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Fungal endophytes are microfungi that live in plants without causing apparent symptoms of infection. This study was conducted to identify endophytic fungi isolated from leaves of coniferous trees in Bohyeon Mountain of Korea. We collected leaves of two species of coniferous trees, Pinus densiflora and Pinus koraiensis, from 11 sites in the study area. A total 58 isolates were obtained and identified using molecular and morphological characteristics. Four species of endophytic fungi were isolated from P. densiflora: Lophodermium conigenum, Leotiomycetes sp., Septoria pini-thunbergii, and Polyporales sp., while two fungal species were isolated from P. koraiensis: Eurotiomycetes sp. and Rhytismataceae sp. The most frequently isolated species were L. conigenum and S. pini-thunbergii.

In this paper, we present a probabilistic approach to determining whether extracted facial features from a video sequence are appropriate for creating a 3D face model. In our approach, the distance between two feature points selected from the MPEG-4 facial object is defined as a random variable for each node of a probability network. To avoid generating an unnatural or non-realistic 3D face model, automatically extracted 2D facial features from a video sequence are fed into the proposed probabilistic network before a corresponding 3D face model is built. Simulation results show that the proposed probabilistic network can be used as a quality control agent to verify the correctness of extracted facial features.