Feature description based on center-symmetric local mapped patterns

C. T. Ferraz, Osmando Pereira, A. Gonzaga
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引用次数: 25

Abstract

Local feature description has gained a lot of interest in many applications, such as texture recognition, image retrieval and face recognition. This paper presents a novel method for local feature description based on gray-level difference mapping, called Center-Symmetric Local Mapped Pattern (CS-LMP). The proposed descriptor is invariant to image scale, rotation, illumination and partial viewpoint changes. Furthermore, this descriptor more effectively captures the nuances of the image pixels. The training set is composed of rotated and scaled images, with changes in illumination and view points. The test set is composed of rotated and scaled images. In our experiments, the descriptor is compared to the Center-Symmetric Local Binary Pattern (CS-LBP). The results show that our descriptor performs favorably compared to the CS-LBP.
基于中心对称局部映射模式的特征描述
局部特征描述在纹理识别、图像检索和人脸识别等领域得到了广泛的应用。提出了一种基于灰度差映射的局部特征描述新方法——中心对称局部映射模式(CS-LMP)。该描述符不受图像尺度、旋转、光照和部分视点变化的影响。此外,该描述符更有效地捕获图像像素的细微差别。训练集由旋转和缩放的图像组成,这些图像随光照和视点的变化而变化。测试集由旋转和缩放的图像组成。在我们的实验中,描述符与中心对称局部二进制模式(CS-LBP)进行了比较。结果表明,与CS-LBP相比,我们的描述符具有更好的性能。
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