Due to rapid advance of computer vision technology, computer assisted image analysis starts to play an important role in several areas including aquaculture. In recent years several computer vision-based methods have been applied to many major operations, e.g. automated fish counting, inspection, and measurement. In this paper we address a problem of overlapping objects in a population image that frequently occurs when objects under investigation are allowed to move freely during operations. We proposed a new skeleton reconstruction algorithm for identifying and isolating individual objects in a cluster of overlapping objects. The algorithm re-assembles initial skeleton of an object cluster based on combination of edge and geometric measures, in order to form correct skeletons of individual objects in a cluster. Skeletons produced by our algorithm will be used as a basis for further automated inspection and measurement tasks. In this paper we apply our algorithm in a field of aquaculture for automated identifying individual fish fry in an overlappingfry cluster. Our algorithm can achieve 93.33 percent accuracy for skeleton reconstruction of each individual fry in a cluster of 2 -7 overlapping fry. The results also show the effectiveness of our algorithm in dealing with various overlapping patterns.

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