The recent sight system requires high stabilization functions for the longer range of observation and the higher kill probability. To this end, it is necessary to compensate rotational disturbances which are not stabilized with the conventional 2-axes stabilization system. This paper proposes a simple method on the rotational motion estimation for the stabilization of the sight system.

In this paper, the false-peaks problem of the conventional correlation-based video tracking is investigated using a simple mathematical analysis. To reduce the false detection problem, a selective-attention correlation measure is proposed. The problem with the conventional correlation measures is that all pixels in the reference block image are equally treated in the computation of the correlation measures irrespective of target or background pixels. Therefore, the more the reference block image includes background pixels, the higher probability of false-peaks is introduced due to the correlation between the background pixels of the reference block and those of the input search image. The proposed selective-attention correlation measure has different consideration according to target and background pixels in the matching process, which conform with the selective-attention property of human visual system. Various computer simulations validated these analyses and confirmed that the proposed selective-attention measure is effective to reduce considerably the probability of the false-peaks.

In this paper, we propose a fast full-search block matching algorithm for motion estimation, based on binary edge information. The binary edge information allows a faster search by reducing the computational complexity. It also reduces error, which is generated by the block located on the boundary of moving objects. After we transform the input image into an edge-based image using Sobel masks, we convert the result into a binary edge image using median-cut quantization. We then perform block matching using the binary edge image. If there exists blocks such that the error of the binary block matching exceeds threshold, we only perform edge intensity-based block matching within those blocks. We improve computational efficiency by eliminating an unnecessary searching process in no-motion regions. Simulation results have shown that the proposed method reduces the computational complexity and provides similar PSNR performance to the Full Search Block Matching Algorithm (FS-BMA)

In this letter we propose a new fast matching algorithm that has no degradation of predicted images such as found in the conventional full search (FS) algorithm, so as to reduce the amount of computation of the FS algorithm for motion estimation in real-time video coding applications. That is, our proposing algorithm reduces only unnecessary computations in the process of motion estimation without decreasing the prediction quality compared to the conventional FS algorithm. The computational reduction comes from rapid elimination of impossible motion vectors. In comparison to the FS algorithm, we obtained faster elimination of inappropriate candidate motion vectors using efficient matching units based on image complexity. Experimentally, we demonstrated that the unnecessary computations were removed by about 30% as compared to the other fast FS algorithms.

An advanced center biased search algorithm for block motion estimation is proposed in this letter. It adopts an innovative center biased search strategy to get correct motion vector. The computational complexity is reduced by strict application of the unimodal error surface assumption and half stop technique. Experimental results show that proposed algorithm has improved performance as compared to the conventional block matching algorithms.

We propose a new and fast full search (FS) motion estimation algorithm for video coding. The computational reduction comes from sequential rejection of impossible candidates with derived formula and subblock norms. Our algorithm reduces more the computations than the recent fast full search (FS) motion estimation algorithms.

In this paper, we propose a multi-level block matching algorithm using motion information in blocks. In the proposed algorithm, the block-level is decided by the motion degree in the block before motion searching procedure, and then adequate motion searching performs according to the block-level. Which improves computational efficiency by eliminating an unnecessary searching process in no motion or low motion regions, and brings more accurate estimation results by deepening motion searching process in high motion regions. Simulation results show that the proposed algorithm brings the lower estimation error--about 20% MSE reduction--with the fewer blocks per frame and the lower computational loading--about 98% operational amount reduction--than full search block matching algorithm with constant block size.

To reduce an amount of computation of full search algorithm for fast motion estimation, we propose a new and fast matching algorithm without any degradation of predicted images. The computational reduction without any degradation comes from adaptive matching scan algorithm according to the image complexity of the reference block in current frame. Experimentally, we significantly reduce the computational load compared with conventional full search algorithm.

Three-dimensional (3-D) instrumentation using an image sequence is a promising instrumentation method for intelligent systems in which accurate 3-D information is required. However, real-time instrumentation is difficult since much computation time and a large memory bandwidth are required. In this paper, a 3-D instrumentation VLSI processor with a concurrent memory-access scheme is proposed. To reduce the access time, frequently used data are stored in a cache register array and are concurrently transferred to processing elements using simple interconnections to the 8-nearest neighbor registers. Based on a row and column memory access pattern, we propose a diagonally interleaved frame memory by which pixel values of a row and column are stored across memory modules. Based on the concurrent memory-access scheme, a 40 GOPS vprocessor is designed and the delay time for the instrumentation is estimated to be 42 ms for a 256256 images.

A new Elastic-Block Matching Algorithm using bilinear space warping is proposed. In this scheme a convex quadrilateral, which minimizes a distortion measure against the current square block, is searched to compensate the shape deformation caused by a rigid body's 3 dimensional depth motion or rotation. The proposed algorithm gives a remarkable improvement in motion-compensated prediction compared with the conventional algorithm.