219,028 research outputs found
Attentional Selection in Object Recognition
A key problem in object recognition is selection, namely, the problem of identifying regions in an image within which to start the recognition process, ideally by isolating regions that are likely to come from a single object. Such a selection mechanism has been found to be crucial in reducing the combinatorial search involved in the matching stage of object recognition. Even though selection is of help in recognition, it has largely remained unsolved because of the difficulty in isolating regions belonging to objects under complex imaging conditions involving occlusions, changing illumination, and object appearances. This thesis presents a novel approach to the selection problem by proposing a computational model of visual attentional selection as a paradigm for selection in recognition. In particular, it proposes two modes of attentional selection, namely, attracted and pay attention modes as being appropriate for data and model-driven selection in recognition. An implementation of this model has led to new ways of extracting color, texture and line group information in images, and their subsequent use in isolating areas of the scene likely to contain the model object. Among the specific results in this thesis are: a method of specifying color by perceptual color categories for fast color region segmentation and color-based localization of objects, and a result showing that the recognition of texture patterns on model objects is possible under changes in orientation and occlusions without detailed segmentation. The thesis also presents an evaluation of the proposed model by integrating with a 3D from 2D object recognition system and recording the improvement in performance. These results indicate that attentional selection can significantly overcome the computational bottleneck in object recognition, both due to a reduction in the number of features, and due to a reduction in the number of matches during recognition using the information derived during selection. Finally, these studies have revealed a surprising use of selection, namely, in the partial solution of the pose of a 3D object
Overcoming the Challenges Associated with Image-based Mapping of Small Bodies in Preparation for the OSIRIS-REx Mission to (101955) Bennu
The OSIRIS-REx Asteroid Sample Return Mission is the third mission in NASA's
New Frontiers Program and is the first U.S. mission to return samples from an
asteroid to Earth. The most important decision ahead of the OSIRIS-REx team is
the selection of a prime sample-site on the surface of asteroid (101955) Bennu.
Mission success hinges on identifying a site that is safe and has regolith that
can readily be ingested by the spacecraft's sampling mechanism. To inform this
mission-critical decision, the surface of Bennu is mapped using the OSIRIS-REx
Camera Suite and the images are used to develop several foundational data
products. Acquiring the necessary inputs to these data products requires
observational strategies that are defined specifically to overcome the
challenges associated with mapping a small irregular body. We present these
strategies in the context of assessing candidate sample-sites at Bennu
according to a framework of decisions regarding the relative safety,
sampleability, and scientific value across the asteroid's surface. To create
data products that aid these assessments, we describe the best practices
developed by the OSIRIS-REx team for image-based mapping of irregular small
bodies. We emphasize the importance of using 3D shape models and the ability to
work in body-fixed rectangular coordinates when dealing with planetary surfaces
that cannot be uniquely addressed by body-fixed latitude and longitude.Comment: 31 pages, 10 figures, 2 table
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels
In the context of scene understanding, a variety of methods exists to
estimate different information channels from mono or stereo images, including
disparity, depth, and normals. Although several advances have been reported in
the recent years for these tasks, the estimated information is often imprecise
particularly near depth discontinuities or creases. Studies have however shown
that precisely such depth edges carry critical cues for the perception of
shape, and play important roles in tasks like depth-based segmentation or
foreground selection. Unfortunately, the currently extracted channels often
carry conflicting signals, making it difficult for subsequent applications to
effectively use them. In this paper, we focus on the problem of obtaining
high-precision depth edges (i.e., depth contours and creases) by jointly
analyzing such unreliable information channels. We propose DepthCut, a
data-driven fusion of the channels using a convolutional neural network trained
on a large dataset with known depth. The resulting depth edges can be used for
segmentation, decomposing a scene into depth layers with relatively flat depth,
or improving the accuracy of the depth estimate near depth edges by
constraining its gradients to agree with these edges. Quantitatively, we
compare against 15 variants of baselines and demonstrate that our depth edges
result in an improved segmentation performance and an improved depth estimate
near depth edges compared to data-agnostic channel fusion. Qualitatively, we
demonstrate that the depth edges result in superior segmentation and depth
orderings.Comment: 12 page
HST morphologies of local Lyman break galaxy analogs I: Evidence for starbursts triggered by merging
Heckman et al. (2005) used the Galaxy Evolution Explorer (GALEX) UV imaging
survey to show that there exists a rare population of nearby compact
UV-luminous galaxies (UVLGs) that closely resembles high redshift Lyman break
galaxies (LBGs). We present HST images in the UV, optical, and Ha, and
resimulate them at the depth and resolution of the GOODS/UDF fields to show
that the morphologies of UVLGs are also similar to those of LBGs. Our sample of
8 LBG analogs thus provides detailed insight into the connection between star
formation and LBG morphology. Faint tidal features or companions can be seen in
all of the rest-frame optical images, suggesting that the starbursts are the
result of a merger or interaction. The UV/optical light is dominated by
unresolved (~100-300 pc) super starburst regions (SSBs). A detailed comparison
with the galaxies Haro 11 and VV 114 at z=0.02 indicates that the SSBs
themselves consist of diffuse stars and (super) star clusters. The structural
features revealed by the new HST images occur on very small physical scales and
are thus not detectable in images of high redshift LBGs, except in a few cases
where they are magnified by gravitational lensing. We propose, therefore, that
LBGs are mergers of gas-rich, relatively low-mass (~10^10 Msun) systems, and
that the mergers trigger the formation of SSBs. If galaxies at high redshifts
are dominated by SSBs, then the faint end slope of the luminosity function is
predicted to have slope alpha~2. Our results are the most direct confirmation
to date of models that predict that the main mode of star formation in the
early universe was highly collisional.Comment: 32 pages, 15 figures. ApJ In pres
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