Real-time image rotation is an essential operation in many application areas such as image processing, computer graphics and pattern recognition. Existing architectures that rely on CORDIC computations for trigonometric operations cause a severe bottleneck in high-throughput applications, especially where high-resolution images are involved. A novel hierarchical method that exploits the symmetrical characteristics of the image to accelerate the rotation of highresolution images is presented. Investigations based on a 512 Â 512 image show that the proposed method yields a speedup of 20Â for a mere 3% increase in area cost when compared with existing techniques. Moreover, the effect of hierarchy on the computational efficiency has been evaluated to provide for area -time flexibility. The proposed technique is highly scalable and significant performance gains are evident for very high-resolution images.

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Lane detection is a problem that has been extensively studied by the research community in the past two decades. However limited literature can be found on techniques to distinguish the various types of lane markings -such as solid, dashed, single, double, zigzag etc. In this paper, we present a modular approach to detect and distinguish a wide range of lane markings. The fundamental processing module for detecting basic lane markings (BLM) is first proposed, after which we show how this can be deployed for distinguishing the various lane marking types. The underlying principle is that any lane marking can be broken down into one or more BLMs. A modular architecture is presented to detect and distinguish the various lane markings using the proposed modules. The techniques are evaluated on the road marking dataset in [8] and is shown to yield a high detection accuracy.

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