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Mask Size Independent and Orientation Invariant Object Finding

机译：蒙面尺寸独立和方向不变对象查找

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Finding an object in images with orientation invariance has many applications in computer vision and pattern recognition. Template matching is a typical approach for finding objects. However, template matching requires pixel additions and multiplications on each pixel in a template and for each pixel in images, which involves a significant amount of pixel operations. Many techniques have been investigated to reduce the computational cost including FFT techniques, partial illumination approaches, and coarse to fine methods. These techniques may work for finding objects with the same orientation. However, when the object orientation in templates is different from the orientation in images, the computational cost is prohibitive even for the latest fast template matching techniques. In this paper, by combining the ideas of moment invariants in pattern recognition, Green theorem from physics and Bresenham line algorithm from computer graphics, we propose a mask size independent and orientation invariant object finding technique. From theoretical analysis and experiments, we demonstrate that this new technique significantly reduces computational cost for orientation free object finding from 0(N~3M~2) of the direct implementation to 0(N~2)
机译：查找与方向不变图像对象在计算机视觉和模式识别许多应用。模板匹配是用于查找对象的典型做法。然而，模板匹配要求在在模板的每个像素与在图像中的每个像素，其中涉及像素的操作的显著量像素加法和乘法。许多技术已经被研究，以减少包括FFT技术，部分照明途径和粗到细方法计算成本。这些技术可能适用于以相同的方向查找对象。然而，当模板的面向对象是从图像中的方向不同，计算成本，甚至最新的快速模板匹配技术望而却步。在本文中，通过模式识别，从物理学和计算机图形学的Bresenham直线算法格林定理不变矩的思想结合起来，我们提出了一个口罩大小的独立和方向不变的对象查找技术。从理论分析和实验，我们证明了这种新技术显著减少了从直接执行0（N〜3M〜2）定向免费对象发现计算成本为0（N〜2）

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