Center Symmetric Local Multilevel Pattern Based Descriptor and Its Application in Image Matching

This paper presents an effective local image region description method, called CS-LMP (Center Symmetric Local Multilevel Pattern) descriptor, and its application in image matching. The CS-LMP operator has no exponential computations, so the CS-LMP descriptor can encode the differences of the local intensity values using multiply quantization levels without increasing the dimension of the descriptor. Compared with the binary/ternary pattern based descriptors, the CS-LMP descriptor has better descriptive ability and computational efficiency. Extensive image matching experimental results testified the effectiveness of the proposed CS-LMP descriptor compared with other existing state-of-the-art descriptors

Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

Analysis of transcriptional networks and chromatin states in normal and abnormal blood cells

Altered myeloid differentiation can lead to a variety of haematological malignancies including the Myelodysplastic Syndrome (MDS), chronic myelomonocytic leukaemia (CMML) and acute myeloid leukaemia (AML). We have studied transcriptome regulation in haematopoietic stem and progenitor cells (HSPC) using different high-throughput technologies. In this thesis, I introduce bioinformatics pipelines and an algorithm for the analysis of next-generation sequencing (NGS) data and highlight methods to integrate different genome-wide datasets to derive chromatin states, transcriptional and post-transcriptional networks in normal and abnormal blood cells. Following an introduction to key concepts relevant to this thesis, in the second chapter, I detail the first genome-wide characterisation of small non-coding RNAs in HSPC in MDS patients. By profiling mRNA expression in the same patients, I developed a novel statistical model that integrated miRNA, transcription factors (TF) and gene expression to identify novel regulatory pathways in MDS. MDS and CMML patients often die following transformation into AML. In the third chapter, I present an analysis of a heptad of HSPC TFs that regulate their own expression by binding enhancers of these genes. The enhancer and the heptad are active in a subset of AMLs, normal HSPC and leukemic stem cells. The heptad and a gene signature derived from enhancer activity, predict clinical outcome in AML, while the expression of four heptad genes further correlated with the underlying genetic mutations in cytogenetically normal AML patients. In the fourth chapter, I describe a novel algorithm (LPCHP) to define histone states from NGS data. LPCHP makes use of signal characteristics such as peak shape, location and frequencies in contrast to other algorithms, which only evaluate read intensities. LPCHP was evaluated and performed well in terms of correlation with gene expression, prediction of histone states, parameter variations and signal-to-noise ratios. In the final chapter, I present preliminary data and outline plans for future work. I propose a systems biology approach to study networks of miRNAs and TFs in MDS and CMML. Sequencing of miRNA and mRNA facilitates network reconstruction where interactions between miRNA and mRNA are predicted at single nucleotide resolution, providing avenues for patient stratification and drug response prediction