Simulation of neural contour mechanisms: from simple to end-stopped cells

Heitger, F.; Rosenthaler, L.; Heydt, Rüdiger von der; Peterhans, E.; Kübler, Olaf

doi:10.1016/0042-6989(92)90039-l

Cited by 264 publications

(173 citation statements)

References 44 publications

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“…In both cases unit activity will be chccreasexl whenever any stimulus impinges on the inhibitory region of the endstopped cell. Peterhans, and Kubler (1992). Can this interference effect be used to explain our data?…”

Section: The Model Of Finkrj and Edelmanmentioning

confidence: 92%

The role of edges and line-ends in illusory contour formation

Lesher

Mingolla²

1993

Vision Research

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Section: The Model Of Finkrj and Edelmanmentioning

confidence: 92%

The role of edges and line-ends in illusory contour formation

Lesher

Mingolla²

1993

Vision Research

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“…Such localized nonlinear computations would include size-and phase-independent complex cell-like tuning, end-stopping, pooling normalizations, etc. (Shapley, 1994, Heeger, 1994Heitger, Rosenthaler, von der Heydt, Peterhans and Kubler, 1992) The question is whether such capabilities would be able to produce the difference in recognition accuracy between recoverable and nonrecoverable images and, at the same time, produce the equivalence of recognition for the complementary images. Although we cannot prove that such developments would endow the system with the needed capabilities, we see no clear path by which such capabilities could actually be achieved with such local operations.…”

Section: Could the Frs Be Modified To Handle The Present Results?mentioning

confidence: 99%

To what extent can matching algorithms based on direct outputs of spatial filters account for human object recognition?

1996

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A number of recent successful models of face recognition posit only two layers, an input layer consisting of a lattice of spatial filters and a single subsequent stage by which those descriptor values are mapped directly onto an object representation layer by standard matching methods such as stochastic optimization. Is this approach sufficient for modeling human object recognition? We tested whether a highly efficient version of such a two-layer model would manifest effects similar to those shown by humans when given the task of recognizing images of objects that had been employed in a series of psychophysical experiments. System accuracy was quite high overall, but was qualitatively different from that evidenced by humans in object recognition tasks. The discrepancy between the system's performance and human performance is likely to be revealed by all models that map filter values directly onto object units. These results suggest that human object recognition (as opposed to face recognition) may be difficult to approximate by models that do not posit hidden units for explicit representation of intermediate entities such as edges, viewpoint invariant classifiers, axes, shocks and/or object parts.

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“…Crossing cells are expected to play a complementary role to endstopped cells which respond to line ends, corners, and junctions, but not to crossings [6,9]. However, from functional brain modeling perspective, it is desirable to model all junction types [12].…”

Section: Discussionmentioning

confidence: 99%

“…A bucket was placed at distances of 1, 1.5, 2, and 2.5 meters at angles of -60, -30, 0, 30, and 60 degrees, under 3 different (normal, left side dimmed, right side dimmed) illumination conditions yielding a total of 60 different images, which were applied to the crossing operator (6). All four crossing points of the sharp (#) on the bucket were determined with high accuracy for all distances and orientations of 0 and ±30 degrees, under all …”

Section: Crossing Cell Responses To Natural Imagesmentioning

confidence: 99%

TiViPE Simulation of a Cortical Crossing Cell Model

Lourens

Barakova

2005

Computational Intelligence and Bioinspired Systems

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Abstract. Many cells in cat and monkey visual cortex (area V1 and area 17) respond to gratings and bar patterns of different orientation between center and surround [18]. It has been shown that these cells respond on average 3.3 times stronger to a crossing pattern than to a single bar [16]. In this paper a computational model for a group of neurons that respond solely to crossing patterns is proposed, and has been implemented in visual programming environment TiViPE [10]. Simulations show that the operator responds very accurately to crossing patterns that have an angular difference between 2 bars of 40 degrees or more, the operator responds appropriately to bar widths that are bound by 50 to 200 percent of the preferred bar width and is insensitive to non-uniform illumination conditions, which appear to be consistent with the experimental results.

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Simulation of neural contour mechanisms: from simple to end-stopped cells

Cited by 264 publications

References 44 publications

The role of edges and line-ends in illusory contour formation

The role of edges and line-ends in illusory contour formation

To what extent can matching algorithms based on direct outputs of spatial filters account for human object recognition?

TiViPE Simulation of a Cortical Crossing Cell Model

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