Fourier Lucas-Kanade Algorithm

In this paper we propose a framework for both gradient descent image and object alignment in the Fourier domain. Our method centers upon the classical Lucas & Kanade (LK) algorithm where we represent the source and template/model in the complex 2D Fourier domain rather than in the spatial 2D domain. We refer to our approach as the Fourier LK (FLK) algorithm. The FLK formulation is advantageous when one pre-processes the source image and template/model with a bank of filters (e.g. oriented edges, Gabor, etc.) as: (i) it can handle substantial illumination variations, (ii) the inefficient pre-processing filter bank step can be subsumed within the FLK algorithm as a sparse diagonal weighting matrix, (iii) unlike traditional LK the computational cost is invariant to the number of filters and as a result far more efficient, and (iv) this approach can be extended to the inverse compositional form of the LK algorithm where nearly all steps (including Fourier transform and filter bank pre-processing) can be pre-computed leading to an extremely efficient and robust approach to gradient descent image matching. Further, these computational savings translate to non-rigid object alignment tasks that are considered extensions of the LK algorithm such as those found in Active Appearance Models (AAMs).

Visual front-end wars : Viola-Jones face detector vs Fourier Lucas-Kanade

The performance of visual speech recognition (VSR) systems are significantly influenced by the accuracy of the visual front-end. The current state-of-the-art VSR systems use off-the-shelf face detectors such as Viola- Jones (VJ) which has limited reliability for changes in illumination and head poses. For a VSR system to perform well under these conditions, an accurate visual front end is required. This is an important problem to be solved in many practical implementations of audio visual speech recognition systems, for example in automotive environments for an efficient human-vehicle computer interface. In this paper, we re-examine the current state-of-the-art VSR by comparing off-the-shelf face detectors with the recently developed Fourier Lucas-Kanade (FLK) image alignment technique. A variety of image alignment and visual speech recognition experiments are performed on a clean dataset as well as with a challenging automotive audio-visual speech dataset. Our results indicate that the FLK image alignment technique can significantly outperform off-the shelf face detectors, but requires frequent fine-tuning.

Association of microRNA 17–92 cluster host gene (MIR17HG) polymorphisms with breast cancer

Breast cancer incidence and mortality rates are increasing despite our current knowledge on the disease. Ninety-five percent of breast cancer cases correspond to sporadic forms of the disease and are believed to involve an interaction between environmental and genetic determinants. The microRNA 17–92 cluster host gene (MIR17HG) has been shown to regulate expression of genes involved in breast cancer development and progression. Study of single-nucleotide polymorphisms (SNPs) located in this cluster gene could help provide a further understanding of its role in breast cancer. Therefore, this study investigated six SNPs in the MIR17HG using two independent Australian Caucasian case–control populations (GRC-BC and GU-CCQ BB populations) to determine association to breast cancer susceptibility. Genotyping was undertaken using chip-based matrix assisted laser desorption ionisation time-of-flight (MALDI-TOF) mass spectrometry (MS). We found significant association between rs4824505 and breast cancer at the allelic level in both study cohorts (GRC-BC p = 0.01 and GU-CCQ BB p = 0.03). Furthermore, haplotypic analysis of results from our combined population determined a significant association between rs4824505/rs7336610 and breast cancer susceptibility (p = 5 × 10−4). Our study is the first to show that the A allele of rs4824505 and the AC haplotype of rs4824505/rs7336610 are associated with risk of breast cancer development. However, definitive validation of this finding requires larger cohorts or populations in different ethnical backgrounds. Finally, functional studies of these SNPs could provide a deeper understanding of the role that MIR17HG plays in the pathophysiology of breast cancer.