The paper presents an adaptive segmentation and activity classification method for filamentous fungi image. Firstly, an
adaptive structuring element (SE) construction algorithm is proposed for image background suppression. Based on
watershed transform method, the color labeled segmentation of fungi image is taken. Secondly, the fungi elements
feature space is described and the feature set for fungi hyphae activity classification is extracted. The growth rate
evaluation of fungi hyphae is achieved by using SVM classifier. Some experimental results demonstrate that the
proposed method is effective for filamentous fungi image processing.
The paper presents an adaptive preprocessing method for infrared image sequence. Firstly an adaptive structuring
element (SE) construction algorithm is proposed. Secondly, based on morphological top-hat operation, the adaptive
background suppression of image sequence is taken. Finally, the image sequence adaptive segmentation is achieved by
using morphological opening operation. Some experimental results demonstrate that our proposed method is effective
and adaptive for infrared image sequence preprocessing.
The need for remote sensing image feature selection methods is discussed in this paper. A central problem in image classification and recognition is the redundancy of image features. To cope with many unnecessary and irrelevant features, we propose a mixture method based on principle component analysis (PCA) and rough set theory to alleviate this situation. The main contribution of this paper is to provide the method for remote sensing image classification with higher accuracy comparing to the single rough set theory and PCA method. Finally, some experimental results demonstrate that our proposed method is effective in feature selection for remote sensing image.
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