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A Multiphase Active Contour Model based on the Hermite Transform for Texture Segmentation

机译:基于Hermite变换的纹理分割多相主动轮廓模型

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Texture is one of the most important elements used by the human visual system (HVS) to distinguish different objects in a scene. Early bio-inspired methods for texture segmentation involve partitioning an image into distinct regions by setting a criterion based on their frequency response and local properties in order to further perform a grouping task. Nevertheless, the correct texture delimitation still remains as an important challenge in image segmentation. The aim of this study is to generate a novel approach to discriminate different textures by comparing internal and external image content in a set of evolving curves. We propose a multiphase formulation with an active contour model applied on the highest energy coefficients generated by the Hermite transform (HT). Local texture features such as scale and orientation are reflected in the HT coefficients which guide the evolution of each curve. This process leads to the enclosure of similar characteristics in a region associated with a level set function. The efficiency of our proposal is evaluated using a variety of synthetic images and real textured scones.
机译:纹理是人类视觉系统(HVS)用来区分场景中不同对象的最重要元素之一。早期的生物启发式纹理分割方法涉及通过基于图像的频率响应和局部属性设置标准,将图像划分为不同的区域,以便进一步执行分组任务。然而,正确的纹理定界仍然是图像分割中的重要挑战。这项研究的目的是通过在一组不断变化的曲线中比较内部和外部图像内容,从而产生一种区分不同纹理的新颖方法。我们提出了一种多相公式,其中将主动轮廓模型应用于由Hermite变换(HT)生成的最高能量系数。局部纹理特征(例如比例和方向)反映在HT系数中,该系数指导每条曲线的演变。该过程导致在与水平设置功能相关联的区域中具有相似特性的封闭。我们使用多种合成图像和真实纹理烤饼来评估我们提案的效率。

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