351 research outputs found
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering
Scene parsing has attracted a lot of attention in computer vision. While
parametric models have proven effective for this task, they cannot easily
incorporate new training data. By contrast, nonparametric approaches, which
bypass any learning phase and directly transfer the labels from the training
data to the query images, can readily exploit new labeled samples as they
become available. Unfortunately, because of the computational cost of their
label transfer procedures, state-of-the-art nonparametric methods typically
filter out most training images to only keep a few relevant ones to label the
query. As such, these methods throw away many images that still contain
valuable information and generally obtain an unbalanced set of labeled samples.
In this paper, we introduce a nonparametric approach to scene parsing that
follows a sample-and-filter strategy. More specifically, we propose to sample
labeled superpixels according to an image similarity score, which allows us to
obtain a balanced set of samples. We then formulate label transfer as an
efficient filtering procedure, which lets us exploit more labeled samples than
existing techniques. Our experiments evidence the benefits of our approach over
state-of-the-art nonparametric methods on two benchmark datasets.Comment: Please refer to the CVPR-2016 version of this manuscrip
Superpixels: An Evaluation of the State-of-the-Art
Superpixels group perceptually similar pixels to create visually meaningful
entities while heavily reducing the number of primitives for subsequent
processing steps. As of these properties, superpixel algorithms have received
much attention since their naming in 2003. By today, publicly available
superpixel algorithms have turned into standard tools in low-level vision. As
such, and due to their quick adoption in a wide range of applications,
appropriate benchmarks are crucial for algorithm selection and comparison.
Until now, the rapidly growing number of algorithms as well as varying
experimental setups hindered the development of a unifying benchmark. We
present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms
utilizing a benchmark focussing on fair comparison and designed to provide new
insights relevant for applications. To this end, we explicitly discuss
parameter optimization and the importance of strictly enforcing connectivity.
Furthermore, by extending well-known metrics, we are able to summarize
algorithm performance independent of the number of generated superpixels,
thereby overcoming a major limitation of available benchmarks. Furthermore, we
discuss runtime, robustness against noise, blur and affine transformations,
implementation details as well as aspects of visual quality. Finally, we
present an overall ranking of superpixel algorithms which redefines the
state-of-the-art and enables researchers to easily select appropriate
algorithms and the corresponding implementations which themselves are made
publicly available as part of our benchmark at
davidstutz.de/projects/superpixel-benchmark/
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Objective: Surgical data science is evolving into a research field that aims
to observe everything occurring within and around the treatment process to
provide situation-aware data-driven assistance. In the context of endoscopic
video analysis, the accurate classification of organs in the field of view of
the camera proffers a technical challenge. Herein, we propose a new approach to
anatomical structure classification and image tagging that features an
intrinsic measure of confidence to estimate its own performance with high
reliability and which can be applied to both RGB and multispectral imaging (MI)
data. Methods: Organ recognition is performed using a superpixel classification
strategy based on textural and reflectance information. Classification
confidence is estimated by analyzing the dispersion of class probabilities.
Assessment of the proposed technology is performed through a comprehensive in
vivo study with seven pigs. Results: When applied to image tagging, mean
accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB)
and 96% (MI) with the confidence measure. Conclusion: Results showed that the
confidence measure had a significant influence on the classification accuracy,
and MI data are better suited for anatomical structure labeling than RGB data.
Significance: This work significantly enhances the state of art in automatic
labeling of endoscopic videos by introducing the use of the confidence metric,
and by being the first study to use MI data for in vivo laparoscopic tissue
classification. The data of our experiments will be released as the first in
vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table
Learning from Weak and Noisy Labels for Semantic Segmentation
This work was partially supported by National Natural Science Foundation of China (61573363 and 61573026), 973 Program of China (2014CB340403 and 2015CB352502), the Fundamental Research Funds for the Central Universities
and the Research Funds of Renmin University of China (15XNLQ01), IBM Global SUR Award Program, European
Research Council FP7 Project SUNNY (313243), and the funding from KAUST
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