In intelligent transportation systems, human detection is an important issue and has been widely used in many applications. Histograms of oriented gradients (HOG) are proven to be able to significantly outperform existing feature sets for human detection. In this paper, we present a low-cost high-speed hardware implementation for HOG feature extraction. The simulation shows that the proposed circuit can achieve 167 MHz with 153-K gate counts by using Taiwan Semiconductor Manufacturing Company 0.13-μm technology. Compared with the previous hardware architectures for HOG feature extraction, our circuit requires fewer hardware costs and achieves faster working speed.

Images are often corrupted by impulse noise in the procedures of image acquisition and transmission. In this paper, we propose an efficient denoising scheme and its VLSI architecture for the removal of random-valued impulse noise. To achieve the goal of low cost, a low-complexity VLSI architecture is proposed. We employ a decision-tree-based impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Furthermore, an adaptive technology is used to enhance the effects of removal of impulse noise. Our extensive experimental results demonstrate that the proposed technique can obtain better performances in terms of both quantitative evaluation and visual quality than the previous lower complexity methods. Moreover, the performance can be comparable to the higher,- complexity methods. The VLSI architecture of our design yields a processing rate of about 200 MHz by using TSMC 0.18 μm technology. Compared with the state-of-the-art techniques, this work can reduce memory storage by more than 99 percent. The design requires only low computational complexity and two line memory buffers. Its hardware cost is low and suitable to be applied to many real-time applications.

Image scaling is an important technique that is widely used in many image processing applications. This paper presents a novel scaling algorithm for the implementation of 2-D image scalar. The proposed interpolation method is based on the interpolation error theorem often mentioned in numerical analysis. A bilateral error-amender is used to make the interpolation more precise, and an edge-weighted scheme enhances the edge features of the scaled images. Extensive experimental results demonstrate that the proposed method can obtain better performance than previous methods in both quantitative evaluation and visual quality. This paper also presents an efficient very large-scale integrated architecture for the proposed method. The cooperation and hardware sharing techniques greatly reduce hardware cost requirements. Using a nine-stage pipeline, the proposed scaling circuit contains 13 k gate counts and yields a processing rate of approximately 278 MHz using TSMC 0.13- μm technology. The hardware cost of the proposed circuit is low, making it a good candidate for high-quality image scaling applications.