Conventional correlation matching algorithms waste great time in invalid area search. This paper proposes a color tracking method based on correlation search area optimization on target characteristic hue decision. By...Conventional correlation matching algorithms waste great time in invalid area search. This paper proposes a color tracking method based on correlation search area optimization on target characteristic hue decision. By quantifying and reducing dimensions of HSV（ hue saturation value） color space, a one-dimensional hue space is constructed. In the space, the target characteristic hue granule set is constructed, which contains attributes such as value, area and average distance between pixels and aiming center. By using granular computing method, the similarity between target and search blocks is obtained and the invalid search areas can be removed. The color tracking experiment has proved that the algorithm can improve real time performance for conventional matching algorithms without precision lost.展开更多
During matching on feature point, gray correlation matching technology is utilized to extract multi-peaks as a coarse matching set. A pair of given corresponding reference points within the left and right images is us...During matching on feature point, gray correlation matching technology is utilized to extract multi-peaks as a coarse matching set. A pair of given corresponding reference points within the left and right images is used to calculate gradients of reference difference between the reference points and each feature point within the multi-peaks set. The unique correspondence is determined by criterion of minimal gradients of reference difference. The obtained correspondence is taken as a new pair of reference points to update the reference points continuously until all feature points in the left (or right) image being matched with the right (or left) image. The gradients of reference difference can be calculated easily by means of pre-setting a pair of obvious feature points in the left and right images as a pair of corresponding reference points. Besides, the efficiency of matching can be improved greatly by taking the obtained matching point as a new pair of reference points, and by updating the reference point continuously. It is proved that the proposed algorithm is valid and reliable by 3D reconstruction on two pairs of actual natural images with abundant and weak texture, respectively.展开更多
文摘Conventional correlation matching algorithms waste great time in invalid area search. This paper proposes a color tracking method based on correlation search area optimization on target characteristic hue decision. By quantifying and reducing dimensions of HSV（ hue saturation value） color space, a one-dimensional hue space is constructed. In the space, the target characteristic hue granule set is constructed, which contains attributes such as value, area and average distance between pixels and aiming center. By using granular computing method, the similarity between target and search blocks is obtained and the invalid search areas can be removed. The color tracking experiment has proved that the algorithm can improve real time performance for conventional matching algorithms without precision lost.
文摘During matching on feature point, gray correlation matching technology is utilized to extract multi-peaks as a coarse matching set. A pair of given corresponding reference points within the left and right images is used to calculate gradients of reference difference between the reference points and each feature point within the multi-peaks set. The unique correspondence is determined by criterion of minimal gradients of reference difference. The obtained correspondence is taken as a new pair of reference points to update the reference points continuously until all feature points in the left (or right) image being matched with the right (or left) image. The gradients of reference difference can be calculated easily by means of pre-setting a pair of obvious feature points in the left and right images as a pair of corresponding reference points. Besides, the efficiency of matching can be improved greatly by taking the obtained matching point as a new pair of reference points, and by updating the reference point continuously. It is proved that the proposed algorithm is valid and reliable by 3D reconstruction on two pairs of actual natural images with abundant and weak texture, respectively.
