Theory of pixel lensing toward M31. II. The velocity anisotropy and flattening of the MACHO distribution

The POINT-AGAPE collaboration is currently searching for massive compact halo objects (MACHOs) toward the Andromeda galaxy (M31). The survey aims to exploit the high inclination of the M31 disk, which causes an asymmetry in the spatial distribution of M31 MACHOs. Here, we investigate the effects of halo velocity anisotropy and flattening on the asymmetry signal using simple halo models. For a spherically symmetric and isotropic halo, we find that the underlying pixel lensing rate in far-disk M31 MACHOs is more than 5 times the rate of near-disk events. We find that the asymmetry is further increased by about 30% if the MACHOs occupy radial orbits rather than tangential orbits, but it is substantially reduced if the MACHOs lie in a flattened halo. However, even for halos with a minor- to major-axis ratio of q = 0.3, the number of M31 MACHOs in the far side outnumber those in the near side by a factor of similar to2. There is also a distance asymmetry, in that the events on the far side are typically farther from the major axis. We show that, if this positional information is exploited in addition to number counts, then the number of candidate events required to confirm asymmetry for a range of flattened and anisotropic halo models is achievable, even with significant contamination by variable stars and foreground microlensing events. For pixel lensing surveys that probe a representative portion of the M31 disk, a sample of around 50 candidates is likely to be sufficient to detect asymmetry within spherical halos, even if half the sample is contaminated, or to detect asymmetry in halos as flat as q = 0.3, provided less than a third of the sample comprises contaminants. We also argue that, provided its mass-to-light ratio is less than 100, the recently observed stellar stream around M31 is not problematic for the detection of asymmetry.

Image, Video and 3D Data Registration: Medical, Satellite and Video Processing Applications with Quality Metrics

Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods.

This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state-of-the-art registration methodologies used in a variety of targeted applications.

Key features:
- Provides a state-of-the-art review of image and video registration techniques, allowing readers to develop an understanding of how well the techniques perform by using specific quality assessment criteria
- Addresses a range of applications from familiar image and video processing domains to satellite and medical imaging among others, enabling readers to discover novel methodologies with utility in their own research
- Discusses quality evaluation metrics for each application domain with an interdisciplinary approach from different research perspectives

The FLP-side of nematodes.

The central role of FMRFamide-like peptides (FLPs) in nematode motor and sensory capabilities makes FLP signalling an appealing target for new parasiticides. Accumulating evidence has revealed an astounding level of FLP sequence conservation and diversity in the phylum Nematoda, and preliminary work has begun to identify the nematode FLP receptor complement in Caenorhabditis elegans, with a view to investigating their basic biology and therapeutic potential. However, much work is needed to clarify the functional aspects of FLP signalling and how these peptides exert their effects at the organismal level. Here, we summarize our current knowledge of nematode FLP signalling.

Thresholding of noisy shoeprint images based on pixel context

In a typical shoeprint classification and retrieval system, the first step is to segment meaningful basic shapes and patterns in a noisy shoeprint image. This step has significant influence on shape descriptors and shoeprint indexing in the later stages. In this paper, we extend a recently developed denoising technique proposed by Buades, called non-local mean filtering, to give a more general model. In this model, the expected result of an operation on a pixel can be estimated by performing the same operation on all of its reference pixels in the same image. A working pixel’s reference pixels are those pixels whose neighbourhoods are similar to the working pixel’s neighbourhood. Similarity is based on the correlation between the local neighbourhoods of the working pixel and the reference pixel. We incorporate a special instance of this general case into thresholding a very noisy shoeprint image. Visual and quantitative comparisons with two benchmarking techniques, by Otsu and Kittler, are conducted in the last section, giving evidence of the effectiveness of our method for thresholding noisy shoeprint images.