64,667 research outputs found
On the Convergence and Consistency of the Blurring Mean-Shift Process
The mean-shift algorithm is a popular algorithm in computer vision and image
processing. It can also be cast as a minimum gamma-divergence estimation. In
this paper we focus on the "blurring" mean shift algorithm, which is one
version of the mean-shift process that successively blurs the dataset. The
analysis of the blurring mean-shift is relatively more complicated compared to
the nonblurring version, yet the algorithm convergence and the estimation
consistency have not been well studied in the literature. In this paper we
prove both the convergence and the consistency of the blurring mean-shift. We
also perform simulation studies to compare the efficiency of the blurring and
the nonblurring versions of the mean-shift algorithms. Our results show that
the blurring mean-shift has more efficiency.Comment: arXiv admin note: text overlap with arXiv:1201.197
System calibration method for Fourier ptychographic microscopy
Fourier ptychographic microscopy (FPM) is a recently proposed quantitative
phase imaging technique with high resolution and wide field-of-view (FOV). In
current FPM imaging platforms, systematic error sources come from the
aberrations, LED intensity fluctuation, parameter imperfections and noise,
which will severely corrupt the reconstruction results with artifacts. Although
these problems have been researched and some special methods have been proposed
respectively, there is no method to solve all of them. However, the systematic
error is a mixture of various sources in the real situation. It is difficult to
distinguish a kind of error source from another due to the similar artifacts.
To this end, we report a system calibration procedure, termed SC-FPM, based on
the simulated annealing (SA) algorithm, LED intensity correction and adaptive
step-size strategy, which involves the evaluation of an error matric at each
iteration step, followed by the re-estimation of accurate parameters. The great
performance has been achieved both in simulation and experiments. The reported
system calibration scheme improves the robustness of FPM and relaxes the
experiment conditions, which makes the FPM more pragmatic.Comment: 18 pages, 9 figure
Kitting in the Wild through Online Domain Adaptation
Technological developments call for increasing perception and action capabilities of robots. Among other skills, vision systems that can adapt to any possible change in the working conditions are needed. Since these conditions are unpredictable, we need benchmarks which allow to assess the generalization and robustness capabilities of our visual recognition algorithms. In this work we focus on robotic kitting in unconstrained scenarios. As a first contribution, we present a new visual dataset for the kitting task. Differently from standard object recognition datasets, we provide images of the same objects acquired under various conditions where camera, illumination and background are changed. This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified. Our second contribution is a novel online adaptation algorithm for deep models, based on batch-normalization layers, which allows to continuously adapt a model to the current working conditions. Differently from standard domain adaptation algorithms, it does not require any image from the target domain at training time. We benchmark the performance of the algorithm on the proposed dataset, showing its capability to fill the gap between the performances of a standard architecture and its counterpart adapted offline to the given target domain
On Adaptive Measurement Inclusion Rate In Real-Time Moving-Horizon Observers
This paper investigates a self adaptation mechanism regarding the rate with
which new measurements have to be incorporated in Moving-Horizon state
estimation algorithms. This investigation can be viewed as the dual of the one
proposed by the author in the context of real-time model predictive control. An
illustrative example is provided in order to assess the relevance of the
proposed updating rule.Comment: 6 pages. 4 Figure
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