The Tilt Illusion and Orientation Sensitivity

An optical—geometrical illusion, described by Delbœuf and not familiar to specialists, is investigated. The results of two experiments show that the divergence between a bar filled with parallel slanting lines and a line drawn above it is clearly related to this angle of the lines which fill the bar. The illusion is already present when this angle is 10°, reaches its maximum at 20°, decreases at 30°, and almost disappears at 40°. These results are similar to those found for the tilt illusion, are slightly different from those found for the rod-and-frame illusion, and differ greatly from those found for the Zöllner illusion. The other variables considered—the distance between the slanting lines filling up the bar, the distance between the upper line and the bar, and the width of the bar—do not influence the illusion as much. Since either the line appears as diverging from the bar, or the bar seems inclined in relation to the line, the illusion should be considered a complex one. The small oblique lines inside the bar induce obliquity in the opposite sense in the display, but which of the elements is seen as diverging from the other depends on which of the two is established as the frame of reference.

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Specialized mechanoreceptor systems in rodent glabrous skin

10.1101/337923 ◽

2018 ◽

Author(s):

Jan Walcher ◽

Julia Ojeda-Alonso ◽

Julia Haseleu ◽

Maria K. Oosthuizen ◽

Ashlee H. Rowe ◽

...

Keyword(s):

Small Group ◽

Rodent Species ◽

Glabrous Skin ◽

Skin Area ◽

Mechanical Stimuli ◽

Orientation Sensitivity ◽

Hairy Skin ◽

Physiological Properties ◽

Sensory Fibers ◽

Very High

AbstractRodents use their forepaws to actively interact with their tactile environment. Studies on the physiology and anatomy of glabrous skin that makes up the majority of the forepaw are almost non-existent in the mouse. Here we developed a preparation to record from single sensory fibers of the forepaw and compared anatomical and physiological receptor properties to those of the hind paw glabrous and hairy skin. We found that the mouse forepaw skin is equipped with a very high density of mechanoreceptors; >3 fold more than hind paw glabrous skin. In addition, rapidly adapting mechanoreceptors that innervate Meissner’s corpuscles of the forepaw were several-fold more sensitive to slowly moving mechanical stimuli compared to their counterparts in the hind paw glabrous skin. All other mechanoreceptors types as well as myelinated nociceptors had physiological properties that were invariant regardless of which skin area they occupied. We discovered a novel D-hair receptor innervating a small group of hairs in the middle of the hind paw glabrous skin in mice. Glabrous D-hair receptors were direction sensitive albeit with an orientation sensitivity opposite to that described for hairy skin D-hair receptors. Glabrous D-hair receptors do not occur in all rodents, but are present in North American and African rodent species that diverged more than 65 million years ago. The function of these specialized hairs is unknown, but they are nevertheless evolutionarily very ancient. Our study reveals novel physiological specializations of mechanoreceptors in the glabrous skin that likely evolved to facilitate tactile exploration.

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Endogenous attention can reduce the tilt illusion, but not crowding

Journal of Vision ◽

10.1167/9.8.126 ◽

2010 ◽

Vol 9 (8) ◽

pp. 126-126

Author(s):

I. Mareschal ◽

J. Solomon ◽

M. Morgan

Keyword(s):

Endogenous Attention ◽

Tilt Illusion

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USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION

10.46793/iccbi21.263bs ◽

2021 ◽

Author(s):

Sandi Baressi Šegota ◽

◽

Simon Lysdahlgaard ◽

Søren Hess ◽

Ronald Antulov

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

High Performance ◽

Data Augmentation ◽

Classification Model ◽

Orientation Sensitivity ◽

X Ray ◽

Artificial Neural Network Ann ◽

Image Orientation ◽

Single Orientation

The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.

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