3,397 research outputs found
A sparse-to-dense method for 3D optical flow estimation in 3D light microscopy image sequences
International audienceWe present a two-stage 3D optical flow estimation method for light microscopy image volumes. The method takes a pair of light microscopy image volumes as input, segments the 2D slices of the source volume in superpixels and sparsely estimates the 3D displacement vectors in the volume pair. A weighted interpolation is then introduced to get a dense 3D flow field. Edges and motion boundaries are considered during the interpolation. Our experimental results show good gain in execution speed, and accuracy evaluated in computer generated 3D data. Promising results on real 3D image sequences are reported
4D Temporally Coherent Light-field Video
Light-field video has recently been used in virtual and augmented reality
applications to increase realism and immersion. However, existing light-field
methods are generally limited to static scenes due to the requirement to
acquire a dense scene representation. The large amount of data and the absence
of methods to infer temporal coherence pose major challenges in storage,
compression and editing compared to conventional video. In this paper, we
propose the first method to extract a spatio-temporally coherent light-field
video representation. A novel method to obtain Epipolar Plane Images (EPIs)
from a spare light-field camera array is proposed. EPIs are used to constrain
scene flow estimation to obtain 4D temporally coherent representations of
dynamic light-fields. Temporal coherence is achieved on a variety of
light-field datasets. Evaluation of the proposed light-field scene flow against
existing multi-view dense correspondence approaches demonstrates a significant
improvement in accuracy of temporal coherence.Comment: Published in 3D Vision (3DV) 201
3D Flow Field Estimation and Assessment for Live Cell Fluorescence Microscopy
International audienceMotivation: The revolution in light sheet microscopy enables the concurrent observation of thousands of dynamic processes, from single molecules to cellular organelles, with high spatiotemporal resolution. However, challenges in the interpretation of multidimensional data requires the fully automaticmeasurement of those motions to link local processes to cellular functions. This includes the design and the implementation of image processing pipelines able to deal with diverse motion types, and 3D visualization tools adapted to the human visual system.Results: Here, we describe a new method for 3D motion estimation that addresses the aforementioned issues. We integrate 3D matching and variational approach to handle a diverse range of motion without any prior on the shape of moving objects. We compare dierent similarity measures to cope with intensity ambiguities and demonstrate the eectiveness of the Census signature for both stages. Additionally, wepresent two intuitive visualization approaches to adapt complex 3D measures into an interpretable 2D view, and a novel way to assess the quality of flow estimates in absence of ground truth
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
10411 Abstracts Collection -- Computational Video
From 10.10.2010 to 15.10.2010, the Dagstuhl Seminar 10411 ``Computational Video \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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