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Browsing by Author "Jackman, James"

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    Deep learning for automatic target recognition with real and synthetic infrared maritime imagery
    (SPIE, 2020-09-20) Westlake, Samuel T.; Volonakis, Timothy N.; Jackman, James; James, David B.; Sherriff, Andy
    Supervised deep learning algorithms are re-defining the state-of-the-art for object detection and classification. However, training these algorithms requires extensive datasets that are typically expensive and time-consuming to collect. In the field of defence and security, this can become impractical when data is of a sensitive nature, such as infrared imagery of military vessels. Consequently, algorithm development and training are often conducted in synthetic environments, but this brings into question the generalisability of the solution to real world data. In this paper we investigate training deep learning algorithms for infrared automatic target recognition without using real-world infrared data. A large synthetic dataset of infrared images of maritime vessels in the long wave infrared waveband was generated using target-missile engagement simulation software and ten high-fidelity computer-aided design models. Multiple approaches to training a YOLOv3 architecture were explored and subsequently evaluated using a video sequence of real-world infrared data. Experiments demonstrated that supplementing the training data with a small sample of semi-labelled pseudo-IR imagery caused a marked improvement in performance. Despite the absence of real infrared training data, high average precision and recall scores of 99% and 93% respectively were achieved on our real-world test data. To further the development and benchmarking of automatic target recognition algorithms this paper also contributes our dataset of photo-realistic synthetic infrared images.
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    Illumination invariance and shadow compensation via spectro-polarimetry technique
    (Maney Publishing, 2013-06-28T00:00:00Z) Ibrahim, Izzati; Yuen, Peter W. T.; Hong, Kan; Chen, Tong; Soori, Umair; Jackman, James; Richardson, Mark A.
    A major problem for obtaining target reflectance via hyperspectral imaging systems is the presence of illumination and shadow effects. These factors are common artefacts, especially when dealing with a hyperspectral imaging system that has sensors in the visible to near infrared region. This region is known to have highly scattered and diffuse radiance which can modify the energy recorded by the imaging system. Shadow effect will lower the target reflectance values due to the small radiant energy impinging on the target surface. Combined with illumination artefacts, such as diffuse scattering from the surrounding targets, background or environment, the shape of the shadowed target reflectance will be altered. In this study we propose a new method to compensate for illumination and shadow effects on hyperspectral imageries by using a polarization technique. This technique, called spectro-polarimetry, estimates the direct and diffuse irradiance based on two images, taken with and without a polarizer. The method is evaluated using a spectral similarity measure, angle and distance metric. The results of indoor and outdoor tests have shown that using the spectro-polarimetry technique can improve the spectral constancy between shadow and full illumination spectra.

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