In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.
This paper presents a novel follicular unit (FU)classification method based on an angle variation of a boundary vector according to the number of hairs in several FU images. The recently developed robotic FU harvest system, ARTAS, classifies through digital imaging the FU type based on the number of hairs with defects in the contour and outline profile of the FU of interest. However, this method has a drawback in that the FU classification is inaccurate because it causes unintended defects in the outline profile of the FU. To overcome this drawback, the proposed method classifies the FU's type by the number of variation points that are calculated using an angle variation a boundary vector. The experimental results show that the proposed method is robust and accurate for various FU shapes, compared to the contour-outline profile FU classification method of the ARTAS system.
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