This paper describes a procedure extracting feature vector of a target cell more precisely in the case of identifying specified cell. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of th...
This paper describes a procedure extracting feature vector of a target cell more precisely in the case of identifying specified cell. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of the object So, the feature vector plays very important role in classifying objects. Because the feature vectors is affected by noises and holes, it is necessary to remove noises contaminated in original image to get feature vector extraction exactly. In this paper, we propose the following method to do to get feature vector extraction exactly. First, by Otsu's optimal threshold selection method and morphological filters such as cleaning, filling and opening filters, we separate objects from background an get rid of isolated particles. After the labeling step by 4-adjacent neighborhood, the labeled image is filtered by the area filter. From this area-filtered image, feature vector such as area, complexity, centroid, rotation angle, effective diameter, the perimeter based on chain code and the width and height based on rotation matrix are extracted. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxn. It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.
This paper describes a procedure extracting feature vector of a target cell more precisely in the case of identifying specified cell. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of the object So, the feature vector plays very important role in classifying objects. Because the feature vectors is affected by noises and holes, it is necessary to remove noises contaminated in original image to get feature vector extraction exactly. In this paper, we propose the following method to do to get feature vector extraction exactly. First, by Otsu's optimal threshold selection method and morphological filters such as cleaning, filling and opening filters, we separate objects from background an get rid of isolated particles. After the labeling step by 4-adjacent neighborhood, the labeled image is filtered by the area filter. From this area-filtered image, feature vector such as area, complexity, centroid, rotation angle, effective diameter, the perimeter based on chain code and the width and height based on rotation matrix are extracted. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxn. It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.
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제안 방법
The previous labeled image is filtered by area filter. Finally, from this area-filtered image, morphological features such as area, perimeter, complexity, centroid, rotation angle, width, height and effective diameter are extracted by the proposed procedure. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxii It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.
So this paper shows basic research results to develop vision analysis system which can be applied for bio process plant and to present a procedure extracting features in order to identify the object cells exactly under background with noise such as obstacles and non-symmetric contour, etc. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of the object So the feature vector plays veiy important role in dassifying objects.
This paper shows basic research results to develop vision analysis system which can be applied for bio-process plant and to present a procedure extracting features in order to identify the object cells exactly under background with noise such as obstacles and non-symmetric contour, etc. To prove the effectiveness, the proposed method is applied for yeast, Zygosac- charomyces rouxii.
이론/모형
13. Eample images by only Otsu's method.
Image segmentation is a fundamental technique for image analysis, whose purpose is to identify the regions of image objects and to extract the objects from their background (Morii, 1995). This paper engages Otsu's variance based thresholding method (Otsu, 1979). He described three possible discriminant criteria based on ratios of the within-class, between-class and total variance, all of which are equivalent, and thus in a given situation any of the three possible discriminant criteria could be chosen (Hannah et al.
In general, two labeling algorithms are used to identify objects into binary image by 4-adjacent or 8-adjacent neighborhood. This paper has applied labeling algorithm called 'frame propagation' using queue to store positions of labeled pixels. g(x, y) is a binary image that we want to do labeling.
성능/효과
Finally, from this area-filtered image, morphological features such as area, perimeter, complexity, centroid, rotation angle, width, height and effective diameter are extracted by the proposed procedure. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxii It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.
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