Visibility in three-dimensional cluttered scenes

Langer, Michael; Mannan, Fahim

doi:10.1364/josaa.29.001794

Cited by 13 publications

(21 citation statements)

References 38 publications

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“…Interestingly, hidden surfaces and hybrid occlusions were 4 often more frequent than a self-occlusion in this simulation. 5 6 Lastly, our results are consistent with the simulations presented by Langer & Mannan (2012), in 7 that we observe more monocular regions when the environments are populated with smaller 8 objects (Langer & Mannan, 2012). For example, in the small-scale environments with the many 9 small objects, over 30% of visible points were monocular, whereas in the large-scale 10 environments less than 3% of visible points were monocular.…”

supporting

confidence: 84%

“…It is important to note that the natural scene dataset used here does not contain objects that are 25 closer than 3 m. This limitation of the dataset should be considered when interpreting the results 26 of our analysis because prior work suggests that the prevalence of monocularly visible regions 27 depends on the distribution of object distances in a scene (Langer & Mannan, 2012). That said, 28…”

mentioning

confidence: 97%

“…Langer & colleagues asked how the probability of points that are visible only monocularly varies 21 as a function of object distance (Langer & Mannan, 2012;Langer et al, 2016). They found that 22 the probability that a given point is monocularly visible increases monotonically as a function of 23 its distance from the viewer, and that this increase in probability is more rapid when the objects 24 are smaller.…”

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Natural image statistics at depth edges modulate perceptual stability

Başgöze¹,

White

Burge

et al. 2020

Preprint

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2Binocular fusion relies on matching points in the two eyes that correspond to the same physical 3 feature in the world. However, not all world features are binocularly visible. In particular, at depth 4 edges parts of a scene are often visible to only one eye (so-called half occlusions). Accurate 5 detection of these monocularly visible regions is likely to be important for stable visual 6 perception. If monocular regions are not detected as such, the visual system may attempt to 7 binocularly fuse non-corresponding points, which can result in unstable percepts. We 8 investigated the hypothesis that the visual system capitalizes upon statistical regularities 9 associated with depth edges in natural scenes to aid binocular fusion and facilitate perceptual 10 stability. By sampling from a large set of stereoscopic natural image patches, we found evidence 11 that monocularly visible regions near depth edges in natural scenes tend to have features more 12 visually similar to the adjacent binocularly visible background region than to the adjacent 13 binocularly visible foreground. The generality of these results was supported by a parametric 14 study of three-dimensional (3D) viewing geometry in simulated environments. In two perceptual 15 experiments, we examined if this statistical regularity may be leveraged by the visual system. 16The results show that perception tended to be more stable when the visual properties of the 17 depth edge were statistically more likely. Exploiting regularities in natural environments may 18 allow the visual system to facilitate fusion and perceptual stability of natural scenes when both 19 binocular and monocular regions are visible. 20 21 Precis: We report an analysis of natural scenes and two perceptual studies aimed at 22 understanding how the visual statistics of depth edges impact perceptual stability. Our results 23 suggest that the visual system exploits natural scene regularities to aid binocular fusion and 24 facilitate perceptual stability. 25 26

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confidence: 84%

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confidence: 97%

mentioning

confidence: 99%

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Natural image statistics at depth edges modulate perceptual stability

Başgöze¹,

White

Burge

et al. 2020

Preprint

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“…is constant within each column and increases from left to right. The parameter k is called the occlusion factor (Langer & Mannan, 2012). It is the expected total area of the surfaces in the clutter per cm 3 .…”

Section: Stimulimentioning

confidence: 99%

Density discrimination with occlusions in 3D clutter

Scaccia

Langer

2019

Journal of Vision

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We examined how well human observers can discriminate the density of surfaces in two halves of a rotating three-dimensional cluttered sphere. The observer's task was to compare the density of the front versus back half or the left versus right half. We measured how the bias and sensitivity in judging the denser half depended on the level of occlusion and on the area and density of the surfaces in the clutter. When occlusion level was low, observers in the front-back task were biased to judge the back as denser, and when occlusion level was high they were biased to judge the front as denser. Weber fractions decreased as density increased for both the front-back and left-right tasks, consistent with previous findings for two-dimensional density discrimination. Weber fractions did not vary significantly with area for the front-back task, but increased with area for the left-right task, and we attribute this difference to occlusions that have different effects in the two tasks. We also ran model observers that compared the image occupancies of the two halves against a known expected difference. As the occlusion level increased, this expected difference followed a similar trend as the biases of the human observers, with a roughly constant offset between them. Weber fractions for human and model observers followed some similar trends, but there were discrepancies as well that can be partly explained by the information available to human versus model observers in carrying out their respective tasks.

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“…Firstly, performing reliable stereo matching can be challenging in agricultural fields, since vegetation has significant depth discontinuities and occlusions which often cause sparse or incorrect matches (Rovira-Más et al 2008;Langer & Mannan 2012). With noisy depth measurements, stereo methods may perform poorly on very young or short crops where the height difference between the crop and soil can become lost in the noise.…”

Section: Stereo Vision-based Crop Row Trackingmentioning

confidence: 99%

Visually-Aided Localisation for Autonomous Agricultural Vehicles

English¹

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This thesis presents an approach to visually-aided navigation of agricultural robots in cropping fields.Many agricultural robots and vehicles navigate solely using sophisticated RTK-GPS/INS systems, however these systems remain expensive or are prone to signal occlusion. Alternatively, navigation in agricultural fields can be performed relative to crop rows using local sensors such as cameras. However, current visual crop row tracking algorithms are applicable to only a subset of fields since they make strong and restrictive assumptions about the appearance of the crop. Additionally, current navigation filters for agricultural robots do not fuse crop row tracking together with global navigation sensors to improve their position estimate.This thesis proposes a localisation system suitable for small agricultural robots that augments low-cost GPS and odometry sensors with novel visual crop row tracking methods. In working towards this goal this thesis provides two main contributions.

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Visibility in three-dimensional cluttered scenes

Cited by 13 publications

References 38 publications

Natural image statistics at depth edges modulate perceptual stability

Natural image statistics at depth edges modulate perceptual stability

Density discrimination with occlusions in 3D clutter

Visually-Aided Localisation for Autonomous Agricultural Vehicles

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