Cut down by some cowardly miscreants’: Plant maiming, or the Malicious Cutting of Plants as an Act of Protest in Eighteenth and Nineteenth Century Rural England

Since the publication of Hobsbawm and Rudé's Captain Swing our understanding of the role(s) of covert protests in Hanoverian rural England has advanced considerably. Whilst we now know much about the dramatic practices of incendiarism and animal maiming and the voices of resistance in seemingly straightforward acquisitive acts, one major gap remains. Despite the fact that almost thirty years have passed since E. P. Thompson brought to our attention that under the notorious ‘Black Act’ the malicious cutting of trees was a capital offence, no subsequent research has been published. This paper seeks to address this major lacuna by systematically analysing the practices and patterns of malicious attacks on plants (‘plant maiming’) in the context of late eighteenth- and early nineteenth-century southern England. It is shown that not only did plant maiming take many different forms, attacking every conceivable type of flora, but also that it was universally understood and practised. In some communities plant maiming was the protestors' weapon of choice. As a social practice it therefore embodied wider community beliefs regarding the defence of plebeian livelihoods and identities.

Robust face recognition using posterior union model based neural networks

Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.