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Accurate and efficient eye gaze estimation is important for emerging consumer electronic systems such as driver monitoring systems and novel user interfaces. Such systems are required to operate reliably in difficult, unconstrained environments with low power consumption and at minimal cost. In this paper a new hardware friendly, convolutional neural network model with minimal computational requirements is introduced and assessed for efficient appearance-based gaze estimation. The model is tested and compared against existing appearance based CNN approaches, achieving better eye gaze accuracy with significantly fewer computational requirements. A brief updated literature review is also provided.

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T he aim of this article is to outline the issues involved in the application of machine vision to the automatic extraction and registration of watermarks from continuous web paper. The correct identification and localization of watermarks are key issues in paper manufacturing. As well as requiring the position of the watermark for defect detection and classification, it is necessary to insure its position on the paper prior to the cutting process. Two paper types are discussed, with and without laid and chain lines (these lines appear as a complex periodic background to the watermark and further complicate the segmentation process). We will examine both morphological and Fourier approaches to the watermark segmentation process, concentrating specifically on those images with complex backgrounds. Finally we detail a system design suitable for real-time implementation.