82,654 research outputs found
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
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and
Automation (ICRA '15), Seattle, WA, US
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Lensfree computational microscopy tools for cell and tissue imaging at the point-of-care and in low-resource settings.
The recent revolution in digital technologies and information processing methods present important opportunities to transform the way optical imaging is performed, particularly toward improving the throughput of microscopes while at the same time reducing their relative cost and complexity. Lensfree computational microscopy is rapidly emerging toward this end, and by discarding lenses and other bulky optical components of conventional imaging systems, and relying on digital computation instead, it can achieve both reflection and transmission mode microscopy over a large field-of-view within compact, cost-effective and mechanically robust architectures. Such high throughput and miniaturized imaging devices can provide a complementary toolset for telemedicine applications and point-of-care diagnostics by facilitating complex and critical tasks such as cytometry and microscopic analysis of e.g., blood smears, Pap tests and tissue samples. In this article, the basics of these lensfree microscopy modalities will be reviewed, and their clinically relevant applications will be discussed
Image formation in synthetic aperture radio telescopes
Next generation radio telescopes will be much larger, more sensitive, have
much larger observation bandwidth and will be capable of pointing multiple
beams simultaneously. Obtaining the sensitivity, resolution and dynamic range
supported by the receivers requires the development of new signal processing
techniques for array and atmospheric calibration as well as new imaging
techniques that are both more accurate and computationally efficient since data
volumes will be much larger. This paper provides a tutorial overview of
existing image formation techniques and outlines some of the future directions
needed for information extraction from future radio telescopes. We describe the
imaging process from measurement equation until deconvolution, both as a
Fourier inversion problem and as an array processing estimation problem. The
latter formulation enables the development of more advanced techniques based on
state of the art array processing. We demonstrate the techniques on simulated
and measured radio telescope data.Comment: 12 page
Phase-Retrieved Tomography enables imaging of a Tumor Spheroid in Mesoscopy Regime
Optical tomographic imaging of biological specimen bases its reliability on
the combination of both accurate experimental measures and advanced
computational techniques. In general, due to high scattering and absorption in
most of the tissues, multi view geometries are required to reduce diffuse halo
and blurring in the reconstructions. Scanning processes are used to acquire the
data but they inevitably introduces perturbation, negating the assumption of
aligned measures. Here we propose an innovative, registration free, imaging
protocol implemented to image a human tumor spheroid at mesoscopic regime. The
technique relies on the calculation of autocorrelation sinogram and object
autocorrelation, finalizing the tomographic reconstruction via a three
dimensional Gerchberg Saxton algorithm that retrieves the missing phase
information. Our method is conceptually simple and focuses on single image
acquisition, regardless of the specimen position in the camera plane. We
demonstrate increased deep resolution abilities, not achievable with the
current approaches, rendering the data alignment process obsolete.Comment: 21 pages, 5 figure
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
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