Feature detection with automatic scale selection
Feature detection with automatic scale selection
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DOI：
10.1023/a:1008045108935
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发表时间：
1998-11-01
影响因子：
19.5
通讯作者：
Lindeberg, T
中科院分区：
文献类型：
--
作者：
Lindeberg, T
世界上的物体根据观察的尺度以不同的方式出现，这一事实具有重要的意义，如果一个人的目的是描述它们的话。这表明，规模的概念是至关重要的，当处理未知的测量数据的自动方法。Witkin（1983）和Koenderink（1984）在他们的开创性工作中提出了通过在所谓的尺度空间表示中以不同尺度表示图像结构来解决这个问题。传统的尺度空间理论建立在这项工作，但是，没有解决的问题，如何选择当地合适的尺度进行进一步的分析。本文提出了一个系统的方法来处理这个问题。一个框架，提出了一个一般的原则，说明当地的极值在不同组合的伽马归一化衍生物的尺度可能的候选人，以对应于有趣的结构的基础上，用于生成关于图像数据中的有趣的尺度水平的假设。具体地说，它是如何显示这个想法可以作为一个主要的机制，在算法自动规模选择，适应当地规模的处理th当地的图像结构。支持所提出的方法是在一个一般的理论调查的行为规模选择方法下重新缩放的输入模式和集成不同类型的早期视觉模块，包括对真实世界和合成数据的实验。支持也给出了一个详细的分析不同类型的功能检测器如何执行时，集成了规模选择机制，然后应用到特征模型模式。具体来说，它是如何提出的方法适用于斑点检测，交界处检测，边缘检测，脊检测和局部频率估计的问题进行了详细的描述。在许多计算机视觉应用中，低层次的视觉模块的性能差构成了一个主要的瓶颈。有人认为，包括自动尺度选择的机制是必不可少的，如果我们要构建视觉系统，自动分析复杂的未知环境。
The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a so-called scale-space representation. Traditional scale-space theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is presented for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of gamma-normalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is shown how this idea can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing of th local image structure.Support for the proposed approach is given in terms of a general theoretical investigation of the behaviour of the scale selection method under rescalings of the input pattern and by integration with different types of early visual modules, including experiments on real-world and synthetic data. Support is also given by a detailed analysis of how different types of feature detectors perform when integrated with a scale selection mechanism and then applied to characteristic model patterns. Specifically, it is described in detail how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation.In many computer vision applications, the poor performance of the low-level vision modules constitutes a major bottleneck. It is argued that the inclusion of mechanisms for automatic scale selection is essential if we are to construct vision systems to automatically analyse complex unknown environments.