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Abstract
Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions.
Highlights
Computer vision studies how to let computers obtain highlevel and abstract information from images and videos
Scharstein and Szeliski [7] divided the stereo matching algorithm into global stereo matching and local stereo matching. e global stereo matching algorithm [8,9,10] usually uses the minimized energy function instead of cost aggregation to select the best parallax value, which can obtain a high-precision disparity map, but the complicated calculation leads to the limitations in practical applications. e local stereo matching algorithm uses the local information of pixels to construct a supported window, calculates the cost of all pixels in the window and aggregates to replace the cost value of a single pixel, and uses the Winner Take All (WTA) [11] algorithm to obtain the disparity map
Based on the above analysis, this paper proposes an antinoise matching method based on HSV color space and improved census transform
Summary
Computer vision studies how to let computers obtain highlevel and abstract information from images and videos. The census-based method only relies on the size relationship between pixels as the basis for similarity judgment in the cost calculation process, so it loses pixel gray information and distance information, which affects the matching accuracy. Based on the above analysis, this paper proposes an antinoise matching method based on HSV color space and improved census transform. Once the exposure or lighting conditions changed, the values of the three components will change significantly At this time, it is difficult to use the RGB color space to make similarity judgments. E improved cost calculation method based on census usually loses color information, which affects the matching accuracy. It can be seen that, due to the inconsistency of exposure and lighting conditions, the values of the three channels of the same pixel in the RGB color space have changed significantly.
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