9,209 research outputs found
When Computer Vision Gazes at Cognition
Joint attention is a core, early-developing form of social interaction. It is
based on our ability to discriminate the third party objects that other people
are looking at. While it has been shown that people can accurately determine
whether another person is looking directly at them versus away, little is known
about human ability to discriminate a third person gaze directed towards
objects that are further away, especially in unconstraint cases where the
looker can move her head and eyes freely. In this paper we address this
question by jointly exploring human psychophysics and a cognitively motivated
computer vision model, which can detect the 3D direction of gaze from 2D face
images. The synthesis of behavioral study and computer vision yields several
interesting discoveries. (1) Human accuracy of discriminating targets
8{\deg}-10{\deg} of visual angle apart is around 40% in a free looking gaze
task; (2) The ability to interpret gaze of different lookers vary dramatically;
(3) This variance can be captured by the computational model; (4) Human
outperforms the current model significantly. These results collectively show
that the acuity of human joint attention is indeed highly impressive, given the
computational challenge of the natural looking task. Moreover, the gap between
human and model performance, as well as the variability of gaze interpretation
across different lookers, require further understanding of the underlying
mechanisms utilized by humans for this challenging task.Comment: Tao Gao and Daniel Harari contributed equally to this wor
Invariant Jordan curves of Sierpiski carpet rational maps
In this paper, we prove that if
is a postcritically finite
rational map with Julia set homeomorphic to the Sierpi\'nski carpet, then there
is an integer , such that, for any , there exists an
-invariant Jordan curve containing the postcritical set of .Comment: 16 pages, 1 figu
LDSO: Direct Sparse Odometry with Loop Closure
In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains this robustness, while at the same time ensuring
repeatability of some of these points by favoring corner features in the
tracking frontend. This repeatability allows to reliably detect loop closure
candidates with a conventional feature-based bag-of-words (BoW) approach. Loop
closure candidates are verified geometrically and Sim(3) relative pose
constraints are estimated by jointly minimizing 2D and 3D geometric error
terms. These constraints are fused with a co-visibility graph of relative poses
extracted from DSO's sliding window optimization. Our evaluation on publicly
available datasets demonstrates that the modified point selection strategy
retains the tracking accuracy and robustness, and the integrated pose-graph
optimization significantly reduces the accumulated rotation-, translation- and
scale-drift, resulting in an overall performance comparable to state-of-the-art
feature-based systems, even without global bundle adjustment
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