Feature-domain super-resolution for IRIS recognition

Uncooperative iris identification systems at a distance suffer from poor resolution of the captured iris images, which significantly degrades iris recognition performance. Superresolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, all existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values. This paper considers transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. This is the first paper to investigate the possibility of feature domain super-resolution for iris recognition, and experiments confirm the validity of the proposed approach.

Optical non-destructive evaluation of articular cartilage integrity : a review

This paper reviews the current status of the application of optical non-destructive methods, particularly infrared (IR) and near infrared (NIR), in the evaluation of the physiological integrity of articular cartilage. It is concluded that a significant amount of work is still required in order to achieve specificity and clinical applicability of these methods in the assessment and treatment of dysfunctional articular joints.

Assessment of corneal dynamics with high-speed swept source optical coherence tomography combined with an air puff system

We present a novel method and instrument for in vivo imaging and measurement of the human corneal dynamics during an air puff. The instrument is based on high-speed swept source optical coherence tomography (ssOCT) combined with a custom adapted air puff chamber from a non-contact tonometer, which uses an air stream to deform the cornea in a non-invasive manner. During the short period of time that the deformation takes place, the ssOCT acquires multiple A-scans in time (M-scan) at the center of the air puff, allowing observation of the dynamics of the anterior and posterior corneal surfaces as well as the anterior lens surface. The dynamics of the measurement are driven by the biomechanical properties of the human eye as well as its intraocular pressure. Thus, the analysis of the M-scan may provide useful information about the biomechanical behavior of the anterior segment during the applanation caused by the air puff. An initial set of controlled clinical experiments are shown to comprehend the performance of the instrument and its potential applicability to further understand the eye biomechanics and intraocular pressure measurements. Limitations and possibilities of the new apparatus are discussed.

Near infrared optical materials from polymeric amorphous carbon synthesized by collisional plasma process

The synthesis of polymerlike amorphous carbon(a-C:H) thin-films by microwave excited collisional hydrocarbon plasma process is reported. Stable and highly aromatic a-C:H were obtained containing significant inclusions of poly(p-phenylene vinylene) (PPV). PPV confers universal optoelectronic properties to the synthesized material. That is a-C:H with tailor-made refractive index are capable of becoming absorption-free in visible (red)-near infrared wavelength range. Production of large aromatic hydrocarbon including phenyl clusters and/or particles is attributed to enhanced coagulation of elemental plasma species under collisional plasma conditions. Detailed structural and morphological changes that occur in a-C:H during the plasma synthesis are also described.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Facial expression recognition using facial movement features

Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using ‘salient’ distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the ‘salient’ patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

Improving the performance of facial expression recognition using dynamic, subtle and regional features

Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based ‘salient’ Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.

Recognition of expertise : an important concept in the acquisition of nephrology nursing expertise

The present article, which is abstracted from a larger study into the acquisition and exercise of nephrology nursing expertise, aims to explore the concept of recognition of expertise. The study used grounded theory methodology and involved 17 registered nurses who were practising in a metropolitan renal unit in New South Wales, Australia. Concurrent data collection and analysis was undertaken, incorporating participant observations and interviews. According to nurses in this study, patients, doctors and other nurses recognized that some nurses were experts while others were not. In addition, being trusted, being a role model and teaching others were important components of being recognized as an expert nephrology nurse. Of importance for nursing, the results of the present study indicate that knowledge and experience are not sufficient to ensure expert practice; recognition of expertise by others is an important function of expertise acquisition.

Evaluation of texture and geometry for dimensional facial expression recognition

Facial expression recognition (FER) algorithms mainly focus on classification into a small discrete set of emotions or representation of emotions using facial action units (AUs). Dimensional representation of emotions as continuous values in an arousal-valence space is relatively less investigated. It is not fully known whether fusion of geometric and texture features will result in better dimensional representation of spontaneous emotions. Moreover, the performance of many previously proposed approaches to dimensional representation has not been evaluated thoroughly on publicly available databases. To address these limitations, this paper presents an evaluation framework for dimensional representation of spontaneous facial expressions using texture and geometric features. SIFT, Gabor and LBP features are extracted around facial fiducial points and fused with FAP distance features. The CFS algorithm is adopted for discriminative texture feature selection. Experimental results evaluated on the publicly accessible NVIE database demonstrate that fusion of texture and geometry does not lead to a much better performance than using texture alone, but does result in a significant performance improvement over geometry alone. LBP features perform the best when fused with geometric features. Distributions of arousal and valence for different emotions obtained via the feature extraction process are compared with those obtained from subjective ground truth values assigned by viewers. Predicted valence is found to have a more similar distribution to ground truth than arousal in terms of covariance or Bhattacharya distance, but it shows a greater distance between the means.