN NETWORK 4.1
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4.1. HMAX AS A BAYBSIAN NETWORK ini
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4.1. HMAX AS A BAYESIAN NETWORK fea
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4.1. HMAX AS A BAYESIAN NETWORK S3
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4.1. HMAX AS A BAYESIAN NETWORK Nod
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4.2. ARCHITECTURES 4.2 Architecture
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4,2. ARCHITECTURES S3 C2 1 node Kj,
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4.2. ARCmm^TVRES S3 I C2 s f S2 o o
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4.2. ARCHITECTURES S4 f C3 S3 I C2
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4.3. LEARNING 4.3.2 S1-C1 CRTs The
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4.3. LEARNING Weight matrix applied
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4.3. LEARNING CI group -1 •B •
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4.3. LEARNING 2. The list of select
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4.3. LEARNING node=«. Therefore, t
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4.3. LEARNING 4.3.4 S2-C2CPTS The w
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4.3. WARNING 60 S3 tealures (object
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4.4. FEEDFORWARD PROCESSING (he sim
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4.4. FEEDFORWARD PROCFSSING Figure
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4.5. FEEDBACK PROCESSING is proport
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4.5. F^mSACK PRCKESSING n^ (U,) i^(
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4.5. FEEDBACK PfiOCESSWG t.(VJ,) ji
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4.5. {REDBACK PROCESSING true vs. r
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4.5. FEEDBACK PROCES.SING 4.5.3.2 D
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4.5. FEEDBACK PROCESSING evidence i
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4.6. SUMMARY OF MODEL APPROXIMATION
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5,1. FEEDFORWARD PROCESSING using t
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5.L FEEDFORWARD PROCESSING Slate =
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5.1. FEEDFORWARD PROCESSING 1^ Onem
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5.1. FEEDFORWARD PROCESSING j • H
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5.1. FEEDFORWARD PROCESSING 5.1.2 O
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5.1. FEEDFORWARD PROCESSING Normal
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5.1. FEEDFORWARD PROCESSING c g ra
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5-1. FEEDFORWARD PROCESSING 4 5 Non
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5.1. FEEDFORWARD PROCESSING 100 8 9
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S.i. FEEDFORWARD PnOCESSING 5.1.2.4
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5.2. FEEDBACK-MEDIATED ILLUSORY CON
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5.2. FEEDBACK MEDIATED ILLUSORY CON
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5.2. FEiiDBACK-MEDWTHD ILLUSORY CON
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5.2. FEEDBACK MEDIATED ILLUSORY CON
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5.2. FEEDBACK-MHDIATED ILLUSORY CON
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5.2. FEEDBACK'MEDIATED ILLUSORY CON
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5.2. FEEDBACK-MEDIATED ILLVSORY CON
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.5.2. FEEDBACK-MEDIATED ELUSORY CON
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5.2. FEEDBACK-MEDIATED SILUSORY CON
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5.2. FEEDBACK-MEDIATED ILLUSORY CON
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6.1. ANALYSIS OF RESULTS dence and
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6.1. ANALYSIS OF RESULTS The graph
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6.t. ANALYSIS OF RESULTS used 10 co
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6.1. ANALYSIS OF RF.SULTS the image
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6.1. ANALYSIS OF RESULTS feedback w
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6.1. ANALYSIS OF RESULTS over lime.
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6.1. ANALYSIS OF RESULTS 6.1.2.5 Fe
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6.1. ANALYSIS Ot RESULTS step. Alth
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6.1. ANALYSIS OF RESULTS operations
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6.J. ANALYSIS OFRBSULTS model could
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6.1. ANALYSIS OF RESULTS Alternativ
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6.2. COMPARISON WITH EXPERIMENTAL B
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6.3. COMPARISON WITH PRKVIOUS MODBL
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6.4. FUTURE WORK to temporal contex
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6.5. CONCLUSIONS AND SUMMARY OF CON
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6.5. CONCLUSIONS AND SUMMARY OF CON
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• The HTM paliems correspond lo t
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Bolz, J.& Gilbert, CD. (1986), CJcn
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Friston, K. & Kiebel, S. (2009). 'C
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Hoffman. K. L. & Lngothetis, N K. (
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Lanyon. L. & Denham. S. (2(X)9), 'M
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Murray, M. M.. Wylie. G, R., Higgin
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Reynolds, J. H. &, Chelazzi, L. (20
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Stanley. D. A. & Rubin, N. (2003),
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