186 research outputs found
Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision
In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from different image regions are combined according to a Bayesian estimator --- the estimated motion maximizes the posterior probability assuming a prior favoring slow and smooth velocities. In reviewing a large number of previously published phenomena we find that the Bayesian estimator predicts a wide range of psychophysical results. This suggests that the seemingly complex set of illusions arise from a single computational strategy that is optimal under reasonable assumptions
Separating Reflections from Images Using Independent Components Analysis
The image of an object can vary dramatically depending on lighting, specularities/reflections and shadows. It is often advantageous to separate these incidental variations from the intrinsic aspects of an image. Along these lines this paper describes a method for photographing objects behind glass and digitally removing the reflections off the glass leaving the image of the objects behind the glass intact. We describe the details of this method which employs simple optical techniques and independent components analysis (ICA) and show its efficacy with several examples
GelSight360: An Omnidirectional Camera-Based Tactile Sensor for Dexterous Robotic Manipulation
Camera-based tactile sensors have shown great promise in enhancing a robot's
ability to perform a variety of dexterous manipulation tasks. Advantages of
their use can be attributed to the high resolution tactile data and 3D depth
map reconstructions they can provide. Unfortunately, many of these tactile
sensors use either a flat sensing surface, sense on only one side of the
sensor's body, or have a bulky form-factor, making it difficult to integrate
the sensors with a variety of robotic grippers. Of the camera-based sensors
that do have all-around, curved sensing surfaces, many cannot provide 3D depth
maps; those that do often require optical designs specified to a particular
sensor geometry. In this work, we introduce GelSight360, a fingertip-like,
omnidirectional, camera-based tactile sensor capable of producing depth maps of
objects deforming the sensor's surface. In addition, we introduce a novel
cross-LED lighting scheme that can be implemented in different all-around
sensor geometries and sizes, allowing the sensor to easily be reconfigured and
attached to different grippers of varying DOFs. With this work, we enable
roboticists to quickly and easily customize high resolution tactile sensors to
fit their robotic system's needs
GelSight Svelte Hand: A Three-finger, Two-DoF, Tactile-rich, Low-cost Robot Hand for Dexterous Manipulation
This paper presents GelSight Svelte Hand, a novel 3-finger 2-DoF tactile
robotic hand that is capable of performing precision grasps, power grasps, and
intermediate grasps. Rich tactile signals are obtained from one camera on each
finger, with an extended sensing area similar to the full length of a human
finger. Each finger of GelSight Svelte Hand is supported by a semi-rigid
endoskeleton and covered with soft silicone materials, which provide both
rigidity and compliance. We describe the design, fabrication, functionalities,
and tactile sensing capability of GelSight Svelte Hand in this paper. More
information is available on our website:
\url{https://gelsight-svelte.alanz.info}.Comment: Submitted and accepted to IROS 2023 workshop on Visuo-Tactile
Perception, Learning, Control for Manipulation and HRI (IROS RoboTac 2023
GelSight Svelte: A Human Finger-shaped Single-camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensing
Camera-based tactile sensing is a low-cost, popular approach to obtain highly
detailed contact geometry information. However, most existing camera-based
tactile sensors are fingertip sensors, and longer fingers often require
extraneous elements to obtain an extended sensing area similar to the full
length of a human finger. Moreover, existing methods to estimate proprioceptive
information such as total forces and torques applied on the finger from
camera-based tactile sensors are not effective when the contact geometry is
complex. We introduce GelSight Svelte, a curved, human finger-sized,
single-camera tactile sensor that is capable of both tactile and proprioceptive
sensing over a large area. GelSight Svelte uses curved mirrors to achieve the
desired shape and sensing coverage. Proprioceptive information, such as the
total bending and twisting torques applied on the finger, is reflected as
deformations on the flexible backbone of GelSight Svelte, which are also
captured by the camera. We train a convolutional neural network to estimate the
bending and twisting torques from the captured images. We conduct gel
deformation experiments at various locations of the finger to evaluate the
tactile sensing capability and proprioceptive sensing accuracy. To demonstrate
the capability and potential uses of GelSight Svelte, we conduct an object
holding task with three different grasping modes that utilize different areas
of the finger. More information is available on our website:
https://gelsight-svelte.alanz.infoComment: Submitted and accepted to 2023 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2023
Visual wetness perception based on image color statistics
Color vision provides humans and animals with the abilities to discriminate colors based on the wavelength composition of light and to determine the location and identity of objects of interest in cluttered scenes (e.g., ripe fruit among foliage). However, we argue that color vision can inform us about much more than color alone. Since a trichromatic image carries more information about the optical properties of a scene than a monochromatic image does, color can help us recognize complex material qualities. Here we show that human vision uses color statistics of an image for the perception of an ecologically important surface condition (i.e., wetness). Psychophysical experiments showed that overall enhancement of chromatic saturation, combined with a luminance tone change that increases the darkness and glossiness of the image, tended to make dry scenes look wetter. Theoretical analysis along with image analysis of real objects indicated that our image transformation, which we call the wetness enhancing transformation, is consistent with actual optical changes produced by surface wetting. Furthermore, we found that the wetness enhancing transformation operator was more effective for the images with many colors (large hue entropy) than for those with few colors (small hue entropy). The hue entropy may be used to separate surface wetness from other surface states having similar optical properties. While surface wetness and surface color might seem to be independent, there are higher order color statistics that can influence wetness judgments, in accord with the ecological statistics. The present findings indicate that the visual system uses color image statistics in an elegant way to help estimate the complex physical status of a scene
The delayed rod afterimage
A flashed background, presented to a dark-adapted eye, can saturate the rod system, making an incremental test patch invisible. But as the afterimage decays, the test can be distinguished. Increment thresholds measured within the decaying afterimage exhibit Weber's law over a wide range. The Penn and Hagins model of rod kinetics correctly predicts Weber's law, but makes incorrect predictions of the latency for the detection to occur. A new model, involving two exponential decays, is able to accommodate the latency data, as well as Weber's law. The model also makes good predictions of the results when the stimulus duration is increased from 100 msec to 1 sec.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24152/1/0000409.pd
Saturation and adaptation in the rod system
A background that is briefly flashed to a dark-adapted eye saturates the rod system. This transient saturation occurs with backgrounds that are as much as 2 log units dimmer than those producing saturation under steady viewing. Rod threshold is highest when the background is first turned on, and falls as adaptation proceeds. The nature of the adaptive processes are studied by presenting flashed backgrounds on pre-adapting fields. The data can be interpreted in terms of two adaptive processes: the first is multiplicative, and occurs rapidly; the second is subtractive, and occurs more slowly.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24151/1/0000408.pd
FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback
In this paper, we address the problem of using visuo-tactile feedback for
6-DoF localization and 3D reconstruction of unknown in-hand objects. We propose
FingerSLAM, a closed-loop factor graph-based pose estimator that combines local
tactile sensing at finger-tip and global vision sensing from a wrist-mount
camera. FingerSLAM is constructed with two constituent pose estimators: a
multi-pass refined tactile-based pose estimator that captures movements from
detailed local textures, and a single-pass vision-based pose estimator that
predicts from a global view of the object. We also design a loop closure
mechanism that actively matches current vision and tactile images to previously
stored key-frames to reduce accumulated error. FingerSLAM incorporates the two
sensing modalities of tactile and vision, as well as the loop closure mechanism
with a factor graph-based optimization framework. Such a framework produces an
optimized pose estimation solution that is more accurate than the standalone
estimators. The estimated poses are then used to reconstruct the shape of the
unknown object incrementally by stitching the local point clouds recovered from
tactile images. We train our system on real-world data collected with 20
objects. We demonstrate reliable visuo-tactile pose estimation and shape
reconstruction through quantitative and qualitative real-world evaluations on 6
objects that are unseen during training.Comment: Submitted and accepted to 2023 IEEE International Conference on
Robotics and Automation (ICRA 2023
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