Lun-Chi Chen

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Locations of moving or missing objects are getting important information for context-aware applications which try to get the locality of an object, and then provide services pertaining to the object. To position an object, most systems use a predefined coordinate to compute object location while sensing the appearance of the target object. Usually, it is(More)
This paper proposes a framework for an Image-based Flood Alarm (IFA) that includes bi-seeded region-based image segmentation for the extraction of a water region of interest from an image, as well as an alarm classifier for identifying the degree of flood risk. When the risk reaches the predetermined threshold, a flood response message reports to the main(More)
Severe weather conditions greatly impair the performance of outdoor imaging. In this study, two region-based image segmentation methods, Grow Cut and Region Growing (RegGro), were applied to rain scenes. This study demonstrates that segmentation accuracy depends on fog and rain stains. In severe rainfall periods, heavy rain and fog reduced the overall image(More)
Recommender systems have been proven useful in numerous contemporary applications and helping users effectively identify items of interest within massive and potentially overwhelming collections. Among the recommender system techniques, the collaborative filtering mechanism is the most successful; it leverages the similar tastes of similar users, which can(More)
This paper focuses on flood-region detection using monitoring images. However, adverse weather affects the outcome of image segmentation methods. In this paper, we present an experimental comparison of an outdoor visual sensing system using region-growing methods with two different growing rules-namely, GrowCut and RegGro. For each growing rule, several(More)
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