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首页> 外文期刊>International journal of image and data fusion >Image feature detection using an improved implementation of maximally stable extremal regions for augmented reality applications
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Image feature detection using an improved implementation of maximally stable extremal regions for augmented reality applications

机译：使用增强的最大现实极端区域改进实现的图像特征检测

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Augmented Reality for all practical purposes requires extensive computation, accurate view alignment and real-time performance. To address some of these limitations, an improved method of feature detection is proposed using Maximally Stable Extremal Regions. The approach used for feature detection extracts the regions of interest using a true flood fill approach for building and maintaining the component tree. This approach has true worst-case linear complexity (Linear-MSER). In the present work, Linear-MSER is implemented at multiple scales of an image in order to increase the affine invariance properties of the detector (MSLinear-MSER). The two detectors, Linear-MSER and MSLinear-MSER, are then combined separately with Scale Invariant Feature Transform and Speeded-Up Robust Feature descriptors for performance comparison. Performance evaluation is done under varying imaging conditions like changes in viewpoint, scale, blur, illumination and JPEG compression. Results show that, MSLinear-MSER+SIFT performs best in terms of time-complexity and number of key-point matches when executed at six octaves and five levels. This observation is true for all image-sets taken into consideration, containing images that are affine transformed in one way or other. To exhibit the efficiency of MSLinear-MSER+SIFT, a prototype of an AR system is also developed and discussed in this article using this approach.
机译：用于所有实际目的的增强现实需要大量计算，准确的视图对齐和实时性能。为了解决这些局限性，提出了一种使用最大稳定极值区域的改进的特征检测方法。用于特征检测的方法使用真正的泛洪填充方法来提取感兴趣区域，以构建和维护组件树。这种方法具有真正的最坏情况下的线性复杂度（Linear-MSER）。在当前工作中，Linear-MSER在图像的多个比例上实现，以增加检测器的仿射不变性（MSLinear-MSER）。然后，将两个检测器（Linear-MSER和MSLinear-MSER）分别与Scale Invariant Feature Transform和Speeded-Up Robust Feature描述符组合在一起以进行性能比较。性能评估是在各种成像条件下进行的，例如视点，比例，模糊，照明和JPEG压缩的变化。结果表明，以六个八度和五个级别执行时，MSLinear-MSER + SIFT在时间复杂度和关键点匹配数方面表现最佳。对于所有考虑到的图像集（包含以一种或其他方式进行仿射变换的图像），此观察结果都是正确的。为了展示MSLinear-MSER + SIFT的效率，本文还使用这种方法开发并讨论了AR系统的原型。

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