2,720 research outputs found
Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light
One of the solutions of depth imaging of moving scene is to project a static
pattern on the object and use just a single image for reconstruction. However,
if the motion of the object is too fast with respect to the exposure time of
the image sensor, patterns on the captured image are blurred and reconstruction
fails. In this paper, we impose multiple projection patterns into each single
captured image to realize temporal super resolution of the depth image
sequences. With our method, multiple patterns are projected onto the object
with higher fps than possible with a camera. In this case, the observed pattern
varies depending on the depth and motion of the object, so we can extract
temporal information of the scene from each single image. The decoding process
is realized using a learning-based approach where no geometric calibration is
needed. Experiments confirm the effectiveness of our method where sequential
shapes are reconstructed from a single image. Both quantitative evaluations and
comparisons with recent techniques were also conducted.Comment: 9 pages, Published at the International Conference on Computer Vision
(ICCV 2017
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
This paper addresses the problem of distributed coding of images whose
correlation is driven by the motion of objects or positioning of the vision
sensors. It concentrates on the problem where images are encoded with
compressed linear measurements. We propose a geometry-based correlation model
in order to describe the common information in pairs of images. We assume that
the constitutive components of natural images can be captured by visual
features that undergo local transformations (e.g., translation) in different
images. We first identify prominent visual features by computing a sparse
approximation of a reference image with a dictionary of geometric basis
functions. We then pose a regularized optimization problem to estimate the
corresponding features in correlated images given by quantized linear
measurements. The estimated features have to comply with the compressed
information and to represent consistent transformation between images. The
correlation model is given by the relative geometric transformations between
corresponding features. We then propose an efficient joint decoding algorithm
that estimates the compressed images such that they stay consistent with both
the quantized measurements and the correlation model. Experimental results show
that the proposed algorithm effectively estimates the correlation between
images in multi-view datasets. In addition, the proposed algorithm provides
effective decoding performance that compares advantageously to independent
coding solutions as well as state-of-the-art distributed coding schemes based
on disparity learning
- …