Jasmine Banks

Quantitative Evaluation of Matching Methods and Validity Measures for Stereo Vision

The authors present a qualitative and quantitative comparison of various similarity measures that form the kernel of common area-based stereo-matching systems. The authors compare classical difference and correlation measures as well as nonparametric measures based on the rank and census transforms for a number of outdoor images. For robotic applications, important considerations include robustness to image defects such as intensity variation and noise, the number of false matches, and computational complexity. In the absence of ground truth data, the authors compare the matching techniques based on the percentage of matches that pass the left-right consistency test. The authors also evaluate the discriminatory power of several match validity measures that are reported in the literature for eliminating false matches and for estimating match confidence. For guidance applications, it is essential to have an estimate of confidence in the three-dimensional points generated by stereo vision. Finally, a new validity measure, the rank constraint, is introduced that is capable of resolving ambiguous matches for rank transform–based matching.

Reliability analysis of the rank transform for stereo matching

The rank transform is a nonparametric technique which has been recently proposed for the stereo matching problem. The motivation behind its application to this problem is its invariance to certain types of image distortion and noise, as well as its amenability to real-time implementation. This paper derives one constraint which must be satisfied for a correct match. This has been termed the rank constraint. Experimental work has shown that this constraint is capable of resolving ambiguous matches, thereby improving matching reliability. A novel matching algorithm incorporating the rank constraint has also been proposed. This modified algorithm consistently resulted in an increased percentage of correct matches, for all test imagery used. Furthermore, the rank constraint has been used to devise a method of identifying regions of an image where the rank transform, and hence matching outcome, is more susceptible to noise. Experimental results have shown that the errors predicted using this technique are consistent with the actual errors which result when images are corrupted with noise. Such a method could be used to identify matches which are likely to be incorrect and/or provide a measure of confidence in a match.

Constructing the hallucinations of psychosis in virtual reality

Schizophrenia is a mental disorder affecting 1-2% of the population and it is estimated 12-16% of hospital beds in Australia are occupied by patients with psychosis. The suicide rate for patients with this diagnosis is higher than that of the general population. Any technique which enhances training and treatment of this disorder will have a significant societal and economic impact. A significant research project using Virtual Reality (VR), in which both visual and auditory hallucinations are simulated, is currently being undertaken at the University of Queensland. The virtual environments created by the new software are expected to enhance the experiential learning outcomes of medical students by enabling them to experience the inner world of a patient with psychosis. In addition the Virtual Environment has the potential to provide a technologically advanced therapeutic setting where behavioral, exposure therapies can be conducted with exactly controlled exposure stimuli with an expected reduction in risk of harm. This paper reports on the current work of the project, previous stages of software development and future educational and clinical applications of the Virtual Environments.

Fast and Robust Stereo Matching Algorithms for Mining Automation

Abstract The mining environment, being complex, irregular, and time-varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper evaluates a number of matching techniques for possible use in a stereo vision sensor for mining automation applications. Area-based techniques have been investigated because they have the potential to yield dense maps, are amenable to fast hardware implementation, and are suited to textured scenes. In addition, two nonparametric transforms, namely, rank and census, have been investigated. Matching algorithms using these transforms were found to have a number of clear advantages, including reliability in the presence of radiometric distortion, low computational complexity, and amenability to hardware implementation.

A Novel Representation of Bioacoustic Events for Content-Based Search in Field Audio Data

Bioacoustic data can provide an important base for environmental monitoring. To explore a large amount of field recordings collected, an automated similarity search algorithm is presented in this paper. A region of an audio defined by frequency and time bounds is provided by a user; the content of the region is used to construct a query. In the retrieving process, our algorithm will automatically scan through recordings to search for similar regions. In detail, we present a feature extraction approach based on the visual content of vocalisations - in this case ridges, and develop a generic regional representation of vocalisations for indexing. Our feature extraction method works best for bird vocalisations showing ridge characteristics. The regional representation method allows the content of an arbitrary region of a continuous recording to be described in a compressed format.

Multispectral Periocular Classification With Multimodal Compact Multi-Linear Pooling

Feature-level fusion approaches for multispectral biometrics are mainly grouped into two categories: 1) concatenation and 2) elementwise multiplication. While concatenation of feature vectors has benefits in allowing all elements to interact, it is difficult to learn output classification. Differently, elementwise multiplication has the benefits in enabling multiplicative interaction, but it is difficult to learn input embedding. In this paper, we propose a novel approach to combine the benefits of both categories based on a compact representation of two feature vectors’ outer product, which is called the multimodal compact multi-linear pooling technique. We first propose to expand the bilinear pooling technique for two inputs to a multi-linear technique to accommodate for multiple inputs (multiple inputs from multiple spectra are frequent in the multispectral biometric context). This fusion approach not only allows all elements to interact and enables multiplicative interaction, but also uses a small number of parameters and low computation complexity. Based on this fusion proposal, we subsequently propose a complete multispectral periocular recognition system. Employing higher order spectra features with an elliptical sampling approach proposed by Algashaam et al., our proposed system achieves the state-of-the-art performance in both our own and the IIIT multispectral periocular data sets. The proposed approach can also be extended to other biometric modalities.

Computation of ECG Signal Features Using MCMC Modelling in Software and FPGA Reconfigurable Hardware

Computational optimisation of clinically important electrocardiogram signal features, within a single heart beat, using a Markov-chain Monte Carlo (MCMC) method is undertaken. A detailed, efficient data-driven software implementation of an MCMC algorithm has been shown. Initially software parallelisation is explored and has been shown that despite the large amount of model parameter inter-dependency that parallelisation is possible. Also, an initial reconfigurable hardware approach is explored for future applicability to real-time computation on a portable ECG device, under continuous extended use.

Elliptical Higher-Order-Spectra Periocular Code

The periocular region has recently emerged as a standalone biometric trait, promising attractive tradeoff between the iris alone and the entire face, especially for cases where neither the iris nor a full facial image can be acquired. This advantage provides another dimension for implementing a robust biometric system performed in non-ideal conditions. Global features [local binary pattern (LBP), Histogram of Gradient (HOG)] and local features have been introduced; however, the performance of these features can deteriorate for images captured in unconstrained and less-cooperative conditions. A particular set of higher order spectral (HOS) features have been proved to be invariant to translation, scale, rotation, brightness level shift, and contrast change. These properties are desirable in the periocular recognition problem to deal with the non-ideal imaging conditions. This paper investigates the HOS features in different configurations for the periocular recognition problem under non-ideal conditions. Specifically, we introduce a new sampling approach for the periocular region based on an elliptical coordinate. This non-linear sampling approach is then combined with the robustness of the HOS features for encoding the periocular region. In addition, we also propose a new technique for combining left and right perioculars. The proposed feature-level fusion approach is based on the state-of-the-art bilinear pooling technique to allow efficient interaction between the features of both perioculars. We show the validity of the proposed approach in encoding discriminant features outperforming or comparing favorably with the state-of-the-art features on the two popular data sets: Face Recognition Grand Challenge and Japanese Female Facial Expression.

White Blood Cell Nuclei Segmentation Using Level Set Methods and Geometric Active Contours

A new method for segmenting white blood cells nuclei in microscopic images is presented. Challenges to accurate segmentation include intra-class variation of the nuclei cell boundaries, non-uniform illumination, and changes in the cell topology due to its orientation and stage of maturity. In this research, level set methods and geometric active contours are used to segment the nucleus of white blood cells from the cytoplasm and the cell wall. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are less computationally complex and adapt better to the curve topology of the cell boundary than parametric active contours, which have been previously used for white blood cell segmentation. Segmentation performance is compared with other segmentation methods using the Berkeley benchmark database and the proposed method is shown to be superior using various indices.

Automatic segmentation of HEp-2 cell Fluorescence microscope images using level set method via geometric active contours

A method for segmenting HEp-2 cells in Indirect Immuno Fluorescence microscope (IIF) images is implemented and evaluated. Challenges to accurate segmentation include the complexity of the data acquired at multiple wavelengths, overlapping cells, and variations in staining. Level set methods via geometric active contours are used to solve this problem. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are able to adapt to the curve topology of the cell boundary. Segmentation performance is evaluated using six indices: boundary displacement error, global consistency error, variation of information, Jaccard distance error, rand index and F-index.