Browsing by Author "Nicolls, Fred C"

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    Multiview active shape models with SIFT descriptors
    (2016) Milborrow, Stephen; Nicolls, Fred C
    This thesis presents techniques for locating landmarks in images of human faces. A modified Active Shape Model (ASM [21]) is introduced that uses a form of SIFT descriptors [68]. Multivariate Adaptive Regression Splines (MARS [40]) are used to efficiently match descriptors around landmarks. This modified ASM is fast and performs well on frontal faces. The model is then extended to also handle non-frontal faces. This is done by first estimating the face's pose, rotating the face upright, then applying one of three ASM submodels specialized for frontal, left, or right three-quarter views. The multiview model is shown to be effective on a variety of datasets.
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    Open Access
    On the classification of time series and cross wavelet phase variance
    (2016) Pienaar, Marc; Nicolls, Fred C
    The continuous wavelet transform (CWT) is arguably one of the best tools to explore underlying characteristic features of time series data. Its application in large time series classification experiments, however, has been severely limited due to the large amount of redundant associated information. By extending the capabilities of the CWT to perform cross wavelet analysis (CWA), common frequency behaviour between two time series is highlighted, and the potential to extract amplitude modulated (AM) and frequency modulation (FM) characteristics in an automated way is possible. Characterisation of AM is relatively straightforward and can be resolved by any number of Euclidean based techniques in both the time and frequency domains. FM on the other hand, is somewhat more difficult as it transcends multiple wavelet scales. In this study, linear combinations of scales are used to extract both AM similarity (derived from global wavelet power spectra) and FM coherency, using a new method developed called cross wavelet phase variance (CWPV). The methodology is applied to large scale classification problems (using benchmark time series), in which the method clearly outperforms other common distance-based measures. Lastly, the approach provides a powerful framework in which AM and FM characteristics common between time series can be explicitly mapped to their corresponding scales, and with some initial optimisation, can be applied to any classification problem.
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    Open Access
    Unsupervised maritime target detection
    (2016) Bachoo, Asheer; Nicolls, Fred C
    The unsupervised detection of maritime targets in grey scale video is a difficult problem in maritime video surveillance. Most approaches assume that the camera is static and employ pixel-wise background modelling techniques for foreground detection; other methods rely on colour or thermal information to detect targets. These methods fail in real-world situations when the static camera assumption is violated, and colour or thermal data is unavailable. In defence and security applications, prior information and training samples of targets may be unavailable for training a classifier; the learning of a one class classifier for the background may be impossible as well. Thus, an unsupervised online approach that attempts to learn from the scene data is highly desirable. In this thesis, the characteristics of the maritime scene and the ocean texture are exploited for foreground detection. Two fast and effective methods are investigated for target detection. Firstly, online regionbased background texture models are explored for describing the appearance of the ocean. This approach avoids the need for frame registration because the model is built spatially rather than temporally. The texture appearance of the ocean is described using Local Binary Pattern (LBP) descriptors. Two models are proposed: one model is a Gaussian Mixture (GMM) and the other, referred to as a Sparse Texture Model (STM), is a set of histogram texture distributions. The foreground detections are optimized using a Graph Cut (GC) that enforces spatial coherence. Secondly, feature tracking is investigated as a means of detecting stable features in an image frame that typically correspond to maritime targets; unstable features are background regions. This approach is a Track-Before-Detect (TBD) concept and it is implemented using a hierarchical scheme for motion estimation, and matching of Scale- Invariant Feature Transform (SIFT) appearance features. The experimental results show that these approaches are feasible for foreground detection in maritime video when the camera is either static or moving. Receiver Operating Characteristic (ROC) curves were generated for five test sequences and the Area Under the ROC Curve (AUC) was analyzed for the performance of the proposed methods. The texture models, without GC optimization, achieved an AUC of 0.85 or greater on four out of the five test videos. At 50% True Positive Rate (TPR), these four test scenarios had a False Positive Rate (FPR) of less than 2%. With the GC optimization, an AUC of greater than 0.8 was achieved for all the test cases and the FPR was reduced in all cases when compared to the results without the GC. In comparison to the state of the art in background modelling for maritime scenes, our texture model methods achieved the best performance or comparable performance. The two texture models executed at a reasonable processing frame rate. The experimental results for TBD show that one may detect target features using a simple track score based on the track length. At 50% TPR a FPR of less than 4% is achieved for four out of the five test scenarios. These results are very promising for maritime target detection.
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    Using multiple view geometry for transmission tower reconstruction
    (2016) Morarjee, Bhavani; Nicolls, Fred C; Boje, Edward
    Automated platforms that conduct power line inspections need to have a vision system which is robust for their harsh working environment. State-of-the-art work in this field focuses on detecting primitive shapes in 2D images in order to isolate power line hardware. Recent trends are starting to explore 3D vision for autonomous platforms, both for navigation and inspection. However, expensive options in the form of specialised hardware is being researched. A cost effective approach would begin with multiple view geometry. Therefore, this study aims to provide a 3D context in the form of a reconstructed transmission pylon that arises from image data. To this end, structure from motion techniques are used to understand multiple view geometry and extract camera extrinsics. Thereafter, a state-of-art line reconstruction algorithm is applied to produce a tower. The pipeline designed is capable of reconstructing a tower up to scale, provided that a known measurement of the scene is provided. Both 2D and 3D hypotheses are formed and scored using edge detection methods before being clustered into a final model. The process of matching 2D lines is based on an exploitation of epipolar geometry, where such 2D lines are detected via the Line Segment Detection (LSD) algorithm. The transmission tower reconstructions contrast their point cloud counterparts, in that no specialised tools or software is required. Instead, this work exploits the wiry nature of the tower and uses camera geometry to evaluate algorithms that are suitable for offline tower reconstruction. [Please note: the fulltext has been deferred until 9 December 2016]