Faculty Of Computer Science Graduation Project 2017 - 2018

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Browsing Faculty Of Computer Science Graduation Project 2017 - 2018 by Subject "Computer Science"

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    Reassemble Destroyed Paper
    (October University for Modern Sciences and Arts, 2018) Youssef, Heba
    This project looks at the challenges of creating the automatic system for reconstruction shredded documents cut vertically (strip) and vertically, horizontally (cross- cut). The un-shredding problem is of interest in the fields of forensics, investigative sciences, archaeology and criminal investigation. All stages of the un-shredding pipeline are analysed, starting from uploaded images of shreds and ending with reconstructed documents. The current bottlenecks in this pipeline are identified and solutions are proposed. At the beginning a formal definition of this problem and references to related work will be given. While there are some approaches published dealing with the reconstruction of destroyed paper in general, there is barely work done in the field of cross-cut shredding then giving an introduction to algorithm of ant-colony. The solutions proposed in this project start with narrowing problem then implement two reconstruction approaches to improve the results. First approach, Character database, Feature matching algorithm, Row Clustering, Estimate error evaluation function and Greedy classifier then get the results. To improve the results that belongs to first approach we implement second approach. Second approach is reconstructing by Ant colony algorithm by estimate different error evaluation function then apply the algorithm.
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    Silhouette - Based Face Recognation
    (October University for Modern Sciences and Arts, 2018) Ahmed, Maryam Ahmed
    A silhouette face is representing the face as one solid shaped color, usually, the color is dark and removes every inline detail of the face but the silhouette’s outline edges are matching the original face’s outline. The silhouette of the face is usually the outline of the face profile, and face profile is considered a very important aspect of face identification it also provides an overall structure view of the face that is seen in the non-frontal view. Normally side view images are captured from a distance which unfortunately leads to capturing the image in a low-resolution quality. The main purpose of this project is to introduce a combination of silhouette detection and side-view face detection in order to detect, process, and compare a person from a side-view image. In this thesis we will be moving step by step starting fromthe step of extracting the face’s outer line from the side-view colored face picture. And start deploying methods and algorithms on these outer line points in order to approach the goal of matching the person with one of the known and saved persons we have. This thesis is introducing a module that can be added to face recognition systems in order to raise the recognition accuracy.