An application of fractal image-set coding in facial recognition

Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a specialized fractal image coding technique that makes fractal codes more suitable for object and face recognition. A fractal code of a gray-scale image can be divided in two parts – geometrical parameters and luminance parameters. We show that fractal codes for an image are not unique and that we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters – which are faster to compute. Results on a subset of the XM2VTS database are presented.

Dopamine and visually regulated eye growth in chick

Retinal image properties such as contrast and spatial frequency play important roles in the development of normal vision. For example, visual environments comprised solely of low contrast and/or low spatial frequencies induce myopia. The visual image is processed by the retina and it then locally controls eye growth. In terms of the retinal neurotransmitters that link visual stimuli to eye growth, there is strong evidence to suggest involvement of the retinal dopamine (DA) system. For example, effectively increasing retinal DA levels by using DA agonists can suppress the development of form-deprivation myopia (FDM). However, whether visual feedback controls eye growth by modulating retinal DA release, and/or some other factors, is still being elucidated. This thesis is chiefly concerned with the relationship between the dopaminergic system and retinal image properties in eye growth control. More specifically, whether the amount of retinal DA release reduces as the complexity of the image degrades was determined. For example, we investigated whether the level of retinal DA release decreased as image contrast decreased. In addition, the effects of spatial frequency, spatial energy distribution slope, and spatial phase on retinal DA release and eye growth were examined. When chicks were 8-days-old, a cone-lens imaging system was applied monocularly (+30 D, 3.3 cm cone). A short-term treatment period (6 hr) and a longer-term treatment period (4.5 days) were used. The short-term treatment tests for the acute reduction in DA release by the visual stimulus, as is seen with diffusers and lenses, whereas the 4.5 day point tests for reduction in DA release after more prolonged exposure to the visual stimulus. In the contrast study, 1.35 cyc/deg square wave grating targets of 95%, 67%, 45%, 12% or 4.2% contrast were used. Blank (0% contrast) targets were included for comparison. In the spatial frequency study, both sine and square wave grating targets with either 0.017 cyc/deg and 0.13 cyc/deg fundamental spatial frequencies and 95% contrast were used. In the spectral slope study, 30% root-mean-squared (RMS) contrast fractal noise targets with spectral fall-off of 1/f0.5, 1/f and 1/f2 were used. In the spatial alignment study, a structured Maltese cross (MX) target, a structured circular patterned (C) target and the scrambled versions of these two targets (SMX and SC) were used. Each treatment group comprised 6 chicks for ocular biometry (refraction and ocular dimension measurement) and 4 for analysis of retinal DA release. Vitreal dihydroxyphenylacetic acid (DOPAC) was analysed through ion-paired reversed phase high performance liquid chromatography with electrochemical detection (HPLC-ED), as a measure of retinal DA release. For the comparison between retinal DA release and eye growth, large reductions in retinal DA release possibly due to the decreased light level inside the cone-lens imaging system were observed across all treated eyes while only those exposed to low contrast, low spatial frequency sine wave grating, 1/f2, C and SC targets had myopic shifts in refraction. Amongst these treatment groups, no acute effect was observed and longer-term effects were only found in the low contrast and 1/f2 groups. These findings suggest that retinal DA release does not causally link visual stimuli properties to eye growth, and these target induced changes in refractive development are not dependent on the level of retinal DA release. Retinal dopaminergic cells might be affected indirectly via other retinal cells that immediately respond to changes in the image contrast of the retinal image.

Motivation, development and validation of a new spectral greenness index : a spectral dimension related to foliage projective cover

A method is presented for the development of a regional Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper plus (ETM+) spectral greenness index, coherent with a six-dimensional index set, based on a single ETM+ spectral image of a reference landscape. The first three indices of the set are determined by a polar transformation of the first three principal components of the reference image and relate to scene brightness, percent foliage projective cover (FPC) and water related features. The remaining three principal components, of diminishing significance with respect to the reference image, complete the set. The reference landscape, a 2200 km2 area containing a mix of cattle pasture, native woodland and forest, is located near Injune in South East Queensland, Australia. The indices developed from the reference image were tested using TM spectral images from 19 regionally dispersed areas in Queensland, representative of dissimilar landscapes containing woody vegetation ranging from tall closed forest to low open woodland. Examples of image transformations and two-dimensional feature space plots are used to demonstrate image interpretations related to the first three indices. Coherent, sensible, interpretations of landscape features in images composed of the first three indices can be made in terms of brightness (red), foliage cover (green) and water (blue). A limited comparison is made with similar existing indices. The proposed greenness index was found to be very strongly related to FPC and insensitive to smoke. A novel Bayesian, bounded space, modelling method, was used to validate the greenness index as a good predictor of FPC. Airborne LiDAR (Light Detection and Ranging) estimates of FPC along transects of the 19 sites provided the training and validation data. Other spectral indices from the set were found to be useful as model covariates that could improve FPC predictions. They act to adjust the greenness/FPC relationship to suit different spectral backgrounds. The inclusion of an external meteorological covariate showed that further improvements to regional-scale predictions of FPC could be gained over those based on spectral indices alone.

Commentary : Biomaterials offer cancer research the third dimension

In a mini review from 2002, Tyler Jacks and Robert Weinberg commented on the pioneering three-dimensional (3D) culture work from Bissell laboratories and concluded: “Suddenly the study of cancer cells in two dimensions seems quaint if not archaic.” The relevance of this statement for planning and executing mechanistic biological studies and advanced drug testing has been largely disregarded by both academic researchers and the pharmaceutical and biomedical industry in the twenty-first century.

David Hicks and Cathie Holden (eds.) (2007). Teaching The Global Dimension. London: Routledge, Taylor & Francis Group, 212 pages.

Teaching The Global Dimension (2007) is intended for primary and secondary teachers, pre-service teachers and educators interested in fostering global concerns in the education system. It aims at linking theory and practice and is structured as follows. Part 1, the global dimension, proposes an educational framework for understanding global concerns. Individual chapters in this section deal with some educational responses to global issues and the ways in which young people might become, in Hick’s terms, more “world-minded”. In the first two chapters, Hicks presents first, some educational responses to global issues that have emerged in recent decades, and second, an outline of the evolution of global education as a specific field. As with all the chapters in this book, most of the examples are drawn from the United Kingdom. Young people’s concerns, student teachers’ views and the teaching of controversial issues, comprise the other chapters in this section. Taken collectively, the chapters in Part 2 articulate the conceptual framework for developing, teaching and evaluating a global dimension across the curriculum. Individual chapters in this section, written by a range of authors, explore eight key concepts considered necessary to underpin appropriate learning experiences in the classroom. These are conflict, social justice, values and perceptions, sustainability, interdependence, human rights, diversity and citizenship. These chapters are engaging and well structured. Their common format consists of a succinct introduction, reference to positive action for change, and examples of recent effective classroom practice. Two chapters comprise the final section of this book and suggest different ways in which the global dimension can be achieved in the primary and the secondary classroom.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.