Edge Detection Using Regression Surface

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  • 回帰面による輝度画像のエッジ抽出法
  • カイキメン ニ ヨル キド ガゾウ ノ エッジ チュウシュツホウ

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Edge detection is the process of characterizing object boundaries and therefore a problem of fundamental importance in image analysis. In general, the development of edge detection commonly uses a multiple stage algorithm such as reducing noise, finding the intensity gradient and tracing edges through the image to detect wide range of edges. While this set of algorithms can produce a significant advantage over the conventional edge detections, the problem of selecting an appropriate parameter on each stage has been taken into account since most of parameters have to be determined interactively referring to the input images. In this study, we proposed a novel high performance edge detection based on regression surface with a single parameter for luminance images. The proposed method fits regression surfaces to the luminance images to reduce noise and calculate the intensity gradient. The parameter to control the resulted edges is a threshold of the non-edge's percentage. This parameter can be set simply with edge and non-edge's histogram as reference. Our evaluation shows that the proposed edge detection with a parameter fixed to basic and photographic images has significant advantage over the Canny operator in which parameters are adaptively adjusted to those.

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