Automatically detecting road sign text from natural scene video

Automatic detection of text on road signs can help drivers keep aware of the traffic situation and surrounding environments by reminding them of the signs ahead. Current systems can only detect constrained road signs or produce unsatisfying performance when dealing with complex scenes in practical use. This paper firstly reviews the existing techniques used for text detection from natural scene. A novel system which detects text on road signs from natural scene video is then proposed. Our detailed approaches and methodology give a promising solution to this problem in order to reduce the running time and improve the recognition rate. © 2006 IEEE

A Deep learning based food recognition system for lifelog images

In this paper, we propose a deep learning based system for food recognition from personal life archive im- ages. The system first identifies the eating moments based on multi-modal information, then tries to focus and enhance the food images available in these moments, and finally, exploits GoogleNet as the core of the learning process to recognise the food category of the images. Preliminary results, experimenting on the food recognition module of the proposed system, show that the proposed system achieves 95.97% classification accuracy on the food images taken from the personal life archive from several lifeloggers, which potentially can be extended and applied in broader scenarios and for different types of food categories

Applying local cooccurring patterns for object detection from aerial images

Developing a spatial searching tool to enhance the search car pabilities of large spatial repositories for Geographical Information System (GIS) update has attracted more and more attention. Typically, objects to be detected are represented by many local features or local parts. Testing images are processed by extracting local features which are then matched with the object's model image. Most existing work that uses local features assumes that each of the local features is independent to each other. However, in many cases, this is not true. In this paper, a method of applying the local cooccurring patterns to disclose the cooccurring relationships between local features for object detection is presented. Features including colour features and edge-based shape features of the interested object are collected. To reveal the cooccurring patterns among multiple local features, a colour cooccurrence histogram is constructed and used to search objects of interest from target images. The method is demonstrated in detecting swimming pools from aerial images. Our experimental results show the feasibility of using this method for effectively reducing the labour work in finding man-made objects of interest from aerial images. © Springer-Verlag Berlin Heidelberg 2007

Linear optical absorption spectra of mesoscopic structures in intense THz fields: free particle properties

We theoretically study the effect of THz radiation on the linear optical absorption spectra of semiconductor structures. A general theoretical framework, based on non-equilibrium Green functions, is formulated, and applied to the calculation of linear optical absorption spectrum for several non-equilibrium mesoscopic structures. We show that a blue-shift occurs and sidebands appear in bulk-like structures, i.e., the dynamical Franz-Keldysh effect [A.-P. Jauho and K. Johnsen, Phys. Rev. Lett. 76, 4576 (1996)]. An analytic calculation leads to the prediction that in the case of superlattices distinct stable steps appear in the absorption spectrum when conditions for dynamical localization are met.Comment: 13 Pages, RevTex using epsf to include 8 ps figures. Submitted to Phys. Rev. B (3 April 97

Bridging the Mid-Infrared-to-Telecom Gap with Silicon Nanophotonic Spectral Translation

Expanding far beyond traditional applications in optical interconnects at telecommunications wavelengths, the silicon nanophotonic integrated circuit platform has recently proven its merits for working with mid-infrared (mid-IR) optical signals in the 2-8 {\mu}m range. Mid-IR integrated optical systems are capable of addressing applications including industrial process and environmental monitoring, threat detection, medical diagnostics, and free-space communication. Rapid progress has led to the demonstration of various silicon components designed for the on-chip processing of mid-IR signals, including waveguides, vertical grating couplers, microcavities, and electrooptic modulators. Even so, a notable obstacle to the continued advancement of chip-scale systems is imposed by the narrow-bandgap semiconductors, such as InSb and HgCdTe, traditionally used to convert mid-IR photons to electrical currents. The cryogenic or multi-stage thermo-electric cooling required to suppress dark current noise, exponentially dependent upon the ratio Eg/kT, can limit the development of small, low-power, and low-cost integrated optical systems for the mid-IR. However, if the mid-IR optical signal could be spectrally translated to shorter wavelengths, for example within the near-infrared telecom band, photodetectors using wider bandgap semiconductors such as InGaAs or Ge could be used to eliminate prohibitive cooling requirements. Moreover, telecom band detectors typically perform with higher detectivity and faster response times when compared with their mid-IR counterparts. Here we address these challenges with a silicon-integrated approach to spectral translation, by employing efficient four-wave mixing (FWM) and large optical parametric gain in silicon nanophotonic wires