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We present a novel algorithm for generating robust and consistent hypotheses for multiple-structure model fitting. Most of the existing methods utilize random sampling which produce varying results especially when outlier ratio is high. For a structure where a model is fitted, the inliers of other structures are regarded as outliers when multiple structures are present. Global optimization has recently been investigated to provide stable and unique solutions, but the computational cost of the algorithms is prohibitively high for most image data with reasonable sizes. The algorithm presented in this paper uses a maximum feasible subsystem (MaxFS) algorithm to generate consistent initial hypotheses only from partial datasets in spatially overlapping local image regions. Our assumption is that each genuine structure will exist as a dominant structure in at least one of the local regions. To refine initial hypotheses estimated from partial datasets and to remove residual tolerance dependency of the MaxFS algorithm, iterative re-weighted L1 (IRL1) minimization is performed for all the image data. Initial weights of IRL1 framework are determined from the initial hypotheses generated in local regions. Our approach is significantly more efficient than those that use only global optimization for all the image data. Experimental results demonstrate that the presented method can generate more reliable and consistent hypotheses than random-sampling methods for estimating single and multiple structures from data with a large amount of outliers. We clearly expose the influence of algorithm parameter settings on the results in our experiments.

We present an efficient deterministic hypothesis generation algorithm for robust fitting of multiple structures based on the maximum feasible subsystem (MaxFS) framework. Despite its advantage, a global optimization method such as MaxFS has two main limitations for geometric model fitting. First, its performance is much influenced by the user-specified inlier scale. Second, it is computationally inefficient for large data. The presented MaxFS-based algorithm iteratively estimates model parameters and inlier scale and also overcomes the second limitation by reducing data for the MaxFS problem. Further it generates hypotheses only with top-n ranked subsets based on matching scores and data fitting residuals. This reduction of data for the MaxFS problem makes the algorithm computationally realistic. Our method, called iterative MaxFS with inlier scale estimation and subset updating (IMaxFS-ISE-SU) in this paper, performs hypothesis generation and fitting alternately until all of true structures are found. The IMaxFS-ISE-SU algorithm generates substantially more reliable hypotheses than random sampling-based methods especially as (pseudo-)outlier ratios increase. Experimental results demonstrate that our method can generate more reliable and consistent hypotheses than random sampling-based methods for estimating multiple structures from data with many outliers., Comment: An extended version of our ICCV 2013 paper

Graphene and related two-dimensional materials are promising candidates for atomically thin, flexible, and transparent optoelectronics. In particular, the strong light-matter interaction in graphene has allowed for the development of state-of-the-art photodetectors, optical modulators, and plasmonic devices. In addition, electrically biased graphene on SiO2 substrates can be used as a low-efficiency emitter in the mid-infrared range. However, emission in the visible range has remained elusive. Here we report the observation of bright visible-light emission from electrically biased suspended graphenes. In these devices, heat transport is greatly minimised; thus hot electrons (~ 2800 K) become spatially localised at the centre of graphene layer, resulting in a 1000-fold enhancement in the thermal radiation efficiency. Moreover, strong optical interference between the suspended graphene and substrate can be utilized to tune the emission spectrum. We also demonstrate the scalability of this technique by realizing arrays of chemical-vapour-deposited graphene bright visible-light emitters. These results pave the way towards the realisation of commercially viable large-scale, atomically-thin, flexible and transparent light emitters and displays with low-operation voltage, and graphene-based, on-chip ultrafast optical communications., Comment: 63 page

In this paper, we present a novel method for rapid high-resolution range sensing using green-blue stripe pattern. We use green and blue for designing high-frequency stripe projection pattern. For accurate and reliable range recovery, we identify the stripe patterns by our color-stripe segmentation and unwrapping algorithms. The experimental result for a naked human face shows the effectiveness of our method., Comment: 7 pages, 5 figures. Updated version of a conference paper

Metal-graphene-metal photodetectors utilize photocurrent generated near the graphene/metal junctions and have many advantages including high speed and broad-band operation. Here, we report on photocurrent generation at ABA/ABC stacking domain junctions in tri-layer graphene with a responsivity of 0.18 A/W. Unlike usual metal-graphene-metal devices, the photocurrent is generated in the middle of the graphene channel, not confined to the vicinity of the metal electrodes. The magnitude and the direction of the photocurrent depend on the back-gate bias. Theoretical calculations show that there is a built-in band offset between the two stacking domains, and the dominant mechanism of the photocurrent is the photo-thermoelectric effect due to the Seebeck coefficient difference.