How do Humans Determine Reflectance Properties under Unknown Illumination?

Under normal viewing conditions, humans find it easy to distinguish between objects made out of different materials such as plastic, metal, or paper. Untextured materials such as these have different surface reflectance properties, including lightness and gloss. With single isolated images and unknown illumination conditions, the task of estimating surface reflectance is highly underconstrained, because many combinations of reflection and illumination are consistent with a given image. In order to work out how humans estimate surface reflectance properties, we asked subjects to match the appearance of isolated spheres taken out of their original contexts. We found that subjects were able to perform the task accurately and reliably without contextual information to specify the illumination. The spheres were rendered under a variety of artificial illuminations, such as a single point light source, and a number of photographically-captured real-world illuminations from both indoor and outdoor scenes. Subjects performed more accurately for stimuli viewed under real-world patterns of illumination than under artificial illuminations, suggesting that subjects use stored assumptions about the regularities of real-world illuminations to solve the ill-posed problem

ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition

Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots could also benefit from such multi-modal sensing ability. In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep neural networks. But the learned features are of a high dimensionality and are redundant due to the differences between the two sensing modalities, which deteriorates the perception performance. To address this, the learned features are paired using maximum covariance analysis. Results of the algorithm on a newly collected dataset of paired visual and tactile data relating to cloth textures show that a good recognition performance of greater than 90% can be achieved by using the proposed DMCA framework. In addition, we find that the perception performance of either vision or tactile sensing can be improved by employing the shared representation space, compared to learning from unimodal data

Baseline and triangulation geometry in a standard plenoptic camera

In this paper, we demonstrate light field triangulation to determine depth distances and baselines in a plenoptic camera. The advancement of micro lenses and image sensors enabled plenoptic cameras to capture a scene from different viewpoints with sufficient spatial resolution. While object distances can be inferred from disparities in a stereo viewpoint pair using triangulation, this concept remains ambiguous when applied in case of plenoptic cameras. We present a geometrical light field model allowing the triangulation to be applied to a plenoptic camera in order to predict object distances or to specify baselines as desired. It is shown that distance estimates from our novel method match those of real objects placed in front of the camera. Additional benchmark tests with an optical design software further validate the model’s accuracy with deviations of less than 0:33 % for several main lens types and focus settings. A variety of applications in the automotive and robotics field can benefit from this estimation model

Does Perceptual Belongingness Affect Lightness Constancy?

Scientists have shown that two equal grey patches may differ in lightness when belonging to different reflecting surfaces. We extend this investigation to the constancy domain. In a CRT simulation of a bipartite field of illumination, we manipulated the arrangement of twelve patches: six squares and six diamonds. Patches of the same shape could be placed: (i) all within the same illumination field; or (ii) forming a row across the illumination fields. Furthermore, we manipulated proximity between the innermost patches and the illumination edge. The patches could be (i) touching (forming an X-junction); or (ii) not touching (not forming an X-junction). Observers were asked to perform a lightness match between two additional patches, one illuminated and the other in shadow. We found better lightness constancy when the patches of the same shape formed a row across the fields, with no effect of X-junctions

Feature pyramid transformer

Feature interactions across space and scales underpin modern visual recognition systems because they introduce beneficial visual contexts. Conventionally, spatial contexts are passively hidden in the CNN's increasing receptive fields or actively encoded by non-local convolution. Yet, the non-local spatial interactions are not across scales, and thus they fail to capture the non-local contexts of objects (or parts) residing in different scales. To this end, we propose a fully active feature interaction across both space and scales, called Feature Pyramid Transformer (FPT). It transforms any feature pyramid into another feature pyramid of the same size but with richer contexts, by using three specially designed transformers in self-level, top-down, and bottom-up interaction fashion. FPT serves as a generic visual backbone with fair computational overhead. We conduct extensive experiments in both instance-level (i.e., object detection and instance segmentation) and pixel-level segmentation tasks, using various backbones and head networks, and observe consistent improvement over all the baselines and the state-of-the-art methods.Comment: Published at the European Conference on Computer Vision, 202

PlenoptiSign: An optical design tool for plenoptic imaging

© 2019 The Authors. Published by Elsevier. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1016/j.softx.2019.100259© 2019 The Authors Plenoptic imaging enables a light-field to be captured by a single monocular objective lens and an array of micro lenses attached to an image sensor. Metric distances of the light-field's depth planes remain unapparent prior to acquisition. Recent research showed that sampled depth locations rely on the parameters of the system's optical components. This paper presents PlenoptiSign, which implements these findings as a Python software package to help assist in an experimental or prototyping stage of a plenoptic system.Published versio

An Educational Program for Blind Infants

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68635/2/10.1177_002246696900300201.pd

Experimental Traumatic Brain Injury Induces Chronic Glutamatergic Dysfunction in Amygdala Circuitry Known to Regulate Anxiety-Like Behavior

Up to 50% of traumatic brain injury (TBI) survivors demonstrate persisting and late-onset anxiety disorders indicative of limbic system dysregulation, yet the pathophysiology underlying the symptoms is unclear. We hypothesize that the development of TBI-induced anxiety-like behavior in an experimental model of TBI is mediated by changes in glutamate neurotransmission within the amygdala. Adult, male Sprague-Dawley rats underwent midline fluid percussion injury or sham surgery. Anxiety-like behavior was assessed at 7 and 28 days post-injury (DPI) followed by assessment of real-time glutamate neurotransmission in the basolateral amygdala (BLA) and central nucleus of the amygdala (CeA) using glutamate-selective microelectrode arrays. The expression of anxiety-like behavior at 28 DPI coincided with decreased evoked glutamate release and slower glutamate clearance in the CeA, not BLA. Numerous factors contribute to the changes in glutamate neurotransmission over time. In two additional animal cohorts, protein levels of glutamatergic transporters (Glt-1 and GLAST) and presynaptic modulators of glutamate release (mGluR2, TrkB, BDNF, and glucocorticoid receptors) were quantified using automated capillary western techniques at 28 DPI. Astrocytosis and microglial activation have been shown to drive maladaptive glutamate signaling and were histologically assessed over 28 DPI. Alterations in glutamate neurotransmission could not be explained by changes in protein levels for glutamate transporters, mGluR2 receptors, astrocytosis, and microglial activation. Presynaptic modulators, BDNF and TrkB, were significantly decreased at 28 DPI in the amygdala. Dysfunction in presynaptic regulation of glutamate neurotransmission may contribute to anxiety-related behavior and serve as a therapeutic target to improve circuit function.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]