| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === Shadow detection is one of major topics in computer vision. Shadow usually
causes interference when preforming image segmentation, tracking, face detection, and
object recognition. Thus, detecting shadow region is an important step before shadow
removal process. It is a challenging task that most of method need users to provide
strokes to indicate location of shadow and only few of methods can automatically detect
shadow region.
This thesis proposed a method that can detect shadow automatically. The pairwise
classification is performed to classify the relationship between all the pair of patches in
the input image, and Gaussian Mixture Model (GMM) is applied to obtain main color
statistic of the shadow patches and enhance accuracy of the detection, then the input
image is upsampled to find the small area of shadow. Finally, the detected shadow
region is refined by mean-shift segmentation.
The experimental result shows the proposed method can generate accurate shadow
map without any user assistance.
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