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Robust object detection and segmentation by peripheral increment sign correlation image

机译：通过外围增量符号相关图像进行稳健的目标检测和分割

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摘要

This paper proposes the peripheral increment sign correlation image, which can be used to evaluate sign changes of the neighborhood brightness. Based on the technique, a robust method is presented for detection and separation of the object emerging from the image time sequence, with the following features. (1) The result is not affected by the brightness distribution of the emerging object. (2) The result is not affected by brightness change of the background. The peripheral increment sign correlation image is constructed from only the trend of the brightness changes in the neighborhood of the pixel under consideration. Consequently, the image is highly robust to brightness changes over the sequence, and the similarity of the original texture pattern can be detected even if there is a brightness change. Furthermore, it does not require complex preprocessing or parameter setting, and a high-density difference image can be directly derived. Through experiments using real images with various conditions, it is verified that difference extraction which is robust to brightness change can be realized, indicating the effectiveness of the approach. An application experiment is performed to extract the emerging object area in a scene, and it is verified that segmentation can be performed while retaining good contours and continuity by adding a relatively simple filter sequence as postprocessing.
机译：本文提出了外围增量符号相关图像，可用于评估邻域亮度的符号变化。基于该技术，提出了一种鲁棒的方法，用于检测和分离从图像时间序列中出现的物体，具有以下特征。 （1）结果不受新兴物体亮度分布的影响。 （2）结果不受背景亮度变化的影响。仅根据所考虑的像素附近的亮度变化的趋势来构造外围增量符号相关图像。因此，该图像对于整个序列上的亮度变化具有很高的鲁棒性，并且即使存在亮度变化，也可以检测到原始纹理图案的相似性。此外，它不需要复杂的预处理或参数设置，并且可以直接导出高密度差异图像。通过在各种条件下使用真实图像进行实验，验证了可以实现对亮度变化具有鲁棒性的差异提取，表明了该方法的有效性。进行了应用实验以提取场景中出现的物体区域，并且通过添加相对简单的滤镜序列作为后处理，证明了可以在保持良好轮廓和连续性的同时进行分割。

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