Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

Methodology for tidal turbine representation in ocean circulation model

The present method proposes the use and adaptation of ocean circulation models as an assessment tool framework for tidal current turbine (TCT) array layout optimization. By adapting both momentum and turbulence transport equations of an existing model, the present TCT representation method is proposed to extend the actuator disc concept to 3-D large-scale ocean circulation models. Through the reproduction of experimental flume tests and grid dependency tests, this method has shown its numerical coherence as well as its ability to simulate accurately both momentum and turbulent turbine-induced perturbations in both near and far wakes in a relatively short period of computation time. Consequently the present TCT representation method is a very promising basis for the development of a TCT array layout optimization tool

A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

The dusty heart of nearby active galaxies. I. High-spatial resolution mid-IR spectro-photometry of Seyfert galaxies

We present 8-13 micron imaging and spectroscopy of 9 type 1 and 10 type 2 AGN obtained with the VLT/VISIR instrument at spatial resolution <100 pc. The emission from the host galaxy sources is resolved out in most cases. The silicate absorption features are moderately deep and emission features are shallow. We compare the mid-IR luminosities to AGN luminosity tracers and found that the mid-IR radiation is emitted quite isotropically. In two cases, IC5063 and MCG-3-34-64, we find evidence for extended dust emission in the narrow-line region. We confirm the correlation between observed silicate feature strength and Hydrogen column density recently found in Spitzer data. In a further step, our 3D clumpy torus model has been used to interpret the data. We show that the strength of the silicate feature and the mid-IR spectral index can be used to get reasonable constraints on the dust distribution in the torus. The mid-IR spectral index, alpha, is almost exclusively determined by the radial dust distribution power-law index, a, and the silicate feature depth is mostly depending on the average number of clouds, N0, along an equatorial line-of-sight and the torus inclination. A comparison of model predictions to our type 1 and type 2 AGN reveals typical average parameters a=-1.0+/-0.5 and N0=5-8, which means that the radial dust distribution is rather shallow. As a proof-of-concept of this method, we compared the model parameters derived from alpha and the silicate feature to more detailed studies of IR SEDs and interferometry and found that the constraints on a and N0 are consistent. Finally, we might have found evidence that the radial structure of the torus changes from low to high AGN luminosities towards steeper dust distributions, and we discuss implications for the IR size-luminosity relation. (abridged)Comment: 22 pages, 13 figues, 6 tables; Accepted for publication in A&A; Note that this is the second submitted paper from the series, but we changed paper order. This one will be referred to as paper I, the previously submitted arXiv:0909.4539 will become paper I

Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers

In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers

From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)