Abstract-This paper proposes perceptual segmentation of natural color images using a fuzzy-based hierarchical algorithm and its application to the segmentation of dermoscopy images. A fuzzy-based homogeneity measure makes a fusion of the color features and the texture features. The proposed hierarchical segmentation method is performed in four stages: simple splitting, local merging, global merging and boundary refinement. The effectiveness of the proposed method is confirmed through computer simulations that demonstrate the applicability of the proposed method to the segmentation of natural color images and dermoscopy images.

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This paper proposes unsupervised perceptual segmentation of natural color images using a fuzzy-based hierarchical algorithm. L a b color space is used to represent color features and statistical geometrical features are adopted as texture features. A fuzzy-based homogeneity measure makes a fusion of color features and texture features. Proposed hierarchical segmentation method is performed in four stages: simple splitting, local merging, global merging and boundary refinement. Experiments on segmentation of natural color images are presented to verify the effectiveness of the proposed method in obtaining perceptual segmentation.

From the academia viewpoint, the process of qualifying engineering students demands they acquire a deep understanding of basic sciences and technological aspects. On the other hand, it is quite a challenge to keep them motivated throughout their graduation period. Taking into account these issues, an integrated approach for teaching can provide good results. Basically, the way that such integration might be accomplished requires a single project per period. In addition, the disciplines taken by the students in a specific period should provide them the necessary background to conclude that project. Focusing on teaching robotics, this chapter describes the authors’ experience with mechatronics engineering students in the 7th-period of their graduation.