13,490 research outputs found
AirCode: Unobtrusive Physical Tags for Digital Fabrication
We present AirCode, a technique that allows the user to tag physically
fabricated objects with given information. An AirCode tag consists of a group
of carefully designed air pockets placed beneath the object surface. These air
pockets are easily produced during the fabrication process of the object,
without any additional material or postprocessing. Meanwhile, the air pockets
affect only the scattering light transport under the surface, and thus are hard
to notice to our naked eyes. But, by using a computational imaging method, the
tags become detectable. We present a tool that automates the design of air
pockets for the user to encode information. AirCode system also allows the user
to retrieve the information from captured images via a robust decoding
algorithm. We demonstrate our tagging technique with applications for metadata
embedding, robotic grasping, as well as conveying object affordances.Comment: ACM UIST 2017 Technical Paper
Local object gist: meaningful shapes and spatial layout at a very early stage of visual processing
In his introduction, Pinna (2010) quoted one of Wertheimer’s observations: “I
stand at the window and see a house, trees, sky. Theoretically I might say there
were 327 brightnesses and nuances of color. Do I have ‘327’? No. I have sky,
house, and trees.” This seems quite remarkable, for Max Wertheimer, together
with Kurt Koffka and Wolfgang Koehler, was a pioneer of Gestalt Theory:
perceptual organisation was tackled considering grouping rules of line and edge
elements in relation to figure-ground segregation, i.e., a meaningful object (the
figure) as perceived against a complex background (the ground).
At the lowest level – line and edge elements – Wertheimer (1923) himself
formulated grouping principles on the basis of proximity, good continuation,
convexity, symmetry and, often forgotten, past experience of the observer. Rubin
(1921) formulated rules for figure-ground segregation using surroundedness, size
and orientation, but also convexity and symmetry. Almost a century of research
into Gestalt later, Pinna and Reeves (2006) introduced the notion of figurality,
meant to represent the integrated set of properties of visual objects, from the
principles of grouping and figure-ground to the colour and volume of objects
with shading. Pinna, in 2010, went one important step further and studied
perceptual meaning, i.e., the interpretation of complex figures on the basis of
past experience of the observer. Re-establishing a link to Wertheimer’s rule about
past experience, he formulated five propositions, three definitions and seven
properties on the basis of observations made on graphically manipulated patterns.
For example, he introduced the illusion of meaning by comics-like elements
suggesting wind, therefore inducing a learned interpretation. His last figure
shows a regular array of squares but with irregular positions on the right side.
This pile of (ir)regular squares can be interpreted as the result of an earthquake
which destroyed part of an apartment block. This is much more intuitive, direct
and economic than describing the complexity of the array of squares
Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty
It is well known that the accuracy of a calibration depends strongly on the
choice of camera poses from which images of a calibration object are acquired.
We present a system -- Calibration Wizard -- that interactively guides a user
towards taking optimal calibration images. For each new image to be taken, the
system computes, from all previously acquired images, the pose that leads to
the globally maximum reduction of expected uncertainty on intrinsic parameters
and then guides the user towards that pose. We also show how to incorporate
uncertainty in corner point position in a novel principled manner, for both,
calibration and computation of the next best pose. Synthetic and real-world
experiments are performed to demonstrate the effectiveness of Calibration
Wizard.Comment: Oral presentation at ICCV 201
A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes
Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016
Automated 3D object modeling from aerial video imagery
Research in physically accurate 3D modeling of a scene is gaining momentum because of its far reaching applications in civilian and defense sectors. The modeled 3D scene must conform both geometrically and spectrally to the real world for all the applications. Geometric modeling of a scene can be achieved in many ways of which the two most popular methods are - a) using multiple 2D passive images of the scene also called as stereo vision and b) using 3D point clouds like Lidar (Light detection and ranging) data. In this research work, we derive the 3D models of objects in a scene using passive aerial video imagery. At present, this geometric modeling requires a lot of manual intervention due to a variety of factors like sensor noise, low contrast conditions during image capture, etc. Hence long time periods, in the order of weeks and months, are required to model even a small scene. This thesis focuses on automating the process of geometric modeling of objects in a scene from passive aerial video imagery. The aerial video frames are stitched into stereo mosaics. These stereo mosaics not only provide the elevation information of a scene but also act as good 3D visualization tools. The 3D information obtained from the stereo mosaics is used to identify the various 3D objects, especially man-made buildings using probabilistic inference provided by Bayesian Networks. The initial 3D building models are further optimized by projecting them on to the individual video frames. The limitations of the state-of-art technology in attaining these goals are presented along with the techniques to overcome them. The improvement that can be achieved in the accuracy of the 3D models when Lidar data is fused with aerial video during the object identification process is also examined
Efficient completeness inspection using real-time 3D color reconstruction with a dual-laser triangulation system
In this chapter, we present the final system resulting from the European Project \u201d3DComplete\u201d aimed at creating a low-cost and flexible quality inspection system capable of capturing 2.5D color data for completeness inspection. The system uses a single color camera to capture at the same time 3D data with laser triangulation and color texture with a special projector of a narrow line of white light, which are then combined into a color 2.5D model in real-time. Many examples of completeness inspection tasks are reported which are extremely difficult to analyze with state-of-the-art 2D-based methods. Our system has been integrated into a real production environment, showing that completeness inspection incorporating 3D technology can be readily achieved in a short time at low costs
A Workstation for microassembly
In this paper, an open-architecture, reconfigurable microassembly workstation for efficient and reliable assembly of micromachined parts is presented. The
workstation is designed to be used as a research tool for investigation of the problems in microassembly. The development of such a workstation includes the design of: (i) a manipulation system consisting of motion stages providing
necessary travel range and precision for the realization of assembly tasks, (ii) a vision system to visualize the microworld and the determination of the position and orientation of micro components to be assembled, (iii) a robust control system and necessary mounts for the end effectors in such a way that according to the task to be realized, the manipulation tools can be easily changed and the system will be ready for the predefined task. In addition
tele-operated and semi-automated assembly concepts are implemented. The design is verified by implementing the range of the tasks in micro-parts manipulation. The versatility of the workstation is demonstrated and high accuracy of positioning is sho
Trifocal Relative Pose from Lines at Points and its Efficient Solution
We present a new minimal problem for relative pose estimation mixing point
features with lines incident at points observed in three views and its
efficient homotopy continuation solver. We demonstrate the generality of the
approach by analyzing and solving an additional problem with mixed point and
line correspondences in three views. The minimal problems include
correspondences of (i) three points and one line and (ii) three points and two
lines through two of the points which is reported and analyzed here for the
first time. These are difficult to solve, as they have 216 and - as shown here
- 312 solutions, but cover important practical situations when line and point
features appear together, e.g., in urban scenes or when observing curves. We
demonstrate that even such difficult problems can be solved robustly using a
suitable homotopy continuation technique and we provide an implementation
optimized for minimal problems that can be integrated into engineering
applications. Our simulated and real experiments demonstrate our solvers in the
camera geometry computation task in structure from motion. We show that new
solvers allow for reconstructing challenging scenes where the standard two-view
initialization of structure from motion fails.Comment: This material is based upon work supported by the National Science
Foundation under Grant No. DMS-1439786 while most authors were in residence
at Brown University's Institute for Computational and Experimental Research
in Mathematics -- ICERM, in Providence, R
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