The PGRs Research Presentations series has started on Wed. 13th Feb, 1pm, Meeting Room, MC3108 (3rd floor).
In each session we expect two PGR presentations. This session we had the following presentations:
| Title: “A probabilistic approach to Correctly and Automatically form of Retinal Vasculature“.
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Title: “Semantic Video Analysis: from Camera Language to Human Language” | |
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By: Touseef Qureshi |
By: Amjad Altadmri |
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| Abstract:
Correct configuration and formation of retinal vasculature is a vital step towards the diagnoses of these cardiovascular diseases. A single minor mistake during the process of connecting broken segments of vessels can lead to a completely incorrect vasculature. Image processing techniques can’t alone solve this problem. On the other hand, we are working on multidimensional scientific approach that integrates Artificial intelligence, image process techniques, statistics and probability. We are working and expecting an optimal approach towards the correct configuration of broken vessels segments at junctions, bridges, and terminals.
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Abstract
The rapidly increasing volume of visual data, available online or via broadcasting, emphasizes the need towards building intelligent tools for indexing, searching, rating, and retrieval. Textual semantic representations, such as tagging, labeling and annotation, are often important parts of videos’ indexing process, due to the advances in text analysis and their intuitive user-friendly nature for representing semantics suitable for search and retrieval.
Ideally, this annotation should simulate the human cognitive way of perceiving and describing videos. While these digital video mediums contain low-level visual data, human beings have the ability to infer more meaningful information from videos. The difference between these low-level contents and its corresponding human perception is referred to as the “semantic gap”. This gap is even harder to be handled in domain-independent uncontrolled videos, mainly due to the lack of any previous information about the analyzed video on one side, and the huge generic knowledge needed to be available on the other.
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