Adaptive background techniques are useful for a wide spectrum of applications, ranging from security surveillance, traffic monitoring to medical and space imaging. With a properly estimated background, moving or new objects can be easily detected and tracked. Existing techniques are not suitable for real-world implementation, either because they are slow or because they do not perform well in the presence of frequent outliers or camera motion. We address the issue by computing a learning rate for each pixel, a function of a local confidence value that estimates whether a pixel is (or not) an outlier, and a global correlation value that detects camera motion. After discussing the role of each parameter, we report experimental results, showing that our technique is fast but efficient, even in a real-world situation. Furthermore, we show that the same method applies equally well to a 3-camera stereoscopic system for depth perception.

In this paper, a new approach to adaptive direction-of-arrival (DOA) estimation based upon a database retrieval technique is proposed. In this method, angles and signal powers are quantized, and a set of true correlation vectors of the array antenna input vectors for various combinations of the quantized angles and signal powers is stored in a database. The k-d tree is then selected as the data structure to facilitate range searching. Estimated a correlation vector, range searching is performed to retrieve several correlation vectors close to it from the k-d tree. The DOA and the signal power are estimated by taking the weighted average of angles and powers associated with the retrieved correlation vectors. Unlike the other high-resolution methods, this method requires no eigenvalue computation, thus allowing a fast computation. It is shown through simulation results that the processing speed of the proposed method is much faster than that of the root-MUSIC that requires the eigenvalue decomposition.

This paper proposes a Mean-Separated and Normalized Vector Quantizer with edge-Adaptive Feedback estimation and variable bit rates (AFMSN-VQ). The basic idea of the AFMSN-VQ is to estimate the statistical parameters of each coding block from its previous coded blocks and then use the estimated parameters to normalize the coding block prior to vector quantization. The edge-adaptive feedback estimator utilizes the interblock correlations of edge connectivity and gray level continuity to accurately estimate the mean and standard deviation of the coding block. The rate-variable VQ is to diminish distortion nonuniformity among image blocks of different activities and to improve the reconstruction quality of edges and contours to which the human vision is sensitive. Simulation results show that up to 2.7dB SNR gain of the AFMSN-VQ over the non-adaptive FMSN-VQ and up to 2.2dB over the 1616 ADCT can be achieved at 0.2-1.0 bit/pixel. Furthermore, the AFMSN-VQ shows a comparable coding performance to ADCT-VQ and A-PE-VQ.