Research on Image Matching Similarity Criterion Based on Maximum Posterior Probability
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摘要: 图像匹配是视觉跟踪领域中的重要环节,利用巴氏(Bhattacharyya) 系数度量模板与待匹配区域之间的统计特征相似性是图像匹配中最有效的方法之一. 但是由于背景特征的影响,有时应用巴氏指标进行匹配得到的最优解的位置并不一定是目标特征的实际位置,因而在视觉跟踪过程中目标定位可能出现偏差,甚至跟踪错误. 本文提出了一种基于后验概率的图像匹配相似性指标,该指标利用搜索区域的统计特征,能有效抑制待匹配区域特征中背景因素的影响,同时突出了目标特征的权重,与巴氏指标相比明显改善了匹配函数的峰值特性. 这种指标的另一突出优点是计算复杂度很低,容易得到全局最优解.与巴氏系数指标的匹配结果进行的比较表明,本文所提出的匹配指标在复杂背景下具有更强的目标识别与分辨能力
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关键词:
- 相似性度量 /
- 图像匹配 /
- 视觉跟踪 /
- 最大后验概率 /
- Bhattacharyya 系数
Abstract: Image matching is an important part for visual tracking. The Bhattacharyya coeffcient is an effcient method in image statistical feature matching. But for the influence of background feature, the optimal location obtained by Bhattacharyya coeffcient may not be the exact target location. Thus, biased or even wrong location may be got in visual tracking. This paper presents an image matching similarity criterion based on maximum posterior probability. The new criterion applies the statistical feature of the searching region, effectively reduces the influence of background feature, and emphasizes the importance of target feature, which distinctly improves the peak modality of matching function compared to that of Bhattacharyya coe±cient. The computation complexity of the new criterion is relatively low, and the global optimal solution can be easily obtained. Compared with the matching criterion of Bhattacharyya coeffcient, experimental results demonstrate that the proposed matching criterion has stronger object detection ability in complex background.
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