Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Environmental microorganisms (EMs) such as bacteria and protozoa are found in every imaginable environments. To explore functions of EMs is an important research field for environmental assessment and treatment. Hover, EMs are traditionally investigated through morphological analysis using microscopes or DNA analysis, which is time and money consuming. To overcome this,  introduce an innovative method which applies content-based image analysis (CBIA) to environmental microbiology. Our method classifies EMs into different categories based on features extracted from microscopic images. Specifically, it consists of three steps: The first is image segmentation which accurately extracts the region of an EM in a microscopic image with a small amount of user interaction. The second step is feature extraction where multiple features are extracted to describe different characteristics of the EM. In particular,  develop an internal structure histogram descriptor which captures the structure of the EM using angles defined on its contour. The last step is fusion which combines classification results by different features to improve the performance. Experimental results validate the effectiveness and practicability of our environmental microbiology method aided by CBIA.
['Chen Li', 'Kimiaki Shirahama', 'Marcin Grzegorzek']
I am writing a research paper on a idea Environmental microbiology aided by content-based image analysis
[{"from": "human", "value": "I am writing a research paper on a idea Environmental microbiology aided by content-based image analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Environmental microorganisms (EMs) such as bacteria and protozoa are found in every imaginable environments. To explore functions of EMs is an important research field for environmental assessment and treatment. Hover, EMs are traditionally investigated through morphological analysis using microscopes or DNA analysis, which is time and money consuming. To overcome this,  introduce an innovative method which applies content-based image analysis (CBIA) to environmental microbiology. Our method classifies EMs into different categories based on features extracted from microscopic images. Specifically, it consists of three steps: The first is image segmentation which accurately extracts the region of an EM in a microscopic image with a small amount of user interaction. The second step is feature extraction where multiple features are extracted to describe different characteristics of the EM. In particular,  develop an internal structure histogram descriptor which captures the structure of the EM using angles defined on its contour. The last step is fusion which combines classification results by different features to improve the performance. Experimental results validate the effectiveness and practicability of our environmental microbiology method aided by CBIA."}]