| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 97 === In this thesis, we present a novel system for recognizing butterfly from a natural image which can be taken from various shooting directions in the real scene. Color is the most important feature for butterfly recognizing. In order to extract features, we first apply the proposed methods - Automatic Region Growing Boundary Thresholding (ARGBT) and Automatic Error Threshold K-means (AET-K-means) to automatically obtain the dominant colors of butterfly, and find their corresponding distribution features. Besides, we provide an interactive method to limit the extraction area to solve the problem on object segmentation, and extract appropriate boundary by the proposed method. In addition, after the recognition process, we also support two feedback mechanisms to meet user’s expectation. Finally, experiments are conducted to demonstrate the performance of the system on the database of 26 Taiwanese common butterfly species. Each species is tested by 134 sample images and 60 natural images, and the results reveal the effectiveness for recognition.
|
|---|
