Initializing Vision Based Trackers Using Semantic Texton Forests

Vision based tracking can provide the spatial location of project related entities such as equipment, workers, and materials in a large-scale congested construction site. It tracks entities in a video stream by inferring their motion. To initiate the process, it is required to determine the pixel areas of the entities to be tracked in the following consecutive video frames. For the purpose of fully automating the process, this paper presents an automated way of initializing trackers using Semantic Texton Forests (STFs) method. STFs method performs simultaneously the segmentation of the image and the classification of the segments based on the low-level semantic information and the context information. In this paper, STFs method is tested in the case of wheel loaders recognition. In the experiments, wheel loaders are further divided into several parts such as wheels and body parts to help learn the context information. The results show 79% accuracy of recognizing the pixel areas of the wheel loader. These results signify that STFs method has the potential to automate the initialization process of vision based tracking.

Adhesive properties of gecko-inspired mimetic via micropatterned carbon nanotube forests

The adhesive properties of the gecko foot have inspired designs of advanced micropatterned surfaces with increased contact areas. We have fabricated micropatterned pillars of vertically aligned carbon nanotube forests with a range of pillar diameters, heights, and spacings (or pitch). We used nanoindentation to measure their elastic and orthogonal adhesion properties and derive their scaling behavior. The patterning of nanotube forests into pillar arrays allows a reduction of the effective modulus from 10 to 15 MPa to 0.1-1 MPa which is useful for developing maximum conformal adhesion. © 2012 American Chemical Society.

Cylindrical Fresnel lenses based on carbon nanotube forests

The forests of carbon nanotubes have been termed as the darkest man-made materials. Such materials exhibit near-perfect optical absorption (reflectance∼0.045%) due to low reflectance and nanoscale surface roughness. We have demonstrated the utilization of these perfectly absorbing forests to produce binary amplitude cylindrical Fresnel lenses. The opaque Fresnel zones are defined by the dark nanotube forests and these lenses display efficient focusing performance at optical wavelengths. Lensing performance was analyzed both computationally and experimentally with good agreement. Such nanostructure based lenses have many potential applications in devices like photovoltaic solar cells. © 2012 American Institute of Physics.

Optimized vertical carbon nanotube forests for multiplex surface-enhanced raman scattering detection

The highly sensitive and molecule-specific technique of surface-enhanced Raman spectroscopy (SERS) generates high signal enhancements via localized optical fields on nanoscale metallic materials, which can be tuned by manipulation of the surface roughness and architecture on the submicrometer level. We investigate gold-functionalized vertically aligned carbon nanotube forests (VACNTs) as low-cost straightforward SERS nanoplatforms. We find that their SERS enhancements depend on their diameter and density, which are systematically optimized for their performance. Modeling of the VACNT-based SERS substrates confirms consistent dependence on structural parameters as observed experimentally. The created nanostructures span over large substrate areas, are readily configurable, and yield uniform and reproducible SERS enhancement factors. Further fabricated micropatterned VACNTs platforms are shown to deliver multiplexed SERS detection. The unique properties of CNTs, which can be synergistically utilized in VACNT-based substrates and patterned arrays, can thus provide new generation platforms for SERS detection. © 2012 American Chemical Society.