Shadows are viewed as undesired information that strongly affects images. Shadows may cause a high risk to present false color tones, to distort the shape of objects, to merge, or to lose objects. This paper proposes a novel approach for the detection and removal of shadows in an image. Firstly the shadow and non shadow region of the original image is identified by HSV color model. The shadow removal is based on exemplar based image inpainting. Finally, the border between the reconstructed shadow and the non shadow areas undergoes bilinear interpolation to yield a smooth transition between them. They would lead to a better fitting of the shadow and non shadow classes, thus resulting in a potentially better reconstruction quality.
In most of the existing Intelligent Tutoring S ystem, the initial clustering of students is based on their previous semester marks. This paper introduces S oftTutor,an ITS for teaching complex data structures in C for 4 th semester B.Tech (Computer S cience and Information Technology)students .Here instead of using their previous semester marks, a pretest is conducted by S oftTutor which includes questions in basics of computer science and a few on abstract reasoning .A XML based architecture is build for analyzing the answers. The dynamic DTD for each answer is constructed by performing apriori algorithm on the training data(collected from 60 students ),to obtain the most frequent answers that are likely to be correct. Later the students are clustered based on their online marks using EM clustering algorithm. The low performing students were given additional assignments based on the clustering results so that they can improve themselves.
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