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Object detection using Non-Redundant Local Binary Patterns

机译:使用非冗余本地二进制模式进行对象检测

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摘要

Local Binary Pattern (LBP) as a descriptor, has been successfully used in various object recognition tasks because of its discriminative property and computational simplicity. In this paper a variant of the LBP referred to as Non-Redundant Local Binary Pattern (NRLBP) is introduced and its application for object detection is demonstrated. Compared with the original LBP descriptor, the NRLBP has advantage of providing a more compact description of object's appearance. Furthermore, the NRLBP is more discriminative since it reflects the relative contrast between the background and foreground. The proposed descriptor is employed to encode human's appearance in a human detection task. Experimental results show that the NRLBP is robust and adaptive with changes of the background and foreground and also outperforms the original LBP in detection task.
机译:局部二进制模式(LBP)作为描述符,由于其具有判别性和计算简单性,已成功用于各种对象识别任务中。在本文中,介绍了一种称为非冗余局部二进制模式(NRLBP)的LBP变体,并演示了其在对象检测中的应用。与原始LBP描述符相比,NRLBP的优势在于可以提供更紧凑的对象外观描述。此外,NRLBP具有更大的判别力,因为它反映了背景和前景之间的相对对比度。提出的描述符用于在人类检测任务中对人类的外观进行编码。实验结果表明,NRLBP具有鲁棒性和自适应性,可以适应背景和前景的变化,在检测任务中也优于原始的LBP。

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