For the first strand of this thesis, we are considering what a disparity defined object is. This Chapter explored an important aspect of object recognition – how the visual system
behaves when an object has a smooth edge that is hard to segregate from its background. In the previous Chapter, we found that smooth edge caused a bias in the perception of depth of the object. Here we used those results to create a quantitative model of the visual system in order to better understand how the shape of our stimulus was interacting with the
perceived peak depth of the object. Having developed a model that could fit participants’ data, we then tested it by attempting to predict the results of Experiment 3 before we ran it on participants. We found that the model performed well, given that it had zero free
parameters.
Here, we briefly review the model’s performance by considering how it has performed at each of the three tasks: modelling Experiment 1; modelling Experiment 2; and predicting participant performance in Experiment 3. We discuss what this tells us about how the visual system is processing the peak depth and averaging across large regions and consider how this relates to the current literature on disparity averaging. Finally, we relate this back to animal camouflage, and consider how the discoveries of the model could be used to help an animal attempting to camouflage itself from a stereoscopic observer.
Given the simplicity and quantitative approach of the model, the model has performed well, and is able to explain over 80% of the variance for most individual participants when fitted to their experimental data from Experiments 1 and 2 (Figure 5.4 and Table 5.2). We were then able to alter the model to predict group behaviour prior to experimentation, with zero free parameters, the majority of participants’ performance at a novel stimulus (Section 5.5). Perhaps the most interesting aspect of the model for the first experiment (Section 5.3) was the shape of the window model used – the only window shape that could fit the data well was square (Section 5.3.4). As both the smooth and sharp objects were square-based, this strongly suggested that the shape of either the smooth or sharp object was prompting a form of segregation across the top of the object, followed by averaging to obtain an estimate of peak depth. This speculation was further strengthened by the best fit window sizes for both Experiments 1 and 2, which showed that the size of the averaging window used was close to the plateau size of both the objects (Figure 5.4 and Table 5.1) – a
parameter that determined the distance between the inflection points in the smooth object and the distance between the edges in the sharp object.
We therefore had two alternative sources of the size of the averaging window: either we had a shape-based averaging, where the visual system segregated the edge of the smooth
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object from its background and then averaged over the area to obtain a depth estimate; or template based averaging, where the coincident display of the well-defined sharp object was used as a template to determine the area of the smooth object over which to average. In Section 5.5.2 we created two models with no free parameters, one to predict the
performance of a template based approach and one to predict a shape based approach. By altering the plateau size of the smooth object, we could create distinct predictions for these two cases, and compare them to participants’ performance. We found that participants were closest to the shape based averaging, where the area averaged over is based on the object’s own size and shape (Section 5.5.4).
The averaging aspect of our shape based averaging is not that remarkable in itself: it is well known (as discussed in the background theory, Section 2.2.4) that the early stages of
disparity extraction result in the averaging across small scales e.g. (Allenmark & Read, 2010, 2011; Filippini & Banks, 2009; Goutcher & Hibbard, 2014; Tyler & Julesz, 1980). These effects are typically thought to occur at the scale of the finest scale disparity detectors at around 5arcmin across (Filippini & Banks, 2009; Harris et al., 1997). However, what we are
hypothesising here that the averaging is occurring over much larger areas – at scales of over 150arcmin. Averaging effects have been observed over this length scale, for example in overlapping transparent planes (Kaufman et al., 1973; Parker & Yang, 1989; Stevenson et al., 1991; Tsirlin et al., 2008), however in this situation the elements of different disparities in close proximity, potentially allowing small scale averaging effects between adjacent elements to account for the large scale percept (Harris, 2014). Here, we have averaging occurring over a large area which is dependent on the shape of an object, with a large lateral separation between elements of different disparities. While averaging specifically over an object has not been proposed before, the long range effects share similarities to some studies on Gestalt grouping (Section 2.2.6). In particular, Deas and Wilcox found that perceptually grouping joining lines into objects causes a reduction in the perceived depth (Deas & Wilcox, 2014) – an effect that could be caused by the averaging mechanism hypothesised here.
The results of this model have potential ramifications on the second strand of this thesis, which explores if depth perception from binocular vision enables camouflaged objects to jump out from their background, making them trivial to detect. Here, we have found an interaction of the stimulus with the visual system that may enable an animal to confound one of the mechanisms of depth perception in a predator, thus removing this advantage. If an animal were to colour itself such that it would be segregated as many objects (called disruptive colouration, see Section 2.1.1 (Cuthill et al., 2005; Osorio & Srinivasan, 1991; Ruxton & Sherratt, 2004) ), then each part of the animal that appeared as a separate object may be individually averaged over, giving each a separate depth estimate. This could
potentially create the impression of many different objects, each divorced in depth from the other. A stereoscopic observer would therefore no longer see the animal as a distinctly
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continuous shape standing above the background, thus ridding the stereoscopic observer of its advantage.
While this is an interesting speculation on the interaction of luminance and disparity, we do not know how the processes responsible for segregating an object based on disparity cues interact with other cues to object segregation. While work has been done on the perception of depth when disparity is placed in conflict with other cues to depth such as shape from shading e.g. (Chen & Tyler, 2015; Lovell et al., 2012), they do not look at interaction of depth perception with cues to object segregation. In the next Chapter we explore this gap: how do disparity based object segregation mechanisms interact with a luminance cue to object segregation?
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6
How does depth segregation interact with
luminance segregation?
Strand 1, How does depth from binocular vision contribute to object perception?
Investigating: If luminance cues influence segregation of depth defined objects.
Task: Which of two objects has a greater peak depth?
Manipulation: One object has a luminance cue to segregation.
Results: Luminance only has an effect when the disparity cue is poor.
Conclusion: Luminance and disparity do interact, but in an RDS it is very hard to make the disparity cue weak enough for the luminance cue to have an effect.
Figure 6.1: Two moths resting on the bark of a Jackfruit tree (one central, one bottom right). Note how brown luminance edges break up the moth into several smaller sections
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6.1
Introduction
In the first strand of this thesis, we are exploring how disparity-defined depth influences object perception. So far, we have been exploring the perception of purely disparity defined objects – in Chapter 4 we have investigated the effect of a smooth edged object on the perception of disparity defined objects. We found that the objects were perceived with a decreased peak depth relative to the objective peak disparity. In Chapter 5 we used computational modelling to explore the mechanisms that could be causing this effect, and concluded that after object segregation the disparities within the object were averaged, possibly to remove errors from the disparity extraction process. However, poor segregation over the smooth object resulted in the inclusion of disparities that were lower than the peak, resulting in a perceptual decrease in their peak depth.
In this Chapter, the third and final experimental Chapter of the first research strand, we are interested to see if object segregation and subsequent averaging is purely driven by the disparity cues to depth, or if other cues to segregation are important. Luminance has been found to improve the judgement of the shape of rectangles (Regan & Hamstra, 1994), and to have an effect on human depth perception (Didyk, Ritschel, Eisemann, Myszkowski, Seidel, et al., 2012; Richards, 1977), but we do not know if this is a combination of luminance and disparity before or after object segregation and averaging as proposed in Chapter 5. To explore this, we introduce a luminance cue to object segregation, and
investigate if there is a change in perceived peak depth with the size of the luminance cue. If the perception of depth in the object is then altered by the luminance cue to segregation, this will indicate that the luminance and disparity cues were combined to segregate the object prior to disparity averaging.
Adding luminance segregation to the disparity defined object created a surprising number of problems with the perception of the object. In this Chapter, we present several
experiments:
1. In Experiment 4, we tested if luminance segregation of the object is a sufficiently strong cue to alter the perceived peak depth of a sharp object. We used the model from Chapter 5 to predict the results, but found that the addition of different luminance dots causes the experiment to be too hard for naïve participants to compete.
2. In Experiment 5, we explore a series of alterations to attempt to improve participants’ performance while still testing the interaction of disparity and
luminance cues to object segregation. This Experiment is the presentation of a series of interesting stimulus manipulations which lead to the creation of Experiment 6, rather than a single standalone experiment.
3. Experiment 6 combines the improvements of Experiment 5 with the luminance segregation methods of Experiment 4. Here, we find that when the disparity signal is
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well defined and the luminance signal poorly defined (because we are only altering the luminance of the dots making up the RDS), then the luminance cue to
segregation has no effect on the perceived depth of the object.
4. In Experiment 7, we investigate if decreasing the reliability of the disparity cue we could result in an interaction of luminance and disparity segregation. We add random noise to the disparity signal, and find that the perceived peak depth of the object is altered by the size of the luminance window, indicating that segregation based on luminance influences the area segregated and averaged over by the object based disparity processing.