25,764 research outputs found
Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences
Results: We present an application that enables the quantitative analysis of
multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence
microscopy images. The image sequences show stem cells together with blood
vessels, enabling quantification of the dynamic behaviors of stem cells in
relation to their vascular niche, with applications in developmental and cancer
biology. Our application automatically segments, tracks, and lineages the image
sequence data and then allows the user to view and edit the results of
automated algorithms in a stereoscopic 3-D window while simultaneously viewing
the stem cell lineage tree in a 2-D window. Using the GPU to store and render
the image sequence data enables a hybrid computational approach. An
inference-based approach utilizing user-provided edits to automatically correct
related mistakes executes interactively on the system CPU while the GPU handles
3-D visualization tasks. Conclusions: By exploiting commodity computer gaming
hardware, we have developed an application that can be run in the laboratory to
facilitate rapid iteration through biological experiments. There is a pressing
need for visualization and analysis tools for 5-D live cell image data. We
combine accurate unsupervised processes with an intuitive visualization of the
results. Our validation interface allows for each data set to be corrected to
100% accuracy, ensuring that downstream data analysis is accurate and
verifiable. Our tool is the first to combine all of these aspects, leveraging
the synergies obtained by utilizing validation information from stereo
visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure and loop
closure detection. Recent random forests based methods exploit randomly sampled
pixel comparison features to predict 3D world locations for 2D image locations
to guide the camera pose optimization. However, these image features are only
sampled randomly in the images, without considering the spatial structures or
geometric information, leading to large errors or failure cases with the
existence of poorly textured areas or in motion blur. Line segment features are
more robust in these environments. In this work, we propose to jointly exploit
points and lines within the framework of uncertainty driven regression forests.
The proposed approach is thoroughly evaluated on three publicly available
datasets against several strong state-of-the-art baselines in terms of several
different error metrics. Experimental results prove the efficacy of our method,
showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
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