Window Matching Algorithm Research Articles

An Improved Velocity Estimation Method for Wideband Multi-Highlight Target Echoes in Active Sonar Systems

In active sonar systems, the target echoes are usually equivalent to a superposition of the Doppler-scaled reflections from multiple highlights. The reflections overlap with each other both in the time and frequency domain, which results in a decreased velocity estimation performance. Recently, the hyperbolic-frequency modulated signal has been widely employed in sonar systems for moving targets due to its Doppler tolerance, while the precise velocity estimation becomes a great challenge under such conditions. In this paper, the echo c is modeled onsidering a target with a constant velocity and multi-highlights. The velocity estimation performance is analyzed though the signal’s matched filter and the wideband ambiguity function. An improved method based on the sliding window matching algorithm is proposed to improve the performance. The method controls the energy of environmental noise and interference by focusing on the dominant target highlight, and applying a designed window which utilizes the Doppler characteristics of hyperbolic-frequency modulated signals. Simulations and lake experiment allow us to compare between the improved method and the conventional matched filter method. The results verify the influence of the multi-highlights in velocity estimation and indicate that the improved method has more effective performance.

Hyperspectral Remote Sensing Data Recovery via Adaptive Window Matching Method

HJ-1A satellite is often used to monitor environmental disaster and plays an important role in environmental changes. Because of the affection of various factors, certain band of HJ-1A hyperspectral remote sensing data is severe loss or distortion, which brings great difficulties for subsequent quantitative processing. A novel adaptive window matching algorithm, which can adjust intelligently size of matching window according to different local feature information of the image, is proposed for HJ-1A satellite hyperspectral data recovery in this paper. The results show that the adaptive matching algorithm has a more superior performance than other algorithms in image quality index, column mean curve, and image correlation coefficient.

A Robust Method for Arabic Car Plates Recognition and Matching Using Chain Code

This paper provides a new and fast method for matching and recognition of characters in Arabic license plate images. For this purpose, various methods have been proposed in literature. However, most of them suffer from: sensitivity to non-uniform illumination distribution, existence of shade in license plate, license plate color and the need for receiving an exact image of the license plate. The main contributions of our work include (I) chain code use to bounded the shape and distinguishing similar characters by local structural features. The moving window matching algorithm has been implemented. The distance measure (squared Euclidean distance) technique has been used for measuring the similarities between the moving window and the plate image. (2) Developing a system architecture combining statistical and structural recognition methods. We tested the method with 300 of plate images captured in different environments from real applications. The result yield 93.93% recognition accuracy.

Regularized Stereo Matching Scheme using Adaptive Disparity Estimation

In this paper, a regularized stereo matching scheme using adaptive disparity estimation is proposed. That is, by adaptively predicting the mutual correlation between stereo image pair using the proposed algorithm, the bandwidth of an input stereo image pair can be compressed to the level of a conventional two-dimensional (2D) image and a predicted image can also be effectively reconstructed using a reference image and disparity vectors. In the adaptive disparity estimation method, feature values are extracted from an input stereo image pair and a matching window for stereo matching is adaptively selected depending on the magnitude of the feature values. Accordingly, this adaptive window matching algorithm (AMA) could improve the overall performance of stereo matching by reducing the mismatch of disparity vectors, which occurs in the conventional dense disparity estimation with a small matching window, and also by reducing a blocking effect, which occurs in the coarse disparity estimation with a large matching window. However, it still has problems of overlap and disallocation of matching windows. Therefore, to alleviate those problems, a new regularized adaptive disparity estimation method is proposed in this paper. That is, the estimated disparity vector is regularized with the neighboring disparity vectors; as a result, the predicted stereo image is found to be more effectively reconstructed. Some experiments with stereo sequences of “Man”, “Hoon”, “Yong”, and “Car” reveal that the proposed algorithm improves the peak signal-to-noise ratios (PSNRs) of the reconstructed images by 8.47 and 1.53 dB, on average, compared with those in the cases of feature and pixel-based window matching algorithm (FPMA) and AMA, respectively.

Effective reconstruction of stereoscopic image pair by using regularized adaptive window matching algorithm

In this paper, an effective method for reconstruction of stereoscopic image pair through the regularized adaptive disparity estimation is proposed. Although the conventional adaptive disparity window matching can sharply improve the PSNR of a reconstructed stereo image, but there still exist some problems of overlapping between the matching windows and disallocation of the matching windows, because the size of the matching window tend to changes adaptively in accordance with the magnitude of the feature values. In the proposed method, the problems relating to the conventional adaptive disparity estimation scheme can be solved and the predicted stereo image can be more effectively reconstructed by regularizing the extimated disparity vector with the neighboring disparity vectors. From the experimental results, it is found that the proposed algorithm show improvements the PSNR of the reconstructed right image by about 2.36 ∼ 2.76 dB, on average, compared with that of conventional algorithms.

Fracture Surface Fractal Dimension Determined Through An Extensive 3-D Reconstruction

AbstractAn efficient image processing method is developed to determine the fractal dimension from a high resolution 3-D reconstruction of fracture surface. The third dimension of the fracture surface is obtained from a stereo pair. The elevation is locally determined through a recursive window matching algorithm centered on the cross-correlation operation. The method allows to reach high vertical and horizontal resolutions of the elevation map.The fractal dimension is obtained by an implementation of the slit-island method. A series of contour lines is extracted from the reconstructed surface. The images of the contour lines are progressively reduced in size and at every step the curve length is estimated. The result is then normalized according to the scale factor to build a Kolmogorov plot.The performance of this method is demonstrated on a synthetic image generated by a fracture surface mathematical model.