Image feature detection and matching in underwater conditions
Image feature detection and matching in underwater conditions
复制标题
水下条件下的图像特征检测与匹配
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Song Wang
中科院分区：
文献类型：
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作者：
K. Oliver;W. Hou;Song Wang
水下成像和图像分析的主要挑战是克服由于水及其成分对光的强烈散射而造成的模糊效应。这种模糊增加了已经具有挑战性的问题，如对象检测和定位的复杂性。用于对象检测和定位的当前最先进的方法通常涉及两个组件：（a）从图像提取一组特征点的特征检测器，以及（B）试图将从目标图像检测到的特征点与对应于感兴趣对象的一组模板特征匹配的特征匹配算法。成功的特征匹配指示目标图像也包含感兴趣对象。对于水下图像，目标图像是在水下条件下拍摄的，而模板特征通常是从一个或多个在水下或不同水下条件下拍摄的训练图像中提取的。此外，目标图像和训练图像中的对象可以显示不同的姿态，包括旋转、缩放、平移变换和视角变化。在本文中，我们研究了各种水下点扩散函数的影响，使用许多不同的特征检测器的图像特征的检测，以及这些功能如何影响这些功能的能力时，他们被用于匹配和目标检测。这项研究为进一步开发适用于水下图像目标检测和定位的鲁棒特征检测器和匹配算法提供了参考。
The main challenge in underwater imaging and image analysis is to overcome the effects of blurring due to the strong scattering of light by the water and its constituents. This blurring adds complexity to already challenging problems like object detection and localization. The current state-of-the-art approaches for object detection and localization normally involve two components: (a) a feature detector that extracts a set of feature points from an image, and (b) a feature matching algorithm that tries to match the feature points detected from a target image to a set of template features corresponding to the object of interest. A successful feature matching indicates that the target image also contains the object of interest. For underwater images, the target image is taken in underwater conditions while the template features are usually extracted from one or more training images that are taken out-of-water or in different underwater conditions. In addition, the objects in the target image and the training images may show different poses, including rotation, scaling, translation transformations, and perspective changes. In this paper we investigate the effects of various underwater point spread functions on the detection of image features using many different feature detectors, and how these functions affect the capability of these features when they are used for matching and object detection. This research provides insight to further develop robust feature detectors and matching algorithms that are suitable for detecting and localizing objects from underwater images.