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Mask Size Independent and Orientation Invariant Object Finding

机译:掩模尺寸独立和方向不变的对象发现

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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:在具有方向不变性的图像中找到对象在计算机视觉和模式识别中有许多应用。模板匹配是查找对象的典型方法。但是,模板匹配要求在模板中的每个像素上以及图像中的每个像素上进行像素相加和相乘,这涉及大量的像素操作。为了降低计算成本,已经研究了许多技术,包括FFT技术,部分照明方法以及从粗糙到精细的方法。这些技术可能适用于查找具有相同方向的对象。但是,当模板中的对象方向与图像中的方向不同时,即使对于最新的快速模板匹配技术,其计算成本也令人望而却步。在本文中,通过结合模式识别中不变矩的思想,物理学中的格林定理和计算机图形学中的布雷森汉线算法,我们提出了一种与掩模尺寸无关和方向不变的目标发现技术。通过理论分析和实验,我们证明了该新技术从直接实现的0(N〜3M〜2)到0(N〜2)显着降低了无方向目标发现的计算成本。

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