Shape-Based Plagiarism Detection for Flowchart Figures in Texts

Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offence that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide this checks. Most, if not all, discard the figures and charts before checking for plagiarism. Discarding the figures and charts results in look holes that people can take advantage. That means people can plagiarized figures and charts easily without the current plagiarism systems detecting it. There are very few papers which talks about flowcharts plagiarism detection. Therefore, there is a need to develop a system that will detect plagiarism in figures and charts. This paper presents a method for detecting flow chart figure plagiarism based on shape-based image processing and multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to different matching sets.Comment: 12 page

Overlapped and shadowed tree crown segmentation based on HSI color model and watershed algoritim

Image provides valuable information to the human and this information could be used to take an effective dissection such as information that comes from satellite sensors. Satellite images let the human have the information from the ground for very wide area. The negative side of satellite image is the resolution is still not much high. Satellite image play a vital role in many area of our live, especially agriculture, where the human can calculate the crown of the tree for very wide area in very short time. The counting of tree will not be accurate without getting good segmentation of these crowns. This work has applied segmentation algorithm to separate crown of coconut palm tree from shadow and the overlapped crown as well. The algorithm has exploited HSI color model to differentiate the color of crown from the color of shadow. The result of using this feature gives very different color for both shadow and crown. After crown detection the algorithm used morphological operation such as image filling to enhance the crown. The following step is removing noise or pixels which considered unwanted objects. Finally, the image was segmented using watershed after applying distance transform on the image. Since this research does not has ground information to measure the accuracy, the evaluation has been done manually, where the crown has counted manually and calculate the accuracy of this work which is 73%