Application Of Image Segmentation And Edge Detection To Image Of Insects

This paper studies the earlier period work of insect recognition, which provides the segmentation image that is easier to extract the characteristics for the insect recognition. Insect's main recognition characteristics including the geometry characteristic, the color characteristic, the texture characteristic and so on, We may carry on the judgment through a series of characteristics data (for example region area, boundary perimeter, hole number, circularity, eccentricity and so on). These data have indirectly reflected insect's many natural characteristics, such as bodily shape, color, number of body spot, whether there is maxilla, antenna and its shape. Therefore we should first to carry on the segmentation operation regarding the insect image, which is advantageous for carries on the characteristic data computation. It mainly carries on three steps to obtain the corresponding segmentation image:1st, separates the insect object from the complex background color image. To avoid disturbance we should remove the insect's background in the image firstly. The background of nature color image is complex. We use the matting method based on the transparency scenery in this paper, compared Ruzon & Tomasi method as well as the Poisson method, and have obtained the experimental result. The result could satisfy the edge detection and the segmentation request.2nd, using the edge detection and the segmentation algorithm obtain the insect's boundary image and the image which has better spot characteristic or the wing grain characteristic segmented image. According to the geometry characteristic which must extract (region area, region perimeter, eccentricity, hole number, circularity and so on), we could obtain the insect's boundary that express insect's overall shape with edge detection method and get the spot or the wing grain of the insect body with image segmentation method, which is the basis for calculating the hole number, circularity etc spot characteristic and the wing grain characteristic data. In this paper we've compared several predicate of first order operators (Roberts, Sobel, Prewitt, Canny), and proposed an improved algorithm which is based on the automatic double threshold value improvement algorithm based on the maximum inter-cluster distance and intra-cluster variance ratio. To withdrawing the spot and wing grain, a segmentation algorithm that based on the fuzzy set entropy method is used to Lepidoptera. And there is a best entropy threshold value segmentation method based on the partial threshold value that is proposed in this paper.3rd, obtain the segmented image of the trunk and the other part of insect. Some insects can not be classified completely accurately by the overall geometry characteristic only, so some partial characteristics such as foot, antenna and so on is needed. But to some insects, the position of the foot and antenna may affect the survey of the overall geometry characteristic data in a certain degree. So a separating image is needed. In this paper an open operational method of mathematics morphology is used to carry on the procession. At the same time, an adaptive method is proposed to choose the structural element size. This improvement could avoid distort phenomenon of the trunk abruption, and reduced remaining phenomenon of the foot, the antenna or maxilla's root.To eliminate the disturbance of the noise (particularly the pulse noise and salt-and-pepper noise influence which causes by equipment and transmission) in image processing, a smooth operation is needed. In this paper we have analyzed several smooth operation methods and improve median filter method by a directional way to reduce the detail loss of smoothing process.