38,725 research outputs found
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data
The Shape Interaction Matrix (SIM) is one of the earliest approaches to
performing subspace clustering (i.e., separating points drawn from a union of
subspaces). In this paper, we revisit the SIM and reveal its connections to
several recent subspace clustering methods. Our analysis lets us derive a
simple, yet effective algorithm to robustify the SIM and make it applicable to
realistic scenarios where the data is corrupted by noise. We justify our method
by intuitive examples and the matrix perturbation theory. We then show how this
approach can be extended to handle missing data, thus yielding an efficient and
general subspace clustering algorithm. We demonstrate the benefits of our
approach over state-of-the-art subspace clustering methods on several
challenging motion segmentation and face clustering problems, where the data
includes corrupted and missing measurements.Comment: This is an extended version of our iccv15 pape
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
Intraoperative segmentation and tracking of minimally invasive instruments is
a prerequisite for computer- and robotic-assisted surgery. Since additional
hardware like tracking systems or the robot encoders are cumbersome and lack
accuracy, surgical vision is evolving as promising techniques to segment and
track the instruments using only the endoscopic images. However, what is
missing so far are common image data sets for consistent evaluation and
benchmarking of algorithms against each other. The paper presents a comparative
validation study of different vision-based methods for instrument segmentation
and tracking in the context of robotic as well as conventional laparoscopic
surgery. The contribution of the paper is twofold: we introduce a comprehensive
validation data set that was provided to the study participants and present the
results of the comparative validation study. Based on the results of the
validation study, we arrive at the conclusion that modern deep learning
approaches outperform other methods in instrument segmentation tasks, but the
results are still not perfect. Furthermore, we show that merging results from
different methods actually significantly increases accuracy in comparison to
the best stand-alone method. On the other hand, the results of the instrument
tracking task show that this is still an open challenge, especially during
challenging scenarios in conventional laparoscopic surgery
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